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The current digital speech deletion and insertion tampering detection methods mainly employes the extraction of phase and frequency features of the Electrical Network Frequency (ENF). However, there are some problems with the existing approaches, such as the alignment problem for speech samples with different durations, the sparsity of ENF features, and the small number of tampered speech samples for training, which lead to low accuracy of deletion and insertion tampering detection. Therefore, this paper proposes a tampering detection method for digital speech deletion and insertion based on ENF Fluctuation Super-vector (ENF-FSV) and deep feature learning representation. By extracting the parameters of ENF phase and frequency fitting curves, feature alignment and dimensionality reduction are achieved, and the alignment and sparsity problems are avoided while the fluctuation information of phase and frequency is extracted. To solve the problem of the insufficient sample size of tampered speech for training, the ENF Universal Background Model (ENF-UBM) is built by a large number of untampered speech samples, and the mean vector is updated to extract ENF-FSV. Considering the shallow representation of ENF features with not highlighting important features, we construct an end-to-end deep neural network to strengthen the attention to the abrupt fluctuation information by the attention mechanism to enhance the representational power of the ENF-FSV features, and then the deep ENF-FSV features extracted by the Residual Network (ResNet) module are fed to the designed classification network for tampering detection. The experimental results show that the method in this paper exhibits higher accuracy and better robustness in the Carioca, New Spanish, and ENF High-sampling Group (ENF-HG) databases when compared with the state-of-the-art methods. [Display omitted] • The ENF fluctuation super vector is extracted for audio tampering detection. • An end-to-end DNN is designed for deletion and insertion tampering detection. • Experiments have verified that the proposed method outperforms the SOTA methods. [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]

• This paper suggests a grating Moiré fringe-based error correction approach that uses an artificial neural network (ANN) and the quartile method (QM). • The approach uses QM for data cleaning operations to eliminate outliers and creates the ANN model with a structure of fully connected layer-normalization layer-fully connected layer for regression training in order to achieve lightweight and stability. • The proposed QM-ANN error correction model's mean absolute error (MAE) can reach 3.3 nm once the QM data has been cleaned and the ANN model has been trained. Many interference factors of the complex and changeable working environment of the grating sensor are the main factors affecting the accuracy; a grating Moiré fringe error correction model based on the quartile method (QM) and artificial neural network (ANN) is proposed in this paper. Firstly, the grating sensor's signals in the working process are collected, and three kinds of interference signals (temperature, humidity and vibration) from the grating sensor working environment are collected simultaneously. Secondly, the QM is used to detect outliers from the collected data and delete data outliers directly. Subsequently, the processed data are input into the ANN model for training. The model is lightweight and stable, because it adopts the cascaded structure of full connection layer-normalization layer-full connection layer, which does not need deploy the complex convolution layer module etc. Finally, the corrected data are output after being processed by the QM-ANN error correction model. The experimental results show that the mean absolute error (MAE) of the proposed QM-ANN error correction model is better than QM-convolutional neural network (CNN), QM-long short-term memory (LSTM) and ANN, CNN, LSTM models without QM. It shows that the proposed model has better robustness and practicability. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven model for predicting the code-based seismic vulnerability index calibrated on a dataset comprising almost 300 buildings. The vulnerability index, estimated following the Italian Seismic Code, involved rigorous investigations, including geometric surveys, experimental tests, and numerical modelling. Leveraging data from these investigations, encompassing approximately 15 categorical and numerical explanatory variables, the authors developed several regression and classification predictive models, such as Logistic Regression and Artificial Neural Networks (ANN). The optimal models perform binary classification to determine the categorization into two macro-classes, defined by an arbitrary vulnerability threshold. The ANN model stands out as the best performer. When adjusting the vulnerability threshold to obtain a balanced dataset, such a model achieves an accuracy higher than 85%. The paper also discusses the importance of each feature by calculating the SHapley Additive exPlanations (SHAP) values. The proposed model can aid decision-makers in allocating resources effectively to mitigate seismic risks of built environments. • Data-driven model for code-based seismic vulnerability assessment. • 15 explanatory variables are considered. • Regression models perform poorly. • Binary classification is the only reliable approach. • The ANN model has an 85% accuracy on binary classification with balanced dataset. [ABSTRACT FROM AUTHOR]

• Temperature distribution within a spinal musculature has been predicted using a novel approach based on deep neural networks. • Spatial and temporal tissue temperature distribution is predicted for bipolar RFA applications. • Using the proposed approach, reduction in computational time compared to FE simulations were achieved. Radiofrequency ablation (RFA) of the medial branch nerve is a widely used therapeutic intervention for facet joint pain. However, denervation of the multifidus muscle is an inevitable consequence of RFA. New ablation techniques with the potential to prevent muscle denervation can be designed using computational simulations. However, depending on the complexity of the model, they could be computationally expensive. As an alternative approach, deep neural networks (DNNs) can be used to predict tissue temperature during RFA procedure. The objective of this paper is to predict the tissue spatial and temporal temperature distributions during RFA using DNNs. First, finite element (FE) models with a range of distances between the probes were run to obtain the temperature readings. The measured temperatures were then used to train the DNNs that predict the spatio-temporal temperature distribution within the tissue. Finally, a separate data obtained from FE simulations were used to test the efficacy of the network. The results presented in this paper demonstrate that the network can achieve an error rate as low as 0.05%, accompanied by a 92% reduction in time compared to FE simulations. The approach proposed in this study will play a major role in the design of new RFA treatments for facet joint pain. [ABSTRACT FROM AUTHOR]

This paper presents an innovative solution to the key exchange problem in the Industrial Internet of Things (IIoT) implementation. Communication between connected devices must be safe and effective if the IIoT is to continue its fast growth. A crucial part of safeguarding the security and integrity of data shared across the IIoT ecosystem is played by cryptographic key exchange approaches. To provide reliable cryptographic key exchange in the IIoT context, this paper offers a unique approach that makes use of hyperchaotic with complex values, variable parameters, and vector-valued neural synchronization. The method addresses the continued demand for effective cryptographic key exchange across IIoT devices by using drive–response techniques to speed up important applications. Long assessment times in conventional algorithms make it difficult to hide neuronal coordination. As a result, this paper offers a condensed evaluation of ANNs' (Artificial Neural Networks) synchronization by coordinating ANNs for session key exchange using a hyperchaotic setting. The recommended approach has several benefits, including the following: (1) introducing a hyperchaotic system to generate synchronized input vectors for ANN synchronization; (2) The adaptive rules of parameters-based control procedures are constructed mathematically; (3) reciprocal alignment of vector-valued ANNs to form a neural network for establishing session keys throughout the IIoT network; and (4) relevant numerical simulations are carried out to assess the scheme's consistency. The recommended approach performs better than other methods that have been published in the literature, widening up opportunities for more effective and reliable industrial applications. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GA) and artificial neural networks (ANN) as intelligent algorithms have been widely applied in engineering fields such as structural design. The GFRP-concrete-steel composite column (GCS), known for its corrosion resistance, has garnered significant attention in its design. However, the application of GA and ANN methods in the context of GCS has primarily been confined to enhancing the predictive accuracy of ANN. This paper introduces a novel approach for the structural optimization of GCS using GA and ANN. A total of 1050 finite element models were generated and analyzed using ANSYS for the purpose of training ANN. The validity of these finite element models was confirmed through comparative analysis with experimental data. The trained ANN was then utilized to predict the loading capacity of GCS for each generation in the GA. By collecting pricing data for GFRP tubes, steel tubes, and concrete under various parameters, pricing fitting formulas for the three materials were derived. Subsequently, the cost of GCS under different parameter sets was calculated. The maximization of the ratio of GCS loading capacity to its cost (LP) was established as the optimization objective. Through the iterative optimization process of the GA-ANN algorithm, the structural optimization was achieved to maximize the economic efficiency of GCS. Comparing the optimization results with numerical simulation results showed that errors were mostly contained within 10 % or even as low as 5 %, thereby validating the accuracy of the GA-ANN algorithm. The applicability of the GA-ANN algorithm to GCS structural optimization was demonstrated by altering a single concrete variable. The method proposed in this paper can be effectively applied to the structural design and optimization of composite columns with a sandwich structure, such as GCS. [ABSTRACT FROM AUTHOR]

