IF 1.6 4区计算机科学

Iet Optoelectronics Pub Date : 2025-10-18 DOI: 10.1049/ote2.70023

引用次数: 0

Off-road distributed-drive electric vehicle trajectory tracking control with constrained Transformer MPC 基于约束变压器MPC的越野分布式驱动电动汽车轨迹跟踪控制

IF 4.6 2区计算机科学

Control Engineering Practice Pub Date : 2025-10-18 DOI: 10.1016/j.conengprac.2025.106608

Fawang Zhang , Sichao Wu , Yimiao Zhang , Hui Liu , Shida Nie , Jingliang Duan , Rui Liu , Changle Xiang

{"title":"Off-road distributed-drive electric vehicle trajectory tracking control with constrained Transformer MPC","authors":"Fawang Zhang , Sichao Wu , Yimiao Zhang , Hui Liu , Shida Nie , Jingliang Duan , Rui Liu , Changle Xiang","doi":"10.1016/j.conengprac.2025.106608","DOIUrl":"10.1016/j.conengprac.2025.106608","url":null,"abstract":"<div><div>Trajectory tracking control for off-road distributed-drive electric vehicles presents significant challenges due to compound slope effects and rollover risks. Existing approaches often neglect the critical impact of coupled slopes on vehicle roll dynamics and face substantial computational burdens during real-time implementation. This paper presents a four-degree-of-freedom vehicle dynamic model that comprehensively captures longitudinal, lateral, yaw, and roll motions while accounting for compound grade effects. We propose a novel Constrained Transformer Model Predictive Control algorithm that enables real-time policy computation while maintaining safety constraints. CarSim co-simulations demonstrate that our approach effectively prevents rollover and improves trajectory tracking accuracy by 72.28% across various off-road scenarios while reducing computational complexity by 213 times compared to conventional online optimization MPC. Real vehicle tests in off-road environments further validate the effectiveness of the proposed algorithm.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"165 ","pages":"Article 106608"},"PeriodicalIF":4.6,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320282","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}

引用次数: 0

Automating software size measurement from python code using language models 使用语言模型从python代码自动化软件大小测量

IF 3.1 2区计算机科学

Automated Software Engineering Pub Date : 2025-10-18 DOI: 10.1007/s10515-025-00571-z

Samet Tenekeci, Hüseyin Ünlü, Bedir Arda Gül, Damla Keleş, Murat Küük, Onur Demirörs

{"title":"Automating software size measurement from python code using language models","authors":"Samet Tenekeci, Hüseyin Ünlü, Bedir Arda Gül, Damla Keleş, Murat Küük, Onur Demirörs","doi":"10.1007/s10515-025-00571-z","DOIUrl":"10.1007/s10515-025-00571-z","url":null,"abstract":"<div><p>Software size is a key input for project planning, effort estimation, and productivity analysis. While pre-trained language models have shown promise in deriving functional size from natural-language requirements, measuring size directly from source code remains under-explored. Yet, code-based size measurement is critical in modern workflows where requirement documents are often incomplete or unavailable, especially in Agile development environments. This exploratory study investigates the use of CodeBERT, a pre-trained bimodal transformer model, for measuring software size directly from Python source code according to two measurement methods: COSMIC Function Points and MicroM. We construct two curated datasets from the Python subset of the CodeSearchNet corpus, and manually annotate each function with its corresponding size. Our experimental results show that CodeBERT can successfully measure COSMIC data movements with up to 91.4% accuracy and generalize to the functional, architectural, and algorithmic event types defined in MicroM, reaching up to 81.5% accuracy. These findings highlight the potential of code-based language models for automated functional size measurement when requirement artifacts are absent or unreliable.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"33 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316540","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}

引用次数: 0

Personalized safety training for construction workers: A large language model-driven multi-agent framework integrated with knowledge graph reasoning 建筑工人个性化安全培训：集成知识图推理的大型语言模型驱动多智能体框架

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2025-10-18 DOI: 10.1016/j.compind.2025.104399

Qihua Chen , Xianfei Yin , Beifei Yuan , Qirong Chen

{"title":"Personalized safety training for construction workers: A large language model-driven multi-agent framework integrated with knowledge graph reasoning","authors":"Qihua Chen , Xianfei Yin , Beifei Yuan , Qirong Chen","doi":"10.1016/j.compind.2025.104399","DOIUrl":"10.1016/j.compind.2025.104399","url":null,"abstract":"<div><div>Construction sites are inherently high-risk environments, making safety training for workers crucial to enhancing operational skills, reinforcing safety awareness, and reducing accident risks. Traditional centralized training often fails to engage workers due to monotonous nature and lack of relevance, leading to low efficiency. Moreover, critical resources such as operating instructions, safety guidelines, and accident reports are frequently mismanaged or underutilized. Therefore, this study proposes ConSTRAG, an innovative personalized construction safety training framework. By integrating large language model-empowered agents with knowledge graph reasoning, ConSTRAG generates tailored training materials, significantly improving the relevance and effectiveness of safety training. Validation tests conducted on a dataset of 11,020 questions achieved an average score of 81.25, exceeding the benchmark by 6.94. The generated personalized training materials were evaluated through an expert questionnaire survey, with an average score of 4.16 out of 5 across five dimensions. This research contributes to overcoming individual heterogeneity in construction safety training, enhances training efficiency and effectiveness, and holds potential for extension to other personnel training industries.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"174 ","pages":"Article 104399"},"PeriodicalIF":9.1,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315230","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}

