计算机科学最新文献

{"title":"Robot digital twin systems in manufacturing: Technologies, applications, trends and challenges","authors":"Qiang Qin ,&nbsp;Zhihao Liu ,&nbsp;Ruirui Zhong ,&nbsp;Xi Vincent Wang ,&nbsp;Lihui Wang ,&nbsp;Magnus Wiktorsson ,&nbsp;Wei Wang","doi":"10.1016/j.rcim.2025.103103","DOIUrl":"10.1016/j.rcim.2025.103103","url":null,"abstract":"<div><div>The manufacturing industry is undergoing a profound transformation toward smart, digital, and flexible production systems under the Industry 4.0 framework. Within this paradigm, Digital Twin (DT) serves as a key enabler, bridging physical and digital domains to simulate, analyse, and optimise manufacturing operations. Concurrently, robotic systems, enhanced by smart sensor perception, Industrial Internet of Things connectivity, and adaptive control mechanisms, are increasingly deployed to handle complex and dynamic tasks. However, the evolving demands of the modern manufacturing industry require a high degree of flexibility and responsiveness, necessitating more intelligent solutions. The Robot Digital Twin (RDT) has emerged as a transformative approach, facilitating dynamic adaptation and continuous operational improvement. This review offers a comprehensive examination of the literature on RDT in manufacturing from both technology and application perspectives, aiming to provide insight for researchers and practitioners in Industry 4.0. The paper introduces a four-layer RDT system architecture and summarises how Industry 4.0 technologies, e.g., the Industrial Internet of Things, Cloud/Edge Computing, 5 G, Virtual Reality, Modelling and Simulation, and Artificial Intelligence, converge and influence the RDT system based on this architecture. Furthermore, the review covers domain-specific and system-level applications, such as assembly, machining, grasping, material handling, human-robot interaction, predictive maintenance, and additive manufacturing systems, with an analysis of their development status. Finally, the trends, practical challenges, and future research directions for RDT systems in manufacturing are summarised at different levels.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103103"},"PeriodicalIF":11.4,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive hybrid response mechanism for dynamic multi-objective optimization and its application in multi-robot task allocation","authors":"Tingting Dong ,&nbsp;Peiwen Wang ,&nbsp;Fei Xue ,&nbsp;Yuge Geng ,&nbsp;Zhihua Cui","doi":"10.1016/j.swevo.2025.102123","DOIUrl":"10.1016/j.swevo.2025.102123","url":null,"abstract":"<div><div>The dynamic changes in task requirements and real-time fluctuations in robot states in multi-robot task allocation (MRTA) increase the complexity of algorithm design. This paper presents an Adaptive Multi-Objective Evolutionary Algorithm with Hybrid Response Mechanism (AMOEAD-HRM) for dynamic multi-objective MRTA, addressing environmental uncertainty through innovative mechanisms. AMOEAD-HRM proposes a GNG-based prediction response mechanism, leveraging Growing Neural Gas (GNG) networks to model the time-varying nature of tasks and robot states. Unlike fixed-architecture predictors, GNG captures data topological structures to construct adaptive predictive models, dynamically adjusting to fluctuations and uncertainties by iteratively optimizing network topology. This enables effective characterization of complex temporal patterns without prior distribution assumptions, providing a robust foundation for predicting dynamic changes. To enhance responsiveness, the algorithm integrates a memory-based response mechanism and a Gaussian polynomial mixture mutation strategy. A dynamic adaptive weight adjustment strategy selects optimal response mechanisms according to environmental variation degrees, balancing prediction accuracy and real-time adaptability to improve system robustness and flexibility. Experimental validation on 19 benchmark problems shows AMOEAD-HRM’s superiority. In dynamic scenarios, it responds 46.1% faster than DNSGA-II. Under high dynamics, its solution sets have 3.4% higher MHV than DNSGA-II. With moderate changes, MHV is 0.34% higher than SGEA.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102123"},"PeriodicalIF":8.5,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolvable psychology informed neural network for memory behavior modeling","authors":"Xiaoxuan Shen ,&nbsp;Zhihai Hu ,&nbsp;Qirong Chen ,&nbsp;Pei Wang","doi":"10.1016/j.ipm.2025.104312","DOIUrl":"10.1016/j.ipm.2025.104312","url":null,"abstract":"<div><div>Memory behavior modeling is a fundamental issue in the fields of cognitive psychology and education. Classical theoretical models of memory are characterized by insufficient accuracy and ongoing controversies, while data-driven memory modeling methods often require large amount of training data and lack interpretability, highlighting the need for new approaches to memory behavior modeling. This paper integrates classic psychological theories of memory to explore the feasibility of knowledge-driven neural networks in memory behavior modeling. It proposes the EPsyINN model, which combines temporal neural networks with sparse differential regression