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Explanation guided cross-modal social image clustering

INFORMATION SCIENCES 2022年 593卷 1-16页

作者： Yan, Xiaoqiang Mao, Yiqiao Ye, Yangdong Yu, Hui Wang, Fei-Yue Zhengzhou Univ Sch Informat Engn Zhengzhou 450052 Peoples R China Univ Portsmouth Sch Creat Technol Portsmouth PO1 2DJ Hants England Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

The integration of visual and semantic information has been found to play a role in increasing the accuracy of social image clustering methods. However, existing approaches are limited by the heterogeneity gap between the visual and semantic modalities, and their performances significantly degrade due to the commonly sparse and incomplete tags in semantic modality. To address these problems, we propose a novel clustering framework to discover reasonable categories in unlab.led social images under the guidance of human explanations. First of all, a novel Explanation Generation Model (EGM) is proposed to automatically boost textual information for the sparse and incomplete tags based on an extra lexical database with human knowledge. Then, a novel clustering algorithm called Group Constrained Information Maximization (GCIM) is proposed to learn image categories. In this algorithm, a new type of constraint named group level side information is unprecedentedly defined to bridge the well-known heterogeneity gap between visual and textual modalities. Finally, an interactive draw-and-merge optimization method is proposed to ensure an optimal solution. Extensive experiments on several social image datasets including NUS-Wide, IAPRTC, MIRFlickr, ESP-Game and COCO demonstrate the superiority of the proposed approach to state-of-the-art baselines. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Social image clustering Human explanation Side information Information maximization Interactive optimization

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Pedestrian Trajectory Prediction by Modeling the Interactions using Social LSTM Extensions

Pedestrian Trajectory Prediction by Modeling the Interaction...

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Chinese Automation Congress (CAC)

作者： Zeze Si Hongxia Zhao Haina Tang Fenghua Zhu Yisheng Lv Hao Lu State Key Laboratory for Management and Control of Complex Systems Institute of Autamation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academic of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China

In many driving situations, human mobility is an important topic in trajectory prediction. Considering the pedestrian trajectory as a sequence generative task, a prediction algorithm based on Social Long Short-Term Memory (Social LSTM) is implemented. In order to simulate the social interaction between pedestrians, Social Pooling (S-Pooling) is used to aggregate the hidden state of pedestrians, while the attention mechanism is utilized to aggregate information differently according to the importance of surrounding pedestrians. Furthermore, Convolutional Neural Networks (CNN) is introduced into Social LSTM model to consider both the interaction between people and the characteristic of scene scale in the prediction process. Experiments are carried out against baseline methods, and the results demonstrated that combining Social LSTM with attention mechanism or CNN can improve the performance of pedestrian trajectory prediction.

关键词： Automation Aggregates Predictive models Prediction algorithms Trajectory Convolutional neural networks Task analysis

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ParaUDA: Invariant Feature Learning With Auxiliary Synthetic Samples for Unsupervised Domain Adaptation

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2022年第11期23卷 20217-20229页

作者： Zhang, Wenwen Wang, Jiangong Wang, Yutong Wang, Fei-Yue Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat 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 999078 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China

Recognizing and locating objects by algorithms are essential and challenging issues for intelligent Transportation systems. However, the increasing demand for much lab.led data hinders the further application of deep learning-based object detection. One of the optimal solutions is to train the target model with an existing dataset and then adapt it to new scenes, namely Unsupervised Domain Adaptation (UDA). However, most of existing methods at the pixel level mainly focus on adapting the model from source domain to target domain and ignore the essence of UDA to learn domain-invariant feature learning. Meanwhile, almost all methods at the feature level ignore to make conditional distributions matched for UDA while conducting feature alignment between source and target domain. Considering these problems, this paper proposes the ParaUDA, a novel framework of learning invariant representations for UDA in two aspects: pixel level and feature level. At the pixel level, we adopt CycleGAN to conduct domain transfer and convert the problem of original unsupervised domain adaptation to supervised domain adaptation. At the feature level, we adopt an adversarial adaption model to learn domain-invariant representation by aligning the distributions of domains between different image pairs with same mixture distributions. We evaluate our proposed framework in different scenes, from synthetic scenes to real scenes, from normal weather to challenging weather, and from scenes across cameras. The results of all the above experiments show that ParaUDA is effective and robust for adapting object detection models from source scenes to target scenes.

