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

This paper focuses on action recognition tasks oriented to the field of human–robot interaction, which is one of the major challenges in the robotic video understanding field. Previous approaches focus on designing temporal models, lack the ability to capture motion information and build contextual correlation models. This may result in robots being unable to effectively understand long-term video actions. To solve these two problems, this paper propose a novel video understanding framework including: an Adaptive Temporal Sensitivity and Motion Capture Network (ATSMC-Net) and a contextual scene reasoning module called Knowledge Function Graph Module (KFG-Module). The proposed ATSMC-Net can adaptively adjust the frame-level and pixel-level sensitive regions of temporal features to effectively capture motion information. To fuse contextual scene information for cross-temporal inference, the KFG-Module is introduced to achieve fine-grained video understanding based on the relationship between objects and actions. We evaluate the method using three public video understanding benchmarks, including Something-Something-V1&V2 and HMDB51. In addition, we present a dataset with real-world application scenarios of human–robot interactions to verify the effectiveness of our approach on mobile robots. The experimental results show that the proposed method can significantly improve the video understanding of the robots. [ABSTRACT FROM AUTHOR]

In this paper, an optimal trajectory tracking control problem for general nonlinear systems is investigated. An adaptive critic control method with the digital twin (DT) theory is developed. Divergent from the existing tracking control methods, the advantages of adaptive dynamic programming (ADP) and the theory of DT are combined in this paper, and the novel multilayer artificial system structure is constructed. The action-critic structure is employed by each artificial system to obtain an approximate optimal control policy. The model network (MN) is built by using the actual input and output data sets of the controlled system, which means the dependence on the dynamics of the system is overcame. Then, the weights of the trained action network (AN) and MN are passed to the real system to realize the optimal tracking control. The feasibility of the algorithm is proved by theoretical analysis. Finally, the algorithm is applied to a simple nonlinear torsional pendulum system and an industrial wastewater treatment system (WWTS), and the effectiveness of the algorithm is verified. The algorithm effectively realizes the tracking control of nonlinear systems. [ABSTRACT FROM AUTHOR]

Semi-Supervised Graph Clustering (SSGC) has emerged as a pivotal field at the intersection of graph clustering and semi-supervised learning (SSL), offering innovative solutions to intricate data analysis problems. However, despite its significance and wide-ranging applications, there exists a notable void in the literature—a comprehensive survey specifically dedicated to SSGC techniques and their diverse applications remains conspicuously absent. Addressing this gap, this paper presents a systematic and comprehensive review of SSGC methodologies, spanning from well-established approaches to cutting-edge developments. Through a meticulous categorization, critical examination, and insightful discussion of these techniques, this survey not only illuminates the current landscape of SSGC but also identifies unexplored avenues for exploration and innovation. In this paper we present a comprehensive survey of conventional graph construction, graph clustering, SSL methods, evaluation metrics and their primary development process. We then provide a taxonomy of SSGC techniques based on their structures and principles and thoroughly analyze and discuss the related models. Furthermore, we review the applications of SSGC in various fields. Lastly, we summarize the limitations of current SSGC algorithms and discuss the future directions of the field. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation of electrical power output (P e) in a combined cycle power plant (CCPP) using ambient temperature (AT), vacuum in the condenser (V), ambient pressure (AP), and relative humidity (RH). The study stresses accurate estimation for better CCPP performance and energy efficiency through responsive control to changing conditions. The novelty lies in applying genetic programming (GP) on a publicly available dataset to generate Symbolic Expressions (SEs) for high-accuracy P e. To address the challenge of numerous GP hyperparameters, a random hyperparameter values search method (RHVS) is introduced to find optimal combinations, resulting in SEs with higher accuracy. SEs are created with varying input variables, and their performance is evaluated using multiple metrics (coefficient of determination (R 2), mean absolute error (M A E), mean square error (M S E), root mean square error (R M S E), mean absolute percentage error (M A P E), Kling–Gupta Efficiency (K G E), and Bland–Altman (B-A) analysis). A key innovation involves combining the best SEs through an Averaging ensemble (AE), leading to a robust estimation accuracy. Notably, the AE Y V E − 2 achieves the highest (P e) accuracy, including R 2 = 0. 9368 , M A E = 3. 3378 , M S E = 18. 4800 , R M S E = 4. 2985 , M A P E = 0. 7354 % , and K G E = 0. 9479. The investigation highlights AT as the most influential variable, underscoring the importance of choosing inputs aligned with physical processes. This paper's outlined procedure, combining GP, hyperparameter optimization, and ensemble techniques, offers an efficient method for estimating P e in CCPP. It promises simplicity and effectiveness in real-world applications. B-A analysis proves valuable for SE selection, enhancing the proposed methodology. • GP symbolic expressions can estimate electrical power output of CCPP. • Symbolic expressions with AT-V, AT-AP, AT-RH, AT-V-AP, AT-V-RH, AT-AP-RH, AT-V-AP-RH as input variables are most accurate. • B-A analysis, R2, MAE, MSE, RMSE, MAPE, and KGE metrics used as filter of large number of expressions. • Lower estimation errors are achieved with averaging ensemble. [ABSTRACT FROM AUTHOR]

