IEEE Transactions on Cognitive and Developmental Systems最新文献_第4页

Guest Editorial: Special Issue on Advancing Machine Intelligence With Neuromorphic Computing 特邀编辑：利用神经形态计算推进机器智能》特刊

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-10-14 DOI: 10.1109/TCDS.2024.3458488

Guoqi Li;Emre Neftci;Rong Xiao;Pablo Lanillos;Kaushik Roy

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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区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-10-14 DOI: 10.1109/TCDS.2024.3459314

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IEEE Transactions on Cognitive and Developmental Systems Information for Authors 电气和电子工程师学会《认知与发展系统》期刊为作者提供的信息

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-10-14 DOI: 10.1109/TCDS.2024.3459316

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IEEE Transactions on Cognitive and Developmental Systems Publication Information 电气和电子工程师学会认知与发展系统论文集》出版信息

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-10-14 DOI: 10.1109/TCDS.2024.3459312

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PDRL: Towards Deeper States and Further Behaviors in Unsupervised Skill Discovery by Progressive Diversity PDRL：渐进式多样性在无监督技能发现中的深层状态和进一步行为

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-10-02 DOI: 10.1109/TCDS.2024.3471645

Ziming He;Chao Song;Jingchen Li;Haobin Shi

{"title":"PDRL: Towards Deeper States and Further Behaviors in Unsupervised Skill Discovery by Progressive Diversity","authors":"Ziming He;Chao Song;Jingchen Li;Haobin Shi","doi":"10.1109/TCDS.2024.3471645","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3471645","url":null,"abstract":"We present progressive diversity reinforcement learning (PDRL), an unsupervised reinforcement learning (URL) method for discovering diverse skills. PDRL encourages learning behaviors that span multiple steps, particularly by introducing “deeper states”—states that require a longer sequence of actions to reach without repetition. To address the challenges of weak skill diversity and weak exploration in partially observable environments, PDRL employs two indications for skill learning to foster exploration and skill diversity, emphasizing each observation and subtrajectory's accuracy compared to its predecessor. Skill latent variables are represented by mappings from states or trajectories, helping to distinguish and recover learned skills. This dual representation promotes exploration and skill diversity without additional modeling or prior knowledge. PDRL also integrates intrinsic rewards through a combination of observations and subtrajectories, effectively preventing skill duplication. Experiments across multiple benchmarks show that PDRL discovers a broader range of skills compared to existing methods. Additionally, pretraining with PDRL accelerates fine-tuning in goal-conditioned reinforcement learning (GCRL) tasks, as demonstrated in Fetch robotic manipulation tasks.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"495-509"},"PeriodicalIF":5.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213661","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

Functional Connectivity Patterns Learning for EEG-Based Emotion Recognition 基于脑电图的情感识别的功能连接模式学习

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-09-30 DOI: 10.1109/TCDS.2024.3470248

Chongxing Shi;C. L. Philip Chen;Shuzhen Li;Tong Zhang

{"title":"Functional Connectivity Patterns Learning for EEG-Based Emotion Recognition","authors":"Chongxing Shi;C. L. Philip Chen;Shuzhen Li;Tong Zhang","doi":"10.1109/TCDS.2024.3470248","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3470248","url":null,"abstract":"Neuroscience research reveals that different emotions are associated with different functional connectivity structures of brain regions. However, many existing electroencephalography (EEG)-based emotion recognition methods use these connectivity patterns broadly without distinguishing between specific emotions. Additionally, the nonstationarity of EEG signals often results in high variations across different periods, leading models to extract time-specific features instead of emotional features. This article proposes a functional connectivity patterns learning network (FCPL) for EEG-based emotion recognition to address these challenges. FCPL includes a coefficient branch, a graph construction module, and a period domain adversarial module. These components capture individual characteristics and specific emotional connectivity patterns and reduce period-related variations, respectively. FCPL achieves state-of-the-art results: 42.04%/28.81% for seven-class subject-dependent/independent experiments on the MPED dataset, 97.45%/89.88% for subject-dependent/independent experiments on the SEED dataset, and 95.98%/96.19% for valence/arousal subject-dependent experiments and 67.90%/65.60% for valence/arousal subject-independent experiments on the DREAMER dataset. This work advances the exploration of functional connectivity structures in EEG signals from coarse-grained emotion-related patterns to fine-grained emotional distinctions, promoting neuroscience, and EEG-based emotion recognition technologies.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"480-494"},"PeriodicalIF":5.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213531","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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HDMTK: Full Integration of Hierarchical Decision-Making and Tactical Knowledge in Multiagent Adversarial Games HDMTK：多智能体对抗博弈中层次决策和战术知识的全面整合

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-09-30 DOI: 10.1109/TCDS.2024.3470068

Wei Li;Boling Hu;Aiguo Song;Kaizhu Huang

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Graph-Laplacian-Processing-Based Multimodal Localization Backend for Robots and Autonomous Systems 基于图拉普拉斯处理的机器人和自主系统多模态定位后端

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-09-26 DOI: 10.1109/TCDS.2024.3468712

Nikos Piperigkos;Christos Anagnostopoulos;Aris S. Lalos;Petros Kapsalas;Duong Van Nguyen

