IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-07-25 DOI: 10.1016/j.conengprac.2025.106499

Weihe Liang, Ruoyan Wang, Chunyan Wang, Wanzhong Zhao, Zhongkai Luan, Qikang Meng

{"title":"Event-triggered tube-based model predictive anti-rollover control for liquid tank trucks considering time-varying parameters","authors":"Weihe Liang, Ruoyan Wang, Chunyan Wang, Wanzhong Zhao, Zhongkai Luan, Qikang Meng","doi":"10.1016/j.conengprac.2025.106499","DOIUrl":"10.1016/j.conengprac.2025.106499","url":null,"abstract":"<div><div>Liquid tank trucks, primarily used for transporting hazardous chemicals, pose a high rollover risk due to the coupled dynamics of sloshing liquid and vehicle motion, and their rollover incidents can lead to severe safety hazards. The liquid sloshing introduces time-varying parameters that challenge the design of anti-rollover controllers. In response to this, this paper proposes an event-triggered, tube-based model predictive anti-rollover control strategy for liquid tank trucks that accounts for time-varying parameters. Firstly, to capture the time-varying characteristics resulting from liquid sloshing, this paper establishes a linear parameter-varying model. After analyzing the influence of liquid sloshing and time-varying parameters on rollover, a time-varying rollover index of the liquid tank truck is obtained using a parameter-state joint estimator for estimating difficult-to-obtain states and time-varying parameters. Then, this paper proposes a tube-based model predictive anti-rollover control strategy, which enhances the robustness of the control strategy to time-varying parameters in liquid tank trucks by incorporating system time-varying parameters within the tube. Furthermore, due to the limited bandwidth of the chassis CAN communication, an event-triggered mechanism is introduced to reduce communication resource consumption. Finally, this paper developed a hardware-in-the-loop anti-rollover test platform to validate the proposed strategy. The test results demonstrate that, under the proposed control strategy, the rollover angle of the liquid tank truck decreased by 35 %, and the lateral acceleration was reduced by 50 %. Additionally, the communication resource occupancy decreased by 39 %. The proposed anti-rollover control strategy effectively reduces the rollover risk and enhances the driving safety of liquid tank trucks.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106499"},"PeriodicalIF":5.4,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703672","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

DiffSBR: A diffusion model for session-based recommendation DiffSBR：基于会话的推荐的扩散模型

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2025-07-25 DOI: 10.1016/j.ipm.2025.104284

Zihe Wang , Bo Jin

{"title":"DiffSBR: A diffusion model for session-based recommendation","authors":"Zihe Wang , Bo Jin","doi":"10.1016/j.ipm.2025.104284","DOIUrl":"10.1016/j.ipm.2025.104284","url":null,"abstract":"<div><div>Session-based recommendation (SBR) focuses on recommending items to anonymous users within short interaction sequences. Existing solutions focus on modeling item representations as fixed embedding vectors within the discriminative learning paradigm, which fail to accurately capture the diverse preferences that user exhibit during dynamic decision-making. We argue that users in the anonymous environment can fundamentally be regarded as a <strong>normative implicit group</strong>, exhibiting both <strong>homogeneous preference</strong> and <strong>heterogeneous preference</strong> when selecting items. To tackle this, we propose a Diffusion Model for Session-based Recommendation (DiffSBR). Specifically, we first model the aforementioned user diverse preferences from both local and global views. Next, we introduce a cluster-aware diffusion model, which directly represents heterogeneous preference clusters as distribution through forward and reverse processes, while indirectly influencing homogeneous preference via the attention mechanism in the final prediction stage, thereby improving the learning of item and session representations and enhancing the next-item recommendation. Experimental results show that DiffSBR outperforms the strong baseline, demonstrating that this sampling-allocation approach accurately reflects the uncertainty and variability in user preferences.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 1","pages":"Article 104284"},"PeriodicalIF":7.4,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703791","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}

引用次数: 0

Privacy-Preserving Sparse Traffic Flow Prediction in IIoT: A Three-Tier Federated Learning Framework 工业物联网中保护隐私的稀疏交通流预测：一个三层联邦学习框架

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-07-25 DOI: 10.1109/jiot.2025.3592772

Jianhao Wei, Tingsen Zhou, Chuang Li, Xin Yao, Limei Liu, Yanhua Wen

引用次数: 0

GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative Models GradBias：揭示文本到图像生成模型中单词对偏见的影响

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-25 DOI: 10.1109/tpami.2025.3592901

Moreno D'Inca, Elia Peruzzo, Massimiliano Mancini, Xingqian Xu, Humphrey Shi, Nicu Sebe

引用次数: 0

Energy Efficiency Optimization for Integrated Sensing and Communications-aided Full Duplex MIMO System with Imperfect CSI 不完全CSI下集成传感与通信辅助全双工MIMO系统的能效优化

IF 6.8 2区计算机科学

IEEE Transactions on Vehicular Technology Pub Date : 2025-07-25 DOI: 10.1109/tvt.2025.3592910