The research uniquely focuses on advancing the field of fuzzy sets, specifically proposing a novel approach using Hexagonal Type-2 Fuzzy Variable (HT2FV), to handle higher degrees of uncertainty. Flooding, a recurrent and impactful phenomenon, necessitates a robust approach to effective disaster management. The study delves into the humanitarian supply chain dynamics during movement crises and flood catastrophes, introducing a novel methodology to harness the potential of HT2FV. Central to the paper is the innovative defuzzification concept of Critical Value (CV) reduction, strategically employed to eliminate fuzzy components within HT2FV, transforming it into crisp data. The research extends its novelty by proposing a comprehensive approach to distributing relief materials, employing a Multi-objective, Multi-level, Multi-modal (MOMLMM) approach, with a primary focus on boats for inundated areas. Significantly, the study aims to optimize the allocation of resources, minimize time and cost under uncertain environments, and enhance disaster relief operations. The paper is the first to use new hexagonal fuzzy numbers (HFNs) and focuses on how they can be used to sort vulnerable areas using the fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods of the Multi-Criteria Decision-Making (MCDM) process. A case study of the 2022 Assam flood is looked at and solved using the Weighted Sum (WS), Neutrosophic Compromise (NC), and Goal Programming (GP) systematically on the LINGO solver to see if the suggested method can be used. Further, sensitivity analysis is employed to establish optimal credibility levels to validate the proposed methods. A real-life case study lends practicality, substantiating the approach's efficacy. At the same time, comprehensive comparative analysis compares and contrasts findings from different studies to synthesize existing knowledge and identify future scope, contributing significantly to the field of fuzzy sets. [ABSTRACT FROM AUTHOR]

Track geometry is one of the critical indicators of railway tracks' condition which requires continuous monitoring and maintenance over time. In this paper, a novel artificial intelligence (AI) based framework is proposed for railway track geometry inspection using vibration data collected from a dedicated measuring high-speed train. This AI-based anomaly track detection approach consists of two main stages. Firstly, a subset of features that best characterizes the track condition is defined. Several dynamic features from time domain data are extracted and importance scores are assigned to them, to determine the most effective subset for the purpose of track condition monitoring. Secondly, a data-driven based anomaly detection approach is developed to assess and identify track geometrical defects. In this stage, the acceleration responses collected from an in-service train traversing on a healthy track zone are employed as input into a One-Class Support Vector Machine (OCSVM) algorithm. The proposed algorithm defines the anomalies as relative changes to the historical behaviour. A comprehensive dataset from field measurements using a Société Nationale des Chemins de Fer Français (SNCF) Réseau IRIS320 highspeed train is used in this paper to implement the proposed approach. In addition, the impact of using different features and different locations/directions of the sensors on the accuracy of detecting geometrical defects is investigated. It is also shown that the OCSVM approach outperforms other algorithms based on Isolation Forest (IF), Local Outlier Factor (LOF), and Robust Mahalanobis Distance (RMD) in terms of recall, precision, and F1-score. The proposed anomaly detection approach has demonstrated a 12% increase in defect detection accuracy compared to the direct utilization of the raw acceleration response, which can facilitate track monitoring using in-service trains while providing cost-efficient maintenance in the future. [Display omitted] [ABSTRACT FROM AUTHOR]

In recent years, transformer-based models have emerged as powerful tools for natural language processing tasks, demonstrating remarkable performance in several domains. However, they still present significant limitations. These shortcomings become more noticeable when dealing with highly specific and complex concepts, particularly within the scientific domain. For example, transformer models have particular difficulties when processing scientific articles due to the domain-specific terminologies and sophisticated ideas often encountered in scientific literature. To overcome these challenges and further enhance the effectiveness of transformers in specific fields, researchers have turned their attention to the concept of knowledge injection. Knowledge injection is the process of incorporating outside knowledge into transformer models to improve their performance on certain tasks. In this paper, we present a comprehensive study of knowledge injection strategies for transformers within the scientific domain. Specifically, we provide a detailed overview and comparative assessment of four primary methodologies, evaluating their efficacy in the task of classifying scientific articles. For this purpose, we constructed a new benchmark including both 24K labelled papers and a knowledge graph of 9.2K triples describing pertinent research topics. We also developed a full codebase to easily re-implement all knowledge injection strategies in different domains. A formal evaluation indicates that the majority of the proposed knowledge injection methodologies significantly outperform the baseline established by Bidirectional Encoder Representations from Transformers. [ABSTRACT FROM AUTHOR]

