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In order to accurately estimate the period of X-ray pulsars to improve navigation performance, this paper proposes a pulsar period estimation method based on photon energy using reverse Hilbert coding and double Convolutional Neural Networks (CNNs) discrimination. This method uses photon energy information to perform epoch folding. It converts one-dimensional contour information into two-dimensional image signals through reverse Hilbert curve and recognizes contour image features through CNN models to determine the optimal period. According to the simulation results, the period estimation accuracy of this method is 0.5350 ns, which is 65.71% higher than the χ 2 epoch folding method. The contour phase accuracy is 0.0004767, which is 41.93% higher than the χ 2 epoch folding method. At the same time, the observation time and noise interference that may affect the accuracy of period estimation are also quantitatively analyzed. In addition, in order to simulate real navigation scenarios, this paper also conducts dynamic period estimation experiments on PSR B0531+21 pulsar in a specific period of time. The method proposed in this paper has the advantages of high precision, fast calculation speed, strong anti-interference ability and accurate dynamic period estimation, which can significantly improve the navigation performance of X-ray pulsars. • The proposed period estimation method uses photon energy information for contour weighted folding. • The proposed method uses reverse Hilbert curve to convert one-dimensional contour information into two-dimensional image information. • Convolutional Neural Network models are used to optimize pulsar period estimation. [Display omitted] [ABSTRACT FROM AUTHOR]

Autonomous spacecraft relative navigation via monocular images became a hot topic in the past few years and, recently, received a further push thanks to the constantly growing field of artificial neural networks and the publication of several spaceborne image datasets. Despite the proliferation of spacecraft relative-state initialization algorithms developed, most architectures adopt computationally expensive solutions relying on convolutional neural networks (CNNs) that provide accurate output at the cost of a high computational burden that seems unfeasible for current spaceborne hardware. The paper addresses this issue by proposing a novel pose initialization algorithm based on lightweight CNNs. Inspired by previous state-of-the-art algorithms, the developed architecture leverages a fast and accurate target detection CNN followed by a line segment detection CNN capable of running with low inference time on mobile devices. The line segments and their junctions are grouped into complex geometrical groups, reducing the solution search space, and subsequently, they are adopted to extract the final pose estimate. As a main outcome, the analyses demonstrate that the lightweight architecture developed scores high accuracy in the pose estimation task, with a mean estimation error of less than 10 cm in translation and 2.5°in rotation. The baseline algorithm scores a mean SLAB error of 0.04552 with a standard deviation of 0.22972 in the test dataset. Detailed analyses demonstrate that the uncertainties on the overall pose score are driven mainly by errors in the relative attitude, which gives the highest contribution to the pose error metric adopted. The analyses on the error distributions point out that the uncertainties on the estimated relative position are higher in the camera boresight axis direction. Concerning the relative attitude, the algorithm proposed has higher uncertainties in estimating directions of the target x and y axes due to ambiguities related to the target geometry. Notably, the target detection CNN trained in this work outperforms the previous top scores in the benchmark dataset. The performances of the proposed algorithm have been investigated further by analyzing the effects on the accuracy due to the relative distance and the presence of background in the images. Lastly, the paper delves into the possibility of adopting a sub-portion of the 2D-to-3D match matrix made by the most complex perceptual groups identified that positively affects the overall run-time, pointing out the performances in terms of accuracy of the estimates and providing a comparison of both the baseline and the reduced match matrix versions against state-of-the-art algorithms concerning relative position and attitude errors and solution availability, highlighting the high accuracy and solution availability of the proposed architectures. • New relative pose initialization algorithm based on the detection of line segments. • Detailed literature review and description of available spaceborne image datasets. • Improvement of state of the art in spacecraft detection using lightweight CNN. • Assessment of accuracy of the proposed architecture and error sources analyses. • Proven feasibility of the approach via comparison with other architectures. [ABSTRACT FROM AUTHOR]

• AI improves infectious waste management. • Ensemble model exceeds 97.81% accuracy. • Reinforcement learning optimizes classification. • Geometric augmentation boosts model robustness. • Study addresses urgent urban waste challenges. This research paper focuses on effective infectious municipal waste management in urban settings, highlighting a dearth of dedicated research in this domain. Unlike general or specific waste types, infectious waste poses distinct health and environmental risks. Leveraging advanced artificial intelligence techniques, we prioritize infectious waste categorization and optimization, integrating metaheuristics into optimization methods to create a robust dual-ensemble framework. Our model, the "Enhanced Artificial Intelligence for Infectious Municipal Waste Classification System," combines ensemble image segmentation methods and diverse convolutional neural network models. Innovative geometric image augmentation enhances model robustness, diversifies training data, and improves accuracy across waste types. A pivotal aspect is the integration of a reinforcement learning-differential evolution algorithm as a decision fusion strategy, optimizing classification by harmonizing outputs from ensemble methods and convolutional neural network models. Computational results, using a newly collected dataset, demonstrate our model's accuracy exceeding 96.54% while using the existing solid waste dataset we achieve the accuracy of 97.81%, outperforming advanced approaches by margins ranging from 2.02% to 8.80%. This research significantly advances sustainable waste management, showcasing artificial intelligence's transformative potential in addressing intricate waste challenges. It establishes a foundational framework prioritizing efficiency, effectiveness, and sustainability for future waste management solutions. Acknowledging the importance of diverse datasets, customization for urban contexts, and practical integration into existing infrastructures, our work contributes to the broader discourse on the role of artificial intelligence in evolving waste management practices. [ABSTRACT FROM AUTHOR]

Deep neural networks have achieved outstanding performance in computer vision tasks. Convolutional Neural Networks (CNNs) typically operate in the spatial domain with raw images, but in practice, images are usually stored and transmitted in their compressed representation where JPEG is one of the most widely used encoder. Also, these networks are computationally intensive and slow. This paper proposes performing the learning and inference processes in the compressed domain in order to reduce the computational complexity and improve the speed of popular CNNs. For this purpose, a novel graph-based frequency channel selection method is proposed to identify and select the most important frequency channels. The computational complexity is reduced by retaining the important frequency components and discarding the insignificant ones as well as eliminating the unnecessary layers of the network. Experimental results show that the modified ResNet-50 operating in the compressed domain is up to 70% faster than the spatial-based traditional ResNet-50 while resulting in similar classification accuracy. Moreover, this paper proposes a preprocessing step with partial encoding to improve the resilience to distortions caused by low-quality encoded images. Finally, we show that training a network with highly compressed data can achieve a good classification accuracy with up to 93% reduction in the storage requirements of the training data. [ABSTRACT FROM AUTHOR]

Pipe wall-thinning is a phenomenon whereby the thickness of pipes in a nuclear power plant decreases over time owing to extended operational years. This thickness reduction is caused by various long-term thermal aging mechanisms such as Flow-Accelerated Corrosion, Liquid Droplet Impingement Erosion, cavitation, and flashing. Reducing the thickness of the secondary system pipes to the point of rupture can lead to severe human casualties and significant economic losses. Consequently, domestic power plant operators regularly manage the power plant pipes. Ongoing research focuses on the identification and management of pipe wall-thinning. However, previous studies have encountered problems in accurately judging the decrease in pipe wall-thinning in the presence of noise. To overcome this, Convolutional Neural Network (CNN) models based on image feature analysis have been used. This approach allows for the differentiation of pipe wall-thinning feature from small-sized noise in a single image. However, there were difficulties in making accurate judgments for large-sized noise that resembled the feature of pipe wall-thinning. This paper aims to analyze the limitations of the current pipe wall-thinning evaluation methods and to achieve accurate pipe wall-thinning discrimination through time-series analysis of continuous pipe wall-thinning data. The proposed method employs a Convolutional Recurrent Neural Network (CRNN) model, integrating the Recurrent Neural Network(RNN) model with the CNN model. The image feature of the pipes, extracted using CNN, are utilized as inputs for the RNN. This enables the observation of how the image features of the pipes change over time. This feature differentiates from the time-series feature of noise that occurs suddenly. Through this method, the paper proposes a new approach for effectively identifying the gradual decrease in pipe wall-thinning, enabling precise assessment of pipe wall-thinning progression. [ABSTRACT FROM AUTHOR]

Over the past decades, deep learning techniques have attracted increased attention from various research and industrial domains aligned with the development of Industry Internet-of-Things(IIoT). Specifically, with the advantage of data-driven methods, industrial organizations are seeking novel proactive strategies supported by analytic models to guarantee the quality of their production by observing degradation or predicting failure ahead of the occurrence of the component or asset. Predictive strategies are expected to promise the influence of unnecessary maintenance interruptions and mitigate the consequence of that, hence, extending the remaining useful life of products. This paper conducts a survey of the utilization of deep learning technologies on engineering applications where they provide satisfactory solutions with respect to specific data types or input signals. 106 primary papers are reviewed on deep learning–driven approaches which mainly explore five of the most popular architectures in the application of predictive maintenance. The main content of this paper summarizes the common advantages of each architecture and, accordingly, points out their limitations, as well as describes the application scopes of fully connected deep neural networks, convolutional neural networks, stacked autoencoders, deep belief networks, and deep recurrent neural networks. Based on the technique discussion for each of them, we intend to provide a comprehensive understanding and guidance of the appropriate usage of deep learning architectures to devise an effective predictive maintenance strategy for the scientific and industrial developers whose expertise lies in the prior domain knowledge of multi-source isomerization data. Moreover, the main content demonstrated the summarization of the decisive factor by which the incremental stages of the approaches were determined, fundamentally including the dataset specification, feature extraction, and the integration of deep learning approaches. [ABSTRACT FROM AUTHOR]

