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A New Perspective for Computational Social systems: Fuzzy Modeling and Reasoning for Social Computing in CPSS

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第1期11卷 101-116页

作者： Wang, Tan Zhu, Yifan Ye, Peijun Gong, Weichao Lu, Hao Mo, Hong Wang, Fei-Yue Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau Peoples R China Chinese Acad Sci Inst Automat Chinese Assoc Automat Beijing 100190 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 10086 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China Changsha Univ Sci & Technol Coll Elect & Informat Engn Changsha 410114 Peoples R China

The evolution of modern mobile terminals, social networks, and other intelligent services makes everyone become a ubiquitous information perceiver, producer, and propagator. Also known as "social sensor" and "social IoT", these individuals and communities generate a huge volume of social signals, which has shown prominent value for mining. These unstructured social signals provide a new perspective in the research of complex systems, which makes the traditional cyber-physical system (CPS)-oriented information computing sublimate to the cyber-physical-social system (CPSS)-oriented knowledge computing. However, there still exist great uncertainties, ambiguities, and complexities in modeling behaviors of social individuals or groups. Especially when we apply big-data-driven learning-based models in specific fields and scenarios, the lack of domain expert knowledge and characteristics of system uncertainty severely limits the performance and accuracy of these models. The introduction of fuzzy system modeling integrates data and knowledge in the social computing area, which has shown its unique advantages in solving the above issues and has drawn more attention to this topic. In this article, we conduct a review of recent advances in social computing with fuzzy technologies in CPSS. First, we briefly review the development of social computing, and analyze the characteristics and advantages of social computing through fuzzy methods. Second, we refine core fuzzy system methods for social computing and elab.rate on existing fuzzy-technology-empowered social computing methodologies. As in a range of social spaces, we also review and analyze related advances in human-in-the-loop systems. We also reveal the trend of decentralized, autonomous, and organized computing in cyber-physical-social space with fuzzy-based methods and proposed a framework to categorize related studies in CPSS. Finally, we conclude the research trends and hotspots based on current studies, and discuss th

关键词： Social computing Computational modeling Fuzzy systems Uncertainty complex systems Analytical models Human in the loop Computational systems cyber-physical-social systems (CPSSs) fuzzy paradigm parallel society social computing

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Dissecting and Mitigating Semantic Discrepancy in Stable Diffusion for Image-to-Image Translation

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IEEE-CAA JOURNAL OF AUTOMATICA SINICA 2025年第4期12卷 705-718页

作者： Yuan, Yifan Yang, Guanqun Wang, James Z. Zhang, Hui Shan, Hongming Wang, Fei-Yue Zhang, Junping Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China Fuxi Team NetEase Corp Hangzhou Peoples R China Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100098 Peoples R China Fudan Univ Inst Sci & Technol Brain inspired Intelligence Shanghai 200433 Peoples R China Fudan Univ MOE Frontiers Ctr Brain Sci Shanghai 200433 Peoples R China Shanghai Ctr Brain Sci & Brain inspired Technol Shanghai 200031 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 Syst Engn Macau Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

Finding suitable initial noise that retains the original image's information is crucial for image-to-image (I2I) translation using text-to-image (T2I) diffusion models. A common approach is to add random noise directly to the original image, as in SDEdit. However, we have observed that this can result in "semantic discrepancy" issues, wherein T2I diffusion models mis-interpret the semantic relationships and generate content not present in the original image. We identify that the noise introduced by SDEdit disrupts the semantic integrity of the image, leading to unintended associations between unrelated regions after U-Net upsampling. Building on the widely-used latent diffusion model, Stable Diffusion, we propose a training-free, plug-and-play method to alleviate semantic discrepancy and enhance the fidelity of the translated image. By leveraging the deterministic nature of denoising diffusion implicit models (DDIMs) inversion, we correct the erroneous features and correlations from the original generative process with accurate ones from DDIM inversion. This approach alleviates semantic discrepancy and surpasses recent DDIM-inversion-based methods such as PnP with fewer priors, achieving a speedup of 11.2 times in experiments conducted on COCO, ImageNet, and ImageNet-R datasets across multiple I2I translation tasks.