In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios where fuzzy set order matters. Building on these principles, the complex q-rung picture fuzzy set (Cq-RPFS) becomes instrumental in representing decision-makers preferences in a two-dimensional manner, enhancing the handling of vague information in real-world scenarios. Expanding on these foundational principles, the paper introduces innovative Frank operations grounded in Frank t-norms within the context of Cq-RPFS. Leveraging these operations, the paper proposes four robust aggregation operators (AOs) under Cq-RPFS: complex q-rung picture fuzzy Frank weighted average (Cq-PFFWA), complex q-rung picture fuzzy Frank weighted geometric (Cq-PFFWG), complex q-rung picture fuzzy Frank ordered weighted average (Cq-PFFWOA), and complex q-rung picture fuzzy Frank ordered weighted geometric (Cq-PFFWOG). These AOs exhibit essential properties such as idempotency, monotonicity, and boundedness. A Multi-Criteria Decision-Making (MCDM) method based on the proposed AOs is suggested to validate these strategies. A real-life case study on India's adoption of AIBSWST serves as a practical application, with thorough analyses, including sensitivity, comparative, and superiority assessments, evaluating the performance of the approaches. A thoughtful discussion of the pros and cons of the proposed AOs accompanies the analysis, emphasizing the significance of the approach in ensuring the cleanliness and health of developing nations. [ABSTRACT FROM AUTHOR]

In the complex landscape of healthcare supply chains, challenges extend well beyond internal operational concerns, necessitating innovative solutions that align with the evolving technological landscape. The metaverse has emerged as a compelling solution to address these challenges by offering the potential for significant enhancements in efficiency and traceability. This paper focuses on both the theoretical underpinnings and practical applications of integrating the metaverse into healthcare supply chain management. Employing the Cyber-Physical-Social System framework, the study comprehensively explores the pivotal roles played by emerging information technologies in constructing the metaverse and overseeing various stages of the healthcare supply chain. The practical application is exemplified through the introduction of the TraceLink example, illustrating the successful healthcare supply chain digitalization. Furthermore, the paper introduces a barrier framework based on the Technology-Organization-Environment framework, addressing obstacles such as obsolete management technologies, data security, organizational structure, and societal acceptance. The examination and comparison of individual barriers using the Bayesian best-worst method further provide a nuanced understanding of the challenges. This comprehensive exploration not only equips research scholars with profound insights for addressing these challenges but also lays the foundation for future endeavors. The envisioned improvements in healthcare service delivery hold substantial benefits for patients and the industry, making this research a pioneering exploration of the metaverse's potential applications within the realm of the healthcare supply chain. [ABSTRACT FROM AUTHOR]

Completeness and consistency are widely acknowledged as indispensable prerequisites for applying fuzzy preference relations in resolving real-world problems. Interactions between decision-makers in the field of group decision-making can have a profound effect on preference changes, especially in situations of incomplete information. However, preference matrices generated using existing models often suffer from low consistency during opinion evolution, which further increases the risk of wrong decisions. To cope with these problems, this paper proposes an opinion evolution method under incomplete information. The method consists of two main components: (1) A preference prediction model based on matrix factorization algorithm and social trust networks under incomplete information, and (2) A model for decision-makers' preference evolution under dynamic trust scenarios. In Part (1), to simplify the preference collection method and reduce the occurrence of inconsistent information, this paper presents the concept of a consistent adjacency fuzzy preference matrix. To advance the utilization of machine learning algorithms for consistent preference relations, this paper employs the matrix factorization method with stochastic gradient descent to predict missing preferences in adjacency fuzzy preference matrix. In Part (2), to address the shortcomings in past opinion evolution models, this paper simulates the change of decision-makers' opinions in different stages of interactions under variable trust weights by using alternative ranking similarity and trust thresholds. To illustrate the practical application of the proposed model, this article uses the enterprise supplier selection problem as a case study. The model's effectiveness and feasibility are then validated through a comparative analysis with existing methods. • A preference gathering method based on adjacency preferences is presented. • A way is proposed that use matrix factorization in consistency preference matrices. • 0–1 variables and dynamic weights are introduced in opinion evolution models. • Adjust preferences through trust thresholds and alternative ranking similarities. [ABSTRACT FROM AUTHOR]

Due to the spatiotemporal, periodic, and wave characteristics of traffic flow, this paper considers the continuous traffic flow on the road as a special kind of fluid, and uses the wave equation in fluid mechanics to describe the fluctuation and undulation characteristics of the traffic flow data. From the traffic flow wave equation, using the partial grey prediction model can effectively reflect the time correlation of traffic flow, and establish the second-order partial grey prediction model of the traffic flow wave equation. In solving the time response equation of the model, the modeling steps of the model were obtained by discretizing the grey differential equations and using iterative recursion. Finally, the validity of the model is verified by three case studies, in which the fitted mean absolute percentage error reaches a minimum of 5.1242%, which is better than the other three algorithms and two partial grey prediction models. Meanwhile, the new model was used to predict the short-term traffic flow for the three cases, and the situation when the traffic flow exhibits different periodicity is discussed separately, and the results show that the predicted data have great similarity with the original data trend. Therefore, the model proposed in this paper is very effective in solving the short-time traffic flow prediction problem. By using this model for real-time traffic flow prediction, the control of traffic signals can be optimized to support traffic planning decisions and enhance traffic safety and environmental protection. • The volatility and correlation of traffic flow data are studied. • A second-order partial grey model based on traffic flow wave equation is proposed. • The problem of poor fitting of fluctuating data by the traditional grey model is solved. • Three case studies show that the new model outperforms other grey models. [ABSTRACT FROM AUTHOR]