引用次数: 0

A systematic exploration of C-to-rust code translation based on large language models: prompt strategies and automated repair 基于大型语言模型的c到rust代码翻译的系统探索：提示策略和自动修复

IF 3.1 2区计算机科学

Automated Software Engineering Pub Date : 2025-10-18 DOI: 10.1007/s10515-025-00570-0

Ruxin Zhang, Shanxin Zhang, Linbo Xie

{"title":"A systematic exploration of C-to-rust code translation based on large language models: prompt strategies and automated repair","authors":"Ruxin Zhang, Shanxin Zhang, Linbo Xie","doi":"10.1007/s10515-025-00570-0","DOIUrl":"10.1007/s10515-025-00570-0","url":null,"abstract":"<div><p>C is widely used in system programming due to its low-level flexibility. However, as demands for memory safety and code reliability grow, Rust has become a more favorable alternative owing to its modern design principles. Migrating existing C code to Rust has therefore emerged as a key approach for enhancing the security and maintainability of software systems. Nevertheless, automating such migrations remains challenging due to fundamental differences between the two languages in terms of language design philosophy, type systems, and levels of abstraction. Most current code transformation tools focus on mappings of basic data types and syntactic replacements, such as handling pointers or conversion of lock mechanisms. These approaches often fail to deeply model the semantic features and programming paradigms of the target language. To address this limitation, this paper proposes RustFlow, a C-to-Rust code translation framework based on large language models (LLMs), designed to generate idiomatic and semantically accurate Rust code. This framework employs a multi-stage progressive architecture, which decomposes the overall translation task into several sequential stages, namely translation, validation, and repair. During the translation phase, a collaborative prompting strategy is employed to guide the LLM in achieving cross-language semantic alignment, thereby improving the accuracy of the generated code. Subsequently, a validation mechanism is introduced to perform syntactic and semantic checks on the generated output, and a conversational iterative repair strategy is employed to further enhance the quality of the final result. Experimental results show that RustFlow outperforms most of the latest baseline approaches, achieving an average improvement of 50.67% in translation performance compared to the base LLM. This work offers a novel technical approach and practical support for efficient and reliable cross-language code migration.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"33 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316543","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}

引用次数: 0

An improved dynamic multi-objective robust evolutionary algorithm and application based on PSTL 一种改进的基于PSTL的动态多目标鲁棒进化算法及其应用

IF 8.5 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-10-18 DOI: 10.1016/j.swevo.2025.102195

Zhongqiang Wu, Mingyang Liu

{"title":"An improved dynamic multi-objective robust evolutionary algorithm and application based on PSTL","authors":"Zhongqiang Wu, Mingyang Liu","doi":"10.1016/j.swevo.2025.102195","DOIUrl":"10.1016/j.swevo.2025.102195","url":null,"abstract":"<div><div>A problem whose optimal solution evolves as environmental parameters change is known as a dynamic multi-objective optimization problem (DMOP). Commonly used approaches to DMOP are generally grouped into two categories: Dynamic Multi-Objective Evolutionary Algorithm (DMOEA) and the Dynamic Multi-Objective Robust Evolutionary Algorithm (DMOREA). DMOEA tracks the dynamic Pareto optimal solution through the dynamic response strategy, but it will lead to a high switching cost. DMOREA looks for robust solutions that is suitable in multiple environments, but the optimization effect is poor. To solve these problems, an improved dynamic multi-objective robust evolutionary algorithm based on preliminary search and transfer learning is proposed. Firstly, the preliminary search strategy is used to generate a high-quality target domain guiding population to avoid the occurrence of negative migration. Transfer learning is used to generate a well-distributed population and accelerate the convergence speed. Then, a switching strategy based on the severity of environmental change is proposed, which evaluates the applicability of DMOREA's robust solutions in future environments, switching between solutions generated by preliminary search and transfer learning or existing robust solutions. The proposed strategy improves the optimization effect of the algorithm while maintaining its robustness. The effectiveness of the proposed algorithm is verified by comparison with other algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102195"},"PeriodicalIF":8.5,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321205","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}

引用次数: 0

A novel probabilistic linguistic group decision-making method driven by DEA cross-efficiency and trust relationship 一种基于DEA交叉效率和信任关系的概率语言群体决策方法

IF 3.5 2区计算机科学

Applied Intelligence Pub Date : 2025-10-18 DOI: 10.1007/s10489-025-06696-8

Feifei Jin, Shuyan Guo, Jinpei Liu

引用次数: 0

Towards integrated dashboards for better management of human-centric issues in software development 朝着集成仪表板的方向发展，以便更好地管理软件开发中以人为中心的问题

IF 3.1 2区计算机科学

Automated Software Engineering Pub Date : 2025-10-18 DOI: 10.1007/s10515-025-00565-x