Raviteja Allu, Mayur Katwe, Keshav Singh, Hyundong Shin

引用次数: 0

Symmetrical GaN-Based Vertical Module With Low Interference Integration and High Thermal Performance for PoL Converters 用于PoL转换器的低干扰集成和高热性能的对称gan垂直模块

IF 7.7 1区工程技术

IEEE Transactions on Industrial Electronics Pub Date : 2025-07-25 DOI: 10.1109/tie.2025.3577322

Longyang Yu, Shenglei Zhao, Xuejing Sun, Wei Mu, Xiufeng Song, Shuzhen You, Yue Hao, Jincheng Zhang

引用次数: 0

Double-Primary With Segmented-Secondary Linear Induction Motor for Mining Industry Conveyor Systems 矿用输送系统用分段二次直线感应电机

IF 7.7 1区工程技术

IEEE Transactions on Industrial Electronics Pub Date : 2025-07-25 DOI: 10.1109/tie.2025.3579109

Roberto A. H. de Oliveira, Frederico J. G. Trad, Felipe S. Costa, Richard M. Stephan, Antonio C. Ferreira, Ivan E. Chabu

引用次数: 0

Efficient Network Localization in the Presence of Likelihood Ambiguity 似然歧义存在下的高效网络定位

IF 6.3 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2025-07-25 DOI: 10.1109/lwc.2025.3592701

Zhenyu Liu, Girim Kwon, Xi Tian, Wenbo Ding, Yuhan Dong, Yuan Shen

引用次数: 0

A short-term load forecasting method considering multiple feature factors based on long short-term memory and an improved temporal convolutional network 基于长短期记忆和改进时间卷积网络的多特征因素短期负荷预测方法

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-07-25 DOI: 10.1016/j.engappai.2025.111649

Yu Mu, Lingrui Kong, Guoqiang Zheng, Zhonge Su, Guodong Wang

{"title":"A short-term load forecasting method considering multiple feature factors based on long short-term memory and an improved temporal convolutional network","authors":"Yu Mu, Lingrui Kong, Guoqiang Zheng, Zhonge Su, Guodong Wang","doi":"10.1016/j.engappai.2025.111649","DOIUrl":"10.1016/j.engappai.2025.111649","url":null,"abstract":"<div><div>In order to address the problems of multi-factor coupling difficulties and low prediction efficiency of existing short-term electricity load forecasting methods, in this paper a short-term load forecasting method is proposed that combines the maximum mutual information coefficient (MIC) algorithm and the Long Short-Term Memory (LSTM)-Improved Temporal Convolutional Network (ITCN) model. Second, based on the problem of low prediction efficiency of the Temporal Convolutional Network (TCN), the TCN was improved (ITCN) by using the single residual block structure and the parallel activation function structure. Finally, the LSTM-ITCN model is designed to extract the short-term temporal features of the given data using LSTM first, and extract the long-term temporal features of the given data using ITCN and make the final prediction. Comparison experiments with Convolutional Neural Network (CNN)-LSTM, CNN-Bidirectional Gated Recurrent Unit (BIGRU), and other prediction methods on different datasets are conducted, and the findings indicate that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>), and Running times values of the proposed method are improved by 10.56%, 10.48%, 8.45%, and 25.64%, respectively, which significantly improves the prediction accuracy and prediction efficiency.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696614","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

Strategy-proof mechanism based on dwarf mongoose optimization for task offloading in vehicle computing 基于矮猫鼬优化的车辆计算任务卸载防策略机制

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-07-25 DOI: 10.1016/j.future.2025.108027

Xi Liu , Jun Liu

{"title":"Strategy-proof mechanism based on dwarf mongoose optimization for task offloading in vehicle computing","authors":"Xi Liu , Jun Liu","doi":"10.1016/j.future.2025.108027","DOIUrl":"10.1016/j.future.2025.108027","url":null,"abstract":"<div><div>Along with intelligent vehicle (IV) development, IVCs can be used as mobile computing platforms to provide users with various services. The aim of this paper is to design an efficient task offloading mechanism to maximize group efficiency in vehicle computing. Considering that sensing data inherently support multi-user sharing, we introduce a resource-sharing model in which multiple users share sensing resources. To provide a scalable service, we propose auction-based dynamic pricing. To achieve a balance between quality and efficiency, the efficient task offloading mechanism proposed in this study is based on dwarf mongoose optimization. The initialization algorithm generates random, best-fit, and greedy allocations based on probability. Convergence characteristics are improved using a new scouting algorithm and a new babysitter algorithm, both of which also contribute to maintaining population diversity. We demonstrate that the proposed mechanism achieves strategy-proofness, group strategy-proofness, individual rationality, budget balance, and consumer sovereignty. The novelty consists in our showing how to design the strategy-proof mechanism based on swarm optimization. Furthermore, the approximate ratio of the proposed mechanism is analyzed. Experimental verifications are conducted to show the proposed mechanism shows good performance in different environments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"174 ","pages":"Article 108027"},"PeriodicalIF":6.2,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711237","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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