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IEEE International Conference on Robotics and Automation (ICRA)

作者： Chi Zhang Hengwang Zhao Chunxiang Wang Xuanlai Tang Ming Yang Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China KEENON Robotics Co. Ltd. China

Visual localization for mobile robots and intelligent vehicles in prior LiDAR maps can achieve high accuracy and low cost. However, algorithms for finding the cross-modal correspondences between images and LiDAR map points are not yet stable. In this paper, we propose a monocular visual localization system in prior LiDAR maps, which is based on the cross-modal registration to optimize the camera pose. To align the point clouds from vision and LiDAR map, a point-to-plane Iterative Closest Point algorithm utilizing semantic consistency is designed, and a decoupling optimization strategy is proposed to compute the affine transformation for the monocular scale ambiguity. Experiments on KITTI dataset show that utilizing the semantic consistency and geometric information of the map makes our system competitive with other methods. On the self-collected dataset, experiments on different light intensities demonstrate the robustness of the system in long-term localization tasks, and the ablation study demonstrates the effectiveness of the proposed algorithms.

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Attention-stacking adaptive fuzzy neural networks for autism diagnosis

Attention-stacking adaptive fuzzy neural networks for autism...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuzhan Zhang Lei Xiao Hui Huang Zhongyi Hu College of Computer Science and Artificial Intelligence Wenzhou University Institute of Intelligent Information System Wenzhou University Key Laboratory of Intelligent Image Processing and Analysis Wenzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The current diagnostic methods for Autism Spectrum Disorder (ASD) based on Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) face two significant challenges. Firstly, the functional connectivity networks (FCNs) of the brain are subject to uncertainty due to noise interference during the rs-fMRI acquisition process. Secondly, variations in equipment parameters across different imaging sites lead to diverse distributions of collected features. To address these issues, this paper introduces a novel Multi-Task Stacked Adaptive Fuzzy Neural Network (AT-STTSK) specifically designed for Autism Spectrum Disorder (ASD) classification. The proposed network seamlessly integrates a Multi-Output TSK (MO-TSK) fuzzy system, a Stacked Autoencoder (SAE), and a Transformer. This integration aims to reduce the impact of uncertainty factors, enhance the model's interpretability, and leverage the multi-head attention mechanisms to capture both local and global information, thus strengthening feature learning. Additionally, we explore the utilization of a Contrastive Learning (CL) strategy to further optimize the model. By incorporating these advancements, our AT-STTSK network offers a promising approach for improving the accuracy and reliability of ASD diagnosis based on rs-fMRI data. The validity of the method in this paper is verified on ABIDE dataset, and the result shows that the accuracy rate can reach 85.7% at most.

关键词： Representation learning Autism Accuracy Uncertainty Attention mechanisms Adaptive systems Noise Contrastive learning Transformers Fuzzy neural networks

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Adaptive Deep Reinforcement Learning for Critical Boundary Scenario Generation 43

Adaptive Deep Reinforcement Learning for Critical Boundary S...

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43rd Chinese control Conference, CCC 2024

作者： Zhou, Junjie Wang, Lin Wang, Xiaofan Meng, Qiang Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai202400 China Ministry of Education of China Key Laboratory of System Control and Information Process Shanghai202400 China National University of Singapore Department of Civil and Environmental Engineering Singapore117576 Singapore Shanghai Institute of Technology School of Electrical and Electronic Engineering Shanghai202400 China

ISBN: (纸本)9789887581581

Critical boundary scenarios play a vital role in the comprehensive testing of autonomous vehicles, as they can significantly expedite testing processes and reduce economic and time costs. To tackle the intricate, multidimensional, and diverse nature of intelligent driving scenarios, this paper proposes an innovative bi-level adaptive deep reinforcement learning (BADRL) approach aimed at generating authentic and diverse critical boundary scenarios. Leveraging naturalistic driving data, background agents are trained to exhibit naturalistic and realistic driving behaviors using a neural-based naturalistic driving behavior model. To address the intricacies of multi-interaction, high-dimensional environments, the scenario complexity model is introduced to evaluate the relative complexity between the traffic environment and the tested autonomous vehicle in real time. By integrating the scenario complexity model, the naturalistic driving behavior learning, intelligent driving testing, and critical boundary scenario generation are concatenated together to form a closed loop. BADRL facilitates the upper-level neural network in learning to adaptively increase the complexity of test scenarios, which are then fed into lower-level models to optimize the behavior of principal traffic participants, thereby generating naturalistic and critical boundary test scenarios. Extensive simulations were carried out to verify the efficacy of the BADRL technique in complex intersection environments. Results indicate that the BADRL approach boosts the efficiency of critical boundary scenario generation by approximately 10% compared to the state-of-the-art method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Deep reinforcement learning

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Diagnosis of Occupational Pneumoconiosis Based on Adaptive Feature Fusion and Multi-Class Loss Function

Diagnosis of Occupational Pneumoconiosis Based on Adaptive F...

