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

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

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

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

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

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

Link prediction is a fundamental problem in complex network analysis, aimed at predicting missing or forthcoming connections. Recent research investigates the potential of Nonnegative Matrix Factorization (NMF) models in reconstructing sparse networks and using deep NMF to uncover the hierarchical structure. Deep models have demonstrated remarkable performance in various domains, but they are susceptible to overfitting, especially on the limited training data. This paper proposes a novel Link Prediction using Adversarial Deep NMF (LPADNMF) to enhance the generalization of network reconstruction in sparse graphs. The main contribution is the introduction of an adversarial training that incorporates a bounded attack on the input, leveraging the L 2 , 1 norm to generate diverse perturbations. This adversarial training aims to improve the model's robustness and prevent overfitting, particularly in scenarios with limited training data. Additionally, the proposed method incorporates first and second-order affinities as input to capture higher-order dependencies and encourage the extraction of informative features from the network structure. To further mitigate overfitting, a smooth L 2 regularization is applied to the model parameters. To optimize the proposed model effectively, we utilize a majorization-minimization algorithm that efficiently updates the perturbation and latent factors in an iterative manner. Our findings demonstrate that the proposed model not only has the ability to uncover and learn complex structures but also possesses generalization capabilities. This method demonstrates superior performance compared to state-of-the-art methods across eight networks. In comparison to the best-performing approach, LPADNMF exhibited improvements of 2.36% in AUC, 4.67% in Precision, 2.21% in Recall, and 3.73% in F-Measure. [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]

The significant gap between the assumed and actual degradation model will undoubtedly lead to a serious performance decrease for deep learning based image super-resolution (SR) methods. To solve this shortcoming, this paper presents an enhanced blind image super-resolution (EBSR) network based on unsupervised degradation representation learning. The proposed EBSR mainly is made up of two branching subnetworks. The first subnetwork is a residual-based degradation encoder, which is responsible to learn a high-dimensional abstract degradation representation vector from the input low-resolution (LR) image patches using contrast learning. Another branch is the degradation-aware fusion SR (DAFSR) network that completes the image SR task with the aid of the degradation representation vector learned by the encoder. To obtain an improved SR performance under various degradation settings, the degradation-aware fusion block (DAFB), the core block of DAFSR, has been elaborated, which embeds the degradation model information learned by the encoder into every layer of the network. Additionally, a shallow feature extraction block (SFEB) is constructed for DAFSR to extract global information from the LR image. The proposed EBSR can flexibly adapt to various degradation models. Extensive experimental results on synthetic datasets and real images show that the proposed EBSR is able to achieve a leading reconstruction performance over other blind SR algorithms. • A boosted degradation representation learning is proposed for blind image SR. • A new degradation encoder is designed using residual connection. • A degradation-aware fusion block is designed to obtain improved SR performance. • A shallow feature extraction block is adopted to extract global informative features. [ABSTRACT FROM AUTHOR]

Semantic segmentation is a well-studied topic and one of the most challenging tasks in computer vision applications, such as autonomous driving. Deep learning approaches based on convolutional neural networks (CNN) have demonstrated exceptional success on this task in recent years. Despite this success, existing approaches are plagued by higher-order inconsistencies between the ground truth images and the ones predicted by the segmentation model. This paper proposes a novel post-processing scheme based on adversarial learning to counter these inconsistencies. Such a scheme can be combined with a variety of existing CNN-based semantic segmentation networks to improve their segmentation performances. The proposed scheme is a Two-Stream Conditional Generative Adversarial Network (TScGAN), with one stream having initial semantic segmentation masks predicted by an existing CNN, while the other stream utilizes scene images to retain high-level information under a supervised residual network structure. In addition, TScGAN incorporates a novel dynamic weighting mechanism, which leads to significant and consistent gains in segmentation performance. Several comparative tests on public benchmark driving databases, including Cityscapes, Mapillary, and Berkeley DeepDrive100K, demonstrate the effectiveness of the proposed method when used with state-of-the-art CNN-based semantic segmentation models. Furthermore, the ablation experiment proved the structural rationality of our two-stream structure. The code for TScGAN could be found at https://github.com/epan-utbm/TScGAN-for-Improving-Semantic-Predictions • Explores various post-processing techniques for enhancing semantic segmentation. • Novel Two-Stream Conditional GAN enhances urban drive scene segmentation. • Enhances feature extraction by implementing dynamic weighting within streams. • Evaluates the model against existing semantic segmentation methodologies. • Tests on Cityscapes, Mapillary, BDD100K reveal major IoU gains with proposed model. [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]

