Search

The core of data series classification and clustering is how to evaluate the similarity between time series. However, for one-dimensional time series, the vast majority of similarity measurement methods currently relied on one-dimensional information. Although some classifications of time series were achieved by the images transformed from time series, they mostly relied on a single imaging mechanism (time domain or frequency domain). In order to give a new idea for time series similarity measurement, this paper proposed a similarity measurement method of time series based on the fused images. According to recurrence plots (RP) and wavelet scalogram (WS), the original time series was first transformed into images. Then, RP and WS were fused into an image. Next, the feature in the image was extracted by ResNet-18. Finally, the similarity of the time series can be obtained by the Euclidean distance between the features extracted by ResNet-18. The proposed method was verified by the time series from UCR Time Series Classification/Clustering Homepage. The results showed that the similarity measurement of time series can be effectively improved by fusing the images obtained by different imaging mechanisms in the time domain and frequency domain. • This paper proposes a new similarity measurement method for time series based on the image fusion of different forms. • The time domain and frequency domain information of time series are represented by recurrence plots and wavelet scalogram. • The characteristics of the image fused by recurrence plots and wavelet scalogram are extracted by Convolutional Neural Network to measure the similarity of time series. • 30 sets of time series show the validity and superiority of the proposed method. • The reasons for the effectiveness of the proposed method are analyzed in detail. [ABSTRACT FROM AUTHOR]

Accurate forecasting of natural gas consumption (NGC) plays an important role in energy supply, energy trading, economic effects and environmental sustainability. NGC forecasts can be used to adjust production and supply plans to improve gas efficiency and reduce carbon emissions and supply chain waste. This paper reviews the research progress on NGC in the past decade, analyzes the typical characteristics of different forecasting strategies, and highlights 163 studies in terms of the technical aspects of feature processing methods, data decomposition methods, forecasting models and optimization algorithms. It also systematically elaborates the application of statistical models, machine learning models, grey models, logistic regression and their combinations in predictive models. Bibliometric methods are also utilized to dissect research hotspots and summarize cutting-edge trends in the field. It is worth mentioning that in the terms of hybrid model structures, the application and performance of various model structures are described and evaluated. In this paper, the future development is discussed from spatiotemporal characteristics, studying reasonable data decomposition layers and fusion models, considering potential data privacy issues, and developing artificial intelligence-supporting models and interpretable frameworks. This paper is expected to provide a multi-technology reference for natural gas forecasting and help researchers to select and develop more accurate forecasting techniques and strategies. • Bibliometric analysis of natural gas consumption forecasting. • Typical features of different forecasting strategies are reviewed. • Characteristics and applications of forecasting models and assisted forecasting techniques are outlined. • The application and performance of various hybrid model structures are described and evaluated. • Existing problems and future research directions in natural gas consumption forecasting are discussed. [ABSTRACT FROM AUTHOR]

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

Artificial intelligence (AI) and Machine learning (ML) are increasingly used for digital twin development in energy and engineering systems, but these models must be fair, unbiased, interpretable, and explainable. It is critical to have confidence in AI's trustworthiness. ML techniques have been useful in predicting important parameters and improving model performance. However, for these AI techniques to be useful in making decisions, they need to be audited, accounted for, and easy to understand. Therefore, the use of explainable AI (XAI) and interpretable machine learning (IML) is crucial for the accurate prediction of prognostics, such as remaining useful life (RUL), in a digital twin system to make it intelligent while ensuring that the AI model is transparent in its decision-making processes and that the predictions it generates can be understood and trusted by users. By using an explainable, interpretable, and trustworthy AI, intelligent digital twin systems can make more accurate predictions of RUL, leading to better maintenance and repair planning and, ultimately, improved system performance. This paper aims to explain the ideas of XAI and IML and justify the important role of AI/ML for the digital twin components, which requires XAI to understand the prediction better. This paper explains the importance and fundamentals of XAI and IML in both local and global aspects in terms of feature selection, model interpretability, and model diagnosis and validation to ensure the reliable use of trustworthy AI/ML applications for RUL prediction. [ABSTRACT FROM AUTHOR]

Data-driven fault diagnosis methods have significantly contributed to the rapid development of prognostics and health management of gearboxes. However, these methods require sufficient labeled data for model training. In real industrial scenarios, labeled data is usually scarce. Thus, a residual attention temporal recurrent network is proposed for fault diagnosis of gearboxes under limited labeled data. More specifically, a fault diagnosis framework with self-supervised sample generation learning is explored in this paper. First, a small amount of labeled data is utilized to self-supervise the generation of new samples with similar semantics in adversarial generation modeling. Second, a temporal recurrent network with residual attention is constructed to extract fault features of vibration signals using two-stage temporal and recursive operations. In addition, the construction of the residual attention mechanism can facilitate the network to have a deeper semantic understanding of the vibration signal. Finally, the model can guarantee a more robust classifier in the proposed fault diagnosis framework using a small number of labeled samples. The extensive validation of the proposed method is performed on the gearbox dataset. The experimental results show that the proposed method can achieve competitive fault diagnosis performance under limited labeled data. • The main contributions of this paper are as follows. • A residual attention temporal recurrent network is proposed for the fault diagnosis. • A two-stage feature extractor embedded with a residual attention mechanism is constructed. • An adversarial generation module is developed to obtain new samples with similar semantics. [ABSTRACT FROM AUTHOR]