Accurate state of health (SOH) estimation is crucial to guarantee the safe and reliable operation of lithium-ion batteries. The recent popular SOH estimation methods are mostly calculated based on the charging data of the battery. However, with the development of fast charging technology, different charging strategies are applied in practical charging scenarios, which increases the difficulty of extracting common SOH degradation features and may lead to inaccurate SOH estimations. This paper proposes an efficient attention mechanism, named Multi-scale Convolutional Attention Mechanism (MCA), to automatically capture and enhance the highly correlated features of SOH decay from the charging data, a hybrid neural network model integrating with the residual module is developed to improve the SOH estimation performance. The results indicate that the proposed MCA exhibits a stronger SOH feature-capturing ability than typical attention mechanisms, and the hybrid model achieves an average error of 0.32 % on the MIT fast charging dataset and 0.37 % on the CALCE dataset. Furthermore, the hybrid model can dynamically modify the attention weights and mitigate the tendency of overfitting according to different training samples, which demonstrates exceptional flexibility. • State of health estimation based on a new hybrid neural network model. • A new attention mechanism is proposed to automatically capture the features. • The use of the residual module improves the stability of the model. • The performance of the proposed model is compared with other mainstream models. [ABSTRACT FROM AUTHOR]

Due to the inevitable light absorption and scattering, underwater images always suffer from severe quality degradation, leading to significant performance decline for various maritime engineering-related applications. In recent years, deep neural networks (DNNs) have been proven to achieve high-quality enhancement of underwater images, which aims to extract and learn abstract features to exhibit superior performance. However, existing solutions often stack multilayer processing units to enrich features, which not only significantly burdens computational load but also encounters difficulties in fully utilizing and interacting between features at different depths. To address the above problems, this paper presents a cost-effective underwater image enhancement network via cascaded feature extraction, termed CFENet. Specifically, we develop a cost-effective cascaded structure for sufficient feature extraction and multiscale establishment of pixel-level long-range dependencies. In addition, we construct a dual-branch structure for effective feature fusion, so that the detailed texture and semantic information in the image can be simultaneously enhanced. Extensive experiments reveal the superiority of our proposed CFENet in both underwater image enhancement effects and computational complexity. Sufficient ablation study is conducted to demonstrate the effectiveness of each component in the network. [ABSTRACT FROM AUTHOR]

Dynamic facial expression recognition (DFER) is of great significance in promoting empathetic machines and metaverse technology. However, dynamic facial expression recognition (DFER) in the wild remains a challenging task, often constrained by complex lighting changes, frequent key-points occlusion, uncertain emotional peaks and severe imbalanced dataset categories. To tackle these problems, this paper presents a depth neural network model based on spatial key-points optimized region feature fusion and temporal self-attention. The method includes three parts: spatial feature extraction module, temporal feature extraction module and region feature fusion module. The intra-frame spatial feature extraction module is composed of the key-points graph convolution network (GCN) and a convolution network (CNN) branch to obtain the global and local feature vectors. The newly proposed region fusion strategy based on face spatial structure is used to obtain the spatial fusion feature of each frame. The inter-frame temporal feature extraction module uses multi-head self-attention model to obtain the temporal information of inter-frames. The experimental results show that our method achieves accuracy of 68.73%, 55.00%, 47.80%, and 47.44% on the DFEW, AFEW, FERV39k, and MAFW datasets. Ablation experiments showed that the GCN module, fusion module, and temporal module improved the accuracy on DFEW by 0.68%, 1.66%, and 3.25%, respectively. The method also achieves competitive results in terms of parameter quantity and inference speed, which demonstrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In the process of composite material molding, the role of robots has become increasingly significant. However, the laying process of composite materials is heavily influenced by factors such as temperature and pressure, which affect the quality of the layup. Currently, existing robot path planning algorithms do not account for temperature constraints, presenting challenges in achieving high-quality composite material layups. This paper proposes the RRT*-MTL (Rapidly-Exploring Random Trees* with Minimum Temperature Loss) path planning algorithm. Firstly, a neural network model for predicting temperature loss is developed. Then, temperature loss is integrated as a constraint into the RRT* (Rapidly-Exploring Random Trees*) path planning algorithm to determine the laying path with minimal temperature loss. Experimental results demonstrate that this algorithm effectively preserves the object's heat retention. [ABSTRACT FROM AUTHOR]

In this paper we present a novel methodology, referenced as ORFEO (Ordinal classifier and Regressor Fusion for Estimating an Ordinal categorical target), to enhance the performance in ordinal classification problems for which the latent variable is observable. ORFEO is an artificial neural network model incorporating two outputs, one for ordinal classification, using the cumulative link model, and one for regression, using a linear model. Both outputs are simultaneously optimised considering a loss function that linearly combines both classification and regression losses. The main motivation behind developing the proposed approach is to enhance the performance of a standard ordinal classifier. This improvement is facilitated by considering the regression output, which allows the model to differentiate between patterns within the same category. The ORFEO model is applied to two problems in the field of marine and ocean engineering: short-term prediction of both significant wave height and flux of energy. Both problems are addressed considering four different coastal zones of the United States of America, using 13 datasets formed by buoys measurements and reanalysis data. A comprehensive comparison against 20 methodologies, including regression and nominal/ordinal classification approaches is performed, by using diverse nominal and ordinal performance metrics. Ranks achieved indicate that ORFEO outperforms all the compared methodologies in terms of all the performance measures, demonstrating the efficacy and robustness of the proposal. Finally, a statistical analysis is conducted, concluding that there are statistically significant differences across ordinal and nominal performance metrics in favour of the proposed ORFEO model. [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]