Efficient forecasting of ship order books holds immense significance in the maritime industry, enabling companies to optimize their operations, allocate resources effectively, and make informed decisions. However, volatile characteristics within historical order books pose challenges in achieving reliable, intelligent, and precise forecasts. This paper presents a novel ensemble deep random vector functional link (edRVFL) algorithm to anticipate future ship order book dynamics. The edRVFL leverages deep feature extraction and ensemble learning to enhance forecasting performance. To further elevate its capabilities, we introduce a discontinuous and parsimonious embedding strategy, which deviates from the conventional dense collection of continuous time steps used in vanilla edRVFL. This parsimonious embedding approach limits the model's complexity and boosts its generalization ability. We extensively evaluate the proposed method using ship order book data, and comparative studies demonstrate its superiority over alternative approaches. Our proposed edRVFL offers a promising solution for accurate and efficient ship order book forecasting, making it a valuable asset in the maritime industry's decision-making processes. The source codes utilized in this research are openly available on GitHub at the following link: https://github.com/crkkkaa/Ship-order-book-forecasting-by-an-ensemble-deep-parsimonious-random-vector-functional-link-network-. • A novel ensemble deep RVFL to anticipate ship order book dynamics is proposed. • We propose a parsimonious embedding algorithm to optimize temporal patterns. • The direct links ablation study is conducted for comparison. [ABSTRACT FROM AUTHOR]

Efficient forecasting of ship order books holds immense significance in the maritime industry, enabling companies to optimize their operations, allocate resources effectively, and make informed decisions. However, volatile characteristics within historical order books pose challenges in achieving reliable, intelligent, and precise forecasts. This paper presents a novel ensemble deep random vector functional link (edRVFL) algorithm to anticipate future ship order book dynamics. The edRVFL leverages deep feature extraction and ensemble learning to enhance forecasting performance. To further elevate its capabilities, we introduce a discontinuous and parsimonious embedding strategy, which deviates from the conventional dense collection of continuous time steps used in vanilla edRVFL. This parsimonious embedding approach limits the model's complexity and boosts its generalization ability. We extensively evaluate the proposed method using ship order book data, and comparative studies demonstrate its superiority over alternative approaches. Our proposed edRVFL offers a promising solution for accurate and efficient ship order book forecasting, making it a valuable asset in the maritime industry's decision-making processes. The source codes utilized in this research are openly available on GitHub at the following link: https://github.com/crkkkaa/Ship-order-book-forecasting-by-an-ensemble-deep-parsimonious-random-vector-functional-link-network-. • A novel ensemble deep RVFL to anticipate ship order book dynamics is proposed. • We propose a parsimonious embedding algorithm to optimize temporal patterns. • The direct links ablation study is conducted for comparison. [ABSTRACT FROM AUTHOR]

Implementing key engineering solutions to optimise the operation of energy industries requires daily electricity demand forecasting and including uncertainty, to promote markets insight analysis as part of their strategic planning, regulating and supplying electricity to consumers. This paper proposes hybrid artificial intelligence models combining convolutional neural networks (CNN) as a feature extraction algorithm with extreme learning machines (ELM) as a framework to predict electricity demand with confidence intervals generated by Kernel Density Estimation (KDE) approaches. In order to develop CELM-KDE model, time-lagged series of daily electricity demand with local climate variables based on the air temperature, atmospheric vapour pressure, evaporation, solar radiation, humidity and sea level pressures are used to train the proposed CELM-KDE hybrid model. In order to fully evaluate the newly developed model from a point-based, as well as a probabilistic prediction strategy, the observed and predicted electricity demand as well as the probability distribution of errors are analysed using KDE method that operates without prior data distribution assumptions. Based on observed and predicted electricity demand and the relevant probabilistic confidence intervals generated by the CELM-KDE model, the final results show that the proposed method attains significantly better probability interval predictions than traditionally-used point-based models. The proposed CELM-KDE model is demonstrated to be highly effective in providing a comprehensive coverage of predicted errors, as well as providing greater insights into the average bandwidth and detailed predicted electricity demand in the testing stage. The results also indicate that the proposed hybrid model is a reliable decision support tool to develop engineering solutions in area of energy modelling, monitoring and forecasting, which could potentially be useful to the industry policymakers. We show that the point-and probabilistic-based electricity demand predictive models can be employed as an effective tool to improve accuracy of forecasting and provision of insights for national electricity markets and key energy industry stakeholder application tools. [Display omitted] • A hybrid deep learning model for daily electricity demand prediction is proposed. • A Kernel Density Estimation is applied to derive probabilistic prediction intervals. • The algorithm quantifies uncertainties in point-based electricity demand predictions. • The proposed model outperforms deep learning models using statistical uncertainty indicators. [ABSTRACT FROM AUTHOR]

