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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Miao, Zibo Pan, Yu Gao, Qing School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms Shenzhen518055 China The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique. © 2024 IEEE.

关键词： Poles

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case 63

Optimal Linear Deception Attacks on Remote State Estimation ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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UniAdapter: All-in-One Control for Flexible Video Generation

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IEEE Transactions on Circuits and systems for Video technology 2025年第6期35卷 6059-6073页

作者： Wang, Cong Hu, Panwen Zhao, Haoyu Guo, Yuanfan Gu, Jiaxi Dong, Xiao Han, Jianhua Xu, Hang Liang, Xiaodan Chinese University of Hong Kong Hong Kong School of Science and Engineering Shenzhen China Fudan University School of Computer Science and Technology Shanghai China Huawei Noah'ark Lab Shanghai201206 China Sun Yat-Sen University School of Artificial Intelligence Zhuhai Campus Zhuhai519082 China Shenzhen Campus of Sun Yat-sen University School of Intelligent Systems Engineering Guangdong No. 66 Gongchang Road Guangming District Shenzhen518107 China Peng Cheng Laboratory and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

Condition-based video generation aims to create video content based on given information that describes specific subjects. However, most existing works can only utilize a single condition to guide the denoising process, thereby limiting their applicability to specific scenarios. Although some works attempt to accommodate multiple conditions within one framework, they often require multiple encoders, leading to inefficiencies in integrating multi-condition features. In this work, we present a framework that, with the support of the proposed Unified Adapter (UniAdapter), enables simultaneous multi-condition control of video generation within a single model. To effectively merge these conditions, we propose a novel Probabilistic Multi-condition Concatenator (PMC) module, which employs a unified encoder to accommodate multiple conditions and concatenate condition features at the pixel level to achieve fine-grained control. Following the PMC module, we employ 2D down-sampling blocks to refine features for injection into the Video Diffusion Model (VDM). Moreover, our UniAdapter is designed to be model-agnostic and compatible with any U-Net-based VDM, offering a versatile solution for improving video generation quality. Experimental results on public benchmarks UCF-101 and MSR-VTT show that our method achieves superior results in both quantitative and qualitative evaluations. © 2025 IEEE.

关键词： Signal encoding

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Sociological-Theory-Based Multitopic Self-Supervised Recommendation

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IEEE Transactions on Neural Networks and Learning systems 2024年第6期36卷 11228-11242页

作者： Qin Zhao Peihan Wu Gang Liu Dongdong An Jie Lian MengChu Zhou Shanghai Engineering Research Center of Intelligent Education and Big Data Shanghai Normal University Shanghai China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Social relationships offer crucial supplementary information for recommendations by leveraging users’ social connections to gain insights into their preferences. However, prevalent social recommendation methods often grapple with the issues of sparsity and noise, which curtail their effectiveness. In addition, these methods overlook the intricacies of user interactions within social networks, which could provide invaluable information. Addressing their deficiencies, this article introduces a novel sociological-theory-based multitopic self-supervised recommendation method (SMSR). This method integrates user attitude information into the construction of social relationships and utilizes dynamic routing to identify and categorize topics, thereby mitigating the impact of social noise on recommendation accuracy. Furthermore, we reveal sophisticated higher order user relations within these topics by using motifs. By combining the light graph convolutional network with balance theory, SMSR efficiently aggregates information from diverse social relations to gain its outstanding performance. Moreover, we have devised and integrated four self-supervised signals, inspired by social theory and derived from heterogeneous graph analysis, to more effectively exploit the rich structural and semantic information inherent in social relationship graphs. Empirical results from extensive experiments on publicly available datasets underscore SMSR’s superiority over the state of the art.

关键词： Social networking (online) Aggregates Urban areas Recommender systems Peer-to-peer computing Contrastive learning Semantics data mining Convolution Accuracy

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Time series prediction and application based on multi-kernel support vector regression 2

Time series prediction and application based on multi-kernel...

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2nd International Symposium on Computer Applications and Information systems, ISCAIS 2023

作者： Yi, Xiaoxing Wen, Xueting Yin, Xiangfeng School of Mathematics and Computational Science Hunan University of Science and Technology Hunