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42nd Chinese control Conference, CCC 2023

作者： Liu, Yaojun Liu, Xingwen Tashi, Nyima College of Electrical Engineering Southwest Minzu University Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission Sichuan Chengdu610225 China Engineering Research Center for Tibetan Information Processing School of Information Science and Technology Tibet University Lhasa850000 China

ISBN: (纸本)9789887581543

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavior, memory-based research on evolutionary games has proliferated in recent years. In the present paper, inspired by the well-known 'Stanford marshmallow experiment' (SME), we proposed a multi-memory mechanism in Snowdrift game (SDG) on spatial lattice networks whose core lies in what follows: Players are divided into two groups according to individual self-control;different memory lengths are set by group and by time in a single realization. The simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary game. More specifically, as memory length increases to an intermediate length, the cooperation level increases as well. For mechanism with short memory length, the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative behavior. These findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Behavioral research

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City-wide Solar Radiation Potential Analysis by Coupling Physical Modelling and Machine Learning

City-wide Solar Radiation Potential Analysis by Coupling Phy...

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2024 Symposium on Spatial Information to Empower the Metaverse

作者： Chen, Xiana Yu, Junxian Tu, Wei Chen, Long Guangdong Key Laboratory of Urban Informatics Shenzhen University Guangdong Shenzhen518060 China Ministry of Natural Resources Key Laboratory for Geo-Environmental Monitoring of Great Bay Area Shenzhen University Guangdong Shenzhen518060 China Shenzhen Key Laboratory of Spatial Smart Sensing and Services Guangdong Shenzhen518060 China School of Architecture and Urban Planning Shenzhen University Guangdong Shenzhen518060 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Addressing the challenges posed by climate change and meeting urban energy demands is of utmost importance in today's world. Building Integrated Photovoltaics (BIPV) emerges as a crucial solution for energy conservation and carbon emissions reduction in urban environments. However, traditional methods of assessing solar radiation on buildings using physical models are often computationally intensive and time-consuming. This paper introduces a novel hybrid approach that integrates physical model-based solar radiation calculation with machine learning techniques to analyze Solar Radiation Potential (SRP) across city-wide building infrastructure. The proposed approach precisely evaluates the SRP of representative blocks by leveraging computing-intensive physical models integrated with 3D building data. Subsequently, two machine learning models are developed to effectively predict the SRP of building roofs and facades across the entire city. To validate the efficacy of this approach, an experiment was conducted in Shenzhen, China, yielding insightful results. The findings reveal that Shenzhen has a huge potential for BIPV solar power generation, with mean annual total building roof and facade solar radiation values of 9.22 * 107kwℎ and 2.47 * 108kwℎ, respectively. It can be further observed that relying solely on rooftop installations is insufficient to meet electricity demand. This study not only provides an innovative alternative for city-wide SRP estimation by combining physical modeling and machine learning but also offers valuable insights for fostering low-carbon urban environments and informing data-driven and model-driven urban planning and management strategies. © Author(s) 2024.

关键词： Solar power generation

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Towards City-Scale Traffic Prediction: Cluster Graph Wavelet Networks 25

Towards City-Scale Traffic Prediction: Cluster Graph Wavelet...

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IEEE 25th International Conference on Intelligent Transportation systems (ITSC)

作者： Wang, Jingcheng Zhang, Yong Lv, Yisheng Hu, Yongli Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing Inst Artificial Intelligence Beijing Key Lab Multimedia & Intelligent Software Beijing 100124 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

With the development of intelligent transportation systems, the potential need for city-level traffic prediction is emerging. However, some large-scale traffic forecasting methods divide urban areas into grids, which cannot achieve nodelevel prediction. Meanwhile, mainstream graph convolution models have limitations on the scale of the input graph. For this problem, we propose cluster graph convolution networks with spectral graph wavelet kernels. The proposed method divides the large graph into subgraphs and then builds localized spatial dependency with spectral graph wavelet convolution. The model could achieve node-level traffic prediction on a large graph with an end-to-end framework. The experiments are conducted on the historical dataset of Beijing, covering more than 8,000 road segments. The experimental results demonstrate the effectiveness of the proposed method.

