IEEE transactions on artificial intelligence最新文献_第4页

Safe Multiagent Reinforcement Learning With Bilevel Optimization in Autonomous Driving

IEEE transactions on artificial intelligence Pub Date : 2024-11-13 DOI: 10.1109/TAI.2024.3497919

Zhi Zheng;Shangding Gu

{"title":"Safe Multiagent Reinforcement Learning With Bilevel Optimization in Autonomous Driving","authors":"Zhi Zheng;Shangding Gu","doi":"10.1109/TAI.2024.3497919","DOIUrl":"https://doi.org/10.1109/TAI.2024.3497919","url":null,"abstract":"Ensuring safety in multiagent reinforcement learning (MARL), particularly when deploying it in real-world applications such as autonomous driving, emerges as a critical challenge. To address this challenge, traditional safe MARL methods extend MARL approaches to incorporate safety considerations, aiming to minimize safety risk values. However, these safe MARL algorithms often fail to model other agents and lack convergence guarantees, particularly in dynamically complex environments. In this study, we propose a safe MARL method grounded in a Stackelberg model with bilevel optimization, for which convergence analysis is provided. Derived from our theoretical analysis, we develop two practical algorithms, namely constrained Stackelberg Q-learning (CSQ) and constrained Stackelberg multiagent deep deterministic policy gradient (CS-MADDPG), designed to facilitate MARL decision-making in some simulated autonomous driving applications such as traffic management. To evaluate the effectiveness of our algorithms, we developed a safe MARL autonomous driving benchmark and conducted experiments on challenging autonomous driving scenarios, such as merges, roundabouts, intersections, and racetracks. The experimental results indicate that our algorithms, CSQ and CS-MADDPG, outperform several strong MARL baselines, such as Bi-AC, MACPO, and MAPPO-L, regarding reward and safety performance.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 4","pages":"829-842"},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IEEE Transactions on Artificial Intelligence Publication Information IEEE Transactions on Artificial Intelligence 出版信息

IEEE transactions on artificial intelligence Pub Date : 2024-11-12 DOI: 10.1109/TAI.2024.3489337

引用次数: 0

Semisupervised Breast MRI Density Segmentation Integrating Fine and Rough Annotations

IEEE transactions on artificial intelligence Pub Date : 2024-11-11 DOI: 10.1109/TAI.2024.3491693

Tianyu Xie;Yue Sun;Hongxu Yang;Shuo Li;Jinhong Song;Qimin Yang;Hao Chen;Mingxiang Wu;Tao Tan

{"title":"Semisupervised Breast MRI Density Segmentation Integrating Fine and Rough Annotations","authors":"Tianyu Xie;Yue Sun;Hongxu Yang;Shuo Li;Jinhong Song;Qimin Yang;Hao Chen;Mingxiang Wu;Tao Tan","doi":"10.1109/TAI.2024.3491693","DOIUrl":"https://doi.org/10.1109/TAI.2024.3491693","url":null,"abstract":"This article introduces an enhanced teacher–student model featuring a novel Vnet architecture that integrates high-pass and low-pass filters to improve the segmentation of breast magnetic resonance imaging (MRI) images. The model effectively utilizes finely annotated, roughly annotated, and unannotated data to achieve precise breast tissue density segmentation. The teacher–student framework incorporates three specialized Vnet networks, each tailored to different types of annotations. By integrating cosine contrast loss functions between finely and roughly annotated models, and innovatively applying high-pass and low-pass filters within the Vnet architecture, the segmentation performance is significantly enhanced. This hybrid filtering approach enables the model to capture both fine-grained and coarse structural details, leading to more accurate segmentation across various MRI image datasets. Experimental results demonstrate the superiority of the proposed method, achieving Dice values of 0.833 on the finely annotated Shenzhen dataset and 0.780 on the Duke dataset, using 15 finely annotated, 15 roughly annotated, and 58 unlabeled samples provided by Shenzhen People's Hospital. These findings underscore its potential clinical application in breast density assessment.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"690-699"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Two-Level Neural-RL-Based Approach for Hierarchical Multiplayer Systems Under Mismatched Uncertainties

IEEE transactions on artificial intelligence Pub Date : 2024-11-08 DOI: 10.1109/TAI.2024.3493833