The critical heat flux (CHF) is an important parameter determining the heat transfer capability of nuclear reactors. Therefore, prediction of CHF with accuracy and correct understanding is essential for the design and safety analysis of nuclear power plants (NPPs) and industrial boiling systems. This paper focuses on the CHF prediction using the Gaussian process regression (GPR), a machine learning (ML) technique for modeling nonlinear systems. The covariance kernel is a critical component of GPR modeling because it contains assumptions about the function we want to know and defines the dependence and similarity between the data. In this study, the prediction of CHF is investigated during training and prediction in two different approaches: isotropic and anisotropic covariance kernels. Besides, we evaluate the influence of various isotropic and anisotropic kernels on the accuracy of CHF prediction and quantification of uncertainty encoded in the posterior variances in each of the approaches. The use of anisotropic kernels, considering different shape parameters in line with different data dimensions, results in higher accuracy and better generalization. In addition, compared to other ML tools such as support vector regression (SVR) and artificial neural networks (ANNs), the GPR with anisotropic kernels has much better performance. The evaluation criteria involving root mean square error (RMSE) and mean absolute percentage error (MAPE) in anisotropic kernels are significantly improved compared to isotropic kernels. For example, in squared exponential and rational quadratic kernels, RMSE and MAPE values improved by {65.81%, 67.28%} and {61.97%, 60.12%} values, respectively. The results of the GPR method with the anisotropic kernels show the relative superiority of this method compared to SVR and ANN. The lowest RMSE and MAPE values of all three methods were compared, and these values have improved in GPR compared to SVR by 96.01% and 94.25% and compared to ANN by 84.40% and 85.51%, respectively. Finally, some valuable ideas and practical findings will be offered based on numerical simulations and comparisons between isotropic and anisotropic kernel assumptions. Since the approach of using anisotropic kernels has high computational time despite its high flexibility, we will address this issue in future studies and also use this approach for other nuclear engineering problems. [Display omitted] • A flexible GPR structure is presented. • We compared isotropic & anisotropic kernels for critical heat flux prediction. • Anisotropic model outperforms isotropic in critical heat flux prediction. • Anisotropic GPR outperforms SVR & ANN for critical heat flux prediction. • Broader applicability to other CHF predictions in nuclear engineering applications. [ABSTRACT FROM AUTHOR]

Wind speed forecasting plays a crucial role in enhancing the efficiency, reliability, and profitability of renewable energy systems. Accurate wind speed forecasting optimizes energy production and grid integration in renewable energy systems. It assists in maintenance scheduling, decision-making in energy markets, and supports risk management for financial planning. This paper investigates the difficult balance between model complexity and forecasting accuracy in wind speed forecasting using Artificial Neural Networks (ANNs) and Wavelet Neural Networks (WNNs). Employing a time series approach, the study develops models for 1-h-ahead wind speed forecasting, utilizing recent averaged data and exploiting correlations between consecutive wind speeds for improved short-term predictions. Models training involve the Backpropagation algorithm, with careful input variable selection to minimize errors. This study emphasizes that simpler ANN and WNN models can outperform complex ones and explores the effectiveness of wavelet filtering techniques and demonstrates the benefits of using wavelet filtering to enhance forecasting accuracy. The robustness of the developed models is validated through K-fold cross-validation, confirming the efficacy of the proposed models. Evaluation across two datasets demonstrates the superiority of the proposed models over the persistence model, with enhancements of 7–14% in RMSE. Additionally, the efficacy of wavelet transforms in enhancing forecasting accuracy compared to regular neural network predictions is emphasized, underscoring the effectiveness of wavelet filtering offering a strategic approach for optimizing wind power management in electrical grids. [Display omitted] • A single-step-ahead time series wind speed forecasting approach is proposed. • ANN and WNN are employed for wind speed forecasting. • Wavelet filtering is proposed to minimize the noise influence and changes. • Hybrid models for wind speed forecasting using feature selection are proposed. • Daubechies wavelet (db3) is efficient and adopted for wind time series filtering. [ABSTRACT FROM AUTHOR]

Owing to the development of deep learning, indoor personnel detection methods based on deep neural networks have been extensively investigated in recent years. However, more complex and deeper network structures may consume more computational resources, which seriously limits the deployment of large-scale deep neural networks on lightweight devices. In view of this, a lightweight anchor-free one-level feature indoor personnel detection method based on transformer (LAOF-IPDT) is proposed in this paper, which is deployed on two embedded devices and achieves good detection accuracy. In the feature extraction backbone, an enhanced cross-stage partial ghost convolution block with ghost convolution and channel shuffle is designed to extract shallow features. Additionally, to obtain more comprehensive global features in the high-level semantic structure, an embedded vision transformer cross-stage partial block is constructed by embedding a lightweight mobile-friendly vision transformer. For the path-aggregation neck, a lightweight feature pyramid network is proposed, which integrates multi-scale feature maps to obtain richer one-level feature representations. Subsequently, a dilated convolution group block is applied to expand the one-level feature receptive field and detection is accomplished using a one-level feature map. For the detection head, an anchor-free mechanism is applied to reduce the hyper-parameter interference of anchor boxes. Extensive experiments on four datasets indicated that the LAOF-IPDT outperforms other lightweight networks in terms of accuracy, speed, model parameters, and model size. For example, the frames per second of the LAOF-IPDT are 8.39 and 14.67 on CPU devices and Jetson Nano devices, respectively, and the mean average precision is 84.49%. [ABSTRACT FROM AUTHOR]

Accurate prediction of air pollutant concentrations can be effective in controlling and preventing air pollution, which is crucial for both the government's policy response and the public's reasonable ability to avoid air pollution. In this study, we propose a model based on the principle of big data correlation and deep learning techniques for predicting the concentration of air pollutant concentrations. The proposed model, named STARes-SaLSTM, consists of a residual neural network with spatiotemporal attention (STAResNet) and a temporal self-attention based on long short-term memory network (SaLSTM). The spatiotemporal attention module in STAResNet enhances the original input information by weighting it in both spatial and temporal dimensions, meanwhile the features of the spatial distribution of pollutant and meteorological information across many cities are deeply extracted using ResNet. A novel self-attention method known as temporal self-attention is developed to extract the temporal dependency of air pollutants. LSTM is used as a sequence encoder in this temporal attention method to calculate queries, keys, and values to acquire a more comprehensive temporal dependence than is possible with normal self-attention. The STARes-SaLSTM model successfully predicts air pollution concentrations in the target city for the future by extracting spatiotemporal correlation of feature sequence. In comparison to previous neural network models and conventional methods, the model increases the accuracy of pollutant concentration prediction. The suggested prediction model works well for the one to 3 h prediction job, with a root mean square error (RMSE) ranging from 6.716 to 11.648. Furthermore, even for the 1–24 h prediction assignment, we executed multi-scale prediction in the target city and obtained a reasonable performance, with an average RMSE value of 20.576. • This paper solves the problem of difficult extraction of spatiotemporal features. • ResNet allows for the extraction of spatially relevant features; • The spatiotemporal attention mechanism is used to spatiotemporal extract features; • The temporal self-attention mechanism obtain temporal dependence of data. [ABSTRACT FROM AUTHOR]

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency. • An investigation into an islanded microgrid's Load Frequency Control (LFC) model framework has been conducted, comprising diesel generator, solar PV system, wind turbine, and EVs operating in vehicle-to-grid mode to supply power to electric loads, consequently impacting the frequency dynamics of the microgrid system. • A novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN) has been implemented to optimize the load frequency model of a microgrid. • An extensive comparative analysis with other controllers, including PID and PSO-PID, is conducted across diverse scenarios involving renewable energy integration and EV charging and discharging dynamics under various load disturbances to reduce settling time, control system frequency, and ensure reliable performance. • The stability analysis validates the efficiency of the proposed PSO-TANN controller in ensuring reliability during uncertainties and disturbances, maintaining overall robustness and stability of the LFC system within the microgrid. [ABSTRACT FROM AUTHOR]