{"title":"Graph-Laplacian-Processing-Based Multimodal Localization Backend for Robots and Autonomous Systems","authors":"Nikos Piperigkos;Christos Anagnostopoulos;Aris S. Lalos;Petros Kapsalas;Duong Van Nguyen","doi":"10.1109/TCDS.2024.3468712","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3468712","url":null,"abstract":"Simultaneous localization and mapping (SLAM) for positioning of robots and autonomous systems (RASs) and mapping of their surrounding environments is a task of major significance in various applications. However, the main disadvantage of traditional SLAM is that the deployed backend modules suffer from accumulative error caused by sharp viewpoint changes, diverse weather conditions, etc. As such, to improve the localization accuracy of the moving agents, we propose a cost-effective and loosely coupled relocalization backend, deployed on top of original SLAM algorithms, which exploits the topologies of poses and landmarks generated either by camera, LiDAR, or mechanical sensors, to couple and fuse them. This novel fusion scheme enhances the decision-making ability and adaptability of autonomous systems, akin to human cognition, by elaborating graph Laplacian processing concept with Kalman filters. Initially designed for cooperative localization of active road users, this approach optimally combines multisensor information through graph signal processing and Bayesian estimation for self-positioning. Conducted experiments were focused on evaluating how our approach can improve the positioning of autonomous ground vehicles, as prominent examples of RASs equipped with sensing capabilities, in challenging outdoor environments. More specifically, experiments were carried out using the CARLA simulator to generate different types of driving trajectories and environmental conditions, as well as real automotive data captured by an operating vehicle in Langen, Germany. Evaluation study demonstrates that localization accuracy is greatly improved both in terms of overall trajectory error as well as loop closing accuracy for each sensor fusion configuration.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 2","pages":"436-453"},"PeriodicalIF":5.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761405","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

Touch Gesture Recognition-Based Physical Human–Robot Interaction for Collaborative Tasks 基于触摸手势识别的人机协作任务物理交互

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-09-24 DOI: 10.1109/TCDS.2024.3466553

Dawoon Jung;Chengyan Gu;Junmin Park;Joono Cheong

{"title":"Touch Gesture Recognition-Based Physical Human–Robot Interaction for Collaborative Tasks","authors":"Dawoon Jung;Chengyan Gu;Junmin Park;Joono Cheong","doi":"10.1109/TCDS.2024.3466553","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3466553","url":null,"abstract":"Human–robot collaboration (HRC) has recently attracted increasing attention as a vital component of next-generation automated manufacturing and assembly tasks, yet physical human–robot interaction (pHRI)—which is an inevitable component of collaboration—is often limited to rudimentary touches. This article therefore proposes a deep-learning-based pHRI method that utilizes predefined types of human touch gestures as intuitive communicative signs for collaborative tasks. To this end, a touch gesture network model is first designed upon the framework of the gated recurrent unit (GRU) network, which accepts a set of ground-truth dynamic responses (energy change, generalized momentum, and external joint torque) of robot manipulators under the action of known types of touch gestures and learns to predict the five representative touch gesture types and the corresponding link toward a random touch gesture input. After training the GRU-based touch gesture model using a collected dataset of dynamic responses of a robot manipulator, a total of 35 outputs (five gesture types with seven links each) is recognized with 96.94% accuracy. The experimental results of recognition accuracy correlated with the touch gesture types, and their strength results are shown to validate the performance and disclose the characteristics of the proposed touch gesture model. An example of an IKEA chair assembly task is also presented to demonstrate a collaborative task using the proposed touch gestures. By developing the proposed pHRI method and demonstrating its applicability, we expect that this method can help position physical interaction as one of the key modalities for communication in real-world HRC applications.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 2","pages":"421-435"},"PeriodicalIF":5.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761496","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

Enhancing Dimensional Image Emotion Detection With a Low-Resource Dataset via Two-Stage Training 基于两阶段训练的低资源数据集增强多维图像情感检测

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-09-23 DOI: 10.1109/TCDS.2024.3465602

SangEun Lee;Seoyun Kim;Yubeen Lee;Jufeng Yang;Eunil Park

{"title":"Enhancing Dimensional Image Emotion Detection With a Low-Resource Dataset via Two-Stage Training","authors":"SangEun Lee;Seoyun Kim;Yubeen Lee;Jufeng Yang;Eunil Park","doi":"10.1109/TCDS.2024.3465602","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3465602","url":null,"abstract":"Image emotion analysis has gained notable attention owing to the growing importance of computationally modeling human emotions. Most previous studies have focused on classifying the feelings evoked by an image into predefined emotion categories. Compared with these categorical approaches which cannot address the ambiguity and complexity of human emotions, recent studies have taken dimensional approaches to address these problems. However, there is still a limitation in that the number of dimensional datasets is significantly smaller for model training, compared with many available categorical datasets. We propose four types of frameworks that use categorical datasets to predict emotion values for a given image in the valence–arousal (VA) space. Specifically, our proposed framework is trained to predict continuous emotion values under the supervision of categorical labels. Extensive experiments demonstrate that our approach showed a positive correlation with the actual VA values of the dimensional dataset. In addition, our framework improves further when a small number of dimensional datasets are available for the fine-tuning process.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"455-464"},"PeriodicalIF":5.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213666","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