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

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]

This paper proposes a novel framework for simulating the dynamics of beams on elastic foundations. Specifically, partial differential equations modeling Euler–Bernoulli and Timoshenko beams on the Winkler foundation are simulated using a causal physics-informed neural network (PINN) coupled with transfer learning. Conventional PINNs encounter challenges in handling large space–time domains, even for problems with closed-form analytical solutions. A causality-respecting PINN loss function is employed to overcome this limitation, effectively capturing the underlying physics. However, it is observed that the causality-respecting PINN lacks generalizability. We propose using solutions to similar problems instead of training from scratch by employing transfer learning while adhering to causality to accelerate convergence and ensure accurate results across diverse scenarios. The primary contribution of this paper lies in introducing a causality-respecting PINN loss function in the context of structural engineering and coupling it with transfer learning to enhance the generalizability of PINNs in simulating the dynamics of beams on elastic foundations. Numerical experiments on the Euler–Bernoulli beam highlight the efficacy of the proposed approach for various initial conditions, including those with noise in the initial data. Furthermore, the potential of the proposed method is demonstrated for the Timoshenko beam in an extended spatial and temporal domain. Several comparisons suggest that the proposed method accurately captures the inherent dynamics, outperforming the state-of-the-art physics-informed methods under standard L 2 -norm metric and accelerating convergence. [ABSTRACT FROM AUTHOR]

In the current landscape, user opinions exert an unprecedented influence on the trajectory of companies. In the field of online review platforms, these opinions, transmitted through text reviews and numerical ratings, significantly shape the credibility of products and services. For this reason, detecting inappropriate reviews becomes crucial. This paper addresses the problem of automatic anomalous review detection using a novel approach based on Anomaly Detection in the field of Natural Language Processing (NLP). Unlike other NLP tasks, anomaly detection in texts is a relatively emerging area. In this paper, we present a pipeline for opinion filtering that poses the problem of discerning between normal opinions containing relevant information about an item and anomalous opinions with unrelated content. Its key functionalities include: Classifying the reviews, assigning normality scores, and generating explanations for each classification, indispensable for the human who normally moderates these platforms. To evaluate the model, several Amazon datasets were used to demonstrate that the performance obtained is robust, obtaining an average F1 score of 91.4 detecting anomalies in the most complex scenario. In addition, a comparative study of three explainability techniques was conducted with 241 participants to measure the impact on understanding the classifications of the model and to rank their perceived usefulness of explanations. As a result, we obtained a system with great potential to automate tasks related to online review platforms, offering insights into anomaly detection applications in textual data and showing the difficulties that arise when the task to be explained presents a subjectivity component. • A pipeline to detect anomalous reviews on online platforms. • Reviews are coded using a pretrained MPNet transformer model. • Anomaly detection is carried out using non-iterative autoencoder networks. • Classifications are accompanied by a normality score and an ad-hoc explanation. • A study has been carried out with 241 people to evaluate the explanations. [ABSTRACT FROM AUTHOR]

The surge of machine learning (ML) applications and increasing usage of data driven approach for resilience enhancement provide great opportunities for applying ML techniques to power outage prediction (POP) during hurricanes. Considering the substantial damage that wind hazards can cause to power system network (PSN) in countries such as the USA and China, researchers have devised numerous approaches for disaster prevention to reduce their susceptibility. Since the selection of optimal ML algorithms for POP during hurricanes is a complex task, this paper helps researchers easily comprehend the numerous prediction techniques used worldwide for power outages during hurricanes. The paper provides a brief review of several categories of hurricanes and the damage it causes to the PSN more specifically to distribution side, the necessary measures to improve the PSN resilience, and finally a sequential approach for optimal ML model selection techniques that researchers and engineers can use to improve the POP during hurricanes. In this study the effectiveness of ML algorithms has been explored with the help of performance evaluation metrics which serve as the basic deciding criteria for optimal model selection. This research also highlights some of the key issues and challenges of integrating machine learning algorithms with existing PSN. • Reviewed various ML algorithms used for distribution system POP during hurricanes. • Effects of damage experienced by power system networks during hurricanes are widely investigated. • Provides an insight into the measures for improving power system resilience against hurricanes. • Provides a stepwise approach for using various ML algorithms in the context of POP. • Identification of key issues, challenges, and discussion of possible future research directions. [ABSTRACT FROM AUTHOR]

Job shop scheduling problems (JSSPs) are intractable combinatorial optimization problems and have been solved by numerous researchers over the last couple of decades, however, their solution still needs improvement. In this paper, a Hybrid Evolution Strategies-Simulated Annealing (HES-SA) algorithm is proposed for minimizing the makespan of JSSPs. The initial solution is randomly generated in the Evolution Strategies (ES) algorithm. Since mutation is the main source of genetic variation, and the performance of the ES algorithm relies on the mutation operator, multi-mutation operators are used in this paper i.e. a random number between 1 and 4 is generated and based on that number, the value used is either swap mutation, insertion mutation, scrambled mutation, or inversion mutation. For reproduction (1 + 9)-ES is used, hence nine offspring are randomly generated from one parent. The ES algorithm often gets trapped in local minima and suffers from premature convergence, hence it is combined with the Simulated Annealing (SA) algorithm, which helps to avoid local minima and increases the local search ability of the Hybrid HES-SA algorithm. In the SA algorithm, an insertion mutation is used and the initial temperature is set at 0.95, which is then gradually reduced for finding good solutions in the neighborhood. The HES-SA algorithm is tested on Fisher, Lawrence, and Yamada benchmark job shop problems and compared with other famous available techniques using the Wilcoxon signed rank test and Friedman test. The computational results show that HES-SA algorithm performs better in terms of makespan values as compared to other famous techniques. • Hybrid evolution strategies algorithm is proposed to solve benchmark job shop problems. • Evolution strategies is combined with Simulated annealing algorithm to avoid local minima and explore better offsprings. • Multi mutation operators are used in the Evolution strategies algorithm for genetic variation. • A multi factor analysis of variance technique is performed for the parameter optimization of Evolution strategies algorithm. • Wilcoxon signed test is performed for the comparing the performance of Hybrid evolution strategies algorithm. [ABSTRACT FROM AUTHOR]

The prevalence of adverse drug reactions (ADRs) associated with medications has emerged as a pressing global concern, impacting patients worldwide. Addressing the mitigation and prevention of ADRs to enhance patient safety stands as a paramount challenge within the healthcare community. This paper introduces a novel approach featuring the complex q-rung picture fuzzy Aczel–Alsina prioritized ordered weighted arithmetic (Cq-RPFAAPOWA) aggregation operator (AO) and the complex q-rung picture fuzzy Aczel–Alsina prioritized ordered weighted geometric (Cq-RPFAAPOWG) AO, each incorporating priority weightings. Furthermore, the paper devises a robust multi-attribute decision-making (MADM) methodology that employs these operators to discern suitable medication alternatives for patients with minimal or no impact from ADRs. In contrast to conventional operators, the Cq-RPFAAPOWA and Cq-RPFAAPOWG AOs effectively curtail potential redundancy in relationship assessments throughout the decision-making process. To exemplify the viability of the proposed MADM method, a comprehensive quantitative model is presented, showcasing its practical applicability. An exhaustive case study in ADR assessment empirically underscores the superiority of this approach over existing strategies. Importantly, the study underscores that drug selection based on the severity of ADRs stands as the most influential factor in elevating the public healthcare system. These findings offer valuable insights into decision-making processes and present actionable recommendations for policymakers, empowering them to make well-informed and impactful enhancements in the selection of pharmacological therapies. [ABSTRACT FROM AUTHOR]

Remote sensing scene (RSS) image classification plays a vital role in various fields such as urban planning and environmental protection. However, due to higher inter-class similarity and intra-class variability, achieving accurate classification for RSS images poses a considerable challenge for current convolutional neural networks (CNNs)-based and visual transformer (ViT)-based methods. To address these issues, this paper proposes a novel dual-encoding method named master–slave encoding network (MSE-Net) from two perspectives of feature extraction and fusion. The master encoder, based on ViT, extracts higher-level semantic features, while the slave encoder, based on CNN, captures relative lower-level spatial structure information. Secondly, to integrate feature information from the two encoders effectively, this paper further develop two fusion strategies. The first strategy involves the auxiliary enhancement units (AEUs), which eliminates semantic divergence between the two encoders, enhances spatial context awareness of the slave encoder and promotes effective feature learning. The interactive perception unit (IPU), as the second strategy, facilitates interaction and integration of the two encoders' representations to extract more discriminative feature information. In addition, we conducted comparative experiments on four widely-used RSS datasets, including RSSCN7, SIRI-WHU, the aerial image dataset (AID) and NWPU-RESISC45 (NWPU45), to verify the effectiveness of MSE-Net. The experimental results demonstrate that MSE-Net achieved state-of-the-art (SOTA) performance across all the datasets. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, the practical stabilization of highly nonlinear Takagi–Sugeno fuzzy complex networks with Markovian jump (HT-SFNM) is investigated via aperiodically intermittent discrete-time observation control (AID-TOC). Compared to existing articles on highly nonlinear complex networks, this paper considers both Takagi–Sugeno fuzzy rules and Markovian jump for the first time, and uses AID-TOC to solve its stability problem. Furthermore, more general polynomial growth conditions are adopted instead of the conventional linear growth conditions, which makes the model more universal. Finally, two numerical examples are presented to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