Electricity load exhibits daily and weekly cyclical patterns as well as random characteristics. At present, prevailing deep learning models cannot learn electricity load cyclical and stochastic features adequately. This results in insufficient prediction accuracy and the scalability of current methods. To tackle these difficulties, this paper proposes a framework for electrical load prediction based on an Attention Mechanism Time Series Depthwise Separable Convolutional Neural Network (ELPF-ATDSCN). The framework starts by using the Maximum Information Coefficient for exogenous variable selection. It then incorporates a seasonal decomposition algorithm with manual feature engineering to extract the cyclical and stochastic features of the electrical load. Subsequently, the framework employs the ATDSCN to learn the cyclical and stochastic features of the electrical load. In addition, the Bayesian algorithm optimizes model hyperparameters for optimal model performance. Experimental results of point and interval load prediction on datasets from the US and Nordic power markets reveal that the ATDSCN model proposed in this paper enhances load prediction accuracy compared with other models. It can provide more reliable predictions for power system operation and dispatch. • A new attention mechanism is designed to improve the model memory capability. • The ATDSCN model is proposed for learning periodic and stochastic features of electricity loads. • A framework for electricity load prediction is proposed to improve the accuracy of electricity load forecasting. • The effectiveness of the proposed method is validated on the real world dataset. [ABSTRACT FROM AUTHOR]

Pipe wall-thinning is a phenomenon whereby the thickness of pipes in a nuclear power plant decreases over time owing to extended operational years. This thickness reduction is caused by various long-term thermal aging mechanisms such as Flow-Accelerated Corrosion, Liquid Droplet Impingement Erosion, cavitation, and flashing. Reducing the thickness of the secondary system pipes to the point of rupture can lead to severe human casualties and significant economic losses. Consequently, domestic power plant operators regularly manage the power plant pipes. Ongoing research focuses on the identification and management of pipe wall-thinning. However, previous studies have encountered problems in accurately judging the decrease in pipe wall-thinning in the presence of noise. To overcome this, Convolutional Neural Network (CNN) models based on image feature analysis have been used. This approach allows for the differentiation of pipe wall-thinning feature from small-sized noise in a single image. However, there were difficulties in making accurate judgments for large-sized noise that resembled the feature of pipe wall-thinning. This paper aims to analyze the limitations of the current pipe wall-thinning evaluation methods and to achieve accurate pipe wall-thinning discrimination through time-series analysis of continuous pipe wall-thinning data. The proposed method employs a Convolutional Recurrent Neural Network (CRNN) model, integrating the Recurrent Neural Network(RNN) model with the CNN model. The image feature of the pipes, extracted using CNN, are utilized as inputs for the RNN. This enables the observation of how the image features of the pipes change over time. This feature differentiates from the time-series feature of noise that occurs suddenly. Through this method, the paper proposes a new approach for effectively identifying the gradual decrease in pipe wall-thinning, enabling precise assessment of pipe wall-thinning progression. [ABSTRACT FROM AUTHOR]

• A multi-physics 3D FEM is established for exciting electrodes of ECS installed around pipeline subjected to LTCTMF. • The LTCTMF behavior is studied at different levels of LTCTMF. • A theoretical model results of LTCTMF compliance over creep time are analyzed using the Modulus degradation Approach. • A deep learning model is designed to predict compliance for various LTCTMF levels not included in previous FEM evaluation. • The proposed method performance is estimated by computing the accuracy rate, regression rate, and F-score indexes. The composite pipeline is a relatively new and viable alternative pipeline to the more commonly used traditional one due to its good mechanical and fatigue properties and lower production cost. For this purpose, it is critical to assess the mechanical and fatigue performance of composite pipeline material under various working conditions, particularly for monitoring long-term creep thermo-mechanical fatigue behavior. In this paper, a long-term creep thermo-mechanical fatigue behavior in a basalt fiber reinforced polymer laminated composite pipeline is detected through an integrated expert system consisting of the electrical capacitance sensors and a deep learning algorithm. First, a multi-physics finite element model is established for the simulation of a long-term creep thermo-mechanical fatigue behavior in basalt fiber reinforced polymer composite pipelines subjected to long-term fatigue loading of internal pressure and thermal effect. Second, theoretical model results of long-term creep thermo-mechanical fatigue compliance (S f (t)) over the time of creep are analyzed in pipeline material using the modulus degradation approach. Finally, an electrical potential change between electrical capacitance sensors electrodes corresponding to S f (t) over the time of creep for some levels of long-term creep thermo-mechanical fatigue (R f %) is recorded and then used in these datasets for training of the novel deep neural network based on one of the most widely used of the deep neural network families is the convolutional neural network, to predict S f (t) in pipeline for various R f % not included in the previous finite element model evaluation (i.e. electrical capacitance sensors technique). In this paper first is detected the long-term creep thermo-mechanical fatigue behavior for R f % = 25 % , 50 % , and 75 % from a finite element model and modulus degradation approach, and then is predicted the long-term creep thermo-mechanical fatigue behavior for R f % = 15 % , 35 % , 60 % , and 85 % respectively via deep neural network. The proposed method results are in good agreement with the experimental results available in the literature, thus verifying the accuracy and reliability of the proposed technique and its applicability to other different composite structures. [ABSTRACT FROM AUTHOR]

To tune the wave energy converter (WEC) controller parameters such as damping to reduce the line force during extreme wave conditions, future knowledge of the line force is required. To achieve this, the incoming wave and system state should be predicted for a few seconds in the future. It is rather an arduous task to predict the future knowledge of waves and the system's dynamic when dealing with breaking and steep waves, and the system is subject to various nonlinear forces. The classical model-based control strategies often rely on linear assumptions to estimate the WEC dynamics for the sake of simplicity. Unlike the model-based, the data-driven approaches are free from modeling errors and the algorithms are trained over the true and noisy data to predict non-linear system behaviors. Using data-driven approaches, we are able to model nonlinear dynamics. However, new questions emerge on the accuracy of the future wave and system state predictions, and how this uncertainty propagates into the final prediction of the line force. As incorrect damping may lead to excessive line force and detrimental damage to the system, these are the knowledge gaps that need to be addressed. The main purpose of this paper is to answer these questions through a survivability strategy for wave energy converters by providing a realistic perspective on the implementation of the neural network approaches by accounting for the errors in the input data. For this purpose, a series of neural networks is designed that first predicts the surface elevation for 0.36 s ahead, i.e. corresponding to 2 s in the full-scale WEC. This future knowledge of the wave elevation is then used to predict the system state (i.e. power take-off (PTO) translator position) for the same prediction horizon based on the PTO damping. This information is then fed to a convolutional neural network (CNN) that predicts the peak line force 0.36 s ahead. This paper also evaluates the predictive capabilities of neural network (NN) models, comparing them to classical autoregressive (AR) and Kalman filter methods. While the AR model exhibits slightly higher accuracy in fixed PTO system configurations, it falters in providing real-time predictions when system parameters undergo variations. In contrast, the NN prediction model excels by establishing a robust relationship between system configuration and output signals. Especially noteworthy is its ability to outperform classical methods with minimal training on a selective set of system configurations. Then, the sensitivity of the peak line force prediction to the uncertainties in the input data from NN models and the prediction horizon is analyzed. The neural network models are trained over the experimental data subjected to extreme sea states for a point absorber wave energy converter. The results suggest that the accuracy of the surface elevation prediction has an insignificant direct effect on the peak force prediction model. However, these uncertainties are reflected in the PTO translator position prediction, and the model is considerably sensitive to the accuracy of this prediction. This sensitivity nonetheless is less notable for higher PTO damping values. • Predicting wave elevation, PTO position, and mooring force via neural networks. • Finding optimum damping to reduce mooring force in extreme wave conditions. • Predicting the mooring force is sensitive to the PTO position prediction. [ABSTRACT FROM AUTHOR]

In personal identity recognition systems, detecting a person's age, gender, and language using voice signal characteristics is a crucial issue, especially because of the importance of security considerations. Age, gender, and language classification problems are important in signal processing because they are used to analyze and understand human behavior, interactions, and preferences. This can be especially useful in the fields of human-computer interaction, psychology, and social science research. In this paper, a new system for detecting a speaker's age, gender, and language based on deep learning models is presented. Deep learning models have shown great efficacy in various fields of signal processing. For this paper, a range of deep models were tested, including convolutional neural networks (CNNs), recurrent neural network (RNN), and a fine-tuning ResNet34 architecture. Additionally, techniques such as transfer learning were applied to improve the efficiency of the proposed system. The input voice signals are preprocessed by applying the spectro-temporal transform to obtain additional features that can be fed to the ResNet34 model, which is designed specifically for the task of voice signal processing. The dataset used in this paper was sourced from the Mozilla common voice initiative, which is dedicated to advancing speech recognition and language identification technologies. The performance of the proposed algorithm was evaluated in the presence of Gaussian noise to determine its robustness. The experimental results demonstrated that the proposed algorithm significantly outperformed basic algorithms and other deep neural networks in terms of age and gender recognition from voice signals. [ABSTRACT FROM AUTHOR]

Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras to record videos and images for understanding fish life and ecology, as well as for preserve the environment. There are currently many permanent underwater camera systems deployed at different places around the globe. In addition, there exists numerous studies that use temporary cameras to survey fish habitats. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. Deep Learning (DL) is a cutting-edge Artificial Intelligence (AI) technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localisation, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring. • Marine environments and fish habitats are widely surveyed using underwater cameras. • Deep learning (DL) offers an effective solution for processing underwater videos. • We write a tutorial on the key concepts and steps of DL for underwater monitoring. • We provide a survey of key DL-based visual fish monitoring methods/datasets. • We shed light on challenges/opportunities of DL for underwater fish monitoring. [ABSTRACT FROM AUTHOR]

This paper investigates a novel CNN-based architecture for image resizing forensics in the presence of Double-JPEG compression. Two sub-problems are addressed as part of this paper: first, the detection of resizing in DJPEG images, and second, determining the factor used to resize the image before the second JPEG compression. The image resizing technique used in this paper is image scaling. The proposed network is based on utilizing specially chosen high pass filters that constitute the pre-processing layer, followed by four convolutional layers, two fully connected layers, and a final softmax layer. The high-pass filters preserve the resizing clues in the form of residuals applied at the pre-processing layer, followed by utilizing the residuals as inputs to a CNN to detect the presence of resizing and also for estimating the resizing factor. The proposed network is fully end-to-end and does not rely on any hand-crafting. Experimental validation is carried out utilizing images from the RAISE dataset. Comparative analysis with four state-of-the-art methods demonstrates the proposed CNN-based architecture's superiority for resizing detection and resize factor determination sub-problems. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the most serious threats to human health among skin lesions. Computer-aided diagnosis methods can assist patients in identifying and detecting skin lesion types early, thereby enabling corresponding treatments. In this paper, we propose a dual-branch neural network model Conformer with Residual Cosine Similarity Attention and Bidirectional Convolutional fusion strategy, named RCSABC-Conformer. The core of this network structure comprises three parts: a Convolutional Neural Network (CNN) branch with Residual Cosine Similarity Attention (RCSA), a Transformer branch, and a Feature Couple Unit with Bidirectional Convolutional strategy (BC-FCU). The RCSA module calculates the cosine similarity value between the feature map generated by the convolutional operation and the feature map of the residual edge to assess whether their semantic information is similar. The semantic information of similar parts is weighted by exponential normalization to enhance the network's memory of similar features of the same type of skin lesion. The BC-FCU module interactively fuses local features and global representations of skin lesion images with different resolutions in the two branches. Specifically, when the global representations is integrated into local features, we introduce a new bidirectional convolution strategy to extract the feature map from both forward and backward directions, and then select the element with the smaller feature value from the two directions to fuse into local features. In this way, we can minimize the interference of the artifact features extracted by the Transformer branch on the CNN branch. In addition, taking advantage of the Transformer branch's capacity to construct global representations, our model can learn contextual semantic information of normal skin and lesion areas to enhance model robustness. We conducted experiments on three datasets, consisting of clinical and dermoscopic skin lesion images, as well as a hybrid of both. The experimental results show that RCSABC-Conformer outperforms both advanced and classical classification methods in terms of classification accuracy across all three datasets, without requiring an increase in the number of parameters and computational complexity. Compared with the baseline model, the classification accuracy of our proposed method improves by 2.40%, 5.39%, and 4.44% on the three datasets, respectively. To the best of our knowledge, this is the first study to apply an interactive fusion dual-branch network for multi-disease classification on different modalities of skin lesion databases. Code will be available at https://github.com/AlenLi817/RCSABC-Conformer. [ABSTRACT FROM AUTHOR]

Skin diseases are reported to contribute 1.79% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency. Our paper proposes an end-to-end framework for skin lesion classification, and our contributions are two-fold. Firstly, two fundamentally different algorithms are proposed for segmenting and extracting features from images during image preprocessing. Secondly, we present a deep convolutional neural network model, S-MobileNet that aims to classify 7 different types of skin lesions. We used the HAM10000 dataset, which consists of 10000 dermatoscopic images from different populations and is publicly available through the International Skin Imaging Collaboration (ISIC) Archive. The image data was preprocessed to make it suitable for modeling. Exploratory data analysis (EDA) was performed to understand various attributes and their relationships within the dataset. A modified version of a Gaussian filtering algorithm and SFTA was applied for image segmentation and feature extraction. The processed dataset was then fed into the S-MobileNet model. This model was designed to be lightweight and was analyzed in three dimensions: using the Relu Activation function, the Mish activation function, and applying compression at intermediary layers. In addition, an alternative approach for compressing layers in the S-MobileNet architecture was applied to ensure a lightweight model that does not compromise on performance. The model was trained using several experiments and assessed using various performance measures, including, loss, accuracy, precision, and the F1-score. Our results demonstrate an improvement in model performance when applying a preprocessing technique. The Mish activation function was shown to outperform Relu. Further, the classification accuracy of the compressed S-MobileNet was shown to outperform S-MobileNet. To conclude, our findings have shown that our proposed deep learning-based S-MobileNet model is the optimal approach for classifying skin lesion images in the HAM10000 dataset. In the future, our approach could be adapted and applied to other datasets, and validated to develop a skin lesion framework that can be utilized in real-time. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the most serious threats to human health among skin lesions. Computer-aided diagnosis methods can assist patients in identifying and detecting skin lesion types early, thereby enabling corresponding treatments. In this paper, we propose a dual-branch neural network model Conformer with Residual Cosine Similarity Attention and Bidirectional Convolutional fusion strategy, named RCSABC-Conformer. The core of this network structure comprises three parts: a Convolutional Neural Network (CNN) branch with Residual Cosine Similarity Attention (RCSA), a Transformer branch, and a Feature Couple Unit with Bidirectional Convolutional strategy (BC-FCU). The RCSA module calculates the cosine similarity value between the feature map generated by the convolutional operation and the feature map of the residual edge to assess whether their semantic information is similar. The semantic information of similar parts is weighted by exponential normalization to enhance the network's memory of similar features of the same type of skin lesion. The BC-FCU module interactively fuses local features and global representations of skin lesion images with different resolutions in the two branches. Specifically, when the global representations is integrated into local features, we introduce a new bidirectional convolution strategy to extract the feature map from both forward and backward directions, and then select the element with the smaller feature value from the two directions to fuse into local features. In this way, we can minimize the interference of the artifact features extracted by the Transformer branch on the CNN branch. In addition, taking advantage of the Transformer branch's capacity to construct global representations, our model can learn contextual semantic information of normal skin and lesion areas to enhance model robustness. We conducted experiments on three datasets, consisting of clinical and dermoscopic skin lesion images, as well as a hybrid of both. The experimental results show that RCSABC-Conformer outperforms both advanced and classical classification methods in terms of classification accuracy across all three datasets, without requiring an increase in the number of parameters and computational complexity. Compared with the baseline model, the classification accuracy of our proposed method improves by 2.40%, 5.39%, and 4.44% on the three datasets, respectively. To the best of our knowledge, this is the first study to apply an interactive fusion dual-branch network for multi-disease classification on different modalities of skin lesion databases. Code will be available at https://github.com/AlenLi817/RCSABC-Conformer. [ABSTRACT FROM AUTHOR]

Segmentation is an important task in a wide range of computer vision applications, including medical image analysis. Recent years have seen an increase in the complexity of medical image segmentation approaches based on sophisticated convolutional neural network architectures. This progress has led to incremental enhancements in performance on widely recognised benchmark datasets. However, most of the existing approaches are computationally demanding, which limits their practical applicability. This paper presents an expand-squeeze dual multiscale residual network (ESDMR-Net), which is a full y convolutional network that is particularly well-suited for resource-constrained computing hardware such as mobile devices. ESDMR-Net focusses on extracting multiscale features, enabling the learning of contextual dependencies among semantically distinct features. The ESDMR-Net architecture allows dual-stream information flow within encoder–decoder pairs. The expansion operation (depthwise separable convolution) makes all of the rich features with multiscale information available to the squeeze operation (bottleneck layer), which then extracts the necessary information for the segmentation task. The Expand-Squeeze (ES) block helps the network pay more attention to under-represented classes, which contributes to improved segmentation accuracy. To enhance the flow of information across multiple resolutions or scales, we integrated dual multiscale residual (DMR) blocks into the skip connection. This integration enables the decoder to access features from various levels of abstraction, ultimately resulting in more comprehensive feature representations. We present experiments on seven datasets from five distinct examples of applications: segmentation of retinal vessels (2 ×), skin lesions (2 ×), digestive tract polyps, lung regions, and cells. Our model demonstrates strong performance, with an F1 score of 0.8287%, 0.8211%, 0.9034%, 0.9451%, 0.9543%, 0.9840%, and 0.8424% on the DRIVE, CHASE, ISIC2017, ISIC2016, CVC-ClinicDB, MC and MoNuSeg datasets, respectively. Remarkably, our model achieves these results despite having significantly fewer trainable parameters, with a reduction of two or even three orders of magnitude. • Multiscale feature extraction to understand contextual dependencies. • The expand-squeeze operation facilitates multiscale information extraction. • Dual-stream information flow within encoder–decoder pairs. • The use of DMR blocks in skip connections improves the information flow. • The deep structure of the network allows it to learn more features. [ABSTRACT FROM AUTHOR]

[Display omitted] • Aerial thermographic inspection is done by thermal cameras embedded in UAVs. • A new approach based on image analysis with two consecutive Neural Networks. • Detect thermal patterns associated with superficial faults in an IoT platform. • A real case study is proposed with thermograms from three PV solar plants. • Accuracy of 99 % for PV panel detection, and 96 % for hot spot detection. • False positives are reduced in comparison with other studies. Aerial thermographic inspection is performed with thermal cameras embedded in unmanned aerial vehicles, being one of the most relevant monitoring techniques for photovoltaic panels. This technique allows the detection of thermal patterns associated with faults in the photovoltaic panels, although image analysis and fault detection require novel processing methodologies. The volume and variety of aerial thermal data together with the need for faster and more efficient analysis for early maintenance operations require efficient Internet of Things architectures to provide fast and effective diagnostics with easy access. This paper presents a new approach based on image analysis with two consecutive convolutional neural networks to detect hot spots in an Internet of Things platform and reduce the number of false positives. The architecture of the platform is designed to automatically process the received data through the implementation of different Convolutional Neural Networks. A real case study is proposed with thermograms from three photovoltaic solar plants with different sizes, shapes of panels and a wide temperature range. The results show an accuracy of 99 % for panel detection and 96 % for hot spot detection with a reduction of false positives compared to other studies, such as Support Vector Machine or different Artificial Neural Networks, demonstrating the robustness of the method. [ABSTRACT FROM AUTHOR]

Distributed generation sources (e.g. photovoltaic, wind power) are raised to fit the energy demands recently in proficient way. Further, adoption of renewable energy sources to the smart grid causes severe issues in quality of power signals. These problems can be overcome with correct identification and classification of power quality disturbances (PQDs) efficiently by the both producer and consumer. In this work, a new method for classification of PQDs based on signal processing technique and deep convolutional neural network (DCNN) is proposed. The proposed method compress the raw data of PQDs by using discrete wavelet transform (DWT) and multi-resolution analysis (MRA). Moreover, CNN has the ability to automatic feature selection and classification in a single stage instead of conventional methods i.e. artificial neural networks (ANNs). In this paper, DWT 1D-CNN is designed to extract and optimize multi-scale characteristics of disturbances. Further, 1-D convolutional, pooling and batch-normalization layers are added to facilitate normalize data, better generalization ability and, effective classification process against noisy data. A typical 11Kv distribution network is simulated in MATLAB to prove the validity of the proposed method in an essential part of smart grid. [ABSTRACT FROM AUTHOR]

• The proposed method had fused multi-level information compensation and U-Net. • The module designed for multi-level information compensation and compression. • The method had fused compressed features and correlation features. Recently, Deep Convolutional Neural Networks have demonstrated high-quality reconstruction in image super-resolution procedure. In this paper, we propose improved image super-resolution reconstruction via multi-level information compensation and U-Net network to address the problem that the image super-resolution reconstruction algorithm based on deep neural networks tends to lose feature information in the feature extraction process, resulting in the lack of texture and edge details in the reconstructed image. Firstly, we design the U-net like network for image super-resolution reconstruction, which performs multi-level feature extraction and channel compression for the input features through the down-channel branch. It fuses the compressed features and extracts the correlation features of different channels through the bottom module, and performs multi-level feature extraction and channel recovery for the compressed correlation features through the up-channel branch. The multi-level information compensation model is then designed to compensate for the information lost in the channel compression process and the information that is difficult to recover in the channel recovery process of U-net like network. The experimental results can show that the proposed algorithm achieves a significant improvement in Peak Signal-to-Noise Ratio and Structure Similarity Index and visual effect compared with the state-of-arts algorithms. The average experimental results of PSNR from proposed method had improved by 1.63 dB, 1.53 dB, 0.97 dB and 0.94 dB compared to SRCNN, HAT, DAT and CARN, respectively. [ABSTRACT FROM AUTHOR]

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case's CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the "gold standard" information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), positive predictive value (PPV) and F1-score. • A novel deep learning network is proposed for lung cancer subtypes classification. • An effective approach to generate pathological priors from CT images. • High-level features of paired pathological and CT images are obtained solely on CT. • Validated on a large-scale multi-center dataset. [ABSTRACT FROM AUTHOR]

Date fruit (Phoenix dactylifera L.) is known for its diversity in terms of genotypes that differ in shape, color, texture, taste, and other physical and sensory attributes, in addition to biochemical compounds. This richness generates complex, long, and tedious work at the scale of farm as well as in handling, processing, and storage facilities to prepare standardized, and homogenous final products that deal with consumer preferences and needs. To alleviate this, an increased importance and particular trend were observed recently for the use of artificial intelligence (AI) techniques and computer vision technology since they are considered as accurate, objective, non-destructive, fast, and efficient emerging technologies. In this context, this manuscript provides comprehensive insights into key applications of AI techniques and computer vision technology, using different classification models and algorithms developed on the basis (for example) of neural network classifiers, such as convolutional and artificial neural network, on harvest and postharvest assessment of date fruit. Thus, the importance of date fruit dataset used in the AI domain was discussed with link to all accessible datasets in the field of date palm. In addition, available and relevant AI tools and approaches applied to the postharvest of date fruit from maturity distinguishing until storage through other key handling operations, sorting, grading, etc., were detailed in the five sections of this paper. In terms of conclusion, this review showed that these smart technologies can play a substantial role and offer new opportunities to improve the whole value chain of date palm sector. • Harvesting and post-harvest handling impact quality and storability of dates. • These operations are tedious and subjective leading to quantitative/qualitative losses. • Artificial intelligence (AI) techniques are emergent technologies that can reduce these losses. • AI techniques are more accurate, fast, non-destructive and objective. [ABSTRACT FROM AUTHOR]

Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. This supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method. • Developing a novel approach to solve the OPF problems in real-time. • Wrap power system characteristics under a mold of an image-like shape. • Overcome the outburst of trainable parameters when training with large-scale systems. • Do not require looking up a new set of hyperparameters or re-coding a framework. • All time-consuming computations are shifted to the offline training process. [ABSTRACT FROM AUTHOR]

Dropout is a popular regularisation tool for deep neural classifiers, but it is applied regardless of the nature of the classification task: nominal or ordinal. Consequently, the order relation between the class labels of ordinal problems is ignored. In this paper, we propose the fusion of standard dropout and a new dropout methodology for ordinal classification regularising deep neural networks to avoid overfitting and improve generalisation, but taking into account the extra information of the ordinal task, which is exploited to improve performance. The correlation between the outputs of every neuron and the target labels is used to guide the dropout process: the higher the neuron is correlated with the expected labels, the lower its probability of being dropped. Given that randomness also plays a crucial role in the regularisation process, a balancing factor (β) is also added to the training process to determine the influence of the ordinality with respect to a constant probability, providing a hybrid ordinal regularisation method. An extensive battery of experiments shows that the new hybrid ordinal dropout methodology perform better than standard dropout, obtaining improved results in most evaluation metrics, including not only ordinal metrics but also nominal ones. • Deep ordinal classification. • Hybrid ordinal dropout as regularisation methodology for deep neural networks. • Fusion of standard and ordinal dropout techniques. • Balance factor for the influence of the ordinality in the hybrid ordinal dropout. • Comparison of the new hybrid ordinal dropout using ordinal benchmark datasets. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have gained immense popularity in recent years, finding their utility in diverse fields such as image recognition, natural language processing, and bio-informatics. Despite the remarkable progress made in deep learning theory, most studies on CNNs, especially in regression tasks, tend to heavily rely on the least squares loss function. However, there are situations where such learning algorithms may not suffice, particularly in the presence of heavy-tailed noises or outliers. This predicament emphasizes the necessity of exploring alternative loss functions that can handle such scenarios more effectively, thereby unleashing the true potential of CNNs. In this paper, we investigate the generalization error of deep CNNs with the rectified linear unit (ReLU) activation function for robust regression problems within an information-theoretic learning framework. Our study demonstrates that when the regression function exhibits an additive ridge structure and the noise possesses a finite p th moment, the empirical risk minimization scheme, generated by the maximum correntropy criterion and deep CNNs, achieves fast convergence rates. Notably, these rates align with the mini-max optimal convergence rates attained by fully connected neural network model with the Huber loss function up to a logarithmic factor. Additionally, we further establish the convergence rates of deep CNNs under the maximum correntropy criterion when the regression function resides in a Sobolev space on the sphere. [ABSTRACT FROM AUTHOR]

With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage systems and reserve. In this paper, a hybrid deep learning model and a convolutional neural network with memory is proposed, to provide intraday (2 h) solar irradiance forecasts using sequentially-collected images from public webcams. The performance of the proposed model is compared to those of a standard time-series forecast models, a linear regression as well as state-of-the-art neural networks. All models are trained and tested using images collected from two webcams on EPFL's campus for just over a year. The results show that the proposed method outperforms all other models and matches the state-of-the-art methodology while providing simplicity of implementation and efficient computation. • A novel method for estimating global horizontal irradiance is proposed. • The method uses images public webcams instead of all-sky cameras. • The method combines images from different cameras to improve forecasts' accuracy. • The method can be extended to include other types of data (meteorological data). • This simple method matches state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multiple advanced reactor designs envision deployment scenarios that feature reactor operations with significantly reduced operating staff compared to present reactors, or even feature fully autonomous operations. Since many of these new reactor designs, like small modular reactors (SMRs) and microreactors, also include extended fuel cycles which limit inspection intervals, load-following capabilities, and reduced access to critical components, safe and reliable semi- or fully autonomous operations must be enabled through an on-line monitoring (OLM) system that effectively detects and diagnoses any faults in the reactor. This paper presents the development of the Fault Detection Module of the previously proposed intelligent OLM system: the Fault Detection and Diagnosis Monitoring System (FDDMS). The Fault Detection Module monitors various plant parameters from 86 sensors throughout the nuclear power plant and accurately defines operations as nominal or abnormal. The methodology leverages the advantages of unsupervised learning and semi-supervised optimization data-driven techniques to successfully detect any fault in the system in real-time without fault information a priori. This allows the Fault Detection Module to identify any unknown fault that may affect the system. When integrated with the rest of the FDDMS architecture, the Fault Detection Module provides power transient dependent fault detection by using a separate model for steady state, ramping up in power, and ramping down in power operations. This makes the FDDMS especially applicable for load-following reactor systems. The Fault Detection Module architecture features a dimensionality reduction algorithm followed by an anomaly detection method. Three dimensionality reduction techniques: 1) Principal Component Analysis (PCA), 2) Deep Neural Network Autoencoder (DNNAE), and 3) Convolutional Neural Network Autoencoder (CNNAE); and three anomaly detection methods: 1) One-Class Support Vector Machine (OC-SVM), 2) Clustering (DBSCAN), and 3) Reconstruction Error (RecErr) thresholding were compared to find the best model for each power transient dataset. Precision rates, recall rates, F1-scores, and accuracies were used to compare model performance. The best models produced total accuracies of 94.50%, 93.63%, and 91.76% for the steady state, ramp up, and ramp down datasets, respectively, or 99.19%, 98.97%, and 97.09% when omitting 1%-severe fault cases. Specifically, the best performing models for the steady state, ramp up, and ramp down cases were the CNNAE-RecErr, DNNAE-DBSCAN, and CNNAE-RecErr, respectively. Recall rates were calculated for various fault types at multiple severity levels. The results show that the Fault Detection Module can identify any type of fault in the system with a delay of a few seconds on average. Additionally, robustness against noisy sensor data was tested, with models maintaining 100% accuracy at various levels of noise, and an illustrative real-time application of the methodology is provided. • The Fault Detection Module of the previously proposed FDDMS is developed, providing accurate identification of unknown faults in real-time for several power transients. • Several data-driven techniques were explored and compared for the dimensionality reduction and anomaly identification steps of the Fault Detection Module. • Demonstrative real-time transient examples of the Fault Detection Module showcase its accurate monitoring capabilities without fault information a priori, and the models showed robustness against sensor error. [ABSTRACT FROM AUTHOR]

To predict the dynamic response of high-speed trains (HSTs) passing through cable-stayed bridges (CSBs), this paper proposed a prediction framework based on the genetic algorithm (GA) and the neural network. Firstly, an HST-CSB coupled dynamic model was established using the rigid-flexible coupling method. Subsequently, a fused Temporal Convolutional Network and Long Short-Term Memory (TCN-LSTM) neural network model was developed, and its hyperparameters were optimized using the GA. The dataset was created based on the HST-CSB coupled dynamic model for training and testing the model. Additionally, the generalization capability of the model after adopting different track deformations was discussed. The main results were as follows: Comparative analysis with various neural network models revealed that the GA-TCN-LSTM model exhibited the most accurate prediction results across different train speeds. The significant temperature fluctuations of the bridge led to a drastic deterioration of the track smoothness, resulting in decreased predictive performance of the neural network models. The GA-TCN-LSTM model could optimize the hyperparameters, demonstrating good robustness and generalization capabilities. The predictive performance of the model improved when using the German low-interference spectrum, attributed to the greater irregularities in the German low-interference spectrum, resulting in larger train dynamic responses and stronger feature characteristics in the training data for the neural network. Different neural network models exhibited varying adaptability to track irregularities, with the GA-TCN-LSTM model demonstrating high accuracy in predicting dynamic responses under different train speeds. • A coupled dynamic model of high-speed train and cable-stayed bridges is established. • A model combining Temporal Convolutional Networks and Long Short-Term Memory network is established. • The genetic algorithm is employed to optimize the hyperparameters to mitigate the impact of them on model. • The generalization capability of the model under different conditions is discussed the performance. [ABSTRACT FROM AUTHOR]

Today, a significant content of the system on chips (SOCs) is dedicated to static random access memory (SRAM) cells. Due to the stress during SRAM operation, MOSFET aging is becoming an increasingly serious problem. The manufacturing variability of the MOSFETs causes further differences in the aging of various devices. This paper presents a simulation methodology using a deep neural network (DNN) to predict the performance of SRAM cells, taking into account the impacts of fabrication variations, temperature, and aging. A dataset of four input features and six output criteria is utilized, which are derived from power consumption, power supply transient current, signal rise time, read static noise margin (RSNM), and butterfly and N-curve. The accuracy of the long short-term memory (LSTM) model in predicting the performance of SRAM cells outperformed the other models such as multilayer perceptron (MLP), one-dimensional convolutional neural network (1D-CNN), gated recurrent unit (GRU), and recurrent neural network (RNN) by 1.85 % to 1.11 %. For instance, by processing the supply current data, the LSTM model achieved 97.41 %, 97.96 %, and 97.41 % accuracy in predicting the RMS power consumption, the signal rise time, and the average power supply current of the SRAM cell, respectively. • DNN successfully predicts the aging and process variation impact on SRAM performance. • Supply current illustrates superior results in performance prediction. • LSTM DNN model outperforms MLP, RNN, 1D-CNN, and GRU, exhibiting sufficient accuracy. [ABSTRACT FROM AUTHOR]

Accurate prediction of critical heat flux (CHF) is important for enhancing the reliability and efficiency of systems used in the energy and power engineering sectors. Traditional physical and mathematical models are constrained by current theoretical maturity. While machine learning (ML) techniques offer alternative ways for CHF modeling, its 'black-box' nature poses risks of producing outcomes that could potentially violate physical laws. In this paper, physics-informed ML (PIML) algorithms amalgamating characteristics of both ML and physical models are investigated. Three physical models for computing CHF (Zuber, CHF Look-Up Table (LUT), and Biasi) were paired with three ML models (Neural Network, Random Forest, Convolutional Neural Network) and trained and validated using the available CHF database. The uncertainty quantification (UQ) study was also conducted to assess the influence of fluctuating inputs on output's stability. Results indicated that PIML algorithms generally had lower rRSMEs than pure data-driven ML, showing higher prediction accuracy. The results of PIML also reflect physical principles more coherently. Among PIML models, LUT-informed Neural Network and LUT-informed Random Forest algorithms balanced computational accuracy with physical fidelity. UQ study revealed that under uncertain inputs, the LUT-informed Neural Network model yielded more concentrated and stable outcomes than LUT-informed Random Forest, evidencing strong robustness. The PIML method shows great potential in CHF prediction. • Physics-informed ML's performance in predicting CHF is compared against pure ML. • Results from physics-informed ML resonate more with theoretical physics, optimizing the "black-box" feature. • LUT-informed RF stands out for CHF prediction accuracy and its alignment with physical laws. • Uncertainty quantification findings pinpoint LUT-informed RF's challenges with uncertain inputs, signaling notable output distribution bias. [ABSTRACT FROM AUTHOR]

Solar radiation is an essential meteorological parameter for building energy efficiency analysis, and the quality of the data directly affects the analysis results. This paper investigates the estimation of hourly solar radiation based on the generation of the typical meteorological year(TMY) using various real meteorological parameters and limited solar radiation data. The focus of this paper is to use two types of neural network algorithms to improve the estimation accuracy and applicability, and to solve the problem of hourly solar radiation data acquisition in non-radiation areas. First, select two city station data and use three methods to generate TMY. Then, two neural network models, BP Neural Network (BP)，Convolutional Neural Network (CNN) are used to estimate the hourly solar radiation data and verify the results. Finally, by constructing a photovoltaic-integrated office building model, the accuracy of the hourly solar radiation estimation model is verified using energy consumption simulation and photovoltaic (PV) power generation simulation. The results show that this paper can well solve the problem of limited radiation data, which provides a new idea for the study of building energy efficiency in areas where radiation data is missing. • Multiple neural network is used to estimate the generation of solar radiation data using other meteorological parameters. • An advanced neural network is used to provide an effective TMY generation method in missing hourly solar radiation data. • The accuracy of the experimental results is verified by energy consumption simulation and photovoltaic power generation. [ABSTRACT FROM AUTHOR]

Quantization methods for convolutional neural network models can be broadly categorized into post-training quantization (PTQ) and quantization aware training (QAT). While PTQ offers the advantage of requiring only a small portion of the data for quantization, the resulting quantized model may not be as effective as QAT. To address this limitation, this paper proposes a novel quantization function named Attention Round. Unlike traditional quantization function that map 32 bit floating-point value w to nearby quantization levels, Attention Round allows w to be mapped to all possible quantization levels in the entire quantization space, expanding the quantization optimization space. The possibilities of mapping w to different quantization levels are inversely correlated with the distance between w and the quantization levels, regulated by a Gaussian decay function. Furthermore, to tackle the challenge of mixed precision quantization, this paper introduces a lossy coding length measure to assign quantization precision to different layers of the model, eliminating the need for solving a combinatorial optimization problem. Experimental evaluations on various models demonstrate the effectiveness of the proposed method. Notably, for ResNet18 and MobileNetV2, the PTQ approach achieves comparable quantization performance to QAT while utilizing only 1024 training data and 10 min for the quantization process. • Attention Round quantization function expands the quantization optimization space. • Mixed precision allocation method improves mixed precision quantization efficiency. • Enriched lightweight CNNs contribute to applications in resource-limited scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time state of health (SOH) estimation model based on a deep learning (DL) framework. The proposed model is a combination of two different architectures; a one-dimensional convolutional neural network (1D-CNN) and a bidirectional gated recurrent unit (BiGRU). The hybrid CNN-BiGRU uses the 1D CNN layers to extract pertinent features from input data and then relies on the BiGRU layers for sequence learning in both directions. To account for all SOH indicators, the proposed approach uses the current, voltage, and temperature measurements, which are readily obtainable from the electric vehicle's battery management system (BMS). This prevents the complex and time-consuming feature extraction used in most related papers. Since the hyperparameters have a significant impact on the performance of neural network models, a Bayesian Optimization (BO) technique based on the Gaussian Process (GP) was considered to tune the CNN-BiGRU model hyperparameters. Accordingly, the objective function was able to converge to a low Mean Squared Error (MSE) of 1.2 × 10 − 5 in just 19 iterations. Afterward, to verify the accuracy of the optimized model, a Lithium-ion battery dataset with several discharge profiles provided by the National Aeronautics and Space Administration (NASA) was used. The obtained results demonstrated the accuracy and robustness of the proposed method compared to other commonly used models. The CNN-BiGRU model yielded a Mean Absolute Error (MAE) of 2.080% and a root-mean-square error (RMSE) of 2.516% in the case of the battery set #C, referring to a set with 70 cycles already used at 24 °C. Additionally, the End of life (EOL) indicator error of zero cycles for the same data. [ABSTRACT FROM AUTHOR]

[Display omitted] • The first time using CBAM-CNN to predict particle size distribution during fluidized bed granulation. • The CBAM-CNN model was optimized by introducing Bayesian optimization and C-Mixup algorithms. • The performance of CBAM-CNN was compared to other classic models. • The modeling convenience of CBAM-CNN was evaluated. Monitoring the particle size distribution (PSD) is crucial for controlling product quality during fluidized bed granulation. This paper proposed a rapid analytical method that quantifies the D10, D50, and D90 values using a Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) framework tailored for deep learning with near-infrared (NIR) spectroscopy. This innovative framework, which fuses CBAM with CNN, excels at extracting intricate features while prioritizing crucial ones, thereby facilitating the creation of a robust multi-output regression model. To expand the training dataset, we incorporated the C-Mixup algorithm, ensuring that the deep learning model was trained comprehensively. Additionally, the Bayesian optimization algorithm was introduced to optimize the hyperparameters, improving the prediction performance of the deep learning model. Compared with the commonly used Partial Least Squares (PLS), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models, the CBAM-CNN model yielded higher prediction accuracy. Furthermore, the CBAM-CNN model avoided spectral preprocessing, preserved the spectral information to the maximum extent, and returned multiple predicted values at one time without degrading the prediction accuracy. Therefore, the CBAM-CNN model showed better prediction performance and modeling convenience for analyzing PSD values in fluidized bed granulation. [ABSTRACT FROM AUTHOR]

In this paper, we present an extensive review of the most recent works on Alzheimer's disease (AD) prediction, focusing on Moderate Cognitive Impairment (MCI) conversion prediction. We aimed to identify the most useful brain-magnetic resonance imaging (MRI) biomarkers and deep learning frameworks used for prediction. To achieve this, we analyzed more than 130 studies and reviewed 7 articles. A closer examination revealed that the hippocampus is an important region of interest (ROI) affected early by AD, and many related features help detect the disease in its early stages. However, when considered alone, this ROI is not sufficient to ensure high prediction performance. Therefore, several other brain regions can also provide additional information to improve prediction accuracy. Concerning state-of-the-art deep neural networks, the U-Net represents the most efficient architecture for hippocampus segmentation. The RESU-Net architecture achieved the highest Dice Similarity Coefficient (DSC) value, equal to 94%.For MCI conversion prediction, the best results were obtained by two models identifying significant landmarks from the entire brain for classification. The multi-stream convolutional neural network achieved the best Area Under the Curve (AUC) and specificity of 94.39% and 99.70%, respectively. Finally, a region ensemble model delivered the highest accuracy of 85.90%, highlighting the need for further research to address this challenging problem. [ABSTRACT FROM AUTHOR]

• Soil reflectance composites were generated with Sentinel-2 L2A data. • A multi-channel CNN learning framework for soil organic carbon prediction is proposed. • A dual input deep learning architecture improves the predictions. • The framework produced results that can be interpreted. The Copernicus Sentinel-2 multispectral imagery data may be aggregated to extract large-scale, bare soil, reflectance composites, which enable soil mapping applications. In this paper, this approach was tested in the German federal state of Bavaria, to provide estimations for soil organic carbon (SOC). Different temporal ranges were considered for the generation of the composites, including multi-annual and seasonal ranges. A novel multi-channel convolutional neural network (CNN) is proposed. By leveraging the advantages of deep learning techniques, it utilizes complementary information from different spectral pre-treatment techniques. The SOC predictions indicated little dissimilarity amongst the different composites, with the best performance attained for the six-year composite containing only spring months (RMSE = 12.03 g C · kg−1, R2 = 0.64, RPIQ = 0.89). It has been demonstrated that these outcomes outperform other well-known machine learning techniques. An ablation analysis was accordingly performed to evaluate the interplay of the CNN's different components to disentangle the advantages of each aspect of the proposed framework. Finally, a DUal inPut deep LearnIng architecture, named DUPLICITE, is proposed, which concatenates deep spectral features derived from the CNN mentioned earlier, as well as topographical and environmental covariates through an artificial neural network (ANN) to exploit their complementarity. The proposed approach was demonstrated to provide an improvement in the overall prediction performance (RMSE = 11.60 gC · kg−1, R2 = 0.67, RPIQ = 0.92). [ABSTRACT FROM AUTHOR]

• For the first time, neural networks were applied to denoise in spatial heterodyne spectroscopy, and significant results were achieved. • Four corresponding network models were constructed for denoising different brightness and noise levels. • The denoising ability of DnCNN in spatial heterodyne interferograms were comprehensively evaluated. • The reasonable selection of four neural network schemes were discussed. Spatial heterodyne spectroscopy is a hyperspectral remote sensing technology. With the continuous improvement of signal detection accuracy, new methods are urgently needed to further reduce the interference of noise on the information contained in spatial heterodyne interferograms. Convolutional neural networks, as an emerging field, have achieved good results in image denoising in recent years. This paper applies convolutional neural networks to the field of spatial heterodyne spectroscopy, constructs and trains four deep convolutional neural network models for denoising spatial heterodyne interferograms under different brightness and Gaussian noise conditions, and compares their performance with other algorithms. The results show that deep convolutional neural networks have significant advantages in denoising spatial heterodyne interferograms. Finally, a comparison of four neural networks was conducted to explore how to reasonably select network models. This work provides new ideas and effective solutions for reducing the interference of noise on spatial heterodyne spectral information and improving signal detection accuracy. [ABSTRACT FROM AUTHOR]

The rapid growth of online music education has led to increased security risks from cyber intrusions. This paper proposes public cloud networks oriented deep neural networks for effective intrusion detection in online music education environments. Specifically, a novel intrusion detection framework is developed, comprising fuzzy logic based feature selection, chronological salp swarm algorithm optimized deep belief networks, and gated recurrent unit integrated convolutional neural networks. Detailed methodologies are presented for the fuzzy logic system, chronological salp optimization, deep belief network architecture, and convolutional neural networks. Comprehensive experiments are conducted on the NSL-KDD and CICIDS2017 datasets. Various deep neural networks are evaluated and compared, including multi-layer perceptrons, convolutional neural networks, deep belief networks, recurrent neural networks, and the proposed models. Experimental results demonstrate that the proposed fuzzy feature selection and chronological salp swarm algorithm optimized deep belief network achieves a test accuracy of 97.33 %, outperforming other peer models. The gated recurrent unit integrated convolutional neural network obtains a test accuracy of 98.46 %, superior to state-of-the-art methods. While the experiments on the newly created dataset for intrusion detection in cloud-based online music education demonstrate that the proposed models outperform the benchmarks. The experiments verify the effectiveness of the proposed deep learning frameworks for intrusion detection in online music education cloud networks. [ABSTRACT FROM AUTHOR]

Performances are a key concern in aerospace vehicles, requiring safer structures with as little consumption as possible. Composite materials replaced aluminum alloys even in primary aerospace structures to achieve higher performances with lighter components. However, random events such as low-velocity impacts may induce damages that are typically more dangerous and mostly not visible than metals. The damage tolerance (DT) approach is adopted for the fatigue design of aircraft, but fracture mechanisms and propagation of failure prediction in composite structures are much more challenging. Consequently, the DT approach is still costly for these types of structures. It can be achieved only through expensive experimental testing and a drastic reduction of allowable stress levels and maintenance intervals by applying scattering factors due to the uncertainties involved in their original estimations. Structural health monitoring (SHM) systems deal mainly with sensorised structures providing signals related to their "load and health status" to reduce maintenance and weights. At the same time, the use of Deep Neural Networks (DNNs) based on strategic engineering criteria, for instance, may represent an effective and efficient analysis tool to promote faster data analysis and classification. In the field of aircraft maintenance, this approach may lead, for example, to a faster awareness of an aircraft/fleet situation or predict failures. Deep learning-based networks provide automatic feature extraction at different levels of abstraction. With the universal function approximation property of neural networks, it learns the inverse mapping from input space (signals) to target space (damage classes). Starting from the well-established Structural Health Monitoring (SHM) technologies, a network of distributed sensors embedded throughout the structure could be used for real-time structural monitoring and data acquisition. Structural data will constitute an enormous amount of information that can be adequately filtered with the help of specific DNNs designed and trained for the structural context and aimed to classify and identify significant parameters. The authors have collaborated for some years to collect wave propagation signals through experimental tests and validated numerical models of healthy and damaged composite structures, and developed machine learning algorithms (mainly dense and convolutional neural networks) aimed at signal classification and analysis for damage detection and localization. This paper presents a brief review of relevant works about SHM employing Machine Learning methodologies and summarizes the most promising approaches developed during the last years jointly by the two research groups and presents a critical analysis of obtained results and subsequent future activities. [ABSTRACT FROM AUTHOR]

Multi-view deep neural networks have shown excellent performance on 3D shape classification tasks. However, global features aggregated from multiple views data often lack content information and spatial relationship, which leads to difficult identification the small variance among subcategories in the same category. To solve this problem, in this paper, a novel multiscale dilated convolution neural network termed as MSDCNN is proposed for multi-view fine-grained 3D shape classification. Firstly, a sequence of views are rendered from 12-viewpoints around the input 3D shape by the sequential view capturing module. Then, the first 22 convolution layers of ResNeXt50 is employed to extract the semantic features of each view, and a global mixed feature map is obtained through the element-wise maximum operation of the 12 output feature maps. Furthermore, attention dilated module (ADM), which combines four concatenated attention dilated block (ADB), is designed to extract larger receptive field features from global mixed feature map to enhance context information among the views. Specifically, each ADB is consisted by an attention mechanism module and a dilated convolution with different dilation rates. In addition, prediction module with label smoothing is proposed to classify features, which contains 3 × 3 convolution and adaptive average pooling. The performance of our method is validated experimentally on the ModelNet10, ModelNet40 and FG3D datasets. Experimental results demonstrate the effectiveness and superiority of the proposed MSDCNN framework for 3D shape fine-grained classification. [ABSTRACT FROM AUTHOR]

An increasing need of running Convolutional Neural Network (CNN) models on mobile devices encourages the studies on efficient and lightweight neural network model. In this paper, an Inverse Residual Multi-Branch Network named IremulbNet is proposed to solve the problem of insufficient classification accuracy in existing lightweight network models. The core module of this model is to reconstruct an inverse residual structure, in which a special feature fusion method, multi-branch feature extraction, and depthwise separable convolution techniques are used to improve the classification accuracy. The performance of model is tested using image databases. Experimental results show that for the fine-grained image dataset Imagenet-woof, IremulbNet achieved 10.9%, 12.2%, and 15.3% higher accuracy than that of MobileNet V3, ShuffleNet V2, and PeleeNet, respectively. Moreover, it can reduce inference time (GPU) about 42.09% and 75.56% compared to classic ResNet50 and DenseNet121. • A lightweight IremulbNet is proposed to achieve high accuracy image recognition with a small number of parameters and operations. • A special feature fusion method, multi-branch feature extraction, and depthwise separable convolution are used to improve the performance of the network. • A significant improvement in recognition accuracy can be obtained in image classification tasks with low computation load. [ABSTRACT FROM AUTHOR]

Hardware Trojans (HT) are the most malicious components attacking modern integrated circuits (ICs). Information leakage, incorrect functionality, and overheating are the major effects of HT insertion. Due to the outsourcing of multi-stage digital circuit design and fabrication, HTs can be inserted by rogues at every stage. The types of HTs and insertion methods evolve continuously and their detection becomes more complex. Consequently, designing integrated circuits robust to HT insertion seems efficient. A prerequisite for such a strategy is the evaluation of integrated circuit vulnerability to HT insertion. There are several factors involved in the vulnerability of fabricated integrated circuits such as the distribution of white spaces in the IC layout, the ratio of unutilized routing resources, the amount of activity of the circuit's nodes, the geometrical properties of the circuit's gates, the delay of various paths, and etc. The interrelation of these diverse factors makes IC vulnerability modeling a challenging problem. In this paper, a deep neural network (DNN) based approach is developed to combine all the effective factors in IC vulnerability analysis. Firstly, we generate a comprehensive dataset containing various types and sizes of hardware Trojans inserted into benchmark circuits. The benchmark circuits used in this study are ISCAS 85, ISCAS 89, and ITC 99. Then using the transfer learning concept seven state-of-the-art deep convolutional neural networks are trained and verified using the constructed dataset to classify every integrated circuit into three vulnerability levels (low, moderate, and highly vulnerable classes). Simulation results show that our proposed method achieves almost 95 % accuracy in the most successful case (VGG16). The lowest accuracy belongs to Inception V3 (∼87.65 %) which is much better than the most successful previous studies (72 %). This is mainly because the DNN-based method handles shortcomings (such as incomplete modeling of vulnerability-related factors) in the previous state-of-the-art methods efficiently. [ABSTRACT FROM AUTHOR]

Pedestrian evacuation dynamics research is crucial for safety engineering and crowd management. Accurate and fast extraction of pedestrian trajectories from experimental videos is essential for reliable evacuation data and effective strategy development. In this paper, we propose a novel method for extracting pedestrian trajectories from evacuation experiment videos based on deep learning techniques. The method consists of two modules: pedestrian detection and location prediction, which use Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, respectively. Experimental results show that our method achieves high accuracy with short time compared to traditional methods. Furthermore, our experiments demonstrate that our method can accurately extract information on pedestrian evacuation dynamics. The proposed method provides a fast and reliable approach to extracting pedestrian evacuation dynamics information, which has the potential to be utilized by researchers, urban planners, and emergency management personnel to develop more effective evacuation strategies, improve crowd management, and ultimately enhance public safety. [ABSTRACT FROM AUTHOR]