In this work, the practical utility of constructed wetlands (CWs) is described as a promising treatment option for micropollutants (MPs) in wastewater with the aid of their eco-friendly, low-energy, economically feasible, and ecologically sustainable nature. This paper offers a comprehensive review on CW technology with respect to the key strategies for MP removal such as phytoremediation, substrate adsorption, and microbial degradation. It explores the important factors controlling the performance of CWs (e.g., in terms of configurations, substrates, plant-microbe interactions, temperature, pH, oxygen levels, hydraulic loading rate, and retention time) along with the discussions on the pivotal role of microbial populations in CWs and plant-microbe cooperative remediation dynamics, particularly in relation to diverse organic MP patterns in CWs. As such, this review aims to provide valuable insights into the key strategies for optimizing MP treatment and for enhancing the efficacy of CW systems. In addition, the process-based models of constructed wetlands along with the numerical simulations based on the artificial neural network (ANN) method are also described in association with the data exploratory techniques. This work is thus expected to help open up new possibilities for the application of plant-microbe cooperative remediation approaches against diverse patterns of organic MPs present in CWs. [Display omitted] • Performance of different CWs is evaluated for micropollutant removal. • Mechanistic insights of the micropollutant removal are offered in relation to plant and microbe CWs. • Factors affecting the removal efficiency of constructed wetlands are discussed. • Strategies are reviewed for optimizing MP treatment and for enhancing the CW efficiency. [ABSTRACT FROM AUTHOR]

The conventional standardized theoretical models (such as Webster, Alcelik, Indo-HCM) used for the delay estimation revolve around the mathematical hypothesis and assumptions, making them static with limitations in accommodating the dynamic traffic behaviors. Recent advances in artificial intelligence make machine learning techniques suitable for estimating vehicle control delay compared to conventional methods. This paper demonstrates the application of several machine learning models developed by focusing on the fluctuations of traffic observed at an intersection having heterogeneous traffic conditions in Ahmedabad city for delay estimation. Several parameters are extracted from on-field and video surveys. However, not all parameters are relevant for accurately estimating vehicle control delay. Hence, a feature selection process consisting of several feature-scoring techniques from filter, wrapper, and embedded methods is applied to all the parameters. This process gave insights into the most statistically relevant independent parameters, and out of all the parameters, cycle time was found to be insignificant, with a feature score of 0 from almost all techniques. Hence, it was removed, and then the prominent parameters were then used to build a vehicle control delay model using support vector regression (SVR), K-nearest neighbor (KNN), artificial neural network (ANN), random forest regression (RF), and decision tree regression (DT). Error distribution, standard deviation of errors, coefficient of Determination (R2), and Root Mean Squared Error (RMSE) are the parameters used for evaluating the performance of the machine learning models. RF outperformed them all with a standard deviation of errors, R2, and RMSE of 11.065, 0.926, and 11.081 on testing data. But ANN, KNN, and DT also performed satisfactorily. Compared with conventional standardized theoretical models, all the machine learning models except SVR performed better. [ABSTRACT FROM AUTHOR]

Medical image analysis, particularly the segmentation of White Blood Cells (WBCs), holds critical importance in scrutinizing the quantity and morphology of these cells in smear images, a pivotal step in disease detection. Existing methodologies often fall short in differentiating between healthy and diseased WBCs, emphasizing color components over internal organizational nuances and external morphologies. This paper presents a pioneering contribution through the introduction of an encoder-decoder deep neural network with an indeterminacy fusion-based model explicitly designed for WBC extraction from blood images. In contrast to prior studies, our approach addresses the inadequacies by delving into the intricacies of WBC structure, characterizing object indeterminacy within the neutrosophic set domain. The encoder-decoder network is fortified by integrating WBC indeterminacy as a fusion component, enabling the segmentation of WBCs into distinct nucleus and cytoplasm regions. The efficacy of our methodology is evaluated across three datasets with varying resolutions. Quantitative metrics such as segmentation accuracy and intersection over union (IoU) are employed to assess the proposed network's performance, which consistently outperforms three original encoder-decoder networks. Notably, our model achieves commendable precision rates, with the highest mean segmentation accuracy reaching 0.95301 across the three datasets. These results underscore the enhanced performance of our segmentation network, attributing its success to the influential role of indeterminacy fusion in augmenting conventional segmentation methods. In summary, this work not only introduces a novel solution for WBC segmentation but establishes its superiority over existing methods. The interpretive insights gained from the results highlight the potential of indeterminacy fusion to drive advancements in the realm of medical image segmentation, emphasizing originality, significance, and superior performance metrics. [ABSTRACT FROM AUTHOR]

Research on automatic translation of sign language to verbal languages has been progressively explored in recent years to assist speech and hearing-impaired people in communicating with non-signers. In this paper, a tiny machine learning (TinyML) solution is proposed for sign language recognition using a low-cost, wearable, internet-of things (IoT) device. A lightweight deep neural network is deployed on the edge device to interpret isolated signs from the Indian sign language using the time-series data collected from the motion sensors of the device. The scarcity of labeled training data is addressed by employing the deep transfer learning approach. Here, the knowledge gained from the data collected using the motion sensors of a different device is used to initialize the model parameters. The performance of the model is assessed in terms of classification accuracy and prediction time for different sampling rates and transferring schemes. The model achieves an average accuracy of 87.18% when all the parameters are retrained with just 4 observations of each sign recorded from the motion sensors of the proposed IoT device. The recognized sign is transmitted to a cloud platform in real-time. A mobile application, SignTalk , is also developed, which wirelessly receives the predicted signs from the cloud and displays it as text. Additionally, text-to-speech conversion is also provided on SignTalk to vocalize the predicted sign for better communication. [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]

Monte Carlo Tree Search (MCTS) is a pronounced empirical search algorithm for agent decision-making, especially when enhanced by Deep Learning (DL), in mastering board games that were once thought to be unconquerable. However, it does not appear to be as equally successful in the domain of real-time video games, where the simulation time limit for exploration is a crucial factor, since they are generally designed to be played by human users and hence require a significant amount of resources for simulation. We in this paper propose a surrogate-assisted MCTS approach, specifically targeting commercial real-time video games by approximating the result of gameplay with a deep-learning-based surrogate model. The key contribution of our work is that we designed a modified MCTS for video games that are both commercial and processed in real-time. Since commercial video games include considerably more complex and dynamic gameplays to satisfy their market consumers, as opposed to their non-commercial analogs, our work can be regarded as having challenged the domain unattempted by precedent studies. We validated the performance of our method by conducting a comparative experiment with other algorithms, including the traditional MCTS, under the environment of a commercial real-time video game. [Display omitted] • A Monte Carlo tree search (MCTS) method applicable to commercial real-time video games is proposed. • The method integrates MCTS with a deep neural network structure to function efficiently in a real-time environment. • Performance verification of the proposed surrogate-assisted MCTS is done via two commercial real-time video games with distinct features. • Our surrogate model can be trained with a comparatively little amount of computational resources in real-time video games. [ABSTRACT FROM AUTHOR]