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

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

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

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

Reinforcement learning-based methods have addressed production scheduling problems with flexible processing constraints. However, delayed rewards arise due to the dynamic arrival of jobs and transportation constraints between two successive operations. The flow time of operations can only be determined after processing due to the possibility that the solution for job sequencing may change if new operations are inserted in dynamic environments. Job sequencing is often overlooked in single-agent-based scheduling methods. The lack of information sharing between multiple agents necessitates that researchers manually design reward functions to fit the relationship between optimization objectives and rewards, thereby reducing the accuracy of the learned policies. Thus, this paper proposes a multi-agent-based scheduling optimization framework that facilitates collaboration between the agents of both machines and jobs to address dynamic flexible job-shop scheduling problems (DFJSP) with transportation time constraints. Then, this paper formulates the Partial Observation Markov Decision Process and constructs a reward-sharing mechanism to tackle the delayed reward issue and facilitate policy learning. Finally, we develop an improved multi-agent dueling double deep Q network algorithm to optimize scheduling policy during long-term training. The results show that, compared with the state-of-the-art methods, the proposed method efficiently shortens the weighted flow time under the trained and unseen scenarios. Additionally, the case study results demonstrate its efficiency and responsiveness. It indicates that the proposed method efficiently addresses production scheduling problems with complex constraints, including the insertion of jobs, transportation time constraints, and flexible processing routes. [ABSTRACT FROM AUTHOR]

Carbon Fiber Reinforced Polymer (CFRP) laminates, prized for their lightweight and high stiffness, are extensively used in aerospace and maritime applications. Bolted joints play a crucial role in connecting these laminates. However, manufacturing variations arise during the assembly process, impacting performance due to material-related factors. Predicting the assembly stress fields of Carbon Fiber Reinforced Polymer bolted joints is of great significance in design optimization, manufacturing process control, and structural health monitoring. The currently prevalent finite element analysis methods incur extremely high computational costs, failing to meet the requirements for real-time prediction of the assembly and multiparametric design of composite bolted joints. Proposing a methodological framework for rapidly predicting the assembly physical field is necessary. This paper introduces a stress prediction framework to enhance analysis and aid material parameter design. The framework is inspired by image processing and artificial intelligence drawing by analogizing the computed physical field results to the generated images. Therefore, the Bolted Tightening Generative Adversarial Network (BT-GAN), a cascaded generative model, is proposed in this paper to predict stress fields of the composite bolted joints during assembly. The model starts with data augmentation of the stress filed results from the finite element analysis in a super-resolution network, which realizes an integral interpolation mapping from coarse-grid to fine-grid results. Then, the results of the data enhancement are fed into the subsequent conditional generative adversarial network for learning. Similar to the text-guided image generation approach, the network learns to understand the physical mapping relationships between different parameters and assembly stress fields. Moreover, the network achieves higher accuracy in stress field prediction by extraction the understanding of multi-scale features through the skip connection and the attention mechanism. This method effectively learns the physical mapping relationship between multiple parameters and the stress field, applying a graph generation approach to end-to-end predictions of the field. Compared to the results of finite element analysis from the coarse-grid, the Structure Similarity Index Measure (SSIM) of the cascaded generative network proposed in this paper has been improved from 0.584 to 0.962 and the Peak Signal-to-Noise Ratio (PSNR) metric has been increased from 17.3 dB to 58.2 dB. What's more, the mean relative error on the maximum values of the stress field has reached 6.9%. The trained model takes only 6.1s to complete a single prediction, significantly improving the prediction efficiency compared with finite element analysis. It is compared with the other networks commonly used for physical field prediction and shows improvement in the metrics proposed in the article. By constructing such an end-to-end stress field prediction framework during assembly, efficient forecasting for the assembly of composite bolted joints can be achieved. This is advantageous for the digital twin modeling of the assembly lines and the effective control of assembly quality, providing a powerful tool for assembly design and analysis. [Display omitted] [ABSTRACT FROM AUTHOR]

In Turkey, the transportation industry's greenhouse gas (GHG) emissions increased by 147.1% between 1990 and 2019. Today, this transportation industry (i.e., freight and passenger) is among the significant contributors to greenhouse gas emissions in Turkey's megacities. Moreover, 65.43% of short-distance trips between home to work and home to school have been made by private automobiles in Istanbul and increasing concerns about environmental pollution have led practitioners to seek practical, robust, and effective solutions to reduce GHG emissions. Shared electric scooters have rapidly become popular for end-users and practitioners in megacities, depending on their valuable advantages. However, the rapid spread of micro-mobility, characterized by e-scooters, has also raised questions about this system's sustainability, suitability, and applicability. Thus, there are some critical and noteworthy gaps in this issue. This study investigates the factors affecting the suitable e-scooter selection for a sustainable urban transport system. Besides, it aims to develop a methodological framework for assessing the available e-scooter alternatives. For this purpose, a novel negotiation approach, a new form of the Delphi technique, was developed with the help of Fermatean fuzzy sets to identify the influential criteria. Also, the current paper presents a consensus-based MULTIMOORA (Multiple Objective Optimization on the basis of Ratio Analysis plus Full Multiplicative Form) decision-making model based on Fermatean fuzzy sets to address the appraisal problem concerning e-scooter selection. The current paper indicated that economic measures such as acquisition price and upkeep costs affect the e-scooter selection processes. In addition, an optimization model based on cross-entropy and dispersion measures is utilized to compute criteria weights. It highlighted that the costs of e-scooters are still high, and operators consider these criteria instead of the technical and operational features of the e-scooters. Finally, the validity check executed to test the robustness and trustworthiness of the model affirms the model's firmness and trustworthiness. [ABSTRACT FROM AUTHOR]

Heterogeneity and uncertainty in a composite microstructure lead to either computational bottlenecks if modeled rigorously or to solution inaccuracies in the stress field and failure predictions if approximated. Although methods suitable for analyzing arbitrary and non-linear microstructures exist, their computational cost makes them impractical to use in large-scale structural analysis. Surrogate models or Reduced Order Models (ROMs) commonly enhance efficiencies but are typically calibrated with a single microstructure. Homogenization methods, such as the Mori–Tanaka method, offer rapid homogenization for a wide range of constituent properties. However, simplifying assumptions, like stress and strain averaging in phases, render the consideration of both deterministic and stochastic variations in microstructure infeasible. This paper illustrates a transformer neural network architecture that captures the knowledge of various microstructures and constituents, enabling it to function as a computationally efficient homogenization surrogate model. Given an image or an abstraction of an arbitrary composite microstructure of linearly elastic fibers in an elastoplastic matrix, the transformer network predicts the history-dependent, non-linear, and homogenized stress–strain response. Two methods for encoding microstructure features were tested: calculating two-point statistics using Principal Component Analysis (PCA) for dimensionality reduction and employing an autoencoder with a Convolutional Neural Network (CNN). Both methods accurately predict the homogenized material response. The developed transformer neural network offers an efficient means for microstructure-to-property translation, generalizable and extendable to a variety of microstructures. The paper describes the network architecture, training and testing data generation, and performance under cycling and random loadings. [ABSTRACT FROM AUTHOR]

Efficient aggregation methods and order relationships are crucial in solving group decision making (GDM) problems with linguistic interval-valued Atanassov intuitionistic fuzzy information. Based on a novel score function, this paper first presents a new method to rank linguistic interval-valued Atanassov intuitionistic fuzzy numbers (LIVAIFNs). The proposed ranking method overcomes the limitations of existing methods. Then, a new linguistic interval-valued Atanassov intuitionistic fuzzy weighted averaging (NLIVAIFWA) aggregation operator (AO) is proposed. The NLIVAIFWA AO of LIVAIFNs presented in this paper can overcome the drawbacks of the linguistic interval-valued Atanassov intuitionistic fuzzy weighted averaging (LIVAIFWA) AO, the linguistic interval-valued Atanassov intuitionistic fuzzy weighted geometric (LIVAIFWG) AO, the linguistic interval-valued Atanassov intuitionistic fuzzy Hamacher weighted averaging (LIVAIFHWA) AO and the improved linguistic interval-valued Atanassov intuitionistic fuzzy weighted averaging (ILIVAIFWA) AO of LIVAIFNs. Furthermore, some primary properties of the NLIVAIFWA AO are discussed, and a novel GDM method based on the provided NLIVAIFWA AO of LIVAIFNs is constructed. Finally, the effectiveness of the proposed GDM method is demonstrated through numerical examples, highlighting its superiority, and advantages. [ABSTRACT FROM AUTHOR]

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

The dynamic and complex environment affects the operational efficiency of automated guided vehicles (AGVs) at automated container terminals, in which artificial intelligent approaches are applied to address optimization problems in dynamic systems. This paper proposes a digital twin-based decision support approach to improve the efficiency of AGV scheduling service. Accordingly, the factors that affect AGV scheduling performance, including conflicts, failures, and battery constraints, are discussed first. Then, the framework and main steps of the proposed approach are described. The physical operation space is mapped into the virtual space, and both spaces keep synchronized to support the verification of solutions. A mathematical programming model and Q-learning algorithm are used to generate the AGV scheduling plan considering battery charging. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach. Comparisons of the digital twin-based approach, genetic algorithm (GA), and particle swarm optimization (PSO) are also made with different scale experiments. It appears that the proposed digital twin-based approach is superior to GA and PSO when solving small- and large-scale cases. A sensitivity analysis is performed concerning the battery utilization rate, task, and AGV number. Experimental results show that an optimal configuration of AGV and task can improve the battery utilization rate and reduce the completion time. • A digital twin-based decision support approach is proposed to improve scheduling. • A mathematical programming model is developed to minimize the total completion time. • Digital twin and Q-learning are used to monitor operation and obtain solutions. • Sensitivity analysis of battery utilization is made to provide equipment maintenance. • The proposed approach is superior based on the comparison of solution performances. [ABSTRACT FROM AUTHOR]