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

Robotic force-based compliance control is a preferred approach to achieve high-precision assembly tasks. When the geometric features of assembly objects are asymmetric or irregular, reinforcement learning (RL) agents are gradually incorporated into the compliance controller to adapt to complex force-pose mapping which is hard to model analytically. Since force-pose mapping is strongly dependent on geometric features, a compliance controller is only optimal for current geometric features. To reduce the learning cost of assembly objects with different geometric features, this paper is devoted to answering how to reconfigure existing controllers for new assembly objects with different geometric features. In this paper, model-based parameters are first reconfigured based on the proposed Equivalent Theory of Compliance Law (ETCL). Then the RL agent is transferred based on the proposed Weighted Dimensional Policy Distillation (WDPD) method. The experiment results demonstrate that the control reconfiguration method costs less time and achieves better control performance, which confirms the validity of proposed methods. [ABSTRACT FROM AUTHOR]

The huge differences in the visual shapes of multi-angle shooting objects leads to the poor performance of deep neural network (DNN). In this paper, an object detection model named TRUST-TECH-based visual clustering network (TTVCNet) for power line inspection is constructed. First, a TRUST-TECH-based visual clustering method (TTVCM) for multi-view-shape unsupervised clustering is proposed and can learn the difference in visual shape according to the object's views, which is the core of TTVCNet. Then, a Cascaded R–CNN object detection model based on TTVCNet is constructed for the power line inspection. Moreover, we apply the bilinear interpolation method and feature enhancement fusion techniques to this object detection model to solve the problem of small sample detection and semantic loss. In this paper, TTVCNet is applied to the MS-COCO 2017 dataset, and the test accuracy is improved up to 65.3%, especially the recognition accuracy of multi-view-shape is greatly improved. In the contrast experiment of self-made power line inspection dataset, the recognition accuracy of TTVCNet has been greatly improved, and the overall recognition accuracy is 86.3%. [ABSTRACT FROM AUTHOR]

Automatic identification and professional evaluation makes musical instrument learning more intelligent. Since a proper hand shape is the basis of fingerings in playing instruments, this paper explores an integration of intelligent recognition technique into hand shape assessment of instrument players in an attempt of taking Chinese zither (Zheng) as an example. The fine-grained image recognition is novelly applied to automatically assessing basic hand shapes, as a tentative exploration of interdisciplinary research. First, this paper formulates an assessment scales by combining fine-grained image features with hand shape evaluation indicators in musical instrument learning. Then, an image dataset for hand shapes of Chinese zither performance (CZ-Dataset V2) is established based on free multi-view acquisition. Finally, we propose a fine-grained hand shape image recognition method using attention mechanism. Experimental results show that the basic instrumental hand shapes can be effectively recognized and reasonable suggestions for hand shape assessment can be provided. [ABSTRACT FROM AUTHOR]

The design of high-performance analog circuits is a time-consuming process that requires the involvement of highly qualified designers and frequent tedious interactions with circuit simulators. This sets a need for a method that accelerates the circuit design process and reduces unnecessary designer-simulator interactions. Existing methods are either slow or unable to deal with complex tasks. This paper proposes a simulation-based optimization method based on a deep reinforcement learning (DRL) agent to optimize analog circuits and accelerate the design process. In this method, the DRL agent exploits the soft-actor-critic algorithm based on an improved reward function considering multiple conditions to expedite the parameters optimization step of analog circuits. The designer provides the RL agent with the circuit testbench, design requirements and circuit parameters. The agent then repetitively interacts with the circuit simulator to tune these parameters. In this work, an incremental RL approach is also proposed to deal with complex circuits. Incrementality is found to be increasingly beneficial as the circuit complexity increases (increased number of design parameters and competing objectives). The effectiveness of the proposed method was validated on a complex circuit defined by a design space comprising as many as 36 dimensions (17 design variables and 19 competing objectives). The reported results demonstrate that our method can significantly accelerate the design process and yield high-performance optimized designs. Notably, the required number of simulation steps is on the order of 1e3, which is one to two orders of magnitude more efficient than similar state-of-the-art DRL approaches. • The paper proposes a novel approach to accelerate circuit design. • It is based on incremental reinforcement learning with an improved reward function. • It optimizes the design process and reduces designer-simulator interactions. • The approach is validated through two case studies in analog circuit design. • It achieves high-performance designs in less steps compared to similar methods. [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]

Performing online condition monitoring for nuclear turbines in the rapidly changing environment is a challenging but imperative task to enhance the safety and reliability of nuclear power plants. Given the nonlinear and dynamic properties of nuclear turbine operation, this paper proposes an innovative method for condition monitoring. Specifically, the paper first redesigns time augmented matrices based on lagged data to reflect the process dynamics. Subsequently, a dynamic auto-regressive model, integrated with the variant of kernel partial least squares, is built between input and output variables, which represents auto-correlations and cross-correlations of operation data simultaneously. The prediction of the model serves as the baseline for the monitoring indicator. Additionally, since the operation process involves variable working excitation and random noise, making static control limits insufficient to satisfy the requirements of condition monitoring, the proposed method utilizes a novel monitoring indicator based on local information increment. The indicator comprehensively incorporates the prediction value and past measurement for monitoring statistics and control limits. Finally, the proposed method is applied to a real nuclear turbine operation process, and the results are compared with three other methods to demonstrate its superiority. [ABSTRACT FROM AUTHOR]