关键词： Translation Correlation Codes Image edge detection Semantics Noise Noise reduction Text to image Diffusion models Erosion DDIM inversion diffusion model image-to-image translation semantic discrepancy stable diffusion

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Correlation analysis and text classification of chemical accident cases based on word embedding

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2022年第0期158卷 698-710页

作者： Jing, Sifeng Liu, Xiwei Gong, Xiaoyan Tang, Ying Xiong, Gang Liu, Sheng Xiang, Shuguang Bi, Rongshan Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Univ Sci & Technol Coll Chem Engn Qingdao 266042 Peoples R China Qingdao Acad Intelligent Ind Inst Smart Educ Syst Qingdao 266044 Peoples R China Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA

Accident precursors can provide valuable clues for risk assessment and risk warning. Trends such as the main characteristics, common causes, and high-frequency types of chemical accidents can provide references for formulating safety-management strategies. However, such information is usually documented in unstructured or semistructured free text related to chemical accident cases, and it can be costly to manually extract the information. Recently, text-mining methods based on deep learning have been shown to be very effective. This study, therefore, developed a text-mining method for chemical accident cases based on word embedding and deep learning. First, the word2vec model was used to obtain word vectors from a text corpus of chemical accident cases. Then, a bidirectional long short-term memory (LSTM) model with an attention mechanism was constructed to classify the types and causes of Chinese chemical accident cases. The case studies revealed the following results: 1) Common trends in chemical accidents (e.g., characteristics, causes, high-frequency types) could be obtained through correlation analysis based on word embedding;2) The developed text-classification model could classify different types of accidents as fires, explosions, poisoning, and others, and the average p (73.1%) and r (72.5%) of the model achieved ideal performance for Chinese text classification;3) The developed text-classification model could classify the causes of accidents as personal unsafe act, personal habitual behavior, unsafe conditions of equipment or materials and vulnerabilities management strategy;p and r were 63.6% for the causes of vulnerabilities management strategy, and the average p and r are both 60.7%;4) the accident precursors of explosion, fire, and poisoning were obtained through correlation analyses of each high-frequency type of chemical accident case based on text classification;5) the text-mining method can provide site managers with an efficient tool for extracting

关键词： Text mining Correlation analysis Text classification Word embedding Deep learning Chemical accident cases

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An Edge Computing Architecture Based on Unikernel

An Edge Computing Architecture Based on Unikernel

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2022 Australian and New Zealand control Conference

作者： Chen, Shichao Xu, Ruijie Sun, Wenqiao Chinese Acad Sci State Key Lab Management & Control Complex Syst I Ctr Natl Railway Intelligent Transportat Syst Eng China Acad Railway Sci Corp Ltd Beijing Peoples R China Chinese Acad Sci Beijing Univ Chem Technol State Key Lab Management & Control Complex Syst I Coll Informat Sci & Technol Beijing Peoples R China China Acad Railway Sci Corp Ltd Transportat & Econ Res Inst Beijing Peoples R China China Acad Railway Sci Corp Ltd Ctr Natl Railway Intelligent Transportat Syst Eng Beijing Peoples R China

ISBN: (数字)9781665498876

ISBN: (纸本)9781665498876

In the era of big data, more and more applications of smart devices are computing-intensive, thus raising the strong demand for task offloading to cloud data centers. However, it gives rise to network delay and privacy data leak issues. Edge computing can effectively solve latency, bandwidth occupation and data privacy problems, but the deployment of applications are also limited by hardware architectures and resources, i.e., computing and storage resources. Therefore, the combination of virtualization technology and edge computing become important in order to realize the rapid deployment of intelligent application in an edge server or an edge node by virtualization technology. The traditional virtual machine (VM) is no longer suitable for resource-constrained devices. Container technique including Docker can effectively solve these problems, but it also depends on an operating system. Unikernel is the state-of-art virtualization technology. In this paper, we combine Unikernel with edge computing to explore its application in an edge computing system. An application architecture of edge computing based on Unikernel is proposed. It is suitable for application in edge computing.