Multi-surrogate assisted evolutionary algorithms, utilizing inexpensive global and local surrogate models to assist evolutionary search, have demonstrated a remarkable ability to solve expensive optimization problems (EOPs). The selected training samples of the local surrogate model used in the current algorithms may not be updated continuously, which could bring about a waste of computing resources. This paper proposes a multi-surrogate multi-tasking genetic algorithm with an adaptive training sample selection strategy (MS-MTGAwA), in which the local surrogate model is updated adaptively based on information from established local models to enhance the capability in exploiting the optimal solution. The adaptive training sample selection strategy (ATS) applies optimal points found in historical local models as training samples. In addition, MS-MTGAwA inherits the optimization framework of a multi-tasking genetic algorithm. The radial basis function model is chosen as the modeling basis of the global and local surrogate models. A set of common benchmark functions whose dimensions vary from 10 to 100 and the tension/compression spring design problem are adopted to validate the performance of the proposed algorithm. Based on the average best fitness value over thirty independent runs on the benchmark functions, MS-MTGAwA ranks first in Friedman's test compared to five state-of-the-art algorithms including S-JADE, GORS-SSLPSO, SAHO, SAMSO, and MS-MTO. The proposed ATS can be expected to be applied in any algorithms with local surrogate to make full use of historical information of the local model to improve the prediction accuracy of the next local model. If this paper is accepted, MATLAB codes associated with this paper will be uploaded to https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE. • Use the predictive best of historical local surrogate models to update the local model. • Design an adaptive training sample selection strategy for the local surrogate model. • Develop a framework that combined multi-surrogate and multi-population-based multitasking. • Propose a multi-surrogate multi-tasking genetic algorithm with an adaptive training sample selection strategy. • MSMTGAwA shows strong competitiveness in comparison with other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Applications in Engineering: In industry, surface defect detection is crucial for improving product quality. However, there are many challenges in industrial inspection scenarios, such as interference from background noise, complex small-target problems, significant variations in target objects, and the problem of finding a balance between inspection speed and accuracy. To address the above problems, this paper proposes an industrial defect-aware network based on cross-layer semantic information guidance and contextual attention enhancement (IDP-Net). Specifically, IDP-Net has four different new features. The contribution of artificial intelligence: Firstly, to solve the industrial surface context and defect similarity problem, this paper proposes a Lightweight Local Global Feature Extraction Network (LLG-Net), unlike other methods, the effective combination of self-attention blocks and convolution blocks ensures gradual integration of global and local features across multiple layers, to improve the detection ability of targets with significant changes in scale, this paper designs a Multiscale Perceptual Feature Aggregation Network (MPA-Net), adequately fuses the shallow fine-grained information and the deep semantic information. Then, to enhance the connection between multi-scale semantic information, an adaptive cross-layer feature fusion module (ACFF) is proposed, which is novel in integrating the characteristics of multiple adjacent levels to help the model better capture the different scale characterisation of the target. Finally, a Region Attention Module (RAM) is proposed and introduced in the detector to enhance the attention to the critical regions around the target object. In particular, this paper proposes a new localisation loss function (MEIoU) that enhances the network's attention to objects at different scales. The experimental results show that 94.3%, 98.7% and 99.5% of mAP@.5 are obtained on steel, PCB and aluminium surface defect datasets, respectively, and 50 FPS is achieved, which is better than the current mainstream detectors and meets the demand of practical industrial production. [ABSTRACT FROM AUTHOR]

Quadratic programming (QP) problems are frequently encountered in science, engineering, and robotic applications. Zeroing neural network (ZNN), as a special type of recurrent neural network (RNN), is an effective solver for solving QP. However, conventional ZNNs cannot solve QP with inequality and/or bound constraints. To obtain the optimal solution of QP with such constraints, the design of ZNN models roughly resorts to three techniques: slack variable, penalty function, and nonlinear complementary problem (NCP) function. Comparative results demonstrated that the NCP function-based ZNN (NCP-ZNN) has superior performance compared to ZNN models based on the other two techniques. Note that there exist various NCP functions that can be used to design different ZNN models by following similar procedures. One research gap is that these ZNN models have not been designed, presented and compared. To fill such a gap, this paper aims to design and investigate various NCP-ZNN models employing different NCP functions. Specifically, six NCP functions are used in this paper: the perturbed Fisher-Burmeister (pFB) function, Mangasarian and Solodov (MS) function, Evtushenko and Purtov I (EPI) function, EPII function, "discrete generalization" to the FB (D-FB) function, and Ranjbar, Effati, and Miri (REM) function. The performance of six NCP-ZNN models is validated by employing them to solve two QP problems in numerical comparisons. All NCP-ZNN models are successfully applied to parallel and serial robots with joint constraints to accomplish path-tracking tasks. Both simulative and experimental results demonstrate that six NCP-ZNN models can handle the joint constraints, with the best performance achieved by the MS-ZNN model. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

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 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]