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

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

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

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

Bearing fault diagnosis plays a vital role in ensuring the safe and reliable operation of rotating machinery. The diagnostic process is more difficult when the fault is in its early stages, as fewer fault components are contained in the vibration signal, making the diagnosis process more difficult. To improve the accuracy and efficiency of fault type identification, a novel multi-scale competitive network for fault diagnosis is proposed in this paper. First, to obtain multi-scale features and fully utilize the features in the intermediate layer, ensuring the completeness of fault information, a novel improved multi-scale feature fusion residual network (IMSFFRN) is proposed to exploit deep features for vibration signals. Specifically, multi-scale features are obtained by convolution with different dilation rates, and features from adjacent intermediate layers are selected for efficient fusion. Second, different features have varying importance in fault detection tasks. To make neurons more sensitive to specific faults, we propose a multiple-winning consciousness self-organizing map (MCSOM) competition layer, in which each neuron learns specific faults through competition, and the neuron that wins the competition updates its weights. This distinguishes the sensitivity of neurons to different faults. Finally, the generalizability of the network is improved by using support vector machines (SVMs) to classify fault classes. To affirm the efficacy of the proposed approach, a comprehensive evaluation is conducted on the CWRU, PU bearing dataset and SEU gear dataset. The results indicate that the accuracies achieved by the method proposed in this paper are 100%, 99.56% and 100%, respectively. It is superior to other methods proposed in this paper. Furthermore, it is observed that the proposed method exhibits high robustness in noisy environments. [ABSTRACT FROM AUTHOR]

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

Automatic music recommendation systems based on human emotions are becoming popular nowadays. Since audio and lyrics can provide a rich set of information regarding a song, a fusion model including both modalities can enhance classification accuracy and is attempted in this paper. The main objective of the paper is to address music mood classification using various attention mechanisms, namely, self-attention (SA), channel attention (CA), and hierarchical attention network (HAN), on a multi-modal music mood classification system. Experimental results show that multi-modal architectures with attention have achieved higher accuracy than multi-modal architectures without attention and uni-modal architectures. Motivated by the performance of attention mechanisms, a new network architecture, HAN-CA-SA based multi-modal classification system, is proposed, which reported an accuracy of 82.35%. ROC and Kappa are also computed to see the efficacy of the proposed model. The proposed model is also evaluated using the K-fold cross-validation technique. The performance of the proposed model is compared with that of XLNet and CNN-BERT systems. In addition, McNemar's statistical hypothesis test is conducted to reaffirm the importance of the proposed approach. [ABSTRACT FROM AUTHOR]

Forecasting stock market via historical financial data is an important issue for market participants because even if the prediction accuracy is only slightly improved, better trading decisions can be made. Historical financial data has evolved from the initial single text or stock price to the fusion of multisource information. However, how to adopt a method that adaptively fuses numerical data and text so that the prediction model can learn time series information in parallel remains a challenging problem. In this paper, we propose a collaborative attention Transformer fusion model for stock movement prediction (CoATSMP), including parallel extraction of text and prices features, parameter-level fusion and a joint feature processing module, that can successfully deeply fuse text and stock prices in view of the soft fusion method. The experiments show that (1) the proposed approach outperforms the baselines, (2) the soft fusion method proposed in this paper has better modeling performance under the CoATSMP framework, which brings greater improvement in the prediction performance, (3) models containing prices and text are better than those using only one data source, and (4) quantitative analysis of experimental results indicates that text plays a relatively more critical role in the CoATSMP framework. Real simulation trading shows that the trading strategy based on CoATSMP can significantly improve profits; thus, the model has practical application value. • Multi-source information provides investors with more comprehensive information. • The effect of the deep fusion method is better than that of the traditional methods. • Quantitative analysis reveals that text plays a relatively more critical role. [ABSTRACT FROM AUTHOR]

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

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

This paper reviews the paraconsistent annotated evidential logic (PAL E τ) research. This non-classical logic has gained attention in several real-world applications, including robotics, intelligent control, decision-making systems, pattern recognition and classification, and medical diagnosis, among others. The low-complexity mathematics and small computational resources required by PAL E τ algorithms make them interesting for expert systems, edge computing, and tiny machine learning (Tiny ML) design. This paper aims to present the recent work in the field of PAL E τ , from its theoretical grounding to practical applications, as well as challenges and trends for this logic. This paper provides a multilevel platform for researchers interested in this area. • Brief historical review of paraconsistent logic until PAL Et. • PAL Et concept, math and basic algorithm. • Systems developed from PAL Et, including Para-Analyzer, PAN, ParaExtrCTR, PANet, PAL2v Filter and PNN. • PAL Et engineering and IA applications. • Perspectives of future PAL Et developments. [ABSTRACT FROM AUTHOR]