Xiangnan Zhong;Zhen Ni

{"title":"A Two-Level Neural-RL-Based Approach for Hierarchical Multiplayer Systems Under Mismatched Uncertainties","authors":"Xiangnan Zhong;Zhen Ni","doi":"10.1109/TAI.2024.3493833","DOIUrl":"https://doi.org/10.1109/TAI.2024.3493833","url":null,"abstract":"AI and reinforcement learning (RL) have attracted great attention in the study of multiplayer systems over the past decade. Despite the advances, most of the studies are focused on synchronized decision-making to attain Nash equilibrium, where all the players take actions simultaneously. On the other hand, however, in complex applications, certain players may have an advantage in making sequential decisions and this situation introduces a hierarchical structure and influences how other players respond. The control design for such system is challenging since it relies on solving the coupled Hamilton–Jacobi equation. The situation becomes more difficult when the learning process is exposed to complex uncertainties with unreliable data being exchanged. Therefore, in this article, we develop a new learning-based control approach for a class of nonlinear hierarchical multiplayer systems subject to mismatched uncertainties. Specifically, we first formulate this new problem as a multiplayer Stackelberg–Nash game in conjunction with the hierarchical robust–optimal transformation. Theoretical analysis confirms the equivalence of this transformation and ensures that the designed control policies can achieve stable equilibrium. Then, a two-level neural-RL-based approach is developed to automatically and adaptively learn the solutions. The stability of this online learning process is also provided. Finally, two numerical examples are presented to demonstrate the effectiveness of the developed learning-based robust control design.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"759-772"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NL-CoWNet: A Deep Convolutional Encoder–Decoder Architecture for OCT Speckle Elimination Using Nonlocal and Subband Modulated DT-CWT Blocks

IEEE transactions on artificial intelligence Pub Date : 2024-11-08 DOI: 10.1109/TAI.2024.3491935

P. S. Arun;Bibin Francis;Varun P. Gopi

{"title":"NL-CoWNet: A Deep Convolutional Encoder–Decoder Architecture for OCT Speckle Elimination Using Nonlocal and Subband Modulated DT-CWT Blocks","authors":"P. S. Arun;Bibin Francis;Varun P. Gopi","doi":"10.1109/TAI.2024.3491935","DOIUrl":"https://doi.org/10.1109/TAI.2024.3491935","url":null,"abstract":"Optical coherence tomography (OCT), a noninvasive diagnostic technology for identifying and treating various ocular diseases, encounters a loss of image quality due to the introduction of speckles during the image creation process, compromising the precision of disease diagnosis. Researchers have proposed numerous deep convolutional networks to address speckle artifacts in OCT images. This article presents a novel deep convolutional encoder–decoder framework called NL-CoWNet for speckle elimination in OCT images. This despeckling architecture consists of an encoder network having the topology of ResNet34, whose certain feature vectors are passed through nonlocal (NL) neural network blocks and a novel subband modulated dual-tree complex wavelet (CoW) transform (DT-CWT) blocks, followed by a decoder unit with upsampling layers and channel-wise squeeze and excitation (CSE) convolutional blocks. Our network architecture has been validated after numerous ablation studies. Qualitative and quantitative assessments with contemporary and established methodologies have proven that NL-CoWNet excels conspicuously in speckle removal while preserving the structural features of the image.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"700-709"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Feature Unsupervised Domain Adaptation for Time-Series Classification

IEEE transactions on artificial intelligence Pub Date : 2024-11-06 DOI: 10.1109/TAI.2024.3491948

