IEEE Transactions on Emerging Topics in Computational Intelligence最新文献

IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information 电气和电子工程师学会《计算智能新课题论文集》出版信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-10-02 DOI: 10.1109/TETCI.2024.3465291

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Guest Editorial Special Issue on Resource Sustainable Computational and Artificial Intelligence 资源可持续计算与人工智能特刊客座编辑

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-10-02 DOI: 10.1109/TETCI.2024.3463048

Joey Tianyi Zhou;Ivor W. Tsang;Yew Soon Ong

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IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors 电气和电子工程师学会《计算智能新课题论文集》（IEEE Transactions on Emerging Topics in Computational Intelligence）给作者的信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-10-02 DOI: 10.1109/TETCI.2024.3465295

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IEEE Computational Intelligence Society Information 电气和电子工程师学会计算智能学会信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-10-02 DOI: 10.1109/TETCI.2024.3465293

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IEEE Computational Intelligence Society Information 电气和电子工程师学会计算智能学会信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-07-23 DOI: 10.1109/TETCI.2024.3427473

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Energy-Efficient and Interpretable Multisensor Human Activity Recognition via Deep Fused Lasso Net 通过深度融合拉索网实现高能效、可解释的多传感器人类活动识别

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-07-23 DOI: 10.1109/TETCI.2024.3430008

Yu Zhou;Jingtao Xie;Xiao Zhang;Wenhui Wu;Sam Kwong

{"title":"Energy-Efficient and Interpretable Multisensor Human Activity Recognition via Deep Fused Lasso Net","authors":"Yu Zhou;Jingtao Xie;Xiao Zhang;Wenhui Wu;Sam Kwong","doi":"10.1109/TETCI.2024.3430008","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3430008","url":null,"abstract":"Utilizing data acquired by multiple wearable sensors can usually guarantee more accurate recognition for deep learning based human activity recognition. However, an increased number of sensors bring high processing cost, influencing real-time activity monitoring. Besides, existing methods rarely consider the interpretability of the recognition model in aspects of both the importance of the sensors and features, causing a gap between deep learning and their extendability in real-world scenario. In this paper, we cast the classical fused lasso model into a deep neural network, proposing a deep fused Lasso net (dfLasso-Net), which can perform sensor selection, feature selection and HAR in one end-to-end structure. Specifically, a two-level weight computing module (TLWCM) consisting of a senor weight net and a feature weight net is designed to measure the importance of sensors and features. In sensor weight net, spatial smoothness between physical channels within each sensor is considered to maximize the usage of selected sensors. And the feature weight net is able to maintain the physical meaning of the hand-crafted features through feature selection inside the sensors. By combining with the learning module for classification, HAR can be performed. We test dfLasso-Net on three multi-sensor based HAR datasets, demonstrating that dfLasso-Net achieves better recognition accuracy with the least number of sensors and provides good model interpretability by visualizing the weights of the sensors and features. Last but not least, dflasso-Net can be used as an effective filter-based feature selection approach with much flexibility.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3576-3588"},"PeriodicalIF":5.3,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376908","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}

引用次数: 0

IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information 电气和电子工程师学会《计算智能新课题论文集》出版信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-07-23 DOI: 10.1109/TETCI.2024.3427471

引用次数: 0

IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors 电气和电子工程师学会《计算智能新课题论文集》（IEEE Transactions on Emerging Topics in Computational Intelligence）给作者的信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-07-23 DOI: 10.1109/TETCI.2024.3427475

引用次数: 0

Alternating-Direction-Method of Multipliers-Based Adaptive Nonnegative Latent Factor Analysis 基于交替方向乘法的自适应非负潜因分析法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-07-22 DOI: 10.1109/TETCI.2024.3420735

Yurong Zhong;Kechen Liu;Shangce Gao;Xin Luo

{"title":"Alternating-Direction-Method of Multipliers-Based Adaptive Nonnegative Latent Factor Analysis","authors":"Yurong Zhong;Kechen Liu;Shangce Gao;Xin Luo","doi":"10.1109/TETCI.2024.3420735","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3420735","url":null,"abstract":"Large scale interaction data are frequently found in industrial applications related with Big Data. Due to the fact that few interactions commonly happen among numerous nodes in real scenes, such data can be quantified into a High-Dimensional and Incomplete (HDI) matrix where most entries are unknown. An alternating-direction-method-based nonnegative latent factor model can perform efficient and accurate representation leaning to an HDI matrix, while its multiple hyper-parameters greatly limit its scalability for real applications. Aiming at implementing a highly-scalable and efficient latent factor model, this paper adopts the principle of particle swarm optimization and the tree-structured parzen estimator algorithm to facilitate the hyper-parameter adaptation mechanism, thereby building an Alternating-direction-method-based Adaptive Nonnegative Latent Factor (A\u0000<sup>2</sup>\u0000NLF) model. Its theoretical convergence is rigorously proved. Empirical studies on several nonnegative HDI matrices from real applications demonstrate that the proposed A\u0000<sup>2</sup>\u0000NLF model obtains higher computational and storage efficiency than several state-of-the-art models, along with significant accuracy gain. Its hyper-parameter adaptation is implemented smoothly, thereby greatly boosting its scalability in real problems.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3544-3558"},"PeriodicalIF":5.3,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376995","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}

引用次数: 0

Efficient Online Planning and Robust Optimal Control for Nonholonomic Mobile Robot in Unstructured Environments 非结构化环境下非全局性移动机器人的高效在线规划和鲁棒性优化控制

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-07-17 DOI: 10.1109/TETCI.2024.3424527

Yingbai Hu;Wei Zhou;Yueyue Liu;Minghao Zeng;Weiping Ding;Shu Li;Guoxin Li;Zheng Li;Alois Knoll

{"title":"Efficient Online Planning and Robust Optimal Control for Nonholonomic Mobile Robot in Unstructured Environments","authors":"Yingbai Hu;Wei Zhou;Yueyue Liu;Minghao Zeng;Weiping Ding;Shu Li;Guoxin Li;Zheng Li;Alois Knoll","doi":"10.1109/TETCI.2024.3424527","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3424527","url":null,"abstract":"In complex environments where occupied and unknown areas exceed the free space, it is essential for robots to utilize efficient methods for environmental perception, trajectory planning, and trajectory tracking. This paper introduces the jump point search (JPS) algorithm as a global planning approach and integrates the complete trajectory and safe trajectory using convex decomposition for local planning purposes. We specifically formulate the planning process as a jerk optimization problem to reduce robot vibrations and improve stability. To address trajectory tracking challenges, we propose an innovative robust control Lyapunov function method. This method efficiently manages disturbances in mobile robot motion, enhancing stability. It considers input constraints such as angular and linear velocity limits, along with optimization metrics like minimal input effort. We utilize a proximal augmented Lagrangian method to solve the optimization problem related to trajectory planning and the robust control Lyapunov function. Through experiments involving different friction forces and torques, we validate the effectiveness of our proposed robust control Lyapunov function controller in managing unknown disturbances. This demonstrates its superior adaptability and robustness compared to conventional model control.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3559-3575"},"PeriodicalIF":5.3,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377138","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}

引用次数: 0