Pavement crack detection continues to be a stubborn problem given the interference of various factors in the actual pavement and the complex topological structure of asphalt pavement. Among all the obstacles, the bottleneck of pavement crack detection lies in the difficulty of segmenting the cracks in the pavement images whose edges are blurred. This paper proposes a multi-scale context feature and cross-attention based on convolutional neural network for accurate and robust pavement crack segmentation. The multi-scale context feature module is built in different deep networks to extract rich crack feature information. Subsequently, in order to effectively promote the seamless integration of features at different levels, we deploy cross-attention modules to each branch. After that, we add deep supervision to each branch to accelerate training. Finally, we integrate the outputs of each branch to obtain the final output diagram. The comparative experiments on various pavement datasets show that the method has better robustness. At the same time, this paper designs a complete system of pavement crack detection (PCD) and develops corresponding engineering application software. The PCD system can record the real-time pavement image data to the edge server, and the client can also monitor the real-time pavement images from the edge server through the HTTP protocol. • A multi-scale context feature module is built in the deep layer for robustly describing the size of the cracks. • The cross-attention module enables each pixel to select individual contextual information from multi-layer features. • In-depth supervision is added to each branch to accelerate the optimization of the network model. • An intelligent detection application software is designed and integrated with multiple modules. [ABSTRACT FROM AUTHOR]

The applications of social robots have continued to rise in number over the years: there are now mainly used in nursing homes and hospitals, performing different tasks like assistance, physical and cognitive rehabilitation, and supervision. However, robots' ability to learn from humans remains a problem to be solved. This paper presents the development of an Active Object Learning architecture in which the robot can identify unknown objects and autonomously initiate the learning process, supported by user interactions. We study the feasibility of generating a real-time dataset, including techniques for reducing image repetition and online classification model training. The work considers the analysis of machine learning metrics such as F1-Score, accuracy, confidence, and hit rate as the robot learns new objects. The accuracy of the classifier remains constant as the number of classes increases, indicating that the regularisation methods reduce overfitting and decrease the generalisation gap. The results prove that the robot can perform the classifications correctly and differentiate between known and unknown objects. The paper also includes a case study to demonstrate the feasibility of the proposed system integrated into a real social robot. [ABSTRACT FROM AUTHOR]

Aiming to identify incipient fault as earlier as possible, early fault detection (EFD) becomes the key step to trigger predictive maintenance. To lower deployment burden, pre-training techniques are employed to provide well-initialized features before fine-tuning different EFD tasks. Current large-scale pre-training models focus on seeking uniformity features to align every task's characteristic. But in practical engineering, degradation data is usually small-scale and under irregular noise interference. The pre-trained uniformity features are easily biased and less discriminative to early fault. To address this concern, this paper proposes a pre-training anomaly detection method with tensor domain adaptation. Running on the classical contrastive learning architecture, the proposed method first builds hypersphere-formed representation of anomaly detection rule. With the merit of tensor decomposition in extracting the intrinsic information from raw signals, the proposed method second designs a new tensorized rule adaptation mechanism to learn the task-invariant detection rule from normal state data that are used for pre-training. The pre-trained feature representation is then obtained from the feature extractor in contrastive learning via back-propagation. This paper takes rolling bearing as the validation object. Experimental results on the IEEE PHM Challenge 2012 and XJTU-SY datasets show that the proposed method can effectively improve the convergence speed and detection accuracy on different EFD tasks with shallow model, deep model and transfer learning, and also prove that the harmony on task-invariant detection rule and task-specific information facilitates the universal applicability of EFD pre-trained features in small scale. Harmony is then believed better than uniformity for the EFD pre-training. • We propose a new pre-training anomaly detection method for cross-machine early fault detection. • We build a tensorized rule adaptation mechanism on contrastive learning architecture to enhance pre-training. • We find a new harmony property of pre-trained feature covering task-invariant detection rule and task-specific information. • We find harmony property better than uniformity property in pre-training with small-scale data. • The proposed method can improve convergence and accuracy on various kinds of downstream tasks. [ABSTRACT FROM AUTHOR]

This paper proposes a machine learning-based underwater image enhancement scheme using an adaptive standardization network and normalization network. The adaptive standardization network is designed to match the distribution of input features. This helps correct the distorted distribution of underwater images and facilitates training. The proposed adaptive normalization network is constructed using two squeeze-and-excitation blocks and the conventional feature normalization method. It is designed to increase the contrast, remove the hazy effect, and restore the brightness. An improved performance of underwater image enhancement is achieved through an appropriate configuration of the two proposed networks. The structure of the proposed network is simple and therefore requires fewer parameters. The simulation results verify that the proposed underwater image enhancement scheme outperforms other state-of-the-art approaches. The proposed method demonstrates outstanding performance both subjectively and objectively in improving underwater images. The code is available on https://github.com/cwoop92. • This paper presents a machine learning-based underwater image enhancement scheme using an adaptive standardization network and normalization network. • The adaptive standardization network consists of a convolution layer before batch normalization and a sigmoid activation function. It is responsible for matching the histogram of the color channel. • The adaptive normalization network is designed using two modified squeeze-and-excitation blocks to construct an adaptive normalization. The adaptive normalization network can adjust the brightness of an image and enhance its contrast. • The simulation results show that the proposed underwater image enhancement scheme outperforms state-of-the-art approaches regarding computational simplicity and enhancement efficiency. [ABSTRACT FROM AUTHOR]