in a unified framework, enabling the joint optimization of neural networks and classical symbolic models. More specifically, to address the controversies in classical psychological theories and the ambiguity of descriptors, it proposes a descriptor evolution method based on differential operators to achieve precise descriptor characterization and advance the evolution of classical symbolic models. Additionally, it introduces a caching mechanism for regression coefficient matrices and an alternating iterative optimization method for multiple modules, effectively alleviating local optima in model optimization. On five large-scale real-world memory behavior datasets, the proposed method surpasses state-of-the-art memory modeling approaches in predictive accuracy, while the evolved classical symbolic models also achieve performance improvements. Ablation experiments validate the effectiveness of the proposed improvements, and application experiments demonstrate its potential to inspire psychological research. The code for the experiments is available at: <span><span>https://github.com/hellowads/PsyINN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 1","pages":"Article 104312"},"PeriodicalIF":6.9,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain-enabled 6G wireless network securities using multi-relational graph attention disentangled cascaded graph convolution networks","authors":"Gunavathie M A ,&nbsp;A. Arivarasi ,&nbsp;Puneet Kumar Aggarwal ,&nbsp;Krishna Prakash Arunachalam","doi":"10.1016/j.compeleceng.2025.110617","DOIUrl":"10.1016/j.compeleceng.2025.110617","url":null,"abstract":"<div><div>The emergence of sixth-generation (6 G) wireless networks introduces transformative capabilities such as ultra-low latency, massive device connectivity, and real-time intelligent services. However, the inherently distributed and heterogeneous architecture of 6 G significantly escalates vulnerabilities to cyber threats, unauthorized access, and data breaches across large-scale decentralized infrastructures. Existing security mechanisms are inadequate in modeling complex multi-relational interactions among network entities and often fail to ensure trust, transparency, and tamper-resistance at scale. To address these challenges, this research proposes a novel Blockchain-Enabled 6 G Wireless Network Security framework integrating Multi-Relational Graph Attention and Disentangled Cascaded Graph Convolution Network (Multi-RACG) model. This hybrid graph-based model captures high-order relational dependencies while disentangling semantic features across graph channels to enable precise, context-aware intrusion detection. A Dandelion Optimization Algorithm (DOA) is employed to fine-tune model parameters and optimize network architecture, ensuring rapid convergence and reduced computational overhead. Additionally, a Proof-of-Work-Based Weighted Mining (PoWBWM) consensus protocol strengthens blockchain operations by incorporating dynamic trust metrics, enhancing data integrity and resilience against malicious manipulation. Experimental results demonstrate the framework's superiority, achieving 99.9 % detection accuracy with minimal false positives and computational loss, positioning it as a highly scalable and intelligent security solution for future 6 G ecosystems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110617"},"PeriodicalIF":4.9,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A feature vector-based modeling attack method on symmetrical obfuscated interconnection PUF","authors":"Huanwei Wang ,&nbsp;Fushan Wei ,&nbsp;Fagen Li ,&nbsp;Jing Jing ,&nbsp;Tieming Liu ,&nbsp;Wei Liu","doi":"10.1016/j.jisa.2025.104187","DOIUrl":"10.1016/j.jisa.2025.104187","url":null,"abstract":"<div><div>Physical unclonable function (PUF) are widely used in solutions such as device authentication and lightweight encryption due to their tamper-resistant, key-free storage and lightweight properties. However, the security of PUFs is threatened by modeling attacks. In this paper, we propose a novel modeling attack method for the symmetrical obfuscated interconnection physical unclonable function (SOI PUF) based on feature vectors. The proposed method introduces an innovative feature vector transformation technique and vector response pair to capture higher-order relationships with complex PUF architectures. Meanwhile, we propose two important principles for designing deep neural network (DNN) attack models. The experiments are systematically validated for the novel SOI PUF and cSOI PUF architectures, and the results show that, under equivalent dataset conditions, the proposed method achieves a higher attack success rate compared to the traditional challenge-response pair-based modeling approaches, achieving an accuracy of 98.42% in modeling SOI PUF. This study provides valuable theoretical and practical insights for enhancing PUF security and designing attack-resistant PUF architectures.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"93 ","pages":"Article 104187"},"PeriodicalIF":3.7,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coordination control of multi-axle distributed drive vehicle with dynamically-triggered DYC intervention and KKT-based torque optimization distribution","authors":"Lie Guo ,&nbsp;Jiaqing Zhao ,&nbsp;Longxin Guan ,&nbsp;Jiahao Wang ,&nbsp;Pingshu Ge ,&nbsp;Linli Xu","doi":"10.1016/j.mechatronics.2025.103397","DOIUrl":"10.1016/j.mechatronics.2025.103397","url":null,"abstract":"<div><div>Multi-axle distributed drive vehicles, characterized by over-actuation, internal dynamics, and nonlinear external disturbances, frequently encounter coordination challenges in lateral path tracking, yaw stability intervention, and longitudinal speed control. These issues can significantly degrade overall control performance, particularly under complex driving conditions. To address them, this paper proposes a coordinated control framework integrating path tracking, yaw stability intervention, longitudinal drive control, and optimal torque distribution. First, a robust path tracking controller based on a linear parameter-varying (LPV) dynamic model is designed and a longitudinal speed controller using a linear sliding mode approach are designed. Subsequently, a direct yaw-moment control (DYC) strategy based on nonsingular terminal sliding mode control (NTSMC) with nonlinear dynamic triggering is introduced to mitigate performance degradation induced by excessive interventions. Finally, an optimal torque distribution method based on the Karush–Kuhn–Tucker (KKT) conditions is developed to ensure the feasibility of the solutions. The effectiveness and superiority of the proposed coordination framework are validated through hardware-in-the-loop (HiL) experiments.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"111 ","pages":"Article 103397"},"PeriodicalIF":3.1,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144851863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-design of a wave energy converter through bi-conjugate impedance matching","authors":"Ryan G. Coe,&nbsp;Giorgio Bacelli,&nbsp;Daniel Gaebele,&nbsp;Alicia Keow,&nbsp;Dominic Forbush","doi":"10.1016/j.mechatronics.2025.103395","DOIUrl":"10.1016/j.mechatronics.2025.103395","url":null,"abstract":"<div><div>As with other oscillatory power conversion systems, the design of wave energy converters can be understood as an impedance matching problem. By representing the wave energy converter as a multi-port network, two separate but related impedance matching conditions can be established. Satisfying these conditions maximizes power transfer to the load. In practice, these impedance matching conditions may be used to influence the design of the system (including the hull, power take-off, controller, mooring, etc.). To this end, this paper considers some example applications of wave energy converter design with the help of the impedance matching framework.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"111 ","pages":"Article 103395"},"PeriodicalIF":3.1,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144851802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of intelligent neuro-structures optimized with Levenberg–Marquardt and Bayesian distribution for dynamical analysis of Caputo–Fabrizio fractional electric circuit models","authors":"Aneela Kausar ,&nbsp;Chuan-Yu Chang ,&nbsp;Sidra Naz ,&nbsp;Muhammad Asif Zahoor Raja ,&nbsp;Rooh Ullah Khan ,&nbsp;Muhammad Safiullah ,&nbsp;Saeeda Naz","doi":"10.1016/j.engappai.2025.111920","DOIUrl":"10.1016/j.engappai.2025.111920","url":null,"abstract":"<div><div>Electrical engineering models utilize interconnected circuits that consist of charged particles to enable the simulation and study of electric devices and systems, taking advantage of effective electron transfer across a completed circuit. Electric circuits involving the Caputo-Fabrizio (CF) fractional derivative have been precisely modeled recently by known solutions, efficiently capturing the system's response. The paper talks about the application of electrical circuit models for analyzing fractional stiff differential equations in an effort to explore different properties of the fractal Resistor-Capacitor (RC) and Resistor-Inductor (RL) circuits. The study employs artificial intelligence-based neurocomputing techniques and backpropagation networks for the purposes of increasing the knowledge on fractal circuit models. The Bayesian Regularization backpropagated neural networks (BR-BNNs) and Levenberg–Marquardt backpropagated neural networks (LM-BNNs) are utilized as efficient procedure for the training. The mathematical equations of CF-fractional RC and RL circuits were implemented to generate synthetic reference datasets and these information's were then used as a target for execution of LM-BNNs and BR-BNNs to find approximate solutions for the models. To validate and compare the accuracy of BR-BNNs and LM-BNNs in solving CF-fractional electric circuit models, the convergence curves on iterative adaptation of mean squared error (MSE) are employed. Results show that BR-BNNs yields MSE of approximately 10⁻¹² to 10⁻¹³ and absolute error within the range of 10⁻⁶ to 10⁻⁸, which providing strong evidence for the effectiveness of BR-BNNs approach than that of LM-BNNs algorithm. To further validate and endorse precision of the results, a performance evaluation via absolute error, statistical instance distribution in error histogram, regression analysis, convergence stability test, and Wilcoxon signed-rank test were exploited for CF-fractional electric circuit models that shows the statistical adequacy.