关键词： Adaptation models Representation learning Feature extraction Task analysis Semantics Generative adversarial networks Object detection Object detection unsupervised domain adaptation distribution alignment domain-invariant representation

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Parallel control for Continuous-Time Linear systems:A Case Study

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 919-928页

作者： Qinglai Wei Hongyang Li Fei-Yue Wang the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the University of Chinese Academy of Sciences Beijing 100049 Qingdao Academy of Intelligent Industries Qingdao 266109China the Institute of Systems Engineering Macao University of Science and Technology

In this paper,a new parallel controller is developed for continuous-time linear *** main contribution of the method is to establish a new parallel control law,where both state and control are considered as the *** structure of the parallel control is provided,and the relationship between the parallel control and traditional feedback controls is *** the situations that the systems are controllab.e and incompletely controllab.e,the properties of the parallel control law are *** parallel controller design algorithms are given under the conditions that the systems are controllab.e and incompletely ***,numerical simulations are carried out to demonstrate the effectiveness and applicability of the present *** Terms-Continuous-time linear systems,digital twin,parallel controller,parallel intelligence,parallel systems.

关键词： Index Terms—Continuous-time linear systems digital twin parallel controller parallel intelligence parallel systems

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Design of Simultaneous state and Unknown Input Filtering Algorithm for a Class of Nonlinear Stochastic systems with Multiple Sensors 8

Design of Simultaneous State and Unknown Input Filtering Alg...

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8th IEEE International Conference on Cloud Computing and Intelligence systems, CCIS 2022

作者： Hu, Zhibin Hu, Jun Chen, Cai Du, Junhua Harbin University of Science and Technology Department of Applied Mathematics Harbin150080 China Harbin University of Science and Technology School of Automation Harbin150080 China Harbin University of Science and Technology Heilongjiang Prov. Key Lab. of Optimization Control and Intelligent Analysis for Complex Systems Harbin150080 China Qiqihar University College of Science Qiqihar161006 China

ISBN: (纸本)9781665477352

In this paper, we investigate the simultaneous state and unknown input (SUI) filtering issue for a class of multi-sensor networked systems (MSNSs). The unknown input with no prior knowledge is introduced in the system state and output. The focus of this paper is to design the local filters with regard to the SUI, which can yield that the local upper bounds of the filtering error covariance for the SUI are derived at each instant. Moreover, the local filter gains of the SUI are designed such that the obtained upper bounds can be minimized. Finally, the proposed joint SUI algorithm is verified by using the simulation example. © 2022 IEEE.

关键词： Stochastic systems

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Multimodal Perception and Decision-Making systems for complex Roads Based on Foundation Models

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2024年第11期54卷 6561-6569页

作者： Fan, Lili Wang, Yutong Zhang, Hui Zeng, Changxian Li, Yunjie Gou, Chao Yu, Hui Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 518107 Peoples R China Univ Portsmouth Sch Creat Technol Portsmouth PO1 2DJ Hants England

Since the inception of Industry 5.0 in 2021, a growing number of researchers have begun to pay their attention to the revolutionary shift it brings. The principles of Industry 5.0, including human-centric, sustainability, and emphasis on ecological and social values, will become the new paradigm for future industrial development. In this transformative landscape, artificial intelligence (AI) plays a pivotal role, and foundation models based on ChatGPT are set to reshape the organizational structure of industries. In this article, we introduce a multimodal perception and decision-making system built upon a foundational model. This system integrates image and point cloud data to enhance perception accuracy and provide ample information for decision making. It is designed to achieve a deep integration of AI and human-centric autonomous driving within the context of Industry 5.0. We introduce a cross-domain learning approach in the system architecture, along with a model training method from foundation models to handle complex road conditions. The proposed method enables road drivable area segmentation on complex unstructured roads. To address the issue of increased variance caused by the residual structure employed in previous works, this article introduces a distribution correction module, which effectively mitigates this problem. Furthermore, to achieve high-performance perception systems in intricate road scenarios, we put forth a multimodal perception fusion method in this study. The experiments demonstrate the superiority of this approach over single-sensor perception. This work contributes to the ongoing discourse on the convergence of AI, human-centric values, and advanced driving systems within the framework of Industry 5.0.

关键词： Autonomous driving camera and four-dimensional (4-D) millimeter wave radar ChatGPT Industry 5.0 multimodal perception and decision making

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Supervised assisted deep reinforcement learning for emergency voltage control of power systems

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NEUROCOMPUTING 2022年第0期475卷 69-79页

作者： Li, Xiaoshuang Wang, Xiao Zheng, Xinhu Dai, Yuxin Yu, Zhihong Zhang, Jun Jason Bu, Guangquan Wang, Fei-Yue 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 Qingdao Acad Intelligent Ind Parallel Intelligence Res Ctr Qingdao 266109 Peoples R China Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China China Elect Power Res Inst State Key Lab Power Grid Safety & Energy Conserva Beijing 100192 Peoples R China