Nannan Lu;Tong Yan;Song Zhu;Jiansheng Qian;Min Han

{"title":"Deep Feature Unsupervised Domain Adaptation for Time-Series Classification","authors":"Nannan Lu;Tong Yan;Song Zhu;Jiansheng Qian;Min Han","doi":"10.1109/TAI.2024.3491948","DOIUrl":"https://doi.org/10.1109/TAI.2024.3491948","url":null,"abstract":"Unsupervised domain adaptation (UDA) for time series classification (TSC) is an important but challenging task. In the process of UDA, feature learning is most critical. Most of the existing works in this area are based on learning domain-invariant feature representation of data with help of some restriction such as MMD. However, they ignored that the mutual effects between the pretrained network and the downstream target network was also conducive to the learning of domain-invariant features. In this article, we propose a deep feature unsupervised domain adaptation (DFUDA) for time series classification. First, we pretrain a network based on consistency learning to ensure the invariant feature extraction from the source and target domains. Then, we propose an end-to-end unsupervised domain adaptation, which includes the layer matching and the unsupervised domain adaptation to promote more confident knowledge transfer. Finally, the pretrained network receives feedback of the domain adaptation's performance. To verify the effectiveness of the proposed method, we perform the comprehensive experiments on fault diagnosis datasets and human activity recognition datasets. The results show that DFUDA outperforms the state of the arts methods for both scenarios.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"725-737"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced LiDAR-Based Localization via Multiresolution Iterative Closest Point Algorithms and Feature Extraction

IEEE transactions on artificial intelligence Pub Date : 2024-11-05 DOI: 10.1109/TAI.2024.3491950

Yecheng Lyu;Xinkai Zhang;Feng Tao

{"title":"Enhanced LiDAR-Based Localization via Multiresolution Iterative Closest Point Algorithms and Feature Extraction","authors":"Yecheng Lyu;Xinkai Zhang;Feng Tao","doi":"10.1109/TAI.2024.3491950","DOIUrl":"https://doi.org/10.1109/TAI.2024.3491950","url":null,"abstract":"Vehicle localization is a critical component in autonomous driving systems, and light detection and ranging (LiDAR)-based methods have become increasingly popular for this task. In this article, we present a novel vehicle localization approach based on the point cloud map generated from LiDAR data. In particular, our approach first uses semantic segmentation and feature point extraction techniques to create an efficient feature point map and a long-lasting map from LiDAR sequences with corresponding poses. We then introduce a map-based online localization method that achieves precise vehicle localization using both LiDAR scans and the two point cloud maps, along with a multiresolution ICP strategy. Comprehensive evaluations are conducted on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) odometry dataset and the collected results demonstrate superior performance over the existing literature in both odometry metrics and absolute translation error. Our multiresolution iterative closest point (ICP)-based method holds significant potential for map-based vehicle localization, offering promising prospects for application in autonomous driving and associated domains.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"738-746"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Swin-MGNet: Swin Transformer Based Multiview Grouping Network for 3-D Object Recognition

IEEE transactions on artificial intelligence Pub Date : 2024-11-05 DOI: 10.1109/TAI.2024.3492163

Xin Ning;Limin Jiang;Weijun Li;Zaiyang Yu;Jinlong Xie;Lusi Li;Prayag Tiwari;Fernando Alonso-Fernandez

{"title":"Swin-MGNet: Swin Transformer Based Multiview Grouping Network for 3-D Object Recognition","authors":"Xin Ning;Limin Jiang;Weijun Li;Zaiyang Yu;Jinlong Xie;Lusi Li;Prayag Tiwari;Fernando Alonso-Fernandez","doi":"10.1109/TAI.2024.3492163","DOIUrl":"https://doi.org/10.1109/TAI.2024.3492163","url":null,"abstract":"Recent developments in Swin Transformer have shown its great potential in various computer vision tasks, including image classification, semantic segmentation, and object detection. However, it is challenging to achieve desired performance by directly employing the Swin Transformer in multiview 3-D object recognition since the Swin Transformer independently extracts the characteristics of each view and relies heavily on a subsequent fusion strategy to unify the multiview information. This leads to the problem of the insufficient extraction of interdependencies between the multiview images. To this end, we propose an aggregation strategy integrated into the Swin Transformer to reinforce the connections between internal features across multiple views, thus leading to a complete interpretation of isolated features extracted by the Swin Transformer. Specifically, we utilize Swin Transformer to learn view-level feature representations from multiview images and then calculate their view discrimination scores. The scores are employed to assign the view-level features to different groups. Finally, a grouping and fusion network is proposed to aggregate the features from view and group levels. The experimental results indicate that our method attains state-of-the-art performance compared with prior approaches in multiview 3-D object recognition tasks. The source code is available at <uri>https://github.com/Qishaohua94/DEST</uri>.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"747-758"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiattribute Deep CNN-Based Approach for Detecting Medicinal Plants and Their Use for Skin Diseases

