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In the production of paper cups in industrial factories, it is tried to print high quality cups with less waste loss with the help of sensors and heating resistances mounted on the paper cup machine. In this study, a system that detects faulty products based on image processing and removes it by controlling the machine with servo motors, asynchronous motors and programmable logic controller (PLC) is designed. For fault product detection, classification has been performed using real-time Haarcascade algorithm and You Only Look Once (YOLO) algorithm which is a deep learning methods, and real-time object detection has been carried out using the OpenCv library. With this study, an effective faulty product detection and removing hardware system was realized by adapting artificial intelligence algorithms to a machine used in industry. Based on the results, a whole system can be applied to systems that involve removing a faulty product from a band in any production, packaging etc. facility is proposed. A hardware consisting of servo motors, asynchronous motors and PLC was designed to separate faulty cups from the existing paper cup production machine in this study. Then, a data set composed of 1068 images was created with images taken from the camera for faulty and faultless paper cups. Using this dataset, the effect of different deep learning methods on performance in the real-time system has been examined and successful results have been obtained. The optimal outcome was achieved, yielding a real-time application accuracy rate of 90.8% through the utilization of the Yolov5x architecture. [ABSTRACT FROM AUTHOR]

The existing learning-based image dehazing methods usually adopt the encoder–decoder architecture with convolutional neural networks to estimate latent haze-free images from hazy images. However, the limited receptive field of convolutional neural networks may not effectively capture structure-level information, causing the model to be unable to the haze density. To solve this problem, this paper proposes a bi-decoder structure with a dense non-pooling encoder to enhance the structural features that are closely related to the haze density. Compared with conventional methods, the main advantage of our method is the integration of an additional coarse decoder in the encoder–decoder architecture, where a hybrid feature convolution (HFC) block is utilized to enlarge the receptive field to extract the structure of the image. Besides the difference in the network, the inputs of the fine and coarse decoders are non-pooling and pooling respectively. Moreover, a multi-scale feature attention (MSFA) module is proposed to selectively enhance the haze-relevant feature outputs of fine and coarse decoders. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms most state-of-the-art methods in terms of image quality and quantitative metrics. Especially in the NH-HAZE dataset, its PSNR exceeds other methods by more than 2.13 dB. In the end, this paper applies this dehazing technology to object detection. The code of this paper and data are available online at https://github.com/Qiaoyu-K/Bi-Decoder-Dehazing. [ABSTRACT FROM AUTHOR]

Computer vision technology has attracted significant interest in the condition monitoring (CM) community due to its potential to automate visual inspection and analysis of structures and components. By facilitating the processing and interpretation of visual information, including images and video data, computer vision holds promise for CM applications. However, it is essential to distinguish computer vision from non-contact CM techniques regarding their underlying principles and methods. While computer vision enables non-contact, remote monitoring, and condition assessment with minimal disruption to daily operations, it is distinct from non-contact CM techniques, which utilize various sensors to assess the condition of assets without physical contact or interference. Building upon the potential of computer vision technology, this survey paper presents a comprehensive overview of the current state-of-the-art CM methods based on computer vision and deep learning (DL) techniques, focusing on their application in monitoring synthetic fiber ropes (SFRs). SFRs are a viable alternative to steel wire ropes for underwater equipment and cranes that handle heavy loads. This is due to their high resistance to frictional wear, high tensile strength, lightweight, and flexibility. New materials, technologies, and processes for CM are being developed to meet the growing demand for SFRs. The paper explores ongoing research in applications that monitor the wear and aging of materials, as well as estimate their remaining useful life. The survey briefly discusses the traditional non-destructive testing and machine learning (ML) methods for CM applications. More importantly, DL-based methods, including supervised, unsupervised, semi-supervised, and self-supervised methods, are discussed in detail, together with the use of deep generative models and the recently developed diffusion models in the generation of synthetic datasets. Furthermore, the paper addresses the difficulties present in DL-based CM applications, including the scarcity of labeled data and the complexity and variety of the models used. The article ends by discussing the benefits of employing DL-based visual methods to understand SFR degradation processes, particularly in monitoring and maintenance. • This paper surveys visual data-based DL techniques for CM of SFRs. • Reviews defect detection DL models: supervised, unsupervised, semi, and self supervised. • Describes DGMs and diffusion models for generating synthetic data. • Presents TL techniques using pre-trained models for CM tasks. • Addresses challenges and opportunities for vision-based CM applications. [ABSTRACT FROM AUTHOR]

In response to the challenges of low clarity and insufficient training samples in underwater concrete crack detection, this paper proposes an improved unsupervised learning method for the underwater concrete crack image enhancement (increase image quality) and augmentation (increase in number of images). Detecting structural defects underwater is vital for ensuring the proper functioning of underwater structures. However, the harsh underwater environment often leads to low-resolution images of concrete cracks, which in turn diminishes detection accuracy. Additionally, the challenges associated with underwater image collection make it difficult to gather an ample number of samples for training deep learning models to effectively detect these defects. Therefore, this paper proposes an unsupervised learning model that can simultaneously enhance and augment underwater concrete crack images in order to achieve better detection results. For the enhancement of underwater concrete crack images, the proposed method significantly improves the recognizability of images in turbid water environments and exhibits significant superiority compared to other similar methods, the values of the three evaluation indicators decreased by 45.2%, 40.4%, and 69.1%, respectively. Regarding the augmentation of underwater concrete crack images, the proposed method can translate images from clear water and waterless environments to muddy water environments. Compared to other methods, improved image quality by at least 61.2%, the proposed method generates images with better authenticity. This validates that the proposed cross domain translation strategy can simultaneously enhancing and augmenting underwater concrete crack images. • An unsupervised learning model is used to improving quality number of underwater carack images. • The residual network is used to extract local features of low-resolution crack images. • The Swin Transformer is used to obtain local and global features of high-resolution crack images. [ABSTRACT FROM AUTHOR]

With the rapid development of smart cities, urban infrastructure systems produce massive data that reflect their real-time operational conditions. These data provide insights for system monitoring and operation, and many existing studies develop various machine learning methods to understand recurrent system conditions. However, the extreme operational conditions, which could cause system failures, are not well explored. Importantly, methods for the recurrent conditions may not be suitable for modeling the failures. To fill this gap, this paper proposes a novel task of failure prediction, which aims to predict system failures before they happen. To solve this task, a generalized model that integrates survival analysis and the temporal convolutional networks, which is called TCNSurv in this paper, is developed to predict the distribution of system failure time. The model mainly contains three components: a data processing module, a time series module, and a survival analysis module. Specifically, the time series module employs Temporal Convolutional Networks to enable the modeling of temporal dependencies in time series data, and the survival analysis module explicitly formulates the probability of system failures. The proposed model is validated on three real-world datasets: vibration, traffic, and electricity, and results show that the developed model outperforms state-of-the-art regression-based models, survival analysis-based models, as well as integrated models. The research outcomes could help to understand the failure patterns of urban infrastructure systems and to develop early warning systems for smart cities. [ABSTRACT FROM AUTHOR]

Artificial intelligence has advanced the applications of sensor-based human motion capture and recognition technology in various engineering fields, such as human–robot collaboration and health monitoring. Deep learning methods can achieve satisfactory recognition results when provided with sufficient labeled data. However, labeling a large dataset is expensive and time-consuming in practical applications. To address this issue, this paper proposes a deep convolutional transformer-based contrastive self-supervised (DCTCSS) model under the bootstrap your own latent (BYOL) framework. The DCTCSS model aims to achieve reliable activity recognition using only a small amount of labeled data. Firstly, a deep convolutional transformer (DCT) model is proposed as the backbone of DCTCSS model, to learn high-level feature representations from unlabeled data in pre-training period. Subsequently, a simple linear classifier is trained with supervised fine-tuning using a limited amount of labeled data to recognize activities. In addition, this paper experimentally formulates a random data augmentation strategy to increase the diversity of input data. The performance of the DCTCSS model is evaluated and compared with several state-of-the-art algorithms on three datasets widely used in daily life, medical monitoring, and intelligent manufacturing. Experimental results show that the DCTCSS model achieves mean F1 scores of 95.64%, 88.39%, and 98.40% on the UCI-HAR, Skoda, and Mhealth datasets, respectively, using only 10% of the labeled data. These results outperform both supervised and unsupervised baseline models. Consequently, the DCTCSS model demonstrates its effectiveness in reducing the dependence on large amounts of labeled data while still achieving competitive activity recognition performance. [ABSTRACT FROM AUTHOR]

The reliability of bolt connections significantly impacts the operational state and lifespan of industrial equipment. Vision-based noncontact methods exhibit high efficiency in bolt loosening detection. However, limited image features hinder measurement accuracy. To improve bolt loosening detection performance, this paper proposes a novel deep learning backbone, the high-resolution cross-scale transformer, to extract high precision keypoints for bolt three-dimensional model construction. Simultaneously, a monocular vision measurement model is established to get the bolt exposed length and evaluate the connection loosening state. The proposed backbone hybridizes the advantages of high-resolution architecture and transformer, realizing global information aggregation and fine-grained image details. A simplified module, dual-scale multi-head self-attention, is designed to reduce the computational redundancy caused by the implementation of high-resolution multi-branch architecture. In the experiment section, the high-resolution cross-scale transformer outperforms other keypoint detection baselines, achieving the top one performance with 91.6 average precision and 84.9 average recall. The monocular vision measurement model realizes a 0.053 mm error with a 0.028 mm standard deviation, satisfying the industrial implementation requirement. Additionally, the model is tested on different industrial situations and an additional outside dataset, indicating the model's robustness and actual environment adaptability. • A monocular vision measurement method for bolt loosening detection is proposed. • It is the first attempt to introduce the transformer mechanism in bolt keypoint detection. • A new keypoint detection backbone is proposed in this paper for bolt feature extraction. • The 3D exposed length of bolts is calculated with a monocular vision system. • The bolt loosening detection method is validated. [ABSTRACT FROM AUTHOR]

This paper delivers an exhaustive analysis of the fusion of multi-sensor technologies, including traditional sensors such as cameras, Light Detection and Ranging(LiDAR), Radio Detection and Ranging(RADAR), and ultrasonic sensors, with Artificial Intelligence(AI) powered methodologies in obstacle detection for Autonomous Vehicles(AVs). With the growing momentum in AVs adoption, a heightened need exists for versatile and resilient obstacle detection systems. Our research delves into study of literatures, where proposed approaches assimilate data from this diverse sensor suite, integrated through Deep Learning(DL) techniques, to refine AV performance. Recent advancements and prevailing challenges within the domain are thoroughly examined, with particular focus on the integration of sensor fusion techniques, the facilitation of real-time processing via edge and fog computing, and the implementation of advanced artificial intelligence architectures, including Convolutional Neural Networks(CNNs), Recurrent Neural Networks(RNNs), and Generative Adversarial Networks(GANs), to enhance data interpretation efficacy. In conclusion, the paper underscores the critical contribution of multi-sensor arrays and deep learning in enhancing the safety and reliability of autonomous vehicles, offering significant perspectives for future research and technological progress. [ABSTRACT FROM AUTHOR]

In recent years, decomposition-based combination models have been widely used in wind power prediction. This type of method decomposes the highly volatile wind power into some relatively smooth subsequences, which reduces the difficulty of modeling. However, this might use information from future data in advance, creating the illusion of high prediction accuracy. Therefore, this paper proposes a wind power ultra-short-term prediction model based on fixed scale dual mode decomposition (FSDMD) and deep learning networks. First, the wind power series after fixed scale blocking is decomposed using ensemble empirical mode decomposition (EEMD), and use the improved variational mode decomposition (VMD) based on Spearman rank order correlation coefficient (SROCC) to decompose the obtained high-frequency components twice. Then, the appropriate mode components were selected by calculating the SROCC and experimental analysis, and combined with the convolutional neural network (CNN) and the bidirectional long short-term memory (BiLSTM) network to train the model. Finally, the historical data of wind turbines in a wind farm in Northwest China is used for example verification, and the comparison with other models in the two scenarios of sufficient and insufficient features. The results show that the proposed FSDMD–CNN–BiLSTM model has high prediction accuracy in both scenarios. Especially in the scenario of insufficient features, compared with CNN-BiLSTM model, RMSE, MAE and MAPE are reduced by 8.20,14.24 and 0.15, respectively. In addition, this paper verifies that mode decomposition can improve the performance of prediction model without using future features, which provides ideas for solving similar problems. [ABSTRACT FROM AUTHOR]

Accurate detection of pavement markings at the pixel level is crucial for enhancing traffic safety. The majority of current advanced deep-learning networks predominantly focus on localized features, neglecting the global context of pavement image. Such networks often result in discontinuous segmentation outcomes and suboptimal recovery of local details. In this paper, a robust model named C-Transformer is proposed to provide an effective solution to this challenge. The contributions of this paper primarily involve two aspects. Firstly, the proposed C-Transformer is designed to succinctly integrate convolution operations and self-attention, facilitating a comprehensive understanding of essential features. Secondly, an efficient Feed-Forward Network called Inverse Residual Feed-Forward Network is also proposed in this paper and deployed in C-Transformer to improve latent representations. Experimental results demonstrate that, compared to other state-of-the-art networks, the proposed C-Transformer achieves a performance enhancement of 0.93% in F-measure and a 1.64% improvement in Intersection-Over-Union. In particular, the robustness and effectiveness of the C-Transformer in accurate pavement marking detection are proved through field test results. This paper illustrates the feasibility of employing a hybrid Convolutional neural network-Transformer-based network for automatic robust pavement marking detection under noisy conditions. [ABSTRACT FROM AUTHOR]

Gathering pipelines are one of the key upstream infrastructures in the gas industry that link production well to the processing plant. Leak detection is critical for ensuring the safety of pipeline transmission. The detection of small leakage in gathering pipelines consistently poses a formidable challenge. In this paper, a process model is built based on health data of supervisory control and data acquisition system from the actual operating pipeline. In the model structure, the convolutional neural network is used to extract the spatial features, the bi-directional long short-term memory is used to extract the temporal features, and the attention mechanism is employed to allocate the model's attention resources reasonably. Next, the residual between the entity pipeline's output data and the process model's output data is used as a monitoring indicator of the operating state of the pipeline. A clustering-based boundary determination method is proposed to recognize the centroid of normal and small leak conditions, and pipeline leak detection is performed by the Euclidean distance between the monitoring indicator and the centroid. This paper explores the feasibility of fast modeling and leak detection with limited hardware. Field tests for the validation of the proposed methods were implemented in two in-service natural gas gathering pipeline. The experimental results demonstrate that the proposed method significantly enhances the detection performance of small-size leak. The leak detection rates of 94.06% and 92.16% evinces the potency of the proposed method applied in the leak detection of gathering pipelines across diverse real-world scenarios. [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]

Fruits and vegetables are major sources of nutrients for the majority of the population across the globe. With the rapid increase in population, the objectives of the future agro-industry are to reduce product loss while increasing product quality and productivity considerably. Consequently, farmers need to be assisted with cutting-edge technologies for sustainable, eco-friendly, and efficient farming. Smart farming for early disease recognition and control is the current hot-spot research objective in the fruitage domain. The precision agriculture era has been revolutionized by federating cutting-edge technologies like machine learning, deep learning, and, the Internet-of-Things. However, the existing studies focused on the impact of individual technology on single or multiple cultivars of edible fruits or vegetables. Limited areas of the fruitage disease remain explored, necessitating further investigation into the research gaps and challenges identified for implementing the smart practices in real-field farmlands. In this paper, a comprehensive survey of recent advancements in fruit and vegetable disease identification and classification is presented. The technology-wise state-of-the-art findings, gaps, challenges, and future opportunities for fruitage disease recognition have been presented, covering 99 research articles. Moreover, the corpus of publicly available fruit and vegetable datasets has been investigated, with the existing gaps, improvements, and future requirements. The research paper concludes with challenges and a future outlook that promises to be a very significant and valuable resource for researchers working in the area of agronomic disease monitoring. [ABSTRACT FROM AUTHOR]

The advancement of deep learning and neural networks has led to the widespread adoption of neural word embeddings as a prominent lexical representation method in natural language processing. With the help of the neural language model trained by the contextual information of large scale text, the neural word embedding obtained by the neural language model captures more semantic correlation in the semantic space, while ignoring the semantic similarity. It will incur high computational cost and time costs during the training process of the model. To better inject semantic similarity into the distribution space and reduce time cost, we perform post processing learning of neural word embeddings using deep metric learning. This paper proposes a lexical enhancement method based on flexible margins and multiple samples learning. In this method, we embed the lexical entailment constraint relations into neural word embeddings. By categorizing the set of lexical constraints and penalizing the negative samples to different degrees according to the gap between categories, and allowing the positive and negative samples to learn from each other in the distributed space. The method we propose significantly improves neural word embeddings. By evaluating neural word embedded vocabulary similarity, the benchmark accuracy is improved to 75%. The method shows great competitiveness in text similarity tasks and text categorization tasks. These findings summarize research results and provide strong support for further applications. • This paper proposes the LexFMSL model to optimize word embeddings. • This paper enhances LEAR model performance through flexible margins and learning methods. • The LexFMSL model achieves good results on different word embedding datasets. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is a crucial detection technique extensively employed in various contexts demanding accurate identification of human actions. Currently, mainstream HAR approaches frequently rely on sequential data generated by wearable sensors, with a primary focus on extracting feature representations that align with various human activities. Nevertheless, complex and diverse human activities encompass various kinds of features and substantial variations in the features emphasized across different activities, making it challenging for current artificial intelligence methods to achieve accurate recognition. To address this challenge, this paper introduces a network framework called the Multi-Feature Combining Attention Neural Network (MFCANN). This framework incorporates diverse feature extraction, along with both local and global feature attention mechanisms. It is composed of stacked Multi-Feature Combining Attention Blocks (MFCAB) designed by us. In contrast to traditional convolutional methods, MFCAB parallelly stacks various convolutional components, enabling the extraction of a broader range of features from human activity data. Additionally, we propose the Intra-Module Attention Block (Intra-MAB) and the Inter-Module Attention Block (Inter-MAB), which simultaneously focus on local fine-grained features within module feature maps and global distinguishing features across module feature maps. This aims to achieve more targeted feature learning, reinforcing the network's ability to distinguish between different human activities. Abundant experimental results demonstrate that the proposed MFCANN in this paper outperforms current mainstream deep learning algorithms in HAR tasks, achieving recognition accuracies of 0.9813, 0.9324, and 0.9930 on the UCI-HAR, USC-HAD, and RealWorld datasets respectively. We release our code at https://github.com/RangerHeart/MFCANN. [ABSTRACT FROM AUTHOR]

In the domain of complex urban traffic networks, real-time Traffic Signal Control (TSC) serves as a pivotal strategy for mitigating congestion. Coordinating signal control across multiple intersections involves considerable complexity. Deep Reinforcement Learning (DRL) has emerged as a robust solution. In recent years, there has been rapid advancement in TSC methods, with numerous researchers employing various novel DRL methodologies. Yet, existing surveys lack timeliness and universality in capturing the latest research. There is a notable gap in current research surveys with respect to the latest developments in TSC. Therefore, the focus of this paper lies in analyzing the most recent papers from the past five years, with the aim to provide a comprehensive and multi-dimensional review of the evolution of DRL in TSC. The survey categorizes current research based on model setups, utilized algorithms, and application scenarios. Finally, this paper highlights potential directions for future TSC research. [ABSTRACT FROM AUTHOR]

Deep metric learning aims to construct an embedding space where samples belonging to the same class are closely grouped together, while samples from different classes are widely separated. Many existing deep metric learning methods focus on maximizing the difference between inter-class features, and semantic related information is obtained by increasing the distance between samples of different classes in the embedding space. However, by compressing all positive samples together and creating large margins between different classes, the local structure between similar samples is inadvertently disrupted. This disregard for the intra-class variance presents in the local structure results in an embedding space that exhibits lower generalizability when faced with unseen classes. Consequently, the network tends to overfit the training set and performs poorly on the test set, potentially leading to crashes during evaluation. Taking this into account, this paper introduces a self-supervised generative assisted ranking framework that offers a semi-supervised perspective on learning intra-class variance within traditional supervised deep metric learning. Specifically, this paper employs various intensities and diversities to synthesize samples based on positive examples, aiming to simulate the complex variations between similar samples. Then, an intra-class ranking loss function is devised based on the principles of self-supervised learning. This loss function constrains the ordering relationship of synthesized samples according to their generation intensity, enabling the network to capture subtle intra-class differences and maintain the intra-class distribution. With the adoption of this approach, a more realistic embedding space can be achieved, preserving both the global and local structures of samples. [Display omitted] • We propose a generative boundary that improves the accuracy of generation. • The generative boundary can balance inter-class variance and intra-class variance. • We propose a ranking loss to learn the intra-class variance. [ABSTRACT FROM AUTHOR]

Quick response codes are widely utilized in commodity anti-counterfeiting, and their traceability via smartphones is an effective authentication method. However, quick response codes are susceptible to illegal duplication, and the images captured by smartphones are easily affected by changes in light and environmental noise, leading to unsatisfactory verification results. To address these issues, this paper introduces a novel approach by combining the squeeze-excitation attention module with bottleneck residual block. It presents a squeeze-excitation bottleneck residual network for printer source identification of quick response codes. The squeeze-excitation attention module pays more attention to the features that represent the printer attributes, reduces the interference of useless information, and has low computational consumption; while the bottleneck residual block has the advantages of few parameters, strong feature extraction capability, and good expandability. Thus, the performance can be improved effectively only with a small increasing in parameters. The experimental results verify that the proposed method achieves an accuracy of 98.77% under smartphone capture conditions, it outperforms other convolutional neural network-based methods in terms of identification accuracy. The deep learning model proposed in this paper can be generalized and applied to the printer source identification of paper content in the civil, criminal investigation and judicial fields. [ABSTRACT FROM AUTHOR]

With the increasing complexity of the power grid, the precision of the thermal power units' execution of automatic generation control (AGC) commands is gradually increasing the impact on the online regulation of the power grid. The deviation between the actual output of thermal power units and the AGC command of the grid will not only affect the consumption of new energy output, but also endanger the safe operation of the grid. This paper introduces "deep learning" technology to solve the problem. Firstly, an AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. The algorithm builds a neural network model to accurately predict the unit output and the confidence evaluation of the prediction results. Next, a high-dimensional input preprocessing strategy based on variational autoencoder (VAE) is proposed to reduce the dimensionality of the model input attributes, improving the convergence and accuracy of the model. Finally, an AGC optimal command fast inversion solution method (AOCFISM) is designed. This method transforms the unit output deviation problem into an objective optimisation problem. And improve the efficiency of solving the optimal AGC command value by constraining the unit command value. The calculation results show that the error of the prediction results of the model proposed in this paper is 5% lower than that of the traditional neural network. The difference between the output value of the optimal AGC command and the expected output value obtained is less than 0.5 MV, which can support AGC online decision-making.© 2017 Elsevier Inc. All rights reserved. • An AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. • Results show the ACEEI-CEA performs well in command prediction compared to the state-of-the-art methods. • ACEEI-CEA achieves confidence assessment of predicted values. • The pre-processing strategy based on variational autoencoder shortens the training time of the model. • The AGC optimal command fast inversion solution method (AOCFISM) can obtain the AGC optimal command value. [ABSTRACT FROM AUTHOR]

The field of comic recognition (CR) has gained significant attention in recent years due to the growing popularity of digital comics. CR involves the automatic identification and extraction of comic panels, speech bubbles, and text from digital comic images, which has numerous applications in the digital media industry. The objective of this review paper is to conduct a systematic literature analysis of the current state of the art in CR techniques and methods. The authors strive to present all major discoveries related to representation models, techniques, tools, datasets, and comparative evaluations of recognition models. The present study utilizes the conventional Systematic Literature Review (SLR) approach and covers a wide range of 72 articles published in 17 prominent journals and 29 notable conferences and workshops. The available literature on recognition models and techniques is broadly categorized into three groups: computer vision (CV) technique being the most commonly used (75%) in the selected studies, followed by non-CV techniques (13.88%) and hybrid techniques (11.11%). Among the widely used datasets in the selected studies, Manga 109 and Ebdtheque are recognized by 50% of them. Within the field of CR, authors such as Jean-Christophe Burie, Christophe Rigaud, Jean-Marc Ogier, and Zhi Tang have received significant attention. The paper summarizes the findings of the CR research and highlights the need for standardization of accuracy norms and datasets. It emphasizes the importance of exploring and developing hybrid techniques for efficient CR tasks. [ABSTRACT FROM AUTHOR]

Autonomous-rail Rapid Transit (ART) is a new type of road public transportation with an automatic driving function, and it is one of the main places for epidemic spread. Scientific wearing of masks has been proven to be an extremely effective way to prevent an epidemic. However, drivers wearing masks make it difficult for traditional driver alert detection methods based on facial information to operate effectively. This paper proposes an ART driver alert detection system based on multimodal information fusion and deep learning, which can be applied to drivers wearing masks. The detection system is mainly divided into three modules: data acquisition module, preprocessing module, and vigilance detection module. The data acquisition module collects real-time face thermal imaging data, and environment temperature information of 13 people in the vigilance and fatigue state of the ART simulated driving environment. The preprocessing module proposes a face thermal imaging key information extraction method based on the constraints between Yolov5 and thermal image interframe constraints and extracts the mean temperature information and the hot voxel information (HV) of the eyes, forehead, and mask areas. Then the extracted information and the environment temperature information are transmitted together to the vigilance detection module for learning and classification. The vigilance detection module proposes a lightweight hybrid-attention optimization network, and this paper extracts training sets and testing sets from the data of 13 subjects. And the training set data is enhanced. Each sample is 90 frames. The maximum accuracy rate of the testing set reaches 99.57%. The feasibility of the detection system proposed in this paper is proved, which makes the thermal imaging and attention model show great potential in the application of traffic safety operation and maintenance. • A contactless vigilance detection method is proposed for ART drivers with face masks. • The multi-modal fusion method uses facial thermal image and environmental data. • A lightweight network model that integrates hybrid-attention mechanism is proposed. • A method for extracting face key information from thermal images is proposed. • The testing set shows that the system is feasible and the accuracy rate is 99.57%. [ABSTRACT FROM AUTHOR]

Industrial products typically lack defects in smart manufacturing systems, which leads to an extremely imbalanced task of recognizing surface defects. With this imbalanced sample distribution, machine learning and deep learning algorithms preferentially learn features from the majority classes, potentially leading to inaccurate results. Addressing the issue of sample imbalance has thus emerged as a critical area of research within the field of industrial intelligent manufacturing. This paper discusses the imbalanced sample problem of industrial product surface defect detection algorithms, and proposes the existence of "four imbalances and two uncertainties". It also summarizes the industrial product surface dataset and innovatively adds the imbalance rate comparison to the dataset. In this study, data re-sampling, data expansion, feature extraction and identification, and re-weighting of category weights are elaborated at the level of data and algorithm respectively. Additionally, the paper explores prospective directions for future research, including supervised and unsupervised learning, transfer learning, anomaly detection, quality prediction, and future challenges. It is hoped to lay a solid foundation for the more far-reaching development of smart manufacturing and surface defect detection methods. And provide some directions for the research of sample imbalance and long-tail problems. [ABSTRACT FROM AUTHOR]

The fault management process is facilitated by equipping power distribution systems with automated devices, especially remote-controlled switches (RCSs). Although RCS plays a key role in improving the system's reliability, it imposes significant investment, installation, and maintenance costs. Hence, RCSs should be optimally located in distribution feeders for the highest profit. In previous works, reliability-oriented mathematical optimization models have been formulated to reach this goal. However, the number of test solutions exponentially grows with the problem size to find the globally optimal solution. This paper uses machine learning to propose a scalable and easy-to-implement model for optimal switch placement in real power distribution systems. At first, the features of candidate points for installing RCSs are introduced. Then, a learning model is applied to deeply explore the relationship between these features and optimal locations for installing RCSs. After training, the learning-based surrogate model directly determines the optimal RCS placement strategy in real power distribution systems by leveraging knowledge gained from past experiences. Simulation results demonstrate that the proposed surrogate model is approximately 29 times faster than the integer programming-based mathematical model, without a significant loss of accuracy, when implemented on a modified 11 kV network connected to Bus 4 of the Roy Billiton test system. This paper also employs explainable artificial intelligence (XAI) tools to select the most important features, where the Hamming loss is decreased by approximately 5%. [ABSTRACT FROM AUTHOR]

Accurate prediction of structural response under earthquake is of great significance for structural damage and performance evaluation. In order to improve the efficiency of structure time history response prediction, this paper proposes a novel SeisFormer model based on the self-attention mechanism and deep learning technology. Through autoregressive prediction, the SeisFormer can achieve real-time prediction of the response time histories of a large number of nodes in the structure under seismic action and can effectively solve the problem of data scarcity. Four case studies are performed to verify the accuracy and efficiency of the proposed methodology, including validation on datasets obtained from elastoplastic seismic analysis of a single-story structure, a three-story structure, and an eleven-story structure, and measured data of a shaking table test model. In addition, this paper further studies the prediction accuracy of the SeisFormer through ablation experiments and comparative experiments. The experimental results show that the SeisFormer can accurately predict the acceleration, velocity, and displacement time histories of numerous nodes in the structure. The prediction accuracy outperforms the LSTM model, and the prediction speed is 193–109,824 times faster than finite element method. Furthermore, with data augmentation through autoregressive prediction, the SeisFormer model can achieve efficient and accurate predictions when training data is exceptionally scarce, enabling engineering applications. • A self-attention mechanism-based method for real-time refined structural response prediction. • An autoregressive data utilizing strategy. • A new deep learning model called SeisFormer. • The SeisFormer outperforms LSTM in structural response prediction under seismic action. • The SeisFormer predicts structural response 193 to 109,824 times faster than the traditional finite element method. [ABSTRACT FROM AUTHOR]

The long-term accumulation of ammonia nitrogen in aquaculture seriously affects the life of fish and even causes large-scale death. Moreover, when the concentration of ammonia nitrogen starts to accumulate, it is a judgment standard to provide early warning through the changes in fish behavior to prevent excessive ammonia nitrogen in water. Therefore, this paper proposes a novel approach to monitoring water quality for aquaculture based on deep learning and three-dimensional movement trajectory. The improved YOLOv8 model was used as the object detection approach to obtain three-dimensional position information of fish by combining Kalman filter, Kuhn Munkres (KM) algorithm, and Kernelized Correlation Filters (KCF) algorithm. The proposed approach was evaluated in the recovery experiment of acute ammonia nitrogen stress of sturgeon, bass, and crucian. The experimental results show that the precision, recall, mAP@0.5 , and mAP@0.5:0.95 of the improved YOLOv8 model are 0.964, 0.914, 0.979, and 0.602, respectively. In addition, the proposed three-dimensional positioning approach can qualitatively and quantitatively analyze the fish behavior in different stages and further explores the fish behavior changes through behavior trajectories, volumes of exercise, spatial distribution, and movement velocity. This research provides a new method and idea for studying the abnormal behavior of aquatic animals under ammonia nitrogen stress and has theoretical and practical significance. • An object detection method combining three-dimensional position information is proposed. • This paper proposes improved YOLOv8 approach combined with multi head self-attention and Wise-IoU. • Quantitative analysis is conducted on the behavioral changes of fish under ammonia nitrogen stress. • This research provides a new method and idea for studying the abnormal behavior of aquatic animals. [ABSTRACT FROM AUTHOR]

For shafting rotation equipment, in the fault diagnosis based on vibration analysis, the sampling signal is a waveform of a short moment, which is quasi-static information at the corresponding moment. A single quasi-static piece of information does not necessarily contain obvious fault information, however, in a longer period much larger than the sampling period, the time series information composed of multiple quasi-static combinations may well-characterize the fault. Traditional deep learning algorithms often focus on fault features in quasi-static information, ignoring time series features, which results in low diagnostic accuracy. To solve the above problems, this paper firstly presents a fault diagnosis framework of time series and deep fusion network (TDFN). Then, a method to extract time series information and quasi-static information based on expert experience knowledge is proposed, and finally studies the method of using this framework to train the network and fuse the two types of features. Different from existing deep learning methods, TDFN includes time series blocks, depth blocks and fusion blocks. This paper uses laboratory data and industrial rotor data for verification, the results show that the overall accuracy of TDFN reached 93.8% and it does not experience a drop in accuracy after fusion. The diagnosis accuracy is higher than traditional networks, providing guiding suggestions for the management and operation of on-site industrial equipment. [ABSTRACT FROM AUTHOR]

The fundus is the only region where arteries, veins and capillaries can be directly observed. Morphological changes of retinal vessels in the fundus are signals for the appearance of many fundus and cardiovascular diseases. Consequently, the segmentation of retinal vessels is crucial for diagnosing and screening various diseases. In recent years, a large amount of research publications has been published on retinal vessel segmentation. This paper offers a comprehensive review of retinal vessel automatic segmentation research, covering both traditional methods and deep learning methods, including unsupervised and supervised learning methods. Especially, the supervised learning methods are summarized and analyzed from three aspects: traditional CNN-based, GAN-based, and UNet-based methods. This paper also presents an overview of the development of retinal vessel automatic segmentation and analyzes the advantages and disadvantages of existing segmentation methods. The results are shown in two at-a-glance tables. Finally, our work provides a faster and better look to recognize and understand the field of retinal vessel segmentation. • The advancements of automatic retinal vessel segmentation have been summarized. • A summary has been conducted from both supervised and unsupervised learning. • We analyzed the advantages and disadvantages of existing vessel segmentation methods. • Our work provides a more efficient and comprehensive way to understanding the field. [ABSTRACT FROM AUTHOR]

The aging of transformer oil–paper insulation (OPI) contributes significantly to transformer failure and reduced service life. Therefore, reliable and precise evaluation of the aging status of transformer insulation condition is crucial in averting transformer failure. This article proposes a novel deep learning-based aging condition assessment technique of transformers by combining FDS (Frequency domain spectroscopy) and squeeze-and-excitation-enabled convolutional neural network (SE-CNN). An intrinsic product-based transformation using a Gramian angular field is employed to dimensionally expand the 1-D FDS signals acquired from OPI samples into 2D images. Each feature extraction channel in the SE-CNN-based aging diagnosis framework is assigned a specific weight, enabling the model to concentrate on the most relevant features while discarding the rest. Furthermore, the effect of moisture variation on the model is also considered under three different conditions to ensure the consequence of compensation for the effect of moisture on FDS. A comparison with traditional machine learning models has also been made to ensure the viability of the proposed methodology. Additionally, the model is interpreted by creating explanation weight plots of the feature coefficients using a local interpretable model-agnostic explanations (LIME) method in Python. The findings demonstrate that the novel aging diagnosis framework visually assesses the transformer's OPI system with the highest accuracy of 99.12% in the test set. Furthermore, it facilitates the automatic extraction of deep aging features, thus providing better generalization and convergence capability. [ABSTRACT FROM AUTHOR]

The classification rate is the most significant factor affecting the selection of an appropriate classification approach for achieving the desired quality of decisions. Several researchers have evaluated the influence of different features on the performance of classification approaches, but cost/loss functions, despite their theoretical impact on the classification rate, have been less considered in the comparative literature review. In this paper, a comparative study has been presented on the influence of different cost/loss functions on the classification rate of different classifiers. For this purpose, five of the most well-known and widely used categories of cost/loss functions and three of the most popular main categories of classification approaches are considered. Also, 23 benchmark datasets from six different domains are selected. In this paper, all modeling of the statistical and intelligent classifiers are run in the Eviews and MATLAB package software, respectively. In addition, all mathematical modeling of models run in the GAMS package software. Empirical results indicate that cost/loss functions can significantly affect the classification rate. Numerical results show that the discrete cost/loss function, by average 3.91% and 8.31% improvement rather than semi-continuous and continuous cost/loss functions, respectively, is the most effective one. This clearly illustrates that the consistency of the cost/loss function with the goal function of the classification approach has a positive direct relationship with the classification rate. Furthermore, nonlinear versions of semi-continuous and continuous cost/loss functions are on average slightly more efficient than linear ones. Numerical results indicate that nonlinear forms can averagely yield an 88.99% classification rate that is overall 0.98% higher than linear ones, which achieved an 88.13% classification rate. Although the type of cost/loss function can affect the classification rate of different classifiers, the improvement amount varies based on the type of classifier used. In general, the improvement amount in statistical classifiers, i.e., 15.07% is significantly higher than others, and intelligent by 7.86% and deep learning by 5.20% are in the second and third places, respectively. This overall demonstrates that the complexity of the used classifiers has a negative relationship with the improvement of the classification rate in changing cost/loss function. Finally, numerical results indicate that the difference between improvement amounts achieved by varying cost/loss functions, in general, is not significant and is independent of the type of classifier, as well as the type and domain/area of data. [ABSTRACT FROM AUTHOR]

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance. [ABSTRACT FROM AUTHOR]

Maintenance planning of networked multi-asset systems is a complex problem due to the inherent individual and collective asset constraints and dynamics as well as the size of the system and interdependencies among assets. Although multi-asset systems have been studied numerous times in the past decades, maintenance planning implications of the system's network characteristics have been barely analysed. Likewise, solutions that consider the network perspective suffer from scalability issues as a network-wide observability is assumed. This paper proposes a network maintenance planning approach based on the decomposition of the multi-asset network into fixed-size localised subnetworks. The overall network maintenance plan is produced by aggregating the subnetwork maintenance plans, which are computed independently via a multi-agent deep reinforcement learning (MARL) algorithm. The results are evaluated against a network-wide approach as well as the commonly-used individual approach. The paper also introduces a systematic approach to integrate the MARL resulting policy in a multi-asset agent-based model. Simulation results of several random asset networks and a large nationwide network infrastructure show that, although a network-wide approach outperforms, on average, other approaches considered, the localised subnetworks approach, provides an acceptable alternative in networks with small-world properties, without the need of a network-wide view. • A Network-wide maintenance approach outperforms individual approaches in networked assets with the topologies analysed. • In analysed networks with small-world properties, the Localised approach shows performance close to the network-wide approach. • A systematic integration of MARL and multi-asset agent-based modelling enables evaluation of maintenance approaches. [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]

Autism spectrum disorder (ASD) is a collection of neuro-developmental disorders associated with social, communicational, and behavioral difficulties. Early detection thereof is necessary to mitigate the adverse effects of this disorder by initiating special education in schools and rehabilitation centers. Two methods are available for diagnosing and rehabilitating ASD. The first is the manual method (i.e., an observation- or interview-based approach), in which the disorder is diagnosed through observation or by interviewing a parent or caregiver. This method is time-consuming, subjective, and mostly comprises the examination of behavioral symptoms. The other method involves automatic diagnosis using traditional machine learning (ML)- and modern deep learning (DL)-based approaches that rely on image analysis. Indeed, the amount of research literature concerned with the evaluation of the usefulness of DL-based methods that process images or video data to diagnose ASD to improve patients' lives has increased significantly. This paper presents a systematic review of the DL-based approach involving the analysis of images or videos in autism research. The review covers studies that were published from 2017 to June 2023 and were indexed in PubMed, IEEE Xplore, ACM Digital Library, and Google Scholar. The results are reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. A total of 130 studies were included in the analysis. Eligible papers were categorized based on the different features extracted as input for the DL-based approach. Existing well-known public and private datasets that include images or videos for autism research are extensively reviewed and discussed in this systematic review. In addition, different rehabilitation strategies that have been shown to be highly beneficial for ASD individuals are included. Finally, various challenges presented by the automated detection, classification, and rehabilitation of ASD are discussed. The review concludes that the use of DL for the precise and affordable diagnosis of autism is increasing substantially. Our findings are expected to significantly benefit researchers, therapists, psychologists, and relevant stakeholders to advance ASD screening, monitoring, and diagnosis with the aid of a DL-based approach that entails image or video analysis. [ABSTRACT FROM AUTHOR]

Ensuring the safety and security of crowded places is a major concern for both the government and the public. Accurately and quickly estimating the number of people in a crowd is crucial for public safety, urban planning, and traffic monitoring. The existing methods have a minimum mean square error of 0.89 and require high storage space, making them inappropriate for crowd counting using low-computation and small-storage devices like single-board computers. Furthermore, these methods suffer from prediction time lag and are not suitable for live streaming. To tackle these challenges, this paper proposes a Deep Convolution Neural Network-based 'CrowdDCNN' model for crowd counting. This model reduces the value of mean square error by 0.29 and the size of the model by 80.01%. Additionally, the prediction time was decreased to 700 ms. Further, the 'NoLag' algorithm introduced in this paper is efficient in live crowd counting. Its O(1) time and space complexity make it appropriate for all devices, including single-board computers, laptops, and GPUs. The reported 0-p value during statistical analysis using Wilcoxon test, Friedman rank test, and paired t-test validates the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Structural topology optimization is a compute-intensive process due to several iterations of simulations required to evaluate the performance of the component during optimization. Deep learning (DL) based approaches can address this challenge, but these methods were demonstrated mainly using 2D shapes and, at best, in low-resolution 3D geometries (typically 3 2 3 ). Further, due to non-manufacturable geometric features, the predicted optimal geometries from DL may not be manufacturable, even using additive manufacturing. In this paper, we develop a DL framework using a multigrid convolutional neural network (CNN) to generate high-resolution topology-optimized 3D geometries with additional checks on the manufacturability of the predicted shapes. Our framework predicts the final optimal topology using the initial strain energy (objective function of structural topology optimization) and target volume fraction (material fraction to be preserved after optimization) as input. We train the network using a multigrid approach, which enables topology optimization at 12 8 3 resolution, which was previously computationally challenging. We first train the multigrid CNN at a lower resolution and then transfer the learned network to continue training at higher resolutions. We use a distributed deep learning framework on a GPU supercomputing cluster to further speed up the training time. Distributed DL significantly speeds up the training time by more than 4 × while achieving similar model performance. Finally, we check the optimal geometries for manufacturability using fused deposition modeling (FDM)-specific manufacturability constraints. The large training dataset (> 60,000 high-resolution topology optimization examples) will be released with the paper to enable further research on this topic. • Multigrid approach to train the neural network at fine resolution of 1283. • Demonstrate the manufacturability by 3D printing several sample models predicted our framework. • A data-parallel distributed deep learning framework to accelerate the training process. • A comprehensive high-resolution data set consisting of more than 60k optimal shapes. [ABSTRACT FROM AUTHOR]

Upper limb amputation is a widespread problem worldwide, leading to massive loss of functionality for the victims. While a few solutions exist, these are often very expensive and involve expensive and dangerous surgical procedures. In this paper, we propose a low-cost, highly functional upper limb prosthesis controlled via Electroencephalogram (EEG) signals captured in real-time, classified into upper limb motion intention using a novel Genetic Algorithm (GA) optimized Long Short-Term Memory (LSTM) deep learning model to rehabilitate amputees. The proposed 3D-printed prosthetic arm has 3 Degrees-of-Freedom (DOF), which allows it to perform complex movements that can accurately emulate an actual human arm. The results discussed later in this paper conclude that our approach, while being low-cost, is highly accurate and functional. [ABSTRACT FROM AUTHOR]

Dynamic loads on engineering structures are often difficult to measure directly. Therefore, indirect identification methods based on dynamic responses are commonly used. However, this approach is often influenced by ill-conditioned matrices, noise interference, unknown structural and/or material parameters, and difficulty of constructing transfer functions when the traditional physics-based model is used. To address these issues, significant strides have been made in data-driven identification of dynamic loads, which are model-free and independent of structural characteristics. This paper tries to present a comprehensive review of dynamic load identification methods based on data-driven techniques, covering two aspects: load localization and load reconstruction. Features of the widely used data-driven techniques such as the geometric method, reference database method, machine learning methods including SVM-based methods and ANN-based methods and deep learning methods are discussed in detail. Additionally, this paper offers insight into the challenges and prospects of the data-driven techniques for dynamic load identification. The review aims to provide valuable insights for identifying dynamic loads in complex structures based on data-driven techniques and suggests future research directions. [ABSTRACT FROM AUTHOR]

As the "heart" of high-speed train, traction systems play an important role in the safe operation of trains, of which the operation and maintenance level is still unable to meet the needs of modern railway transportation. Fortunately, multifarious advanced fault prognosis methods have been developed to deal with the dilemma. Among them, deep learning ones have received special attention due to their unique advantages. This paper first reveals the structural characteristics of traction systems in high-speed trains. Then, various representative deep learning based prognosis methods are compared and summarized, focusing on the analysis of their pros and cons. Finally, we point out the challenges and speculate the future trends in this field. This paper may serve as a referee for the interested researchers in fault prognosis and high-speed train traction systems fields, along with the potential directions. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT) system for oil field production, oil-well pump leakage is one of the most challenging faults to detect. Once the pump leakage fault occurs, it will severely impact crude oil production. Therefore, accurate diagnosis of oil-well pump leakage faults is highly necessary. However, achieving accurate diagnosis through existing artificial intelligence methods remains a challenge due to the prolonged and progressive nature of oil-well pump leakage faults. This paper proposes a novel methodology for diagnosing oil-well pump leakage. Specifically, we propose a time-series data transformation method that ingeniously transforms difficult-to-identify pump leakage long time-series data variation features into image pixel-level texture features, and based on the characteristics of differences in image texture features under different states, we propose the structure of convolutional residual blocks with attributes and spatial attention for oil-well pump leakage fault diagnosis, which enables more accurate fault diagnosis by assigning more attention weights to extract valuable fault information. Through extensive experimentation, we found that the diagnosis process proposed in this paper outperforms sequence models such as LSTM and GRU, achieving a diagnostic accuracy of 98.36% for oil-well pump leakage faults. Moreover, it leads to a significant improvement of 19.40% and 10.36% in accuracy compared to conventional CNN and ResNet models, respectively. Therefore, in the actual production of oilfields, this method can effectively meet the detection requirements for oil-well pump leakage. • A multivariate time series transformation method for oil well sensor time series data is proposed. • Transformation of time series data into texture features of the image allows for better differentiation of oil well pumps operating states. • An oil-well pump leakage fault diagnosis model with convolutional residual blocks with Attribute and Spatial attention is proposed. [ABSTRACT FROM AUTHOR]

This paper introduces a deep learning-based multi-objective optimization framework, applying for advancing the design of the Cross-Laminated Timber Coupled Wall system. While traditional optimization methods often struggle with the curse of dimensionality in high-dimensional problems, the approach proposed in this study employs an autoencoder to effectively reduce the dimensionality of the design space. Subsequently, a neural network establishes a mapping between input variables and latent spaces, with another neural network forming the crucial link between these latent variables and the output responses. The proposed framework is integrated into the design process of a 20-story Cross-Laminated Timber Coupled Wall system, where uncertainties inherent in connection elements are systematically addressed to optimize the structural parameters. The non-dominated sorting genetic algorithm-II is utilized to estimate optimal design variables by minimizing three conflicting objective functions, thereby generating a Pareto front. This optimized design is then bench-marked against three deterministic models with varying coupling beam shear strengths. A two-dimensional numerical model developed in OpenSees facilitates nonlinear time history analysis using 50 bi-directional ground motion records, representative of the seismicity of Vancouver, Canada. The results of this study not only highlight the efficacy of the deep learning-based framework in enhancing the structural integrity and resilience of high-rise timber structures in seismic regions but also significantly contribute to the evolution of computational approaches in structural engineering. • An autoencoder based deep learning-based framework is proposed. • Proposed method is used in high-dimensional multi-objective optimization to alleviate the curse of dimensionality. • A cross-laminated timber coupled wall building is considered for demonstration purposes. • Optimization under uncertainty for designing timber structures is established. • Results are compared with performances obtained from three deterministic design approaches. [ABSTRACT FROM AUTHOR]

In advancing electronic systems, the replicable nature of devices used in critical environments raises concerns regarding system security. It is of great importance to establish security by identifying electronic devices and computers based on their unique electrical signals, rather than assigning an artificial identity number for security purposes. This paper presents a methodology for identifying electronic devices by capturing their Conducted Emission signal over cables using a Line Impedance Stabilization Network (LISN), followed by applying deep learning techniques. After the extraction of the identities specific to each device based on their electronic characteristics, the establishment of security has been accomplished through these characteristics. The identification process was analyzed through theoretical approaches, and signals were examined by acquiring simulation-based data. After performing the analyses, the data was converted into spectrograms for training the deep learning model using the Conducted Emission signals, and a Convolutional Neural Network (CNN) was used due to its recognized efficacy in handling two-dimensional data formats like spectrogram for the deep learning algorithm. It was observed that with an accuracy of 99.9%, identification was achieved in deep learning results utilizing the data obtained through simulations. Real-time data from 15 devices was collected after training the system with simulation-based data, demonstrating a 99.8% accuracy in accurately identifying the devices. Furthermore, the developed deep learning model was deployed on an Embedded Linux edge device, tested within the system for one day, and achieved an average accuracy of 99.54% in accurately identifying registered devices. [ABSTRACT FROM AUTHOR]

Although additive manufacturing processes have matured in many areas, difficulties with regard to printing accuracy persist. Possible defects are, e.g. , the generation of unwanted internal pores or a lack of fusion between layers. In general, defects result in a deviation between as-planned and as-built geometries. Assessing the influence of such deviations on effective (mechanical) properties requires either experimental testing or numerical investigation, both of which are often too complex for being used in time-critical production settings. Previous work with the Finite Cell Method (FCM) has shown that image-based computational homogenization can assist in principle in quality monitoring of produced parts by providing effective mechanical properties of as-built geometries. However, conducting FCM computations online for each and every part of a series production demands a prohibitive amount of computational resources. The paper at hand suggests a remedy to this problem by using generative adversarial networks. This manuscript presents an integrated computational workflow for outlier detection and property monitoring, which in principle consists of four steps: (i) Carry out experiments under varying process conditions and collect respective (micro)-structural images. (ii) Compute effective properties for these images using FCM. (iii) Compose a generative adversarial network (GAN) training dataset from those images that meet desired effective properties. (iv) Use the GAN discriminator to detect outliers in series production. To this end, the discriminator is used as a classifier on as-built parts to judge whether an as-built structure is acceptable or defective. The viability of the approach is demonstrated on additively manufactured lattice structures whose geometry is acquired after production via computed tomography. The methodology is not only applicable for automated property monitoring but potentially also for reliability estimates of neural network-based property predictors. • Presentation of an integrated computational workflow for quality monitoring of additively manufactured structures. • Generative adversarial network-based (micro)-structure generation for additive manufacturing • Discriminator as a reliability indicator for image-based structural property monitoring. [ABSTRACT FROM AUTHOR]

Image super-resolution is a visual task that reconstructs low-resolution images into high-resolution ones. Currently, many researchers favor applying Transformer-based methods to image super-resolution tasks, which have yielded promising results. However, due to the need to capture long-range dependencies across the entire image, existing research on Vision Transformers (ViT) for super-resolution reconstruction incurs high computational costs, thereby increasing system overhead. Additionally, some researchers have proposed methods based on manually sparse attention mechanisms. However, these approaches, which acquire receptive fields in a manner similar to traditional convolutions, do not fully exploit the advantages of Transformers in extracting global information, resulting in suboptimal reconstruction performance. To leverage the Transformer's ability to capture long-range dependencies, this paper introduces a novel network called RASHAT. In RASHAT, we propose an Adaptive Sparse Hybrid Attention Block (ASHAB). This module introduces a Bi-level Routing Attention(BRA), incorporating both Channel Attention(CA) and Switch Window Multi-head Self-attention((S)W-MSA). These components are designed to capture long-range dependencies, global context, and local dependencies within the image. Additionally, the model employs an Overlapping Cross-Attention Block(OCAB) to enhance information interaction between neighboring pixels. During model training, we introduce a novel composite loss function that combines frequency domain loss with pixel loss, further improving model performance. Extensive experiments demonstrate that benefiting from the sparse attention provided by the Bi-Level Routing Attention (BRA), RASHAT achieves similar performance to the current state-of-the-art results (20.8M) with significantly fewer parameters (11.6M). These results hold across multiple commonly used datasets. • Designed an image super-resolution network based on hybrid attention. • Implement adaptive sparse attention with BRA to capture long-distance dependencies. • Using ASHAB to simultaneously capture global, local, and long-range dependencies. • propose and use a hybrid loss function to obtain frequency domain supervision. • Our method achieves similar performance to SOTA with fewer parameters. [ABSTRACT FROM AUTHOR]