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Chinese Automation Congress (CAC)

作者： Qian Zhao Yinan Guo Dunwei Gong China University of Mining and Technology Xuzhou China China University of Mining and Technology Beijing China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Qingdao University of Science and Technology Qingdao China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Pneumoconiosis, as the most prevalent and severe occupational disease in China, is categorized into four stages based on the area of small shadows in lungs. Previous diagnostic methods have limited accuracy for identifying different stages due to complex and diverse lesions of pneumoconiosis, as well as the difficulty of collecting rich X-ray images of patients. To address the issues, a novel diagnostic method based on adaptive feature fusion and multi-class loss function is proposed. First, the prototype network is introduced to establish a diagnostic model under the insufficient images, with the purpose of distinguishing the disease stage by the distance between the features of various samples. Following that, the multi-channel and multi-level feature fusion modules are proposed, respectively. These modules incorporate a gate-based adaptive selection mechanism to efficiently integrate the most important features from different channels and levels. Finally, the multi-class loss function is designed to train the diagnostic model of pneumoconiosis. The experimental results on a self-built dataset of chest X-ray radiographs demonstrate that the proposed method outperforms ten comparative methods in terms of comprehensive performance.

关键词： Training Adaptation models Accuracy Lungs Prototypes Logic gates Lesions X-ray imaging Diseases Biomedical imaging

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High Performance Distributed control Law for Large-Scale Linear systems: A Partitioned Distributed Observer Approach

arXiv

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arXiv 2024年

作者： Xu, Haotian Liu, Shuai Shi, Ling Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

In recent years, the distributed-observer-based distributed control law has shown powerful ability to arbitrarily approximate the centralized control performance. However, the traditional distributed observer requires each local observer to reconstruct the state information of the whole system, which is unrealistic for large-scale scenarios. To fill this gap, this paper develops a greedy-idea-based large-scale system partition algorithm, which can significantly reduce the dimension of local observers. Then, the partitioned distributed observer for large-scale systems is proposed to overcome the problem that the system dynamics are difficult to estimate due to the coupling between partitions. Furthermore, the two-layer Lyapunov analysis method is adopted and the dynamic transformation lemma of compact errors is proved, which solves the problem of analyzing stability of the error dynamic of the partitioned distributed observer. Finally, it is proved that the distributed control law based on the partitioned distributed observer can also arbitrarily approximate the control performance of the centralized control law, and the dimension of the local observer is greatly reduced compared with the traditional method. The simulation results show that when the similarity between the physical network and the communication network is about 80%, the local observer dimension is greatly reduced by 90% and the relative error between the performance of the distributed control law and that of the centralized control law is less than 1%. © 2024, CC BY.

关键词： Continuous time systems

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection 30

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Cheng, Long He, Haoyang Gu, Yan Liu, Qingzhi Zhao, Zhiming Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Wageningen University and Research Information Technology Group Netherlands University of Amsterdam Research Group of Multi-Scale Networked Systems Netherlands

ISBN: (纸本)9798331515966

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of 53.18% and costs by 61.98% compared to basic techniques, and achieving 20.26% and 25.71% improvements over the latest advanced methods, respectively. © 2024 IEEE.

关键词： Deep reinforcement learning

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Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement Learning

arXiv

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arXiv 2023年

作者： Wang, Siqi Li, Shaoyuan Yin, Li Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China The Institute of Systems Engineering Macau University of Science and Technology Taipa China

This paper investigates the problem of designing control policies that satisfy high-level specifications described by signal temporal logic (STL) in unknown, stochastic environments. While many existing works concentrate on optimizing the spatial robustness of a system, our work takes a step further by also considering temporal robustness as a critical metric to quantify the tolerance of time uncertainty in STL. To this end, we formulate two relevant control objectives to enhance the temporal robustness of the synthesized policies. The first objective is to maximize the probability of being temporally robust for a given threshold. The second objective is to maximize the worst-case spatial robustness value within a bounded time shift. We use reinforcement learning to solve both control synthesis problems for unknown systems. Specifically, we approximate both control objectives in a way that enables us to apply the standard Q-learning algorithm. Theoretical bounds in terms of the approximations are also derived. We present case studies to demonstrate the feasibility of our approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Combining Transformer and Convolutional Neural Network for Smoke Detection 9th

Combining Transformer and Convolutional Neural Network for...