关键词： Convolution Roads Clustering methods Urban areas Predictive models Feature extraction Prediction algorithms

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A Novel Camera Calibration Pattern Robust to Incomplete Pattern Projection

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IEEE SENSORS JOURNAL 2021年第8期21卷 10051-10060页

作者： Gao, Zhang Zhu, Mingzhu Yu, Junzhi Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ ChineseAcademy Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ State Key Lab Turbulence & Complex Syst Dept Adv & Robot BIC ESATColl Engn Beijing 100871 Peoples R China

When calibrating multi-camera systems with existing camera calibration toolboxes, it is oftentimes required that calibration boards are fully captured by all cameras to establish correspondence of X-junctions automatically. It becomes impractical when different cameras share limited common field of views, resulting in the incomplete samples of the calibration board. This article proposes a practical solution containing a modified checkerboard pattern and image processing algorithms. Corresponding world and image points for calibration algorithms are provided by the proposed solution when calibration boards are partially captured. X-junctions are categorized in two types and recognized in a group as an identification unit. The proposed pattern is arranged to ensure uniqueness of each identification unit;thus X-junctions can be positioned by supporting algorithms and correspondences are founded. No burden is introduced in sampling compared to traditional calibration methods. Experimental results of calibrating a multi-camera system verify that the use of the partially captured images brings benefits to calibration accuracy. Furthermore, the proposed method can substantially facilitate the calibration process.

关键词： Calibration Feature extraction Cameras Sensors Visualization complex systems Tools Camera calibration overlapping field of view calibration pattern incomplete sample

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3D Vehicle Detection With RSU LiDAR for Autonomous Mine

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第1期70卷 344-355页

作者： Wang, Guojun Wu, Jian Xu, Tong Tian, Bin Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Waytous Inc Beijing 100080 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Shandong Peoples R China

With the development of intelligent and connected vehicles, RSU (roadside unit) sensors are playing an increasingly important role for environment perception. For vehicle detection in autonomous mine, lack of diversity data on RSU LiDAR limits the application of deep learning based methods. To solve this issue, a voxel-based background filtering module is introduced into 3D object detectors for vehicle detection with RSU LiDAR in mine environments. The proposed background filtering method models average height and the number of points for each voxel as Gaussian distribution to generate a background table. To address the impact of the false negative points of the background filtering module, we also propose a multivariate Gaussian loss to model bounding box uncertainty. The predicted covariances between variates help to learn the relationship between the missed parts and the visible ones. Besides, a background filtering based data augmentation method for vehicle detection is also proposed in this paper. Three RSU LiDAR datasets with different terrains in the BaoLi mine area are used for comprehensive experiment evaluations. Experiments show that the proposed background filtering module and multivariate Gaussian loss can significantly improve the generalization ability and performance of several state-of-the-art 3D detectors on different terrain data. Moreover, most background voxels are filtered out, the inference time of the 3D detectors is about 2x faster. Besides, the effectiveness of the proposed data augmentation method is also demonstrated.

关键词： Laser radar Three-dimensional displays Filtering Vehicle detection Detectors Roads Feature extraction Background filtering 3D object detection deep learning roadside LiDAR point cloud

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ChatGPT for Computational Social systems: From Conversational Applications to Human-Oriented Operating systems

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第2期10卷 414-425页

作者： Wang, Fei-Yue Li, Juanjuan Qin, Rui Zhu, Jing Mo, Hong Hu, Bin Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210006 Peoples R China Changsha Univ Sci & Technol Coll Elect & Informat Engn Changsha 410114 Peoples R China Beijing Inst Technol Sch Med Technol Beijing 100081 Peoples R China

Welcome to the second issue of the IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems (TCSS) of 2023. According to the latest update of CiteScoreTracker from Elsevier Scopus released on February 5, 2023, the CitesSore of TCSS has reached a historical high of 9.6. Many thanks to all for your great effort and support.

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The Image Data and Backbone in Weakly Supervised Fine-Grained Visual Categorization: A Revisit and Further Thinking

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2024年第1期34卷 2-16页

作者： Ye, Shuo Wang, Yu Peng, Qinmu You, Xinge Chen, C. L. Philip Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Shenzhen Res Inst Shenzhen 518000 Peoples R China Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau 99999 Peoples R China Dalian Maritime Univ Coll Nav Dalian 116026 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China

Weakly-supervised fine-grained visual categorization (FGVC) aims to achieve subclass classification within the same large class using only label information. Compared to general images, fine-grained images have similar appearances and features, and are often affected by disturbances such as viewpoint, lighting, and occlusion during data collection, resulting in significant intra-class variance and small inter-class variance. To achieve FGVC, carefully designed models are often needed to explore the locally discriminative regions of the image. This paper revisits high-quality FGVC publications based on deep learning and analyzes from two new perspective: fine-grained image data and backbone. We address two ignored but interesting problems in FGVC. First, we argue that the reasons for exacerbating intra-class variance are not the same in data of animal, plant, and commodity types, and it is necessary to consider the effects of posture, covariate shift, and structural changes. Additionally, the "soft boundary" between subclasses intensifies the difficulty of classification. Second, we highlight that convolutional networks and self-attention networks have different receptive fields and shape biases, leading to performance differences when processing different types of fine-grained data. Overall, our analysis provides new insights into recent advances, challenges, and future directions for FGVC based on deep learning, which can help researchers develop more effective models for FGVC.