Medical image segmentation is a challenging task owing to its aliasing artifacts, existence of mixed pixels, interference of noise and blurring effect, etc. The brain and the spinal cord together constitute the Central Nervous System (CNS), with the brain being the most complex component. Brain segmentation is crucial for surgical planning, disease detection, and therapeutic interventions. A unique entropy based soft clustering approach (EMT2FCM) for the brain MRI imaging on both real and simulated datasets is proposed in this paper. This paper presents a refined version of the type-2 Fuzzy C-Means (FCM) technique (EMT2FCM) for isolating diverse tissues in Magnetic Resonance (MR) brain images, achieved through an enhanced entropy-based membership function. Difficulty in edge detection occurs due to the vagueness caused by mixed pixels. To overcome the problem with mixed pixels a new membership function proposal has been given. EMT2FCM, i.e., fuzzy membership function with Shannon's entropy-based variation, is able to handle uncertainty and vagueness in images offered by mixed pixels, by implementing new distance criteria modification for the calculation of optimum cluster. The approach has been validated using datasets from BRAINWEB (simulated) and IBSR (real-life). Following the deployment of our new algorithm, we assessed its performance using six observation criteria. The results demonstrated significant improvements in clustering performance, as evidenced by enhanced Mean Squared Error (MSE), Dice Index, and Jaccard Index, Accuracy, Sensitivity and Partition Indices validation measures. The evaluation of EMT2FCM in comparison to other state-of-the-art methods is established to reduce segmentation errors significantly. [Display omitted] [ABSTRACT FROM AUTHOR]

A novel fault diagnosis procedure is proposed in this paper to estimate faults using a linear parameter varying (LPV) model whose scheduling parameters depend on the fault. A wrong determination of the operating conditions could lead the system to an undesired performance or even to an unstable situation, when classical fault diagnosis approaches are applied. This paper addresses this issue by formulating fault diagnosis as a dynamic optimization problem, solved by using a novel hybrid technique that combines a Luenberger-based observer with artificial intelligent (AI) optimization-based algorithms. The observer supervises the health of the system, while AI-based algorithms are able to reconstruct the faulty signal in real-time when the observer determines that the system is under a fault. The efficiency of the proposed fault diagnosis scheme, the three AI-based algorithms based on artificial bee colony and particle swarm optimization, and the gradient-based algorithm developed in this paper, are assessed using a numerical example. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges of model evaluation and optimization that arise from domain differences between the target and source domains during model deployment. Current methods for model accuracy evaluation require a fully annotated test set. However, obtaining additional human labels for every unique application scenario can be costly and time-intensive. To tackle this problem, this paper proposes an instance segmentation model evaluation method based on domain differences, which can give the prediction accuracy of the model on unlabeled test sets. Moreover, to enhance deployment accuracy cost-effectively, this paper proposes an "effective operation"-based labeling cost computation method and a weighted uncertainty sample selection method. The former accurately computes labeling costs for instance segmentation, while the latter selects the most valuable samples from the target domain for labeling and training. Model evaluation experiments demonstrate that this method's root mean square error (RMSE) on Cityscapes is approximately 4% less than that of other existing model evaluation methods. Model optimization experiments demonstrate that the proposed method achieves greater model accuracy than comparative methods under four distinct data partitioning protocols. The code is available at https://github.com/licongguan/Lamer. [ABSTRACT FROM AUTHOR]

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

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

Most of the problems associated with physical, biological and engineering systems are complex and are usually modeled with the help of linear as well as non-linear differential equations. In general, the exact solutions of non-linear differential equations may not be obtained by analytical methods and thus, in this paper, a numerical method is proposed to find approximate solutions of differential equations using Chebyshev polynomials and metaheuristic optimization algorithms. In this paper, Ordinary differential equations (ODEs) are modeled as optimization problems to determine their approximate solutions by considering Chebyshev polynomials as the base approximation function, taking initial and/or boundary conditions as constraints. The unknown coefficients of the polynomials are calculated using Differential Evolution (DE) in order to get the approximate solution with minimal error. The proposed method is applied to find the approximate solution to both Initial and Boundary Value Problems. For all test problems considered in this paper, the algorithms have been programmed using MATLAB software to run on computer. The effectiveness of the method is demonstrated by graphical comparison of the computed approximate solutions with that of the exact ones. Further, the performance of the method proposed in this paper is shown by finding out the Root Mean Square Error (RMSE) between the exact and approximate solution, which is found to be better than that of the related existing papers. [ABSTRACT FROM AUTHOR]