Liam Todd, Kashumi Madampe, Hourieh Khalajzadeh, Mojtaba Shahin, John Grundy

{"title":"Towards integrated dashboards for better management of human-centric issues in software development","authors":"Liam Todd, Kashumi Madampe, Hourieh Khalajzadeh, Mojtaba Shahin, John Grundy","doi":"10.1007/s10515-025-00565-x","DOIUrl":"10.1007/s10515-025-00565-x","url":null,"abstract":"<div><p>GitHub and Jira projects typically contain many issues and issue comments used to track project tasks and defects. An important class of issues that needs appropriate consideration is called “<i>human-centric issues</i>”. These issues relate to different human characteristics of end users that need to be identified, tracked and managed differently from traditional technical-related issues. Current management of these human-centric issues during defect management is limited. We introduce a novel dashboard – the (Human-centric Issue Visualiser – HCIV) that categorises and tags these HCIss. We built HCIV prototypes for the two platforms, GitHub and Jira. These tag issues and present them in various visual forms to software practitioners. Using the dashboard, human-centric issues can be prioritised and tracked, and machine learning-generated classifications can be overridden. To reflect these interactions, associated GitHub and Jira issue tags are updated while the user interacts with our dashboard. The user evaluations of our dashboard prototypes show their potential for human-centric issue management. A demo of the GitHub version of the tool being used can be viewed at https://youtu.be/v49aiRiDIPs, and the Jira version can be viewed at https://youtu.be/qQM72SErmqs.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"33 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316539","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}

引用次数: 0

Seasonal Characterisation of Sonar Performance for Effective Underwater Surveillance in the Marmara Sea 马尔马拉海有效水下监测声纳性能的季节特征

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-10-18 DOI: 10.1049/rsn2.70085

Murat Murat, Ugur Kesen

{"title":"Seasonal Characterisation of Sonar Performance for Effective Underwater Surveillance in the Marmara Sea","authors":"Murat Murat, Ugur Kesen","doi":"10.1049/rsn2.70085","DOIUrl":"https://doi.org/10.1049/rsn2.70085","url":null,"abstract":"<p>This study analyses sonar performance for underwater object detection in four regions of the Marmara Sea, using oceanographic data from the Turkish Naval Forces and open source datasets. Simulations were conducted with LYBIN acoustic modelling software across four seasons (January, May, July and October), evaluating variable-depth sonar (VDS) and hull-mounted sonar (HMS) systems for coverage and detection performance. Results identified optimal sonar coverage zones, highlighting seasonal impacts on propagation, with temperature and salinity fluctuations directly influencing performance. Seasonal stratification in the Marmara Sea generates surface ducts and shadow zones that strongly constrain HMS performance, while VDS consistently mitigates these effects. Simulations demonstrate that VDS reduces shadowed areas by 25% across all seasons and regions, extending reliable detection ranges compared with HMS. The study provides a foundation for designing efficient underwater surveillance systems in the Marmara Sea, offering insights for optimising operational strategies. Future research should explore diverse marine conditions and sonar configurations to enhance detection capabilities.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards hybrid modeling with mechanistic and real-time data embed iterative co-optimization for industrial processes 面向工业过程机械与实时数据融合的混合建模与迭代协同优化

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-18 DOI: 10.1016/j.jprocont.2025.103567

Mingyu Liang, Yi Zheng, Shaoyuan Li

{"title":"Towards hybrid modeling with mechanistic and real-time data embed iterative co-optimization for industrial processes","authors":"Mingyu Liang, Yi Zheng, Shaoyuan Li","doi":"10.1016/j.jprocont.2025.103567","DOIUrl":"10.1016/j.jprocont.2025.103567","url":null,"abstract":"<div><div>This paper addresses the hybrid modeling challenges arising from incomplete mechanistic models and operational data noise interference in process industries, proposing a two-layer joint iterative optimization framework for updating parameters of hybrid models integrating mechanistic and data-driven models. The framework achieves real-time anomaly elimination through an outlier screening algorithm, while employing a bidirectional feedback algorithm to enable continuous collaboration and mutual constraints between mechanistic and data-driven models during parameter identification and iterative updates, ensuring robust hybrid model predictions. The proposed method resolves hybrid modeling and updating under conditions of mechanistic model information deficiency. Additionally, by incorporating model uncertainty and prior knowledge, it accomplishes a knowledge-incorporated hybrid modeling process, demonstrating significant practical value. Unlike conventional hybrid modeling approaches where mechanistic knowledge merely guides the modeling process, our method achieves dynamic co-evolution between mechanistic and data-driven models. This paper elaborates on three key aspects: (1) using mechanistic models to screen anomalous data; (2) incorporating mechanistic parameter uncertainty and prior knowledge through Bayesian methods to design knowledge-guided parameter updating method; (3) implementation details of the two-layer joint iterative optimization algorithm. Comparative experiments validate the method’s superior performance under multiple operating conditions and anomalies, demonstrating its scientific validity and practical value in dynamic optimization processes.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103567"},"PeriodicalIF":3.9,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324807","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}

引用次数: 0

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