Microseismic technology has been widely used in geological hazard monitoring. However, the effective identification and classification of microseismic events in mines have always been a challenge in hazard monitoring. In this paper, a new method combining the empirical mode decomposition (EMD) algorithm and artificial neural network is proposed for the classification and identification of microseismic waveforms. This is the first study to explore such a model in the microseismic monitoring of coal mines. The data for this study were collected from a coal mine in northern China. A total of 2768 microseismic events and 2435 non-microseismic events were manually selected from a large amount of data. Firstly, the microseismic waveform reconstruction was performed using the 4th to 8th Intrinsic Mode Function (IMF) obtained by the EMD algorithm. Secondly, the peak factor, clearance factor, impulse factor, kurtosis, skewness, and waveform factor of each reconstructed microseismic waveform were used as features. Subsequently, the convolutional neural network (CNN) was used to classify microseismic events. Among them, data from 3600 microseismic events were used as the training set, and data from 1603 microseismic events were used as the test set. Finally, the original waveform data were used as input and compared with the classification results processed with the EMD algorithm. The classification methods of the BP neural network and Radial Basis Function (RBF) neural network were used for verification. The results of the processed features showed a significant increase in accuracy. The classification accuracy of CNN, BP neural network, and RBF neural network are 97.37%, 91.99%, and 94.23%, respectively. The results show that the utilization of the feature processing technique and CNN algorithm in this study demonstrates superior efficacy in microseismic event classification, which can be used in practice. • The EMD algorithm and ANN was constructed to classify microseismic waveforms. • Six features of the reconstructed waveform were selected as the classification basis. • The EMD algorithm was used to make the six waveform characteristics more significant. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) can result in suboptimal monitoring performance due to nonlinearity, dynamics, and local characteristics in modern complex industrial processes. To surmount these limitations, this paper first proposes a novel data construction method to model the short-term autocorrelation and spatial correlations as a three-dimensional matrix and then reorder the elements of it to better encode the local and temporal structures. Subsequently, we design a new structure called Long-range Discriminative Attention (LDA) based on the self-attention mechanism to enlarge the receptive field of the original convolutional neural networks (CNNs) to extract global features. Finally, we propose a monitoring model named Long-range Discriminative Attention Autoencoder (LDCA) based on LDA to extract structural features between long-range and local variables from the constructed matrix. The effectiveness of the method in fault detection is verified by numerical examples and a three-phase flow process. • Model the spatiotemporal correlation into a three-dimensional tensor. • Reorder elements in data tensor to present the meaning of images. • Long-range discriminative attention mechanism for feature extraction. [ABSTRACT FROM AUTHOR]

Dynamic analysis of forced-vibration systems in civil engineering could be computationally inefficient or even hard to converge if the systems are stiff or highly complicated. Rapid advances in machine learning make it possible to formulate surrogate models for forced-vibration systems using neural networks. The widely used neural networks such as the convolutional neural network (CNN), recurrent neural network (RNN), etc., usually require a constant sampling rate and data length, thus they are difficult to be implemented for real-time calculation of the dynamic system with varying sampling rates. Recently, the continuous-time state-space neural network (CSNN) has shown the capability to lift these restrictions and has been drawing growing attention from the community. In this paper, we propose a generalized CSNN model for various forced-vibration systems (linear and nonlinear). The CSNN model comprises two sets of independent neural networks aiming to compute the state derivative and system response, respectively. Both neural networks adopt linear and nonlinear layers in parallel, instead of only fully connected nonlinear layers as adopted in the literature. This configuration is aimed to enhance the CSNN model with its capability to recognize the linear and nonlinear behaviors of systems. Additionally, the bias options in the CSNN model are all turned off to improve the stability of the model in the long-term time-series forecast, premised on the assumption that the forced-vibration systems are dissipative systems without drift, which is the most common case in civil engineering. Integration on the state derivative at the current time step is executed to obtain the state at the next time step using the explicit 4th-order Runge–Kutta method. Both numerical and experimental illustrative examples are provided, demonstrating that the CSNN model can achieve high performance and training efficiency with a few hyper-parameters, and thus is highly promising for engineering applications. • A novel continuous-time state-space neural network (CSNN) model is proposed for data-driven modeling of forced-vibration linear/nonlinear systems. • The CSNN model does not require any prior knowledge of the system, and is not restricted to the pre-designated sampling rate and input data length. • The CSNN model has much fewer hyper-parameters than traditional neural network models and is unlikely to induce overfitting. • Numerical and experimental studies demonstrate that the CSNN model is superior to LSTM and PhyCNN in modeling both linear and nonlinear systems. [ABSTRACT FROM AUTHOR]

Recently, convolutional neural networks (CNNs), which obtain powerful deep features in an end-to-end manner, have achieved powerful performance in remote sensing scene classification. However, the average or maximum pooling operations defined in the spatial domain and coarser-resolution features with high levels cannot extract reliable features and clear boundaries for small-scale targets in remote sensing scene imagery. This paper attempts to address these problems and proposes a multi-domain sematic high-order network for scene classification, named MSHNet. First, wavelet-spatial and detachable pooling blocks defined in the wavelet and spatial domains are inserted at the end of the convolutional block to learn the features in a more structural fusion manner. Second, multi-scale and multi-resolution semantic embedding modules are proposed to take full advantage of the complementary information and effectively maintain the spatial structures of learned deep features. Third, we employ a factorized bilinear coding approach to obtain compact and discriminative second-order features. MSHNet is thoroughly evaluated on two publicly available benchmarks, i.e., AID (Aerial Image Dataset) and NWPU-RESISC45 (Northwestern Polytechnical University-Remote Sensing Image Scene Classification 45). The extensive results illustrate that our MSHNet is competitive with other related multi-scale deep neural networks. [Display omitted] • Wavelet-spatial and detachable pooling maintain structures of learned features. • Factorized bilinear coding method with semantic features captures rich statistics. • MSHNet obtains competitive performance on NWPU-RESISC45 and AID datasets. [ABSTRACT FROM AUTHOR]

Spatiotemporal assessment of solar potential is one of the most promising solutions to find suitable locations for future photovoltaic systems' placement. However, accurate assessment of solar potential on large areas can be challenging due to missing data or computational complexity. In this paper, a fully convolutional neural network based method for high-resolution spatiotemporal assessment of solar potential by using remote sensing data is presented. The method is trained and validated on the area of 32 km2 of the Maribor city, Slovenia, and tested on the 6 different locations in Slovenia and Germany. The proposed method was tested against the simulation algorithm, which utilized isotropic Liu Jordan diffuse model or anisotropic Perez model with sky view factor based shading. On average, a normalized root mean square error (NRMSE) of 6.06% and mean absolute percentage error (MAPE) of 4.29% was achieved in test locations against the simulation based on Liu-Jordan model. When training the fully convolutional neural network models against the ground truth, generated with more advanced Perez diffuse model, the average NRMSE was 8.37% and MAPE of 10.90% was achieved across all test locations. Additionally, it was shown that the proposed method can assess solar potential in high-resolution from the input in lower resolution with higher accuracy than the simulation algorithm, while being up to 1500-times faster. [ABSTRACT FROM AUTHOR]

Spatiotemporal assessment of solar potential is one of the most promising solutions to find suitable locations for future photovoltaic systems' placement. However, accurate assessment of solar potential on large areas can be challenging due to missing data or computational complexity. In this paper, a fully convolutional neural network based method for high-resolution spatiotemporal assessment of solar potential by using remote sensing data is presented. The method is trained and validated on the area of 32 km2 of the Maribor city, Slovenia, and tested on the 6 different locations in Slovenia and Germany. The proposed method was tested against the simulation algorithm, which utilized isotropic Liu Jordan diffuse model or anisotropic Perez model with sky view factor based shading. On average, a normalized root mean square error (NRMSE) of 6.06% and mean absolute percentage error (MAPE) of 4.29% was achieved in test locations against the simulation based on Liu-Jordan model. When training the fully convolutional neural network models against the ground truth, generated with more advanced Perez diffuse model, the average NRMSE was 8.37% and MAPE of 10.90% was achieved across all test locations. Additionally, it was shown that the proposed method can assess solar potential in high-resolution from the input in lower resolution with higher accuracy than the simulation algorithm, while being up to 1500-times faster. [ABSTRACT FROM AUTHOR]

As the deep neural networks (DNNs) with satisfied expression ability usually require large scale to gain adequate performance, and deploying large-scale DNNs on resource-limited environments is still a challenge, neural network compression becomes a hot topic nowadays. Among the multiple compression methods, tensor decomposition reveals many specific advantages, such as regular data structure comes from linear algebra, convenient approach of training from scratch, ideal compression ratio, etc. Nevertheless, for some compact neural modules such as two-dimensional/three-dimensional (2D/3D) convolutional kernels, traditional tensor decomposition in the way of approximation has encountered intractable obstacles. Fortunately, some works utilize the tensor mapping approach, which just regards the data structure of tensor decomposition as neural layers, to reconstruct the convolutional kernel into a new lightweight module with several thinner convolutional kernels. The only two flies in the ointment are there lack necessary theories of tensor mapping, and there is still no tensor mapping way to compress high-order three-dimensional convolutional neural networks (3DCNNs). In this paper, we first deeply analyze the tensor mapping theory including convergence and precision, which separately establishes the rationality of tensor mapping and its superiority over the traditional tensor approximation, according to the Lottery Ticket Hypothesis. Then we propose an efficient method termed as 3D-KCPNet, to compress 3DCNNs based on the novel Kronecker canonical polyadic (KCP) tensor decomposition. The proposed method can not only compress the 3D convolutional kernels, but also convert a 3D convolution to efficient 1 × 1 × 1 and 2D depthwise convolutions. The experiments on the video recognition datasets VIVA Challenge, UCF11, UCF50, and UCF101 show that the accuracy of 3D-KCPNet can surpass its original baseline model and the corresponding tensor approximation model. [ABSTRACT FROM AUTHOR]