关键词： Fine-grained visual categorization deep learning weakly supervised learning

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Tabular Learning-Based Traffic Event Prediction for Intelligent Social Transportation System

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第3期10卷 1199-1210页

作者： Sun, Chen Li, Shen Cao, Dongpu Wang, Fei-Yue Khajepour, Amir Univ Waterloo Dept Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada Tsinghua Univ Dept Civil Engn Beijing 100084 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100049 Peoples R China

Accurate forecasting of future traffic is a critical contemporary problem for transportation research. However, it is difficult to understand the feature patterns of traffic events due to the complexity of the traffic environment, heterogeneous factors, and lack of abnormal samples. This article proposes a framework to integrate the social traffic data and use the TabNet model to facilitate the representation learning task in traffic event prediction. With the tabular learning and model interpretability analysis, the importance of common traffic external factors toward traffic events is studied. The study has practical significance for regulating traffic planning and the development of the operational boundary for autonomous driving systems.

关键词： Accidents Data models Predictive models Roads Analytical models Vehicles Sun Crowd sourcing social transportation system tabular learning traffic prediction

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So Many Heads, So Many Wits: Multimodal Graph Reasoning for Text-Based Visual Question Answering

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2024年第2期54卷 854-865页

作者： Zheng, Wenbo Yan, Lan Wang, Fei-Yue Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya 572000 Peoples R China Hunan Univ Coll Comp Sci & Engn Changsha 410082 Hunan Peoples R China Natl Supercomp Ctr Changsha 410082 Hunan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

While texts related to images convey fundamental messages for scene understanding and reasoning, text-based visual question answering tasks concentrate on visual questions that require reading texts from images. However, most current methods add multimodal features that are independently extracted from a given image into a reasoning model without considering their inter-and intra-relationships according to three modalities (i.e., scene texts, questions, and images). To this end, we propose a novel text-based visual question answering model, multimodal graph reasoning. Our model first extracts intramodality relationships by taking the representations from identical modalities as semantic graphs. Then, we present graph multihead self-attention, which boosts each graph representation through graph-by-graph aggregation to capture the intermodality relationship. It is a case of "so many heads, so many wits" in the sense that as more semantic graphs are involved in this process, each graph representation becomes more effective. Finally, these representations are reprojected, and we perform answer prediction with their outputs. The experimental results demonstrate that our approach realizes substantially better performance compared with other state-of-the-art models.

关键词： Graph attention graph reasoning multimodal graph self-attention text-based visual question answering

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Cooperative Target Tracking in Aquatic Environment Using Dual Robotic Dolphins

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2021年第8期51卷 4782-4792页

作者： Liu, Jincun Wu, Zhengxing Yu, Junzhi Xue, Zhibin Peking Univ Dept Mech & Engn Sci Coll Engn BIC ESATState Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qinghai Univ Coll Chem Engn Xining 810016 Peoples R China

This article proposes a modified rapidly exploring random tree (RRT)-based path planner and behavior-based cooperative tracking strategy for a dual robotic dolphin system to fulfill a cooperative target-tracking task. Specifically, with full consideration of both task requirements and mechatronic configuration, a robotic dolphin with a waist-caudal propulsive mechanism for thrust forces and differential bilateral flippers for maneuverability is developed. To satisfy the demand of fast path planning, a variant RRT algorithm is employed to generate feasible paths for the dual robotic dolphins with fewer waypoints, faster convergence speed, and better stability. Furthermore, a behavior-based approach in conjunction with centralized architecture is implemented to achieve high-level decision-making. Finally, simulations, analysis, as well as field experiments are carried out to verify the effectiveness of the proposed control scheme. The success of the experiments further offers insight into the mechanisms of cooperative multirobot target tracking in aquatic environments.

关键词： Dolphins Robot kinematics Path planning Target tracking Turning Task analysis Behavior based biomimetic robotic dolphin cooperative target tracking rapidly exploring random tree (RRT)

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