Soil water retention curves (SWRC) are constitutive relationships that are critical for predicting the hydro-mechanical behaviour of unsaturated soil in a wide range of applications in geoenvironmental and geotechnical engineering, hydrology, and agricultural and soil science. Directly measuring SWRC can be costly and time-consuming. Recent research has shown that machine learning, especially artificial neural networks (ANNs), can effectively predict SWRC using easy-to-measure soil properties as inputs. However, significant questions remain about prediction accuracy and what are the most effective input parameters to use, and only shallow neural networks with one hidden layer have been developed in the past for this purpose. In this study, effectiveness of deep neural networks (DNNs) - ANNs with more than one hidden layer - for SWRC prediction is explored for a range of models with different combinations that require easy-to-measure input parameters. Specifically, the study investigates whether switching from a shallow neural network (SNN) to a deep neural network (DNN) can enhance the accuracy of SWRC predictions for these models. Significant differences are found in seven out of thirteen cases when shifting from SNN to DNN. It was also found that models including soil texture, augmented by either dry density or soil porosity as input parameters, perform well. Specific gravity, soil depth, and organic content, on the other hand, do not add to the predictive capability of these models. Findings of this paper can help improve SWRC prediction using machine learning and can lead to more accurate modelling of unsaturated soil behaviour. [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]

The critical heat flux (CHF) is an important parameter determining the heat transfer capability of nuclear reactors. Therefore, prediction of CHF with accuracy and correct understanding is essential for the design and safety analysis of nuclear power plants (NPPs) and industrial boiling systems. This paper focuses on the CHF prediction using the Gaussian process regression (GPR), a machine learning (ML) technique for modeling nonlinear systems. The covariance kernel is a critical component of GPR modeling because it contains assumptions about the function we want to know and defines the dependence and similarity between the data. In this study, the prediction of CHF is investigated during training and prediction in two different approaches: isotropic and anisotropic covariance kernels. Besides, we evaluate the influence of various isotropic and anisotropic kernels on the accuracy of CHF prediction and quantification of uncertainty encoded in the posterior variances in each of the approaches. The use of anisotropic kernels, considering different shape parameters in line with different data dimensions, results in higher accuracy and better generalization. In addition, compared to other ML tools such as support vector regression (SVR) and artificial neural networks (ANNs), the GPR with anisotropic kernels has much better performance. The evaluation criteria involving root mean square error (RMSE) and mean absolute percentage error (MAPE) in anisotropic kernels are significantly improved compared to isotropic kernels. For example, in squared exponential and rational quadratic kernels, RMSE and MAPE values improved by {65.81%, 67.28%} and {61.97%, 60.12%} values, respectively. The results of the GPR method with the anisotropic kernels show the relative superiority of this method compared to SVR and ANN. The lowest RMSE and MAPE values of all three methods were compared, and these values have improved in GPR compared to SVR by 96.01% and 94.25% and compared to ANN by 84.40% and 85.51%, respectively. Finally, some valuable ideas and practical findings will be offered based on numerical simulations and comparisons between isotropic and anisotropic kernel assumptions. Since the approach of using anisotropic kernels has high computational time despite its high flexibility, we will address this issue in future studies and also use this approach for other nuclear engineering problems. [Display omitted] • A flexible GPR structure is presented. • We compared isotropic & anisotropic kernels for critical heat flux prediction. • Anisotropic model outperforms isotropic in critical heat flux prediction. • Anisotropic GPR outperforms SVR & ANN for critical heat flux prediction. • Broader applicability to other CHF predictions in nuclear engineering applications. [ABSTRACT FROM AUTHOR]

Monte Carlo Tree Search (MCTS) is a pronounced empirical search algorithm for agent decision-making, especially when enhanced by Deep Learning (DL), in mastering board games that were once thought to be unconquerable. However, it does not appear to be as equally successful in the domain of real-time video games, where the simulation time limit for exploration is a crucial factor, since they are generally designed to be played by human users and hence require a significant amount of resources for simulation. We in this paper propose a surrogate-assisted MCTS approach, specifically targeting commercial real-time video games by approximating the result of gameplay with a deep-learning-based surrogate model. The key contribution of our work is that we designed a modified MCTS for video games that are both commercial and processed in real-time. Since commercial video games include considerably more complex and dynamic gameplays to satisfy their market consumers, as opposed to their non-commercial analogs, our work can be regarded as having challenged the domain unattempted by precedent studies. We validated the performance of our method by conducting a comparative experiment with other algorithms, including the traditional MCTS, under the environment of a commercial real-time video game. [Display omitted] • A Monte Carlo tree search (MCTS) method applicable to commercial real-time video games is proposed. • The method integrates MCTS with a deep neural network structure to function efficiently in a real-time environment. • Performance verification of the proposed surrogate-assisted MCTS is done via two commercial real-time video games with distinct features. • Our surrogate model can be trained with a comparatively little amount of computational resources in real-time video games. [ABSTRACT FROM AUTHOR]

Soil water retention curves (SWRC) are constitutive relationships that are critical for predicting the hydro-mechanical behaviour of unsaturated soil in a wide range of applications in geoenvironmental and geotechnical engineering, hydrology, and agricultural and soil science. Directly measuring SWRC can be costly and time-consuming. Recent research has shown that machine learning, especially artificial neural networks (ANNs), can effectively predict SWRC using easy-to-measure soil properties as inputs. However, significant questions remain about prediction accuracy and what are the most effective input parameters to use, and only shallow neural networks with one hidden layer have been developed in the past for this purpose. In this study, effectiveness of deep neural networks (DNNs) - ANNs with more than one hidden layer - for SWRC prediction is explored for a range of models with different combinations that require easy-to-measure input parameters. Specifically, the study investigates whether switching from a shallow neural network (SNN) to a deep neural network (DNN) can enhance the accuracy of SWRC predictions for these models. Significant differences are found in seven out of thirteen cases when shifting from SNN to DNN. It was also found that models including soil texture, augmented by either dry density or soil porosity as input parameters, perform well. Specific gravity, soil depth, and organic content, on the other hand, do not add to the predictive capability of these models. Findings of this paper can help improve SWRC prediction using machine learning and can lead to more accurate modelling of unsaturated soil behaviour. [ABSTRACT FROM AUTHOR]

The extraction of fault-induced repetitive transients which possess cyclo-stationarity is the key to the fault diagnosis of rotating machinery, which is of considerable significance for ensuring the safe and reliable operation of machinery equipment. Traditional deconvolution methods mainly aim to recover fault-related impulsive features from the time domain and are prone to give poor fault diagnosis results under heavy interference conditions. To solve this problem, a spectrum sparse deep deconvolution method (SSDD) with a deep neural network structure is proposed in this paper. The proposed method uses an envelope spectrum sparse criterion as the cost function to seek an optimal inverse filter through a deep neural network. Firstly, a special band-averaging strategy is designed to initialize the filters in the input layer of the neural network with a window method to provide a direction for deconvolution. Secondly, envelope spectral kurtosis that can depict the sparse feature in the envelope spectrum domain is taken as the cost function to guide the training of the deep network and lock the fault information. Then, the optimal weights are realized by the eigenvalue algorithm, and the weak sparse features are enhanced and extracted layer by layer. Finally, the most significant fault information is obtained through dimension reduction. The simulated and experimental data analysis results verified that the proposed method is superior to traditional deconvolution methods in fault diagnosis performance and robustness to random impulses and strong background noise. [ABSTRACT FROM AUTHOR]