Photovoltaic defect detection is an essential aspect of research on building-distributed photovoltaic systems. Existing photovoltaic defect detection models based on deep learning, such as YOLOv5 and YOLOv8, have significantly improved the accuracy of photovoltaic defect detection. However, these models are too large, and their feature extraction ability is insufficient, leading to low detection efficiency and inability to cope with the continuous evolution of defects. Therefore, this study proposes an accurate and lightweight YOLOv8 (You Only Look Once v8) GD algorithm. The algorithm is an improved version of YOLOv8, wherein DW-Conv (DepthWise-Conv) is applied to the YOLOv8 backbone network. Moreover, convolution is replaced with the GSConv (Group-shuffle Conv) and the BiFPN (bidirectional feature pyramid network) structure is added to the architecture. Several electroluminescent photovoltaic defect datasets are used to verify the effectiveness of the proposed method. The final experimental results show that the map@0.5 and map@0.5 ∼ 0.95 of YOLOv8-GD are 92.8% and 63.1%, respectively, which are 4.2% and 5.7% higher than those of the original algorithm, respectively, and the model volume is reduced by 16.7%. Thus, the proposed algorithm shows considerable potential in the field of photovoltaic defect detection. • This paper proposes an improved method based on YOLOv8 for building distributed PV defects, named YOLOv8-GD. • YOLOv8-GD includes improved methods such as GSConv, BiFPN and DW-Conv, which shows better accuracy and speed. • YOLOv8-GD can be used for defect detection and health monitoring in building distributed PV. [ABSTRACT FROM AUTHOR]

3D face reconstruction and dense alignment play a key role in digital humans. Learning-based approaches have been developed by employing cascaded convolutional neural networks for feature extraction and spatial-semantic relationship construction. However, these networks often struggle with accurately capturing the complex relationships among various facial components and attributes, particularly in unconstrained environments. Furthermore, the mappings modeled by convolutional weights are at low levels, which are usually implicit and local, and lack global discrimination. In this paper, we propose a global and instance-level perceived relationship matrices-based network (PRMNet) to recover high-fidelity 3D faces and perform accurate dense alignment in unconstrained environments. Specifically, a Key Information Extraction Module (KIEM) extracts crucial features from global and local feature banks. This reduces reasoning costs while maintaining high perceptual quality. Learnable global and instance-level perceived feature relationship matrices are then integrated into the Feature Relationship Reasoning Module (FRRM). This calibration combines key information from both the global macroscopic and sample-specific microscopic views, allowing accurate construction of spatial-semantic relationships to harvest both global discrimination and local relevance. Finally, we introduce a Spatial Reasoning and Guidance Module (SRGM), designed to recalibrate the joint weight responses of feature extraction and its enhancement paths using various attention mechanisms, thereby further enhancing global discrimination ability. Extensive quantitative and qualitative experiments on the benchmark datasets show that our PRMNet outperforms the state-of-the-art. Codes and all resources will be publicly available at https://github.com/Ray-tju/PRMNet. [ABSTRACT FROM AUTHOR]