This paper presents a framework to predict the entire displacement time histories of all floors of buildings using a novel double-head neural network composed of causal Convolution Neural Network layers and Long Short-Term Memory layers. State-of-the-art deep techniques have been effectively employed to construct a surrogate model as an alternative to traditional time-consuming and expert-reliant approaches in evaluating damages and response modeling. An innovative loss function is introduced, facilitating the concurrent implementation of stacking, normalization, and multi-task learning capabilities, directing the network to simultaneously learn the time-series data and its rate of variation at every time step. This approach proves beneficial, particularly in maximum displacement and residual displacement predictions, and when dealing with displacement time series predictions in highly nonlinear regions. A comprehensive database is developed including ground motions and roof floor accelerations of steel buildings with eccentrically braced frames (EBF) and moment-resisting frames (MRF) as inputs and displacement time histories of all floors as outputs. The database involves 2,210,000 and 1,092,000 time steps, for EBF and MRF resulting from 44 far-field and 40 large magnitude-small distance ground motions, respectively, through incremental dynamic analysis. The performance of the proposed method is evaluated on scaled new ground motions in different damage states. Results confirm that the proposed stacked network predicts displacement time histories of the case study buildings with 95% accuracy for unseen datasets. In addition, the suggested loss function enhances the prediction performance by a minimum of 12.5% compared to other state-of-the-art loss functions when using the same deep learning architecture advanced in this research. Finally, a graphical user interface is developed to make the proposed approach time-efficient and expert-needless. • Implements a hybrid stacked CNN-LSTM architecture to predict displacement time history of multi-story buildings. • Uses a large database of ground motions and roof accelerations for eccentrically braced and moment-resisting frame buildings. • Introduces a novel loss function with stacking, normalizing, and multi-task learning effects. • Improves the displacement time series predictions in high non-linear regions for all floors. • Develops a graphical user interface for testing the proposed network time-efficient and user-friendly. [ABSTRACT FROM AUTHOR]

Drones have become a crucial element in current wildfire and smoke detection applications. Several deep learning architectures have been developed to detect fire and smoke using either colour-based methodologies or semantic segmentation techniques with impressive results. However, the computational demands of these models reduce their usability on memory-restricted devices such as drones. To overcome this memory constraint whilst maintaining the high detection capabilities of deep learning models, this paper proposes two lightweight architectures for fire and smoke detection in forest environments. The approaches use the Deeplabv3+ architecture for image segmentation as baseline. The novelty lies in the incorporation of vision transformers and a lightweight convolutional neural network architecture that heavily reduces the model complexity, whilst maintaining state-of-the-art performance. Two datasets for fire and smoke segmentation, based on the Corsican, FLAME, SMOKE5K, and AI-For-Mankind datasets, are created to cover different real-world scenarios of wildfire to produce models with better detection capabilities. Experiments are conducted to show the benefits of the proposed approach and its relevance in current drone-based wildfire detection applications. [ABSTRACT FROM AUTHOR]

Industry 4.0 is of great significance for the development of oil industry. One of the pivotal steps towards achieving oil Industry 4.0 is accurately mastering the oilfield production dynamics, especially the changes of process parameters. Among many process parameters, the accurate modeling of water cut is extremely critical and difficult. In this paper, under laboratory conditions, oil–water flows with various preset water cuts are simulated. A fluid sensor, equipped with eight concave-shaped conductive electrodes, is employed to capture multi-channel measurement data, continuously recording the oil–water flow process from multiple angles. Subsequently, a novel flow behavior-heuristic deep learning model, named FBHWC model, is developed to model the relationship between the measurement data and water cut, achieving water cut measurement. The FBHWC model is guided by the complex flow behaviors of oil–water flows and consists of two key modules. Particularly, the multi-level feature fusion module focuses on the high-order feature extraction and fusion of sensor measurement data, while the multi-scale measurement module uses fully convolutional design to achieve accurate measurement of water cut. Experimental results show that the FBHWC model has excellent performance in measuring water cut, with mean square error of 0.016%. All these open up a new venue for exploring industrial multiphase flows through combining multi-electrode sensor and deep learning. [ABSTRACT FROM AUTHOR]

The effectiveness of knee rehabilitation systems in aiding patients with rehabilitation training has been well-documented. Presently, there is an increasing emphasis on the wearing comfort and user engagement of these systems. In this paper, a wearable knee rehabilitation system (WKRS) based on the graphene textile composite sensor (GTCS), which subjects perform rehabilitation training by controlling the ascent and descent of a bird in the visual feedback game utilized GTCS, is proposed and investigated. To obtain an accurate and smooth estimated knee joint angle, we propose an improved tree boosting regression algorithm based on extreme gradient boosting (XGBoost), called improved XGBoost (IXGB). Specifically, the estimated results are smoothed by two steps: curving the preliminary estimating results within the same interval and smoothing the preliminary estimating results using the weighted moving average method. An online experiment with ten subjects validates the effectiveness of the WKRS and IXGB. Results indicate that the IXGB significantly enhances the smoothness of estimated value while maintaining estimation accuracy compared to XGBoost, random forest regression (RFR) and support vector regression (SVR). IXGB achieves an average increase of 33.43 % in root mean square error, 23.83 % in R-squared, and a 62.63% decrease in smoothness. [ABSTRACT FROM AUTHOR]

Reconstructing gene regulatory networks (GRNs) is an important task to reveal the regulatory relationship between genes and understand the mechanism of intracellular gene expression regulation. With the development of single-cell ribonucleic acid sequencing (scRNA-seq) technology, researchers begin to attempt to infer GRN within cells. In this paper, we propose graph attention network with convolutional layer (GATCL) to infer the latent interactions between transcription factors (TFs) and target genes in GRN. Firstly, GATCL uses graph attention network (GAT), which can effectively extract information about genes and TFs. Secondly, we combine multi-head attention layer with one-head attention layer and propose a new method of using convolution instead of weight matrix. Thirdly, we use the exponential linear unit (ELU) activation function to replace the leaky rectified linear unit (LReLU) commonly used in GAT, which further improves the accuracy of GATCL. The AUROC of our method on seven scRNA-seq datasets with four types of ground-truth networks reached an average of 0.827, which is higher than other state-of-the-art models. GATCL applies a supervised deep learning algorithm to solve the problems existing in the inference of GRN from scRNA-seq in the engineering field, and the effectiveness of this method is verified by a large number of experiments. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs), particularly quadrotors, have seen a surge in applications ranging from surveillance to delivery services. However, autonomous control remains a challenge due to the complexity of their dynamics and the need for real-time responsiveness. This paper addresses the significant problem of autonomous quadrotor control by introducing a novel learning method that integrates a deep neural network with advanced control techniques. Novelty of the work is distribution of different learning tasks in different layers of learning and combining the total learning system in a applicable UAV. The purpose is to enhance the quadrotor's ability to perform complex maneuvers autonomously, which is crucial for expanding its application scope. The proposed method begins with the acquisition of direction control using a fractional order proportional–integral–derivative (FOPID) controller. The learning parameters are refined through a fuzzy method, ensuring adaptability and precision. For the optimization of the FOPID parameters, we apply two stochastic algorithms: Genetic Algorithm and Particle Swarm Optimization, comparing their efficacy. Our approach successfully teaches the quadrotor to generate and control a flight path autonomously. The second stage of learning involves proximal policy optimization, enabling the execution of intricate maneuvers. The experimental setup includes a quadrotor on a test stand with three degrees of freedom, where we validate the method through rigorous simulation and physical tests. The results demonstrate that our method not only teaches the quadrotor complex maneuvers but also ensures robust and precise execution, marking a significant advancement in UAV control systems. The implications of this research extend to improving UAV reliability for critical applications, potentially transforming the landscape of autonomous aerial operations. [ABSTRACT FROM AUTHOR]