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111920"},"PeriodicalIF":8.0,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallel genetic algorithms on hybrid servers: Design, implementation, and optimization for performance and energy","authors":"Amr Abdelhafez ,&nbsp;Ravi Reddy Manumachu ,&nbsp;Alexey Lastovetsky","doi":"10.1016/j.swevo.2025.102110","DOIUrl":"10.1016/j.swevo.2025.102110","url":null,"abstract":"<div><div>Parallel Genetic Algorithms (PGAs) have been widely applied to accelerate solutions for real-world problems such as energy optimization in building constructions, data preprocessing and model selection steps in data mining, real-time control of multilevel inverters in electronics, land-use planning, nanoscience, optimal power flow in power systems, and road traffic management.</div><div>The state-of-the-art research proposes PGAs optimized solely for performance and for solving optimization problems on a multicore CPU, GPU, or clusters of multicore CPUs. However, no research has analyzed PGAs for heterogeneous hybrid platforms comprising multicore CPUs and multiple accelerators that utilize all computing devices in parallel. Furthermore, no definitive comparative research comprehensively investigates the energy consumption of PGAs in hybrid systems versus multicore CPUs or GPUs.</div><div>We address the above gaps in the prior art in this work. First, we present a novel parallelization approach (HPIGA) tailored for heterogeneous hybrid platforms, featuring a portable implementation that utilizes all available computational devices, including multicore CPUs and GPUs. We conduct a comprehensive investigation into the performance and energy profiles of this approach. We compare it with three other traditional parallel approaches across a range of dimensions, varying from 100 dimensions and up to 5000 dimensions. The results showed HPIGA’s competitive energy consumption behavior and promising performance compared to other traditional approaches under the study.</div><div>Moreover, we formulate a bi-objective optimization problem of a PGA employing a parallel island model and executing on a hybrid server comprising <span><math><mi>p</mi></math></span> compute devices. The problem has two objectives: performance and energy. The decision variable used in our bi-objective optimization problem is workload distribution, which is proportional to the number of islands. We study the efficacy of our proposed PGA on a hybrid server platform with an Intel Icelake multicore CPU and two Nvidia A40 GPUs, analyzing execution time and dynamic energy profiles under two power governors. The resulting Pareto front graphs provide valuable insights, serving as crucial benchmarks for the future development and use of efficient, energy-aware optimization techniques across diverse computational devices.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102110"},"PeriodicalIF":8.5,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust zero-watermarking method for medical images based on FFST and Daisy descriptor","authors":"Guangyun Yang ,&nbsp;Xinhui Lu ,&nbsp;Yu Lu ,&nbsp;Xiangguang Xiong","doi":"10.1016/j.jisa.2025.104193","DOIUrl":"10.1016/j.jisa.2025.104193","url":null,"abstract":"<div><div>With the continuous development of digital medical imaging technologies, ensuring the security of the medical images has become critically important. In this study,the Daisy descriptors’ stability against attacks was first experimented with, and the findings show that it provides superior robustness. With this, a robust zero-watermarking method is designed to maintain medical image integrity and enable copyright protection by combining the fast finite Shearlet transform (FFST), Daisy descriptor, and Hessenberg decomposition. First, FFST was performed on the medical image to extract the low-frequency component and divide it into blocks of equal size. Second, each block’s Daisy descriptor matrix is calculated and its 8<span><math><mo>×</mo></math></span> 8 block is selected, after which the Hessenberg decomposition is performed for each block, and a feature image is derived from the magnitude comparison between the maximum value of each block and the global mean. Additionally, the copyrighted image is first encrypted by using a 2D Logistic-Sine coupling mapping, and then combined with the feature image through an exclusive OR operation to produce an unrecognizable binary image. The experimental results on ten medical images and three benchmark image databases (COVID-19, OASIS-1, and SIPI) show that the proposed method is highly resistant to most attacks, and the normalized correlation coefficient is always maintained higher than 0.95. Compared to typical methods, our method achieves superior robustness and improves the average performance by approximately 3.2%.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"93 ","pages":"Article 104193"},"PeriodicalIF":3.7,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}