Multi-view stereo (MVS) reconstruction is a key task of image-based 3D reconstruction, and deep learning-based methods can achieve better results than traditional algorithms. However, most of the current deep learning-based MVS methods use convolutional neural networks (CNNs) to extract image features, which cannot achieve the aggregation of long-distance context information and capture robust global information. In addition, in the process of fusing depth maps into point clouds, the confidence filters will filter out the depth values with low confidence in weak texture areas. These problems will lead to the low completeness of 3D reconstruction of weak texture and texture-less areas. To address the above problems, this paper proposes SA-MVSNet based on the PatchmatchNet with a self-attentive mechanism. First, we design a coarse-to-fine network framework to advance depth map estimation. In the feature extraction network, a module with a pyramid structure based on Swin Transformer Block is used to replace the original Feature Pyramid Network (FPN), and the self-correlation between weak texture areas is enhanced by applying a global self-attention mechanism. Then, we also propose a self-attention-based adaptive propagation module (SA-AP), which applies a self-attention calculation within depth value propagation window to obtain the relative weight values of current pixel and others, and then adaptively samples the depth values of neighbors on the same surface for propagation. Experiments show that SA-MVSNet has significantly improved the completeness of 3D reconstruction for the images with weak texture on DTU (provided by Danish Technical University), BlendedMVS, and Tanks and Temple datasets. Our code is available at https://github.com/miaowang525/SA-MVSNet. • SA-MVSNet proposed a multi-scale feature extraction module based on Swin Transformer. • A global self-attention mechanism is applied to enhance the self-correlation between weak textured areas. • SA-MVSNet proposed a self-attention-based adaptive propagation module (SA-AP). • Apply window self-attention mechanism to enhance the confidence of depth value. • SA-MVSNet has significantly improved the completeness of 3D reconstruction on three datasets. [ABSTRACT FROM AUTHOR]

Currently, the fusion of surface Electromyography (EMG) and deep learning is gradually showing immense potential in the research of Lower Limb Motion Intention Recognition (LLMIR). Nevertheless, most deep learning algorithms have poor interpretability without special design or the help of other post-hoc analysis tools, as well as unsatisfactory performance in cross-subject prediction. Hence, this paper presents a novel Interpretable Dual-Branch EMG Network (IDB-EMGNet), in which one branch is dedicated to lower limb motion recognition, and the other is able to predict knee joint angles in advance. The shallow feature extraction module of IDB-EMGNet is constructed using an ante-hoc interpretable SincNet technique, which enables the detection of the spectral range of EMG used for the LLMIR task. An improved bottleneck block with shuffle attention is designed for deep feature extraction, which enhances model performance with only a little increase in complexity. The performance of IDB-EMGNet in both intra-subject and inter-subject scenarios is investigated, where the latter integrates the transfer learning technique. Specifically, by conducting model pre-training on source-domain subjects and transferring the learned knowledge to target-domain subjects, satisfactory performance can be achieved even with less computing resource. Experimental results on two publicly available datasets indicate that the proposed approach exhibits superior applicability to both normal subjects and knee-pathology patients, showing a promising prospect in the controller design of human-robot collaborative exoskeletons. • A novel IDB-EMGNet is proposed for simultaneous discrete and continuous lower limb motion intention recognition. • Interpretability of IDB-EMGNet is enhanced via an ante-hoc SincConv technique. • An IB-Neck block is designed to improve model prediction performance. • Transfer learning technique is utilized to improve model generalization in inter-subject prediction. • The proposed approach performs well for both normal subjects and knee-pathology patients. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate emotion recognition using a multimodal approach that exploits convolutional neural networks (CNNs) with multiple input. Multimodal approaches allow different modalities to cooperate in order to achieve generally better performances because different features are extracted from different pieces of information. In this work, the facial frames, the optical flow computed from consecutive facial frames, and the Mel Spectrograms (from the word melody) are extracted from videos and combined together in different ways to understand which modality combination works better. Several experiments are run on the models by first considering one modality at a time so that good accuracy results are found on each modality. Afterward, the models are concatenated to create a final model that allows multiple inputs. For the experiments the datasets used are BAUM-1 ((Bahçeşehir University Multimodal Affective Database - 1) and RAVDESS (Ryerson Audio–Visual Database of Emotional Speech and Song), which both collect two distinguished sets of videos based on the different intensity of the expression, that is acted/strong or spontaneous/normal, providing the representations of the following emotional states that will be taken into consideration: angry, disgust, fearful, happy and sad. The performances of the proposed models are shown through accuracy results and some confusion matrices, demonstrating better accuracy than the compared proposals in the literature. The best accuracy achieved on BAUM-1 dataset is about 95%, while on RAVDESS it is about 95.5%. [Display omitted] • Emotion recognition through multimodal architectures. • Comparison of early to late fusion of 1-input models. • Synchronization of video and audio channels. [ABSTRACT FROM AUTHOR]