关键词： unikernel docker virtualization technology edge computing

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Consensus control of Leader-Following Multi-Agent systems in Directed Topology With Heterogeneous Disturbances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 423-431页

作者： Qinglai Wei Xin Wang Xiangnan Zhong Naiqi Wu the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China the Department of Computer and Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and TechnologyMacao 999078China

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown *** main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic *** traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent *** introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local *** the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is *** simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

关键词： Consensus control directed topology external disturbance multi-agent(MA)systems

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Bridging the Micro and Macro: Calibration of Agent-Based Model Using Mean-Field Dynamics

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第11期52卷 11397-11406页

作者： Ye, Peijun Chen, Yuanyuan Zhu, Fenghua Lv, Yisheng Lu, Wanze Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Univ Technol Sch Artificial Intelligence & Automat Beijing 100124 Peoples R China Qingdao Acad Intelligent Ind Parallel Intelligence Res Ctr Qingdao 266109 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China

Calibration of agent-based models (ABM) is an essential stage when they are applied to reproduce the actual behaviors of distributed systems. Unlike traditional methods that suffer from the repeated trial and error and slow convergence of iteration, this article proposes a new ABM calibration approach by establishing a link between agent microbehavioral parameters and systemic macro-observations. With the assumption that the agent behavior can be formulated as a high-order Markovian process, the new approach starts with a search for an optimal transfer probability through a macrostate transfer equation. Then, each agent's microparameter values are computed using mean-field approximation, where his complex dependencies with others are approximated by an expected aggregate state. To compress the agent state space, principal component analysis is also introduced to avoid high dimensions of the macrostate transfer equation. The proposed method is validated in two scenarios: 1) population evolution and 2) urban travel demand analysis. Experimental results demonstrate that compared with the machine-learning surrogate and evolutionary optimization, our method can achieve higher accuracies with much lower computational complexities.

关键词： Calibration Computational modeling Mathematical model Machine learning Aggregates Optimization Bayes methods Agent-based model (ABM) calibration Markovian process

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Marker or Markerless? Mode-Switchable Optical Tactile Sensing for Diverse Robot Tasks

arXiv

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arXiv 2024年

作者： Ou, Ni Chen, Zhuo Luo, Shan The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China The Robot Perception Lab. Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom

Optical tactile sensors play a pivotal role in robot perception and manipulation tasks. The membrane of these sensors can be painted with markers or remain markerless, enabling them to function in either marker or markerless mode. However, this uni-modal selection means the sensor is only suitable for either manipulation or perception tasks. While markers are vital for manipulation, they can also obstruct the camera, thereby impeding perception. The dilemma of selecting between marker and markerless modes presents a significant obstacle. To address this issue, we propose a novel mode-switchable optical tactile sensing approach that facilitates transitions between the two modes. The marker-to-markerless transition is achieved through a generative model, whereas its inverse transition is realized using a sparsely supervised regressive model. Our approach allows a single-mode optical sensor to operate effectively in both marker and markerless modes without the need for additional hardware, making it well-suited for both perception and manipulation tasks. Extensive experiments validate the effectiveness of our method. For perception tasks, our approach decreases the number of categories that include misclassified samples by 2 and improves contact area segmentation IoU by 3.53%. For manipulation tasks, our method attains a high success rate of 92.59% in slip detection. Code, dataset and demo videos are availab.e at the project website https://***/Marker-Markerless-Transition/ Copyright © 2024, The Authors. All rights reserved.

