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ClusterST: Clustering Spatial-Temporal Network for Traffic Forecasting

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第1期24卷 706-717页

作者： Luo, Guiyang Zhang, Hui Yuan, Quan Li, Jinglin Wang, Fei-Yue Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100876 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Innovat Ctr Parallel Vis Qingdao 266000 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China

Traffic forecasting aims to capture complex spatial-temporal dependencies and non-linear dynamics, which plays an indispensable role in intelligent transportation systems and other domains like neuroscience, climate, etc. Most recent works rely on graph convolutional networks (GCN) to model the dependencies and the dynamics. However, the over-smoothing issue of GCN would produce indistinguishable features among nodes, leading to poor expressivity and weak capability of modeling complex dependencies and dynamics. To address this issue, we present a novel clustering spatial-temporal (ClusterST) unit, which incorporates unsupervised learning into GCN for extracting discriminative features. Specifically, we first exploit a neural network to learn a dynamic clustering, i.e., learning to partition the neighbors of each node into clusters at each time step. Two probabilistic losses are proposed to improve the separability of clusters. Then, the extracted features of different clusters can be distinguished. Based on the dynamically formed clusters, a vanilla GCN is applied to aggregate features within each cluster. By purely exploiting such a ClusterST unit, large improvements over the state-of-the-art are achieved. Furthermore, ClusterST units with a different number of clusters can be regarded as basic components to construct an inception-like ClusterST network for going deeper. We evaluate the framework on two real-world large-scale traffic datasets and observe an average improvement of $18.19\%$ and $7.62\%$ over state-of-the-art baselines, respectively. The code and models will be publicly available.

关键词： Index Terms- Traffic forecasting graph convolutional network spatial-temporal networks over-smoothing

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A Self-Attention-Based Deep Reinforcement Learning Approach for AGV Dispatching systems

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2024年第6期35卷 7911-7922页

作者： Wei, Qinglai Yan, Yutian Zhang, Jie Xiao, Jun Wang, Cong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Hong Kong Appl Sci & Technol Res Inst Co Ltd Hong Kong Peoples R China

The automated guided vehicle (AGV) dispatching problem is to develop a rule to assign transportation tasks to certain vehicles. This article proposes a new deep reinforcement learning approach with a self-attention mechanism to dynamically dispatch the tasks to AGV. The AGV dispatching system is modeled as a less complicated Markov decision process (MDP) using vehicle-initiated rules to dispatch a workcenter to an idle AGV. In order to deal with the highly dynamical environment, the self-attention mechanism is introduced to calculate the importance of different information. The invalid action masking technique is performed to alleviate false actions. A multimodal structure is employed to mix the features of various sources. Comparative experiments are performed to show the effectiveness of the proposed method. The properties of the learned policies are also investigated under different environment settings. It is discovered that the policies explore and learn the properties of different systems, and also smooth the traffic congestion. Under certain environment settings, the policy converges to a heuristic rule that assigns the idle AGV to the workcenter with the shortest queue length, which shows the adaptiveness of the proposed method.

关键词： Automated guided vehicle (AGV) dispatching deep learning reinforcement learning (RL) self-attention

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Generalized Zero-Shot Learning via Implicit Attribute Composition

Generalized Zero-Shot Learning via Implicit Attribute Compos...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Zhou, Lei Liu, Yang Li, Qiang School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China College of Computer Science and Technology Zhejiang University Hangzhou China Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9798350337020

Zero-shot learning (ZSL) is an important but challenging task in computer vision that aims to identify unseen classes without matching training samples. Current cutting-edge ZSL methods based on locality focus on acquiring the explicit locality of distinguishing characteristics, which could face a lack of adequate supervision at the class attribute level. This paper introduces a novel approach called IAC, which aims to learn Implicit Attribute Composition for ZSL. This method is more comprehensive compared to attribute localization that solely focuses on class-level attribute supervision. IAC utilizes subspace representations that efficiently capture the inherent structure of high-dimensional image features. Then, we learn implicit attribute composition through subspace representation learning. The superiority of the proposed IAC compared to the state-of-the-art is demonstrated through sufficient experiments conducted on three commonly used ZSL datasets, CUB, SUN, and AwA2. © 2023 IEEE.

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ACP-Incorporated Perturbation-Resistant Neural Dynamics controller for Autonomous Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第4期9卷 4675-4686页

作者： Liufu, Ying Jin, Long Shang, Mingsheng Wang, Xingxia Wang, Fei-Yue Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100098 Peoples R China

Autonomous vehicle control systems are unavoidably influenced by diverse noise perturbations from the unpredictable external environment and internal system. In this consideration, based on the model predictive control (MPC) strategy, a perturbation-resistant neural dynamics (PRND) controller equipped with the noise-suppression ability for the path-tracking control of autonomous vehicles is newly designed in this paper, under the framework of artificial systems, computational experiments, and parallel execution (ACP). In addition, theoretical analyses show that the proposed ACP-incorporated PRND controller can behave with exponential convergence and strong robustness under different noise scenarios. Lastly, computational experiments are conducted and parallelly executed on the CarSim-Simulink platform and E-Car physical platform to demonstrate the effectiveness and superiority of the proposed controller. Overall, this paper provides a new perspective for designing neural-dynamics-based controllers for autonomous vehicles, thereby guaranteeing reliable control performance and effectively resisting noise perturbations.