In this paper, we propose a lightweight approach for industrial defect detection that is modeled based on anomaly detection using point pattern data. Most recent works use global features for feature extraction to summarize image content. However, global features are not robust against lighting and viewpoint changes and do not describe images' geometrical information which, thus, cannot be fully utilized in manufacturing industry. To the best of our knowledge, we are the first to propose using transfer learning of local/point pattern features to overcome these limitations and capture geometrical information of the interested image regions. We model these local/point pattern features as a random finite set (RFS). In addition, we propose RFS energy, in contrast to RFS likelihood as an anomaly score. The similarity distribution of point pattern features of the normal sample has been modeled as a multivariate Gaussian. Parameters learning of the proposed RFS energy does not require any heavy computation. We evaluate the proposed approach on the MVTec AD dataset, a multi-object defect detection dataset. Experimental results show the outstanding performance of our proposed approach compared to the state-of-the-art methods, and the proposed RFS energy outperforms the state-of-the-art in the few shot learning settings. Also, we evaluated the proposed approach on needle dataset, a real life application, to detect defective needle samples given three different views. The results show superior performance compared to DiffeNet. [Display omitted] • A lightweight approach for defect detection based on anomaly detection is proposed. • Transfer learning of deep local features is used instead of global features. • We model these local/point pattern features as a random finite set (RFS). • We propose RFS energy, in contrast to RFS likelihood as an anomaly score. • Results show the outstanding performance of our proposed approach compared to the state-of-the-art methods in few-shot learning. [ABSTRACT FROM AUTHOR]

Induction motors are widely used in various industrial applications due to their simplicity, robustness, and high efficiency. In recent years, deep learning approaches have shown great potential for detecting and diagnosing faults in induction motors. However, it was observed that the convolutional neural network (CNN) might miss the crucial long-range temporal dependencies of the significant features. At the same time, the recursive neural network (RNN) may face issues such as vanishing gradient and less computational efficiency if the data is too long with complex patterns. This paper proposes a novel deep-learning model that combines a one-dimensional CNN and an RNN into two independent pipelines. The spatial, temporal, long-term, and short-term features can be extracted from the raw input signal. A multi-head mechanism, a state-of-the-art deep learning technique in natural language processing (NLP), enables the model to focus more on relevant features and prevent overfitting. Specifically, our method simplifies the conventional fault diagnosis workflow while maintaining competitive accuracy compared to several state-of-the-art methods. By leveraging the ability to detect faults directly from raw, unprocessed electrical signals, our approach offers a practical, cost-effective solution for real-time fault detection in industrial applications. This innovation has the potential to significantly reduce downtime and the associated operational costs, thereby providing immediate practical value in various industrial settings. [ABSTRACT FROM AUTHOR]