Detecting surface defects in the industry is essential for improving the quality of industrial products and maintaining product safety. However, problems such as the similarity of defects, significant variation in the scale of the target object, and the balance between detection speed and accuracy in industrial inspection scenarios have been considerable research topics in this field. This paper proposes an industrial defect detection network based on convolutional attention-guided and aggregated multiscale features to address these issues (ICA-Net). Firstly, for similarity defects in complex backgrounds, this paper proposes a backbone network with a combination of lightweight convolutional blocks and self-attentive modules to fully extract images' local and global information and enhance the network's expressiveness. Secondly, to make full use of the shallow fine-grained features and deep semantic features of the backbone network to improve the detection capability of defects with significant scale changes, this paper designs a cross-layer multiscale feature fusion network (CEF-Net), which fully fuses the features of adjacent layers and cross-layers through a reweighting feature strategy to enrich the network feature transfer path and ensure the efficient fusion of different scale features in the network. At the same time, the fine-grained feature fusion module (FFM) is used to fuse elements from multiple layers to extract more contextual information, enhance the extraction of fine-grained features and improve the detection capability of complex small targets. Finally, to address the problems of inaccurate regression localization and low detection accuracy of defects in existing industrial algorithms, a new IoU loss function (G-IOU) is proposed for regressing the intersection part of the predicted frame and the actual structure according to the aspect ratio of the real frame during the model regression to improve the accuracy and stability of detection. The experimental results show that 94.1%, 98.6%, 99.4%, 98.8% and 96.5% of mAP@.5 are obtained on steel, PCB, aluminium, automobile and Xsteel steel metal surface defect datasets, respectively, and 48 FPS is achieved, which is superior to the current mainstream detectors and meets the needs of practical industrial production. [ABSTRACT FROM AUTHOR]

In recent years, more and more manufacturing factories have employed unmanned electric vehicles (UEVs) to perform the materials delivery, and efficient routing and scheduling approaches are required to improve the delivery efficiency. However, most of existing papers studied the routing problem of fuel vehicles or the drones. This paper investigates the optimization of UEVs delivery routes and charging strategies. Due to the limited payload capacity and battery capacity, the UEV must return to the depot multiple times to load the materials and go to the recharging station during the route to charge, and thus the problem is modeled as a multi-trip UEV routing and scheduling problem, which assigns the recharging station to the UEV's route and optimizes a set of UEV's trips. The investigated problem is firstly formulated as a mixed integer programming model. Then, considering the routing and scheduling challenges, we develop an efficient double-adaptive variable neighborhood search (DA-GVNS), which incorporates the general variable neighborhood search with a new adaptive mechanism, for this problem. The experimental results on the test and benchmark instances highlight that the DA-GVNS outperforms other existing algorithms. Furthermore, sensitivity analyses are conducted to study the impact of model's characteristics on the optimality and complexity of solutions. Finally, a case study is proposed to present how the developed approach works for a manufacturing factory. This paper can offer decision-makers a valuable approach to construct a high-quality routing and scheduling scheme for the UEV delivery while providing potential managerial insights for implementing efficient green manufacturing systems. [ABSTRACT FROM AUTHOR]

The discovery of frequent closed high utility itemsets (FCHUIs) and frequent generators of high utility itemsets (FGHUIs) is significant because they serve as important and concise representations of frequent high utility itemsets (FHUIs), offering a brief but essential summary that can be considerably smaller. Besides, they facilitate the generation of nonredundant high utility association rules that are crucial for decision-makers. However, the challenge lies in the difficulty of mining these representations due to scalability issues, high memory usage, and long runtimes, particularly when dealing with dense and large datasets. To address this issue, this paper proposes a novel approach for efficiently mining FCHUIs and FGHUIs using a novel weak lower bound named w l b u on the utility. The approach includes effective pruning strategies for early eliminating non-closed and/or non-generator high utility branches in the prefix search tree based on w l b u. These pruning strategies allow faster execution with lower memory usage. In addition, the paper presents two novel algorithms, FCGHUI-Miner and FGHUI-Miner, which can simultaneously discover both FGHUIs and FCHUIs or solely mine FGHUIs, respectively. The experimental results demonstrate that the proposed algorithms outperform state-of-the-art algorithms in terms of efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Today, intelligent optimization has become a science that few researchers have not used in dealing with problems in their field. Diversity and flexibility have made the use, efficiency, and usefulness of various nature-inspired optimization methods, such as evolutionary and meta-heuristic algorithms, more evident in such problems. This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965–2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories. More than 320 new algorithms have been reviewed. All specifications including authors, year, abbreviation, inspired source, controls, and their application are considered in this regard. Statistical analyzes of papers and publishers, annually and for 57 years, along with their ranking, are also examined in detail. Among the key achievements of the paper include: the most number of algorithms with 47.71% (156 methods) have been from the swarm category, and most of them were published in the five years of 2021 (72, 22.02%), 2020 (39, 11.93%), 2022 (31, 9.48%), 2019 (26, 7.95%), and 2016 (21, 6.42%) respectively; the top five rankings of publishers of reviewed algorithms/papers were also: "Proceedings of the Congress" (33, 10.09%), "Applied Soft Computing" (19, 5.81%), "Expert Systems with Applications" (18, 5.51%), "Knowledge-Based Systems" (12, 3.67%), "Engineering Applications of Artificial Intelligence" (12, 3.67%), "Advances in Engineering Software" (12, 3.67%), " Neural Computing and Applications " (12, 3.67%), and " Information Sciences " (11, 3.36%). The paper's data is available at: https://github.com/ali-ece. [Display omitted] • A detailed overview of optimization and its variants. • A literature review on state-of-the-art algorithms from 1965 to 2022. • Statistical analyzes and explanations in each year and during the 57 years. [ABSTRACT FROM AUTHOR]

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

Since the multi-time scales and strong nonlinearity are objectively existed in the practical industrial processes, it is extremely challenging to design control policy for optimizing the industrial operation. Therefore, this paper focuses on the multi-time scale optimal operational control (OOC) problem with unmodeled dynamics. Under the framework of reinforcement learning (RL) technology, a self-learning model-free method by integrating zero-sum game and singular perturbation (SP) theories is proposed in this paper, such that composite controllers with signal compensation can be found under which the suboptimal operation of the whole process can be achieved using only measured data. The remarkable highlight in this paper is the novel design method of the composite controllers with signal compensations for unknown nonlinear systems with multi-rate operations. Finally, the effectiveness of the proposed method is verified by a practical example and a numerical example. [ABSTRACT FROM AUTHOR]

The wind turbine has rapidly developed worldwide with increasing height and scale, resulting in the increased risk of lightning strikes. When wind turbines were stroke by the lightning, they will be damaged, causing economic loss and outage. Lightning risk assessment can guide the improvement of lightning protection and the design of the wind farms to efficiently prevent lightning damages. The traditional lightning risk assessment methods rely on subjective features to some extent. The existing lightning risk assessment methods based on machine learning demand abundant labeled data. It is extremely difficult to label and acquire the data. This paper proposed a lightning risk assessment method based on semi-supervised learning to address the challenges of labeling negative samples and limited labeled data. The semi-supervised K-means algorithm is proposed to divide all data into three parts. The Laplacian support vector machine (LapSVM) with hyperparameters optimized by the particle swarm optimization (PSO) is used to assess the lightning risk. The proposed method has better performance than the standard SVM and neural network (NN). Moreover, previous researches did not consider lightning protection ability. This paper introduces the receptor number into lightning risk assessment. The assessment results suggest that there is higher lightning risk in areas with a great number of wind turbines, high lightning density, and strong lightning strength. The ocean area is more likely to have low lightning risk. The results are valuable for lightning protection optimization of existing wind farms and can give guidance for the plan of new wind farms. [ABSTRACT FROM AUTHOR]

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

This paper presents a comprehensive review of Automatic Facial Recognition Systems using integrative and systematic mapping approach. The review is grounded on criteria-attribute scheme formulated in proposed Face Recognition framework. The proposed framework provides a unified platform to identify, categorize and understand wholesome Face Recognition taxonomy based on different criteria (Modality, Dimensionality and Feature Quality) and their corresponding attributes (Unimodal-Multimodal, 2D-3D and Physiological-Behavioral). The framework facilitates a user to understand and select attributes across different criteria. The user selection of criteria-attribute is assisted through several selection parameters (Dataset Availability, Application, User Preference, System Complexity and Time Complexity). Depending on the user selection, a criteria-attribute scheme based model is formulated for Face Recognition. This paper also provides critical mathematical insights to understand each attribute extensively. Existing works are analyzed and compared comprehensively and quantitatively based on popular datasets and proposed criteria-attribute framework. [ABSTRACT FROM AUTHOR]

Object detection is a crucial task in computer vision (CV). With the rapid advancement of Transformer-based models in natural language processing (NLP) and various visual tasks, Transformer structures are becoming increasingly prevalent in CV tasks. In recent years, numerous Transformer-based object detectors have been proposed, achieving performance comparable to mainstream convolutional neural network-based (CNN-based) approaches. To provide researchers with a comprehensive understanding of the development, advantages, disadvantages, and future potential of Transformer-based object detectors in Artificial Intelligence (AI), this paper systematically reviews the mainstream methods and analyzes the limitations and challenges encountered in their current applications, while also offering insights into future research directions. We have reviewed a large number of papers, selected the most prominent Transformer detection methods, and divided them into Transformer Neck and Transformer Backbone categories for introduction and comparative analysis. Furthermore, we have constructed a benchmark using the COCO2017 dataset to evaluate different object detection algorithms. Finally, we summarize the challenges and prospects in this field. [ABSTRACT FROM AUTHOR]

Nowadays, economic and environmental concerns in production have become increasingly significant. To address these issues, the Combined Economic and Emission Dispatch (CEED) problem has been introduced to optimize the power generation process by considering fuel cost and emitted substances. However, due to the nonlinearity and nonconvexity of the objective function, the optimization of CEED remains a challenge. In this paper, we develop a Reinforcement Learning-based Adaptive Differential Evolution (RLADE) algorithm to enhance the optimization performance. The mutation strategy and crossover probability of RLADE are optimized using Reinforcement Learning (RL) to respectively ensure better convergence speed and searchability. Additionally, two modifications of RL, namely the adaptive population size-based state division and fitness-ranking-based reward mechanism, are proposed to improve the accuracy of state division and reward calculation in RL. The experiments conducted in this paper consider two objective formulation methods of CEED problems, namely the quadratic and cubic criterion functions. The mean values and standard deviations of the obtained solutions were utilized to assess the performance of RLADE, as well as other comparative algorithms, namely DE algorithm and two RL-based DE variants. The results clearly demonstrate that RLADE surpasses its counterparts with proportion of 100%, 85.7%, and 100% for the 6-unit and 11-unit quadratic CEED problems, as well as cubic criterion functions, in terms of both search accuracy and convergence ability. Furthermore, the significance of RLADE's superiority is confirmed through the Wilcoxon's signed rank test. [ABSTRACT FROM AUTHOR]