关键词： Autonomous vehicles Vehicle dynamics Kinematics Task analysis control systems Robustness Predictive control Model predictive control (MPC) neural dynamics autonomous vehicles artificial systems computational experiments and parallel execution (ACP)

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Machine-learning-assisted single-vessel analysis of nanoparticle permeability in tumour vasculatures

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NATURE NANOTECHNOLOGY 2023年第6期18卷 657-+页

作者： Zhu, Mingsheng Zhuang, Jie Li, Zhe Liu, Qiqi Zhao, Rongping Gao, Zhanxia Midgley, Adam C. Qi, Tianyi Tian, Jingwei Zhang, Zhixuan Kong, Deling Tian, Jie Yan, Xiyun Huang, Xinglu Nankai Univ Coll Life Sci Key Lab Bioact Mat State Key Lab Med Chem BiolMinist Educ Tianjin Peoples R China Nankai Univ Frontiers Sci Ctr Cell Responses Tianjin Peoples R China Nankai Univ Sch Med Tianjin Peoples R China Univ Sci & Technol China Sch Cyberspace Sci & Technol Hefei Peoples R China Chinese Acad Sci Inst Automation Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Inst Biophys CAS Engn Lab Nanozymes Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

The central dogma that nanoparticle delivery to tumours requires enhanced leakiness of vasculatures is a topic of debate. To address this, we propose a single-vessel quantitative analysis method by taking advantage of protein-based nanoprobes and image-segmentation-based machine learning (nano-ISML). Using nano-ISML, >67,000 individual blood vessels from 32 tumour models were quantified, revealing highly heterogenous vascular permeability of protein-based nanoparticles. There was a >13-fold difference in the percentage of high-permeability vessels in different tumours and >100-fold penetration ability in vessels with the highest permeability compared with vessels with the lowest permeability. Our data suggest passive extravasation and transendothelial transport were the dominant mechanisms for high- and low-permeability tumour vessels, respectively. To exemplify the nano-ISML-assisted rational design of nanomedicines, genetically tailored protein nanoparticles with improved transendothelial transport in low-permeability tumours were developed. Our study delineates the heterogeneity of tumour vascular permeability and defines a direction for the rational design of next-generation anticancer nanomedicines. Using genetically tailored protein-based nanoprobes and taking advantage of image-segmentation-based machine learning, a high-throughput assessment of vascular permeability of individual blood vessels in 32 different tumours is quantified. These insights are valuable in developing personalized anticancer nanomedicine therapeutics and strategies modulating vascular permeability to treat tumours.

关键词： Drug delivery Nanobiotechnology Nanotechnology in cancer

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Convex Optimization-Based Trajectory Planning for Quadrotors Landing on Aerial Vehicle Carriers

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 138-150页

作者： Shen, Zhipeng Zhou, Guanzhong Huang, Hailong Huang, Chao Wang, Yutong Wang, Fei-Yue Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Engn Macau 999078 Peoples R China

This paper presents a novel trajectory planning algorithm for quadrotors landing on aerial vehicle carriers (AVCs). The algorithm involves a quadrotor trajectory planning method based on the lossless convexification (LC) theory and a sequential convex programming (SCP) method enabling quadrotors to autonomously land on both static and moving AVCs in a three-dimensional space. By incorporating landing cone constraints, the safety of the quadrotor during landing is ensured. The LC method transforms the original nonconvex optimal control problem (OCP) into a convex optimization problem, enabling the efficient computation of a 3-degree-of-freedom (3-DoF) safe landing trajectory. The designed SCP algorithm utilizes the 3-DoF trajectory as an initial guess and iteratively solves convex subproblems to obtain a safe, agile, and accurate landing trajectory for the complete 6-DoF quadrotor dynamics. Real-world experiments validate the effectiveness and real-time performance of the proposed method.

关键词： Quadrotors Trajectory Trajectory planning 3-DOF Heuristic algorithms 6-DOF Vehicle dynamics Aerial vehicle carrier convex optimization motion and trajectory planning quadrotor autonomous landing

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Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study

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CANCER 2023年第3期129卷 356-366页