Skin cancer is the most prevalent type of cancer worldwide. Early detection is essential as it could be fatal at later stages. The classification of skin lesions is challenging since there are many variations, including changes in color, shape, size, high intra-class variation, and high inter-class similarity. In this paper, a unique class-wise attention method is proposed that considers each class equally while extracting additional discriminative information of skin lesions. The proposed attention mechanism is employed in a progressive manner to incorporate discriminative feature information from multiple scales. The proposed approach obtained competitive performance against more than 15 state-of-the-art methods including HAM1000 and ISIC 2019 leaderboard winners. The proposed method achieved 97.40% accuracy on the HAM10000 and 94.9% accuracy on the ISIC 2019 dataset. • A novel attention based mechanism is proposed for skin lesions classification. • The problem of significant intra-class variance, high inter-class similarity, and class imbalance is addressed by a class-wise attention mechanism. • The important features for the classification are extracted without the burden of additional parameters using a progressive class-wise attention mechanism. • The final three layers of the baseline model are discarded and the global average pooling (GAP) and classification output layers are added to reduce the number of parameters, while maintaining performance. • The proposed network is robust, end-to-end trainable and has good generalization on unseen data. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has experienced phenomenal expansion over the past few years and has emerged as one of the most dynamic sectors of the international market. Android has become the driving force behind the rapid development of the IoT, and malware is one of the most severe concerns. Android is an open-source platform with a significant number of users. The identification of Android malware has become more critical in cybersecurity due to the meteoric rise of Android malware over the past few years and the severity of the damage it inflicts on smartphone users. Existing traditional Android malware detection methods based on machine learning have achieved encouraging performance. Still, the solution requires a massive workforce of feature engineering to build dynamic or static features and continuous learning through pre-extracted features to maintain high performance in identifying malware. Therefore, technologies for end-to-end malware detection without human interaction are necessary. In response to the rapid evolution of Android malware, a convolutional neural network that can acquire knowledge without requiring feature extraction is ideally suited. This paper offers a unique classification approach for malware based on a convolutional neural network, with feature extraction selected from the current pre-trained models DenseNet169, Xception, InceptionV3, ResNet50, and VGG16. The proposed methods have an advantage over existing detection methods due to their end-to-end learning process. Our suggested approaches convert Android APK files to binary codes and RGB images for usage as inputs to deep learning models. These models are trained and evaluated using the CICInvesAndMal2019 dataset, which consists of 333 benign and 428 malicious applications. Experiments demonstrate that the proposed approaches achieve a classification accuracy of 95.24%, 95.24%, and 95.83% for DenseNet169, InceptionV3, and VGG16, respectively, on a 761-sample dataset of malware, which is superior to other compared algorithms. Compared to previous methods, our suggested methods are more applicable to Android IoT devices since they do not require manual feature engineering and consume fewer resources. [ABSTRACT FROM AUTHOR]

Segmentation of bowel sounds (BS) events is a significant task of automatic BS monitoring. Recently, deep learning (DL) has been utilized to realize the segmentation of BS events. However, most researchers treat BS segmentation as a traditional classification problem, which causes that more precise locations for the occurrence of BS events are unable to obtained. Besides, the performance of segmentation of BS events is easily affected by thresholds of model output in practical applications. To tackle these issues, in this paper, a lightweight DL-based BS segmentation algorithm is proposed. The one-dimensional convolution layers and bidirectional gate recurrent unit (GRU) layers are adopted to enhance the ability of feature extraction. A loss function named shape loss is proposed to reduce the sensitivity of model to thresholds. Moreover, a portable BS monitoring device is developed to realize data acquisition, display and algorithm deployment. Based on this device, experiments on human and rats are conducted to verify the effectiveness of our proposed approach. Experimental results shows that the proposed method outperforms other comparison methods with the higher f1-scores and lower sensitivity to thresholds. [ABSTRACT FROM AUTHOR]

Automatic extraction of flame area plays an important role in forest fire detection, which can accurately understand the spatial distribution and development trend of forest fire, so as to effectively realize the protection of forest resources. However, due to the instability and spread of fires, and the complexity of the background, accurate early fire detection is extremely challenging. At the same time, the image pixel proportion of the flame area in early stage is much smaller than that in the background, which causes a serious class imbalance problem. With the fast development of deep learning, some achievements have been made in flame extraction, but there are still some deficiencies in the existing networks, such as limited feature representation, poor feature capturing ability on micro objects, insufficiency processing of local features, etc. This paper proposes an attention-based dual-encoding segmentation network, abbreviated as ADE-Net, for pixelwise early fire detection. To realize strong feature representation, a dual-encoding path, consisting of semantic units and spatial units, is introduced to extract richer features, and an attention fusion module (AFM) is introduced to fully integrate spatial and semantic information and achieve effective feature aggregation. In addition, faced with the class imbalance problem, a multi-attention fusion (MAF) module is introduced to obtain more discriminating features to make the segmentation network to focus on the key pixel areas. Furthermore, a feature enhancement module, named attention-guided enhancement (AGE) module, is proposed to enrich the feature representation of local feature maps. Finally, to realize better multi-scale global feature extraction and fusion, a global context fusion (GCF) module is proposed into the bottleneck layer for multi-scale feature enhancement. Experimental results show that the proposed ADE-Net has a good early fire detection ability from remote sensing images, and it has obtained a competitive advantage compared with advanced segmentation models. [ABSTRACT FROM AUTHOR]