This paper introduces a novel parallel deep neural network for fault diagnosis of drilling pumps. It integrates the Convolutional Block Attention Module with the AlexNet and synchronizes with the Anomaly Transformer model to delve meticulously into both the time and time-frequency domains of signals. The method prioritizes the singular extraction and subsequent amalgamation of features, facilitating a detailed view of diagnostic data and mitigating the risk of interference and overfitting. The integration of the anomaly attention of the Anomaly Transformer with the features of the Convolutional Block Attention Module results in a distinctive dual attention mechanism that is critical to the methodology. This mechanism emphasizes essential features in both the time domain and the time-frequency domain, improving the accuracy of fault diagnosis. Verification with on-site data underscores the preeminence of the approach over existing models, signaling improved reliability and accuracy in diagnosing faults in drilling pumps. This meticulous approach offers promising advances in the study and application of fault diagnosis in energy equipment, demonstrating increased efficiency and refined accuracy. • A novel Parallel Deep Neural Network-Based Fault Diagnosis Framework is proposed. • Time and time-frequency domains of signals are extracted and fused for Fault Diagnosis. • Integrating the Transformer with CBAM to consider the essential features of signals. [ABSTRACT FROM AUTHOR]

To optimize the operation of his wind farm, the farm manager needs to make precise diagnostic decisions for scheduling efficiently the maintenance actions. To assist him in this task, this paper proposes an approach aimed at designing a hybrid diagnoser model for two main goals. The first one is to detect anomalies at an early stage, and the second one is to provide specific deductions or explanations about the potential cause of the problem. In this way, the developed system can help the maintenance manager understand why a particular decision needs to be made. To do this, an original approach coupling the artificial intelligence technique with a discrete event system is proposed to diagnose, identify, and explain a probable source of a problem. Thus, the proposed method uses a hybrid approach consisting of two model blocks. The first block consists of autoencoder models to extract feature representation to diagnose the health state of the system. The second one is a discrete event-based model to create and visualize rule-based anomaly alerts and triggers to provide plausible explanations to the operator to improve his task. Then, this work introduces a methodology to jointly train these two models and learn all parameters of the hybrid diagnoser. It is shown how the information extracted from the neural network model is used to automatically construct the event-based model to explain the occurrence of an anomaly. The proposed system achieved significant results in explaining and detecting early five types of anomalies in wind turbine systems with accuracy up to 80%. The results demonstrate that the approach can deliver performance gains of up to 20% compared to standard techniques such as Long short-term memory and Random cut forest. • Anomalies detection and explanation in wind turbine systems. • Hybrid learning of an autoencoder and event-based model. • Computation of a set of rules for identifying the root causes of anomalies. • Explainable predictive maintenance for improving decision-making processes. [ABSTRACT FROM AUTHOR]

Digital image watermarking technology has made a significant contribution to the copyright protection of digital images. In recent years, researchers have focused on designing various watermarking algorithms to enhance resistance against different forms of attacks. However, the evolution of watermarking attack technology has been sluggish, thus impeding possible advancements in digital copyright protection. Existing watermarking attack methods exhibit a notable drawback, causing substantial deterioration in visual quality and undermining the practical utility of attacked images. In this paper, we propose a high imperceptibility watermarking attack network, named HIWANet, based on deep neural networks. To enhance the watermarking attack ability, a feature extraction module (FEM) is aimed to better capture watermark information features, and a watermarking attack module (WAM) is constructed to learn high-level abstract features of the images. In addition, to ensure the imperceptibility of the watermarking attack, an asymmetric loss function is designed to maintain the quality of the attacked watermarked image. In the experiments, we randomly select 2000 color images from the PASCAL VOC2012 database as the dataset, with the training and test sets containing 1000 distinct images. Compared to traditional watermarking attack methods, our HIWANet achieves a significant increase in bit error rate (improved by 242%), indicating a higher attack ability. Meanwhile, it brings more than 26% improvement in the attack imperceptibility. Furthermore, our HIWANet also offers significant advantages compared to deep learning-based watermarking attack methods. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a prevalent complication of diabetes, affecting a substantial number of individuals worldwide and being a leading cause of blindness. The accurate and automated detection of DR is crucial for effectively managing symptoms such as vision loss and blindness. Recently, there has been significant interest in exploring the applicability of CapsNet for DR grading regarding its success in various vision tasks. However, the performance of traditional CapsNet in DR grading is constrained by the insufficient utilization of capsule features during the training phase. To enhance its performance, this paper proposes a hybrid neural network model called graph neural network (GNN)-fused CapsNet (GF-CapsNet) for DR grading. The model combines various components including ResNet-18 for feature extraction via transfer learning, a PrimaryCaps layer for encoding capsule features, and multi-head prediction that uses GNN-based feature fusion and transformation. Experimental results obtained from two public datasets (Kaggle APTOS 2019 and IDRiD) demonstrate that GF-CapsNet outperforms traditional CapsNet and several other state-of-the-art methods in terms of capturing DR lesions and grading DR. In addition, an investigation into the internal routing process demonstrates that our method mitigates the potential misassignment problem associated with traditional CapsNet. Moreover, the use of the class activation mapping technique for feature map visualization provides an explanation of our model's superior performance in the DR grading task. [ABSTRACT FROM AUTHOR]

The issue of Twin Noisy Labels, known as Noisy Annotations and Noisy Correspondences, increases the challenge in the engineering application of Visible–Infrared Person Re-identification (VI-ReID). This paper proposes an novel Modality Blur and Batch Alignment (MBBA) framework to address this issue in the practical application of VI-ReID. The MBBA consists of the Label Confidence Learning (LCL) module, the Modality Blur Learning (MBL) module, and the Batch Alignment Learning (BAL) module. The LCL utilizes the memorization effect of deep neural networks to estimate the confidence level of identity labels and rectify the noisy annotations and the noisy correspondences. The MBL uses the noiseless modality labels and the center loss to blur the boundary among modality data to make the discrimination of the learned latent feature space robust. Based on the designed alignment loss, the BAL employs the self-attention mechanism to align the significant prediction distributions among the cross-modal sample pairs from a batch-size perspective. The mean Average Precision (mAP) is improved by 4.06% and 5.34% compared to state-of-the-art methods on the 20% noisy RegDB dataset. And our MBBA exhibits contemporary state-of-the-art performance on Twin Noisy Labels based VI-ReID. Our MBBA is available at https://github.com/SWU-CS-MediaLab/MBBA. [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]

The increasing popularity of mobile applications has catalyzed advancements in object tracking algorithms for mobile platforms. This paper introduces a lightweight object tracking algorithm, designed for real-time and accurate tracking on mobile devices. We propose a feature extraction network with a quantization-friendly architecture and a novel mask-based padding method to enhance tracking precision. By refining MobileNetV3's structure, we mitigate accuracy degradation during quantization and reduce computational demands. The proposed padding technique addresses the outlier generation issue common in traditional padding methods, thereby enhancing positional accuracy. Additionally, an extent adjustment module is incorporated to facilitate prompt error correction during suboptimal tracking. Comprehensive evaluations on OTB50, OTB100, VOT2018, UAV123, and LaSOT demonstrate the superiority of our method over several state-of-the-art trackers in both speed and accuracy. Notably, our feature extraction model is only 3.4 MB, running at 243fps on an RTX 3090 GPU, and can adapt to various sizes of video inputs. • A quantization-friendly structure is designed to reduce the network model size without compromising accuracy. • A mask-based padding method is introduced to better adapt to tracking tasks. • An extent adjustment module is proposed to detect the tracking quality and adjust the size of the search area. • The proposed method achieves excellent tracking accuracy and surpasses similar algorithms in tracking speed. [ABSTRACT FROM AUTHOR]