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9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Gong, Yafei Lian, Xinkang Ma, Xuanchao Xia, Zhifang Zhou, Chengxu Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China National Information Center Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning Jinzhou China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

Smoke detection plays a crucial role in the safety production of petrochemical enterprises and fire prevention. Image-based machine learning and deep learning methods have been widely studied. Recently, many works have applied the transformer to solve problems faced by computer vision tasks (such as classification and object detection). To our knowledge, there are few studies using the transformer structure to detect smoke. In order to research the application potential and improve the performance of the transformer in the smoke detection field, we propose a model consisting of two transformer encoders and a convolutional neural network (CNN) module. The first transformer encoder can be used to establish the global relationship of an image, and the CNN structure can provide additional local information to the transformer. The fusion of global information and local information is conducive to the second transfer encoder to make better decisions. Experiments results on large-size dataset for industrial smoke detection illustrate the effectiveness of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smoke

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HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios

arXiv

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arXiv 2024年

作者： Jiang, Mingyang Li, Yueyuan Zhang, Songan Chen, Siyuan Wang, Chunxiang Yang, Ming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China

Automated parking stands as a highly anticipated application of autonomous driving technology. However, existing path planning methodologies fall short of addressing this need due to their incapability to handle the diverse and complex parking scenarios in reality. While non-learning methods provide reliable planning results, they are vulnerable to intricate occasions, whereas learning-based ones are good at exploration but unstable in converging to feasible solutions. To leverage the strengths of both approaches, we introduce Hybrid pOlicy Path plannEr (HOPE). This novel solution integrates a reinforcement learning agent with Reeds-Shepp curves, enabling effective planning across diverse scenarios. HOPE guides the exploration of the reinforcement learning agent by applying an action mask mechanism and employs a transformer to integrate the perceived environmental information with the mask. To facilitate the training and evaluation of the proposed planner, we propose a criterion for categorizing the difficulty level of parking scenarios based on space and obstacle distribution. Experimental results demonstrate that our approach outperforms typical rule-based algorithms and traditional reinforcement learning methods, showing higher planning success rates and generalization across various scenarios. We also conduct real-world experiments to verify the practicability of HOPE. The code for our solution is openly available on https://***/jiamiya/HOPE. Copyright © 2024, The Authors. All rights reserved.

关键词： Parking

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Polarized Modulation for RIS-Aided Vehicular Communication Networks

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IEEE Internet of Things Journal 2025年第12期12卷 18616-18627页

作者： Shang, Pingping Xie, Longhui Li, Jun Feng, Li Hai, Han Xu, Binbin Li, Jie Yu, Ling Hubei University of Technology School of Electrical and Electronic Engineering Wuhan430068 China Hubei University of Technology Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan430068 China Guangzhou University Research Center of Intelligent Communication Engineering School of Electronics and Communication Engineering Guangzhou510006 China Key Laboratory of On-Chip Communication and Sensor Chip of Guangdong Higher Education Institutes Guangzhou510006 China Donghua University School of Information Science and Technology Shanghai201620 China Maritime Communication Research Institute Wuhan430068 China Guangdong Provincial Hospital of Chinese Medicine Guangzhou510120 China State Key Laboratory of Traditional Chinese Medicine Syndrome Guangzhou510120 China

The reconfigurable intelligent surface (RIS) stands as an emergent technique poised to revolutionize future vehicular communication. In order to facilitate the application of RIS in vehicular communication, this paper proposes two effective RIS-aided schemes tailored for polarization modulation (PM) aiming to achieve remarkable spectral efficiency. Specifically, Scheme I proposes a novel index modulation (IM) aided PM-RIS framework that innovatively encodes information across three distinct dimensions, the indices of dual-polarized antennas (DP-AEs), PM symbols and the reflecting surface indices of the RIS. Scheme II employs a deep neural network method to analyze the channel state information (CSI) for transmit antenna selection, that enhances the reliability and interference immunity in dynamic conditions while reducing computational complexity. Furthermore, a stepwise estimation (SE) method is introduced at the detection stage, which significantly reduces the complexity associated with information decoding. The simulation results highlight the superiority of the proposed system. Compared with traditional PM systems, it demonstrates enhanced performance in terms of spectral efficiency, channel stability, and bit error rate (BER). © 2014 IEEE.

关键词： Signal modulation

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