关键词： Tactile sensors

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ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2022年第10期23卷 19201-19212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 100088 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Beijing Univ Posts & Telecommun Sci & Technol Commun Networks Lab Beijing 100088 Peoples R China State Key Lab Networking & Switching Technol Shijiahznang 050081 Hebei 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

Accurate spatial-temporal prediction is a fundamental building block of many real-world applications such as traffic scheduling and management, environment policy making, and public safety. This problem is still challenging due to nonlinear, complicated, and dynamic spatial-temporal dependencies. To address these challenges, we propose a novel embedded spatial-temporal network (ESTNet), which extracts efficient features to model the dynamic correlations and then exploits three-dimension convolution to synchronously model the spatial-temporal dependencies. Specifically, we propose multi-range graph convolution networks for extracting multi-scale static features from the fine-grained road network. Meanwhile, dynamic features are extracted from real-time traffic using a gated recurrent unit network. These features can be applied to identify the dynamic and flexible correlations among sensors and make it possible to exploit a three-dimension convolution unit (3DCon) to simultaneously model the spatial-temporal dependencies. Furthermore, we propose a residual network by stacking multiple 3DCon to capture the nonlinear and complicated dependencies. The effectiveness and superiority of ESTNet are verified on two real-world datasets, and experiments show ESTNet outperforms the state-of-the-art with a significant margin. The code and models will be publicly availab.e.

关键词： Roads Correlation Feature extraction Sensors Deep learning Convolution Sensor phenomena and characterization Traffic forecasting graph convolutional network spatial-temporal networks

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An IVC-Based Nuclear Emergency Parallel Evacuation System

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2021年第4期8卷 844-855页

作者： Tan, Ke Yang, Linyao Liu, Xin Xu, Yancai Lin, Jiazhen Wang, Xiao Wang, Fei-Yue State Key Lab Nucl Power Safety Monitoring Techno Shenzhen 518108 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China

Nuclear emergency evacuation is challenging and dangerous, with time constraints, resource limitations, and radiation exposure risks. The development of the Internet of Things (IoT) and artificial intelligence enables us to build an intelligent evacuation system to help mitigate this problem a great deal. In this article, we design the nuclear emergency parallel evacuation system based on the artificial systems (A), computational experiments (C), and parallel execution (P) approach and intelligent vehicle collab.rative systems (IVCs). In this system, the evacuation risks of different regions under various possible scenarios are simulated and evaluated in the artificial systems. With data adversarially generated from the artificial systems and collected from sensors all over the area, an optimization model is proposed to find the optimal evacuation plans for emergent evacuation scenarios in the computational experiments. Eventually, the most suitable running strategy of autonomous buses will be selected and carried out based on the parallel execution in accordance with the real scene. A case study is conducted, and results indicate that the system can serve as an efficient tool for future nuclear emergency evacuation planning.

关键词： Computational modeling Planning Optimization Roads Mathematical model Collab.ration Power generation ACP intelligent vehicle collab.rative system nuclear evacuation optimization parallel intelligence

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Machine-learning-based monitoring and optimization of processing parameters in 3D printing

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2023年第9期36卷 1362-1378页

作者： Tamir, Tariku Sinshaw Xiong, Gang Fang, Qihang Yang, Yong Shen, Zhen Zhou, MengChu Jiang, Jingchao Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Cloud Comp Ctr Dongguan Peoples R China Chinese Acad Sci Shanghai Inst Ceram State Key Lab High Performance Ceram & Superfine Shanghai Peoples R China New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Newark NJ 07102 USA Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Macau 999078 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Shatin Hong Kong Peoples R China

Additive manufacturing (AM), commonly known as 3D printing, is a rapidly growing technology. Guaranteeing the quality and mechanical strength of printed parts is an active research area. Most of the existing methods adopt open-loop-like Machine Learning (ML) algorithms that can be used only for predicting properties of printed parts without any quality assuring mechanism. Some closed-loop approaches, on the other hand, consider a single adjustable processing parameter to monitor the properties of a printed part. This work proposes both open-loop and closed-loop ML models and integrates them to monitor the effects of processing parameters on the quality of printed parts. By using experimental 3D printing data, an open-loop classification model formulates the relationship between processing parameters and printed part properties. Then, a closed-loop control algorithm that combines open-loop ML models and a fuzzy inference system is constructed to generate optimized processing parameters for better printed part properties. The proposed system realizes the application of a closed-loop control system to AM.

关键词： Additive manufacturing closed-loop 3D printing digital manufacturing machine learning processing parameters

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