Facial image deblurring is an active research area that aims to restore blurry face images to clear ones. However, this task requires special consideration to restore the detailed elements of facial structures, such as eyes, nose, and mouth. Additionally, facial occlusions and varying illuminance conditions in common environments can degrade the deblurring performance. Previous studies have not accounted for these conditions, necessitating the development of deblurring method that considers these factors. In this paper, we propose a novel approach, called Illuminance-robust Multi-stage DeblurNet with Channel Attention (IMDeCA), which leverages semantic mask and landmark information of the face to restore detailed facial structures. Our approach is robust to various illuminance conditions and facial occlusions. The proposed network comprises a multi-stage structure that extracts facial semantic feature maps, reconstructs clear images, and improves illuminance. We also consider facial landmark information in the loss function to ensure well-restored facial structures even in the presence of facial occlusions. Furthermore, we construct a new facial image dataset, named BIO, which includes Blurred images with various types of Illuminance conditions and facial Occlusions. Through extensive experiments on this dataset, we demonstrate the superior performance of our proposed network, outperforming the latest existing methods. [ABSTRACT FROM AUTHOR]

In weakly coupled few mode fibers (FMFs), used in optical communication systems to increase data capacity, the difference between the effective refractive indices of successive spatial modes is a critical parameter. In this paper, we demonstrate that nanostructured FMFs composed of two different types of subwavelength glass rods can be used to effectively control the propagation properties of individual spatial modes. However, the design and optimization of such fibers proves challenging due to the large number of parameters associated with their free-form refractive index distribution. To address this problem, we use an optimization method based on generative inverse design networks (GIDNs) modified to account for the optical properties of weakly coupled FMFs. Our results show that a nanostructured FMF with a core containing multiple angularly modulated concentric rings can achieve differences between the effective refractive indices of 10 spatial modes exceeding 1.2 × 10−3, surpassing other forms of weakly coupled FMFs. Importantly, the parameters of the structure ensure the feasibility of fabricating the designed fibers. Compared to traditional FMF optimization using a deep neural network with a large, randomly generated data set, our method achieves comparable accuracy in predicting refractive index differences, while achieving better spatial mode separation with a six times smaller data set. By using our proposed approach, which could potentially be applied to the design of other types of optical fibers characterized by a large number of parameters, we can significantly reduce the number of training examples required and the time needed to achieve high-quality results. [ABSTRACT FROM AUTHOR]

Orbit recovery problems are a class of problems that often arise in practice and various forms. In these problems, we aim to estimate an unknown function after being distorted by a group action and observed via a known operator. Typically, the observations are contaminated with a non-trivial level of noise. Two particular orbit recovery problems of interest in this paper are multireference alignment and single-particle cryo-EM modeling. In order to suppress the noise, we suggest using the method of moments approach for both problems while introducing deep neural network priors. In particular, our neural networks should output the signals and the distribution of group elements, with moments being the input. In the multireference alignment case, we demonstrate the advantage of using the NN to accelerate the convergence for the reconstruction of signals from the moments. Finally, we use our method to reconstruct simulated and biological volumes in the cryo-EM setting. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hardware architecture for a Feed-Forward Neural Network (FFNN) with the objective of minimizing the number of execution clock cycles needed for the network's computation. The proposed architecture depends mainly on using two physical layers that are multiplexed and reused during the computation of the FFNN to achieve an efficient parallel design. Two physical layers are designed to handle the computation of different sizes of Neural Networks (NN). The proposed FFNN architecture hardware resources are independent of the NN's number of layers, instead, they depend only on the number of neurons in the largest layer. This versatile architecture serves as an accelerator in Deep Neural Network (DNN) computations as it exploits parallelism by making the two physical layers work in parallel through the computations. The proposed implementation was implemented with 18-bit fixed point representation reaching 200 MHz clock speed on a Spartan7 FPGA. Furthermore, the proposed architecture achieves a lower neuron computation factor compared to previous works in the literature. [Display omitted] [ABSTRACT FROM AUTHOR]

• A two-layer control method is proposed to deal with calculation complexity. • The clustered temperature transfer processes of bitumen tanks are analyzed. • A data-driven method is introduced to enhance computational efficiency. • A real-time predictive control method is developed to deal with uncertainties. Uncertainties and computational complexity are two growing challenges in scheduling industrial heating loads. In this paper, a data-driven real-time predictive control approach is proposed to deal with these challenges in the industrial scheduling of bitumen tanks. Specifically, predictive control technology is utilized to leverage the updated information to mitigate the negative impact of past uncertainties in equipment parameters and external environmental factors, which may lead to temperature constraint violations in the bitumen tank operation processes. Meanwhile, a data-driven method using artificial neural networks (ANN) is developed to ensure efficient computation for real-time predictive control. Moreover, a two-layer control method is devised to reduce the calculation time for day-ahead optimal scheduling of a large scale of bitumen tanks, aiming to generate sufficient high-quality data for training ANN. In the two-layer control method, the clustered temperature transfer processes of bitumen tanks are analyzed and modeled for the first time. Simulation results indicate that the two-layer control method can significantly reduce the computational time required for the day-ahead optimal scheduling of bitumen tanks, facilitating the generation of a large amount of high-quality data for training ANN. Subsequently, the application of ANN enables real-time predictive control, helping to eliminate the negative impact of uncertainties. [ABSTRACT FROM AUTHOR]

Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data-driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations and data labels. To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and 123-bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real-world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other data-driven methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids. • An unsupervised neural network model combines the GANs and autoencoder for FDI attacks. • The proposed model utilizes the outputs of autoencoder and critic score for attack detection. • The proposed model is tested on unbalanced power distribution grids integrated with DERs. • Simulation results verify the model's superior performance compared to other data-driven models. [ABSTRACT FROM AUTHOR]

• A flat plate heat pipe with dimensions of 500 × 200 × 3 mm3 and containing 19 independent microchannels inside is developed. • CFD modeling based on the VOF model and improved Lee model was performed to reproduce the heat transfer behaviors. • The ANN model agrees well with the simulated data with MSE and R of 1.30 × 10-5 and 0.9990, respectively. • The model constructed can provide an effective tool for the adaptation and optimization of heat pipe in 5G base station. To address the significantly increased heat dissipation requirements of 5G base stations, a flat plate heat pipe (FPHP) of 500 × 200 × 3 mm3 comprising of 19 independent channels was developed. To solve the problem of simulation distortion due to the high superheat of the initial boiling of the working fluid in the ultra-long channel of 490 × 5 × 1.5 mm3, this paper adopts the Lee model considering the superheat, and simulates based on the Volume of fluid method, and the simulation results are compared with the experiments, and the average relative error is less than 5 %. The FPHP was explored at 100–500 W heat power and filling ratio of 30 %-70 %, depending on the application scenarios of the base station. By partitioning the 19 channels and calculating the thermal resistance of the FPHP based, an Artificial neural network (ANN) model with 10 hidden layer nodes is established based on 675 data sets. The results showed that the mean square error and correlation coefficient were 1.30 × 10-5 and 0.9990. The ANN prediction results are validated, the mean relative error is 5.4 %, demonstrating that the ANN model can be an effective tool to optimize the FPHP for the telecommunication equipment. [ABSTRACT FROM AUTHOR]