作者： Gu, Jionghui Tong, Tong Xu, Dong Cheng, Fang Fang, Chengyu He, Chang Wang, Jing Wang, Baohua Yang, Xin Wang, Kun Tian, Jie Jiang, Tian'an Zhejiang Univ Affiliated Hosp 1 Coll Med Dept Ultrasound Hangzhou Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Zhejiang Canc Hosp Dept Ultrasound Canc Hosp Hangzhou Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Med & Engn Beijing Peoples R China Zhejiang Prov Key Lab Pulsed Elect Field Technol M Hangzhou Peoples R China Zhejiang Univ Affiliated Hosp 1 Coll Med Dept Ultrasound Hangzhou 310003 Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Background: Neoadjuvant chemotherapy (NAC) can downstage tumors and axillary lymph nodes in breast cancer (BC) patients. However, tumors and axillary response to NAC are not parallel and vary among patients. This study aims to explore the feasibility of deep learning radiomics nomogram (DLRN) for independently predicting the status of tumors and lymph node metastasis (LNM) after NAC. Methods: In total, 484 BC patients who completed NAC from two hospitals (H1: 297 patients in the training cohort and 99 patients in the validation cohort;H2: 88 patients in the test cohort) were retrospectively enrolled. The authors developed two deep learning radiomics (DLR) models for personalized prediction of the tumor pathologic complete response (PCR) to NAC (DLR-PCR) and the LNM status (DLR-LNM) after NAC based on pre-NAC and after-NAC ultrasonography images. Furthermore, they proposed two DLRNs (DLRN-PCR and DLRN-LNM) for two different tasks based on the clinical characteristics and DLR scores, which were generated from both DLR-PCR and DLR-LNM. Results: In the validation and test cohorts, DLRN-PCR exhibited areas under the receiver operating characteristic curves (AUCs) of 0.903 and 0.896 with sensitivities of 91.2% and 75.0%, respectively. DLRN-LNM achieved AUCs of 0.853 and 0.863, specificities of 82.0% and 81.8%, and negative predictive values of 81.3% and 87.2% in the validation and test cohorts, respectively. The two DLRN models achieved satisfactory predictive performance based on different BC subtypes. Conclusions: The proposed DLRN models have the potential to accurately predict the tumor PCR and LNM status after NAC. In this study, we proposed two deep learning radiomics nomogram models based on pre-neoadjuvant chemotherapy (NAC) and preoperative ultrasonography images for independently predicting the status of tumor and axillary lymph node (ALN) after NAC. A more comprehensive assessment of the patient's condition after NAC can be achieved by predicting the status of

关键词： breast cancer deep learning lymph node metastasis neoadjuvant chemotherapy pathologic complete response treatment decision ultrasonography

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PFedSim: An Efficient Federated control Method for Clustered Training

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 779-782页

作者： Chen, Yizhu Wang, Xiao Tian, Yonglin Wang, Fei Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Parallel Intelligence Res Ctr Qingdao 266000 Peoples R China

To further promote the implementation and application of artificial intelligence technology under data silos, federated ecology has defined a set of standardized and systematic solutions from upstream data to downstream applications from a systematic perspective. As the core of the federated ecology, federated control can transform the abstract tasks of federated management into concrete execution rules. Clustered training is a common cooperation rule for the personalized federated training task of multi-party cooperation. However, it is difficult to generalize in large-scale cooperation scenarios because of increased data transmission pressure and server storage burden. Therefore, this paper designs a personalized federated control method based on similarity measures(pFedSim) so that the server can accurately distribute the cluster model to clients, thereby reducing the transmission cost. Compared with the existing clustered training method IFCA, pFedSim transfers the selection rights of cluster models from the client to the server. Model selection preference, accurate distribution of cluster models. Experiments show that in the personalized training task with s clusters, the method can obtain the same model level with 1/s data transmission. It is better than the current optimal clustering training method Macro-Acc increased by 2.25%.

关键词： Federated control personalized federated training clustered training

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Neural-Dynamics-Based Active Steering control for Autonomous Vehicles with Noises 35

Neural-Dynamics-Based Active Steering Control for Autonomous...

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IEEE Intelligent Vehicles Symposium (IV)

作者： Ying Liufu Jin, Long Wang, Fei-Yue Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100098 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9798350348811;9798350348828

This paper introduces a neural-dynamics-based active steering control (NDASC) scheme developed under artificial systems, computational experiments, and parallel execution (ACP) framework, aimed at enhancing the stability and reliability of autonomous vehicles in noisy environments. Based on the Taylor expansion theorem, noises can be represented in the form of polynomials for the desired accuracy, and therefore polynomial noises can be viewed as a more generalized representation of noises. Then, the proposed NDASC scheme includes a model predictive active steering control (MPASC) strategy solved by a polynomial noise resilience neural dynamics (PNRND) model. Computational experiments parallelly implemented upon the CarSim-Simulink platform substantiate the effectiveness and robustness of the proposed NDASC scheme, providing significant theoretical and practical insights for control strategies of autonomous vehicles under various noisy environments.

关键词： Autonomous vehicles polynomial noise resilience neural dynamics (PNRND) model predictive active steering control (MPASC) noises

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Continuous-Time Linear Parallel Output Regulation

Continuous-Time Linear Parallel Output Regulation

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2021 China Automation Congress, CAC 2021

作者： Li, Hongyang Wei, Qinglai Wang, Fei-Yue The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426473

A novel parallel output regulation method is presented in this paper. The framework of the parallel output regulation problem is first introduced. Then, the existence of parallel controller is analyzed detailly in the situation that only the measurable information is obtained. Finally, the simulation example is given to show the performance of the presented method. © 2021 IEEE

关键词： Continuous time systems

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