IEEE transactions on artificial intelligence Pub Date : 2024-11-05 DOI: 10.1109/TAI.2024.3491938

Prachi Dalvi;Dhananjay R. Kalbande;Surendra Singh Rathod;Harshal Dalvi;Amey Agarwal

{"title":"Multiattribute Deep CNN-Based Approach for Detecting Medicinal Plants and Their Use for Skin Diseases","authors":"Prachi Dalvi;Dhananjay R. Kalbande;Surendra Singh Rathod;Harshal Dalvi;Amey Agarwal","doi":"10.1109/TAI.2024.3491938","DOIUrl":"https://doi.org/10.1109/TAI.2024.3491938","url":null,"abstract":"Skin health is a critical concern for humans, especially in geographical areas where environmental conditions and lifestyle factors adversely affect their condition, leading to a prevalence of skin diseases. This issue is exacerbated in rural regions, like parts of India, where a notable dermatologist shortage exists, leading to overlooked skin diseases. In response, the use of medicinal plants for dermatological purposes has been a longstanding tradition. However, traditional plant identification often relies on a single attribute, such as leaves or flowers, which can be unreliable due to seasonal variations. This article introduces a novel approach for accurately identifying medicinal plants using a multiattribute deep convolutional neural network. This approach aims to bridge the gap in healthcare access by empowering individuals to recognize and utilize these plants effectively. Our objective is to develop a robust deep CNN model trained on a diverse dataset of images encompassing leaves, trunks, and seeds of medicinal plants associated with skin health. Our findings demonstrate that the model achieves high accuracy in plant identification, effectively addressing the limitations of single-attribute methods. This research not only contributes to the field of medicinal plant classification but also empowers individuals to make informed decisions about their skin health while preserving valuable traditional knowledge.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"710-724"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Better Accuracy-Efficiency Trade-Offs: Dynamic Activity Inference via Collaborative Learning From Various Width-Resolution Configurations 迈向更好的准确性和效率的权衡：通过协作学习从各种宽度分辨率配置的动态活动推断

IEEE transactions on artificial intelligence Pub Date : 2024-11-04 DOI: 10.1109/TAI.2024.3489532

Lutong Qin;Lei Zhang;Chengrun Li;Chaoda Song;Dongzhou Cheng;Shuoyuan Wang;Hao Wu;Aiguo Song

{"title":"Towards Better Accuracy-Efficiency Trade-Offs: Dynamic Activity Inference via Collaborative Learning From Various Width-Resolution Configurations","authors":"Lutong Qin;Lei Zhang;Chengrun Li;Chaoda Song;Dongzhou Cheng;Shuoyuan Wang;Hao Wu;Aiguo Song","doi":"10.1109/TAI.2024.3489532","DOIUrl":"https://doi.org/10.1109/TAI.2024.3489532","url":null,"abstract":"Recently, deep neural networks have triumphed over a large variety of human activity recognition (HAR) applications on resource-constrained mobile devices. However, most existing works are static and ignore the fact that the computational budget usually changes drastically across various devices, which prevent real-world HAR deployment. It still remains a major challenge: how to adaptively and instantly tradeoff accuracy and latency at runtime for on-device activity inference using time series sensor data? To address this issue, this article introduces a new collaborative learning scheme by training a set of subnetworks executed at varying network widths when fueled with different sensor input resolutions as data augmentation, which can instantly switch on-the-fly at different width-resolution configurations for flexible and dynamic activity inference under varying resource budgets. Particularly, it offers a promising performance-boosting solution by utilizing self-distillation to transfer the unique knowledge among multiple width-resolution configuration, which can capture stronger feature representations for activity recognition. Extensive experiments and ablation studies on three public HAR benchmark datasets validate the effectiveness and efficiency of our approach. A real implementation is evaluated on a mobile device. This discovery opens up the possibility to directly access accuracy-latency spectrum of deep learning models in versatile real-world HAR deployments. Code is available at \u0000<uri>https://github.com/Lutong-Qin/Collaborative_HAR</uri>\u0000.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6723-6738"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0