The increasing complexity of power systems makes existing deep reinforcement learning-based emergency voltage control methods face challenges in learning speed and data utilization efficiency. Meanwhile, the accumulated data containing expert experience and domain knowledge has not been fully utilized to improve the performance of the deep reinforcement learning methods. To address the above issues, a novel hybrid emergency voltage control method that combines expert experience and machine intelligence is proposed in this paper. Specifically, the expert experience in the off-line demonstration is extracted through a behavioral cloning model and the deep reinforcement learning method is applied to discover and learn new knowledge autonomously. A special supervised expert loss is designed to utilize the pre-trained behavioral cloning model to assist the self-learning process. The demonstration is dynamically updated during the training process such that the behavioral cloning model and the deep reinforcement learning model can facilitate each other continuously. Experiments are conducted on the open-source RLGC platform to validate the performance and the experimental results show that the proposed method can effectively improve the learning speed and the applicability of the model to different test situations. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Deep reinforcement learning Behavioral cloning Dynamic demonstration Emergency control

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PCUNet: A Context-Aware Deep Network for Coarse-to-Fine Point Cloud Completion

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IEEE SENSORS JOURNAL 2022年第15期22卷 15098-15110页

作者： Zhao, Meihua Xiong, Gang Zhou, Mengchu Shen, Zhen Liu, Sheng Han, Yunjun Wang, Fei-Yue 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 Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Cloud Comp Ctr Guangdong Engn Res Ctr 3D Printing & Intelligent Dongguan 523808 Peoples R China New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Newark NJ 07102 USA Qingdao Acad Intelligent Ind Intelligent Mfg Ctr Qingdao 266109 Peoples R China

Point cloud completion aims at predicting a complete 3D shape from an incomplete input. It has important applications in the fields of intelligent manufacturing, augmented reality, virtual reality, self-driving cars, and intelligent robotics. Although deep learning-based point cloud completion technology has developed rapidly in recent years, there are still unsolved problems. Previous approaches predict each point independently and ignore contextual information. And, they usually predict a complete 3D shape based on a global feature vector extracted from an incomplete input, which leads to missing of some fine-grained details. In this paper, motivated by the transposed convolution and the "UNet" structure in neural networks for image processing, we propose a context-aware deep network termed as PCUNet for coarse-to-fine point cloud completion. It adopts an encoder-decoder structure, in which the encoder follows the design of the relation-shape convolutional neural network (RS-CNN), and the decoder consists of fully-connected layers and two stacked decoder modules for predicting complete point clouds. The contributions are twofold. First, we design the decoder module as a coordinate-guided context-aware upsampling module, in which contextual information can be taken into full account by neighbor aggregation. Second, to preserve fine-grained details in the input, we propose attention-enhanced skip connections for effective information propagation from the encoder to the decoder. Experiments are conducted on the widely used PCN and KITTI datasets. The results show that our proposed approach achieves competitive performance compared to the existing state-of-the-art approaches in terms of the Chamfer distance and the computational complexity metrics.

关键词： Point cloud compression Three-dimensional displays Decoding Shape Feature extraction Sensors Neural networks Point cloud completion deep learning context-aware attention-enhanced skip connections

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Gait Planning and Motion control Based on Vrep Simulation for Quadruped Robot

Gait Planning and Motion Control Based on Vrep Simulation fo...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Linqi Zhou Zhihua Chen Jun Liu Zhi Liu Yumeng Chen Liting Zhang key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot’s foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper.

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Research on Attention-Based Predictive Model in Multi-Agent Reinforcement Learning

Research on Attention-Based Predictive Model in Multi-Agent ...

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2022 Chinese Automation Congress, CAC 2022

作者： Hong, Weixin Wu, Huaiyu Shen, Zhen Xiong, Gang Hu, Bin Dong, Xisong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China

ISBN: (纸本)9781665465335

Agent actions planning is a challenging problem in multi-agent reinforcement learning. Recent methods typically build their predictive models by full connection layers, but the shortage of utilizing action and observation information in complex multi-agent environment limits the performance of those methods. To address this issue, we propose a novel predictive model based on self-attention mechanism which can effectively use information in the environment. Specifically, we use the MADDPG algorithm to make a dataset where the input is the observations and actions and the output is the predicted observations and rewards. We also introduce the multi-head attention mechanism into the predictive model so that the predictive model can better combine different information for prediction. We perform extensive experiments to assess the proposed method and compare it with the representative baseline approach. Results demonstrate that our method outperforms competitors with higher accuracy in different tasks. Our implementation will be publicly availab.e. © 2022 IEEE.

关键词： Reinforcement learning

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