Machine-learning (ML) techniques have bloomed in recent years, especially in fluid mechanics applications. In this paper, we trained, validated and compared two types of ML-based models to augment Reynolds-averaged Navier–Stokes (RANS) simulations. The methodology was tested in a series of flows around bumps, characterized by different levels of flow separation and curvatures. Initially, the ML-based models were trained in three configurations presenting attached flow, small and moderate separation and tested in two configurations presenting incipient and large separation. The output quantity of the machine-learning model is the turbulent viscosity as done in Volpiani et al. (2022). The new models based on artificial neural networks (NN) and random forest (RF) improved the results if compared to the baseline Spalart–Allmaras model, in terms of velocity field and skin-friction profiles. We noted that NN has better extrapolation properties than RF, but the skin-friction distribution can present small oscillations when using certain input features. These oscillations can be reduced if the RF model is employed. One major advantages of RF is that raw quantities can be given as input features, avoiding normalization issues (such as division by zero) and allowing a larger number of universal inputs. At the end, we propose a mixed NN-RF model that combines the strengths of each method and, as a result, improves considerably the RANS prediction capability, even for a case with strong separation where the Boussinesq hypothesis (and therefore the eddy-viscosity assumption) lacks accuracy. • We combine neural networks and random forest to create a new data-driven RANS model. • The new machine learning-based model does not require to transport unclosed terms. • The new model improves the velocity, wall-pressure and skin-friction distributions. • It was proved to be robust even in predicting extrapolated flows. [ABSTRACT FROM AUTHOR]

• A new AI-based model learning method for HCF of the joints in large-span steel structures is proposed. • The PSO-ANN-ABC AI algorithm is applied to firstly predict fatigue resistance for welded spherical joints. • A subroutine for HCF damages is developed to solve the problem non-stationary amplitudes and frequencies. Welded spherical joints are critical components in large-span bridges and spatial steel structures prone to high cycle fatigue (HCF) loadings. Challenges exist for assessing fatigue life of this joints in small data from physical experiments and no detailed fatigue classification in design codes. This paper investigates the fatigue resistance of welded spherical joints and the real-time pre-damage evolution under non-stationary HCF loadings by proposing AI-based macro model learning from fine FE models and fatigue tests. Firstly, the structural features of welded spherical joints about stress concentration and weld defects were analyzed and quantified, so modified S-N curve was built. Then, based on artificial intelligence (AI-based) learning, stress concentration factors (SCF) were obtained by an artificial neural network with particle swarm optimization and the modified fatigue resistance of welded spherical joints was derived by artificial bee colony algorithms. Finally, the macro linkage element was proposed as a user element subroutine (UEL), by which non-stationary fatigue pre-damage evolution based on the time-domain incremental analytical method was realized. The results not only indicated the damage value close to the ones derived by commonly used rain flow count methods, but also provided the pre-damage evolution curve for real-time monitoring. [ABSTRACT FROM AUTHOR]

To mitigate the high cost of integrated ship design, in this paper, we propose an innovative integrated hull design methodology utilizing artificial intelligence-aided design technology. The AIAD methodology leverages a deep neural network model constructed from multiple fidelities datasets to curtail the exponential growth in data requirements resulting from the proliferation of design variables caused by integration. Combining the proposed bump-free deformation method and optimization algorithms with the multi-fidelity deep network, the AIAD-based design approach facilitates the achievement of optimal hull design solutions with remarkable fidelity while minimizing design workload. Utilizing the developed design method, we performed a numerical test on integrated-hull design for container ships. Through a comparative analysis and sensitive analysis of design results and workload, the optimized results obtained demonstrated the effectiveness of the proposed AIAA integrated-hull design methodology in achieving optimal designs while preserving high fidelity and low workload. The proposed new approach has shown promises to expedite ship integrated-hull design, addressing the longstanding issue of its high cost. • An artificial intelligence-based design method is proposed, reducing the workload brought by the integrated-hull design. • Balanced the conflicting relationship between fidelity and cost of the deep learning model by using multi-fidelity datasets. • A bump-free deformation method is provided, which solves the local concave-convex after deformation. • Carried out the integrated-hull design of a container ship hull, achieving a 3% reduction in resistance. [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]

Detecting and tracking the position of multiple targets indoors is a challenging measurement problem due to the inherent difficulty to cluster correctly the sensor data associated to a given target and to track the position of each cluster with adequate accuracy. This problem is critical especially in rooms filled with fixed or moving objects hampering target detection and whenever the paths of different targets cross one another. In this paper, a robust Multiple Targets Tracking (MTT) algorithm exploiting the clouds of points collected from a mmWave-FMCW radar is presented. The proposed solution consists of four main steps. First, the possible outliers of a raw radar data set are removed using a neural network model. Next, the cleaned-up radar data are clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Then, a Kalman Filter (KF) is used to track the position of the centroid of each cluster. Finally, a Structured Branching Multiple Hypothesis Testing (SBMHT) algorithm is applied and updated over reasonably short time intervals to decide which detected tracks are supposed to be confirmed and which ones instead should be discarded. The proposed MTT technique was validated experimentally using the data sets collected from a 60-GHz TI IWR6843 radar platform. The reported results show that the developed algorithm, if properly tuned, is faster and returns more accurate results than other MTT techniques. In particular, the percentage of detection errors is negligible and the planar positioning accuracy is within about 30 cm with 90% probability when up to five targets move freely within the same room. [Display omitted] • A Multiple Targets Tracking (MTT) algorithm exploiting indoor radar data is proposed. • Robust outlier removal and clustering based on a Neural Network and DBSCAN. • A Structured Branching Multiple Hypothesis Testing (SBMHT) algorithm used to confirm/discard tracks. • Planar positioning accuracy (with a 60-GHz radar) is within ±30 cm when 5 people move in the room. • Probability of detection errors and processing latency lower than other MTT algorithms. [ABSTRACT FROM AUTHOR]

• The UCS of rock was correlated using only three non-destructive test indexes; • A parametric analysis was performed to obtain the optimum ANN architecture; • Metaheuristic techniques were applied to optimize the ANN weights and biases; • The accuracy of the developed model is higher than the available in the literature; • An explicit equation was extracted for practical engineering design. The unconfined compressive strength (UCS) of intact rocks is crucial for engineering applications, but traditional laboratory testing is often impractical, especially for historic buildings lacking sufficient core samples. Non-destructive tests like the Schmidt hammer rebound number and compressional wave velocity offer solutions, but correlating these with UCS requires complex mathematical models. This paper introduces a novel approach using an artificial neural network (ANN) to simultaneously correlate UCS with three non-destructive test indexes: Schmidt hammer rebound number, compressional wave velocity, and open-effective porosity. The proposed ANN model outperforms existing methods, providing accurate UCS predictions for various rock types. Contour maps generated from the model offer practical tools for geotechnical and geological engineers, facilitating decision-making in the field and enhancing educational resources. This integrated approach promises to streamline UCS estimation, improving efficiency and accuracy in engineering assessments of intact rock materials. [ABSTRACT FROM AUTHOR]