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

For the first time, the critical problem of selecting the most sustainable strategy for urban parcel delivery is addressed and solved in this paper. It aims to help not only postal and logistics companies but also regulatory agencies and city authorities find an environmentally friendly, socially responsible, and economically viable solution. Three alternatives for organizing urban parcel delivery are defined, which are traditional, united consolidation center, and inner-city hubs. According to the three pillars of sustainability, an assessment framework for practitioners is proposed. This paper introduces a linguistic q-rung orthopair fuzzy Choquet integral approach to evaluate sustainable strategies for urban parcel delivery. The linguistic q-rung orthopair fuzzy Choquet integral averaging and linguistic q-rung orthopair fuzzy Choquet integral geometric operators have been constructed for the first time for this purpose. The operator-related qualities are demonstrated. The operators are used to create a multi-criteria group decision-making approach. The case study of the city of Belgrade illustrates the practical applicability of the linguistic q-rung orthopair fuzzy Choquet integral approach. This research findings show that the united consolidation center is the most sustainable strategy for organizing urban parcel delivery. [ABSTRACT FROM AUTHOR]

Fault diagnosis is an important technology for performing intelligent manufacturing. To simultaneously maintain high manufacturing quality and low failure rate for manufacturing systems, it is of great value to accurately locate the fault element, evaluate the fault severity and find the fault root cause. In order to effectively perform feature extraction for rotating machinery fault diagnosis, a robust discriminant latent variable manifold learning (RDLVML) algorithm is proposed in this paper. Specifically, RDLVML algorithm could effectively select the features of the high-dimensional fault data, and extract the low-dimensional fault features with better discrimination. Through the idea of discriminative learning, the accuracy of fault diagnosis was improved by increasing the aggregation of the same state feature samples and the difference between different classes of feature samples. A novel weighted neighborhood graph is proposed by constructing the q -Rényi kernel function, which could effectively suppress the interference of outliers and noise, and make the RDLVML algorithm more robust. Moreover, the fault diagnosis method of rotating machinery based on RDLVML is proposed in this paper. Through experimental verifications and comparisons with several classical feature selection algorithms, rotating machinery fault diagnosis could be more accurately performed by this method. [ABSTRACT FROM AUTHOR]

Welding is widely used in metal components. The firm of weld components is very important in different applications, such as buildings, bridges, cars and airplanes, etc. Weld seam quality inspection is essential to ensure product quality. The area and shape of the weld seam are the basis for quality assessment. So the segmentation of the weld area is very important for quality assessment. To address the problem of segmentation of the weld seam region, a weld seam segmentation network based on heat map detail guidance with Matting is proposed in this paper, which provides a new idea for fine-grained segmentation of the weld seam region. The existing DCNN-based semantic segmentation algorithm model has a poor segmentation effect at the boundary and jagged segmentation boundary, which are unacceptable for the weld segmentation problem that requires clear and precise boundary positioning. To solve this problem, three innovations are made in this paper on the DCNN-based semantic segmentation network. (1) A heat map detail guidance module makes the segmentation boundary information focus on shallow features and enhances the representation of boundary information. (2) A segmentation head improvement method for fine-grained semantic segmentation is proposed. (3) In response to the loss of process details in the coding and decoding of the semantic segmentation network, resulting in poor segmentation boundary accuracy, this paper introduces a matting algorithm to calibrate the boundary of the weld seam segmentation region. Through many experiments on industrial weld data sets, the effectiveness of our method is demonstrated, and the MIoU (Mean Intersection over Union) reaches 96.32%. It is worth noting that this performance is comparable to human manual segmentation (MIoU 96.38%). [ABSTRACT FROM AUTHOR]

Detecting faults in high-speed and high-power diesel engines under complex variable operating conditions is highly challenging. Online vibration monitoring systems have been used in such diesel engines in key fields, in which vibration sensors are installed on each cylinder to enable comprehensive monitoring. In this paper, a fault detection method for diesel engines under variable operating conditions is proposed based on multi-sensor signal multi-scale fusion. Firstly, a preprocessing framework is established for the raw vibration signals collected from each cylinder to eliminate random interference and system noise. Then, the resulting signals are phase-aligned based on the engine firing sequence and analyzed using a signal correlation algorithm to produce a multi-sensor multi-scale similarity matrix (MSMSSM). Finally, a multi-branch residual convolutional neural network (MBRCNN) model is constructed with the MSMSSM as the input to detect abnormal health states of the diesel engine. Fault simulation experiments are conducted on a 12-cylinder V-type high-speed and high-power diesel engine test rig. The comparative test results indicate that the proposed MSMSSM-MBRCNN method shows both the highest accuracy of 95.28% and the lowest standard deviation of 3.57% compared to other typical methods. The multi-sensor signals multi-scale fusion method proposed in this paper fully utilizes the key information that remains basically consistent in the synchronous acquisition signals of multiple sensors under different operating conditions. This can effectively reduce the interference of operating condition changes and improve the accuracy and robustness of fault detection. • A new fault detection method is proposed for high-speed and high-power diesel engines under variable operating conditions. • An angular domain multi-scale correlation analysis algorithm is developed for utilizing the consistency of operating condition information in multi-sensor signals. • A multi-branch residual convolutional neural network fault identification model is established for effective multi-scale similarity matrices fusion. • The proposed fault detection method achieved a well performance in experimental tests on a V12 diesel engine rig under complex conditions. [ABSTRACT FROM AUTHOR]

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