Pub Date : 2024-10-02DOI: 10.1109/TETCI.2024.3465291
{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information","authors":"","doi":"10.1109/TETCI.2024.3465291","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3465291","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"C2-C2"},"PeriodicalIF":5.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703867","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-02DOI: 10.1109/TETCI.2024.3463048
Joey Tianyi Zhou;Ivor W. Tsang;Yew Soon Ong
In Recent years, the rapid advancements in computational and artificial intelligence (C/AI) have led to successful applications across various disciplines, driven by neural networks and powerful computing hardware. However, these achievements come with a significant challenge: the resource-intensive nature of current AI systems, particularly deep learning models, results in substantial energy consumption and carbon emissions throughout their lifecycle. This resource demand underscores the urgent need to develop resource-constrained AI and computational intelligence methods. Sustainable C/AI approaches are crucial not only to mitigate the environmental impact of AI systems but also to enhance their role as tools for promoting sustainability in industries like reliability engineering, material design, and manufacturing.
{"title":"Guest Editorial Special Issue on Resource Sustainable Computational and Artificial Intelligence","authors":"Joey Tianyi Zhou;Ivor W. Tsang;Yew Soon Ong","doi":"10.1109/TETCI.2024.3463048","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3463048","url":null,"abstract":"In Recent years, the rapid advancements in computational and artificial intelligence (C/AI) have led to successful applications across various disciplines, driven by neural networks and powerful computing hardware. However, these achievements come with a significant challenge: the resource-intensive nature of current AI systems, particularly deep learning models, results in substantial energy consumption and carbon emissions throughout their lifecycle. This resource demand underscores the urgent need to develop resource-constrained AI and computational intelligence methods. Sustainable C/AI approaches are crucial not only to mitigate the environmental impact of AI systems but also to enhance their role as tools for promoting sustainability in industries like reliability engineering, material design, and manufacturing.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3196-3198"},"PeriodicalIF":5.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703865","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-02DOI: 10.1109/TETCI.2024.3465295
{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors","authors":"","doi":"10.1109/TETCI.2024.3465295","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3465295","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"C4-C4"},"PeriodicalIF":5.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703869","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-02DOI: 10.1109/TETCI.2024.3465293
{"title":"IEEE Computational Intelligence Society Information","authors":"","doi":"10.1109/TETCI.2024.3465293","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3465293","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"C3-C3"},"PeriodicalIF":5.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703866","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1109/TETCI.2024.3427473
{"title":"IEEE Computational Intelligence Society Information","authors":"","doi":"10.1109/TETCI.2024.3427473","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3427473","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"C3-C3"},"PeriodicalIF":5.3,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10607836","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
利用多个可穿戴传感器获取的数据通常可以保证基于深度学习的人类活动识别更加准确。然而，传感器数量的增加会带来高昂的处理成本，影响实时活动监测。此外，现有方法很少从传感器和特征的重要性两方面考虑识别模型的可解释性，导致深度学习与其在实际场景中的可扩展性之间存在差距。本文将经典的融合拉索模型转化为深度神经网络，提出了一种深度融合拉索网络（dfLasso-Net），它可以在一个端到端的结构中完成传感器选择、特征选择和HAR。具体来说，设计了一个由传感器权重网和特征权重网组成的两级权重计算模块（TLWCM）来衡量传感器和特征的重要性。在传感器权重网中，考虑了每个传感器内物理通道之间的空间平滑性，以最大限度地提高所选传感器的使用率。而特征权重网能够通过传感器内部的特征选择，保持手工创建特征的物理意义。通过与用于分类的学习模块相结合，可以执行 HAR。我们在三个基于多传感器的 HAR 数据集上测试了 dfLasso-Net，结果表明 dfLassoNet 以最少的传感器数量实现了更高的识别准确率，并通过可视化传感器和特征的权重提供了良好的模型可解释性。最后但并非最不重要的一点是，dflasso-Net 可以作为一种有效的基于滤波器的特征选择方法，具有很大的灵活性。
{"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}
Pub Date : 2024-07-23DOI: 10.1109/TETCI.2024.3427471
{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information","authors":"","doi":"10.1109/TETCI.2024.3427471","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3427471","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"C2-C2"},"PeriodicalIF":5.3,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10607837","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1109/TETCI.2024.3427475
{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors","authors":"","doi":"10.1109/TETCI.2024.3427475","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3427475","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"C4-C4"},"PeriodicalIF":5.3,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10607838","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1109/TETCI.2024.3420735
Yurong Zhong;Kechen Liu;Shangce Gao;Xin Luo
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 2 NLF) model. Its theoretical convergence is rigorously proved. Empirical studies on several nonnegative HDI matrices from real applications demonstrate that the proposed A 2 NLF 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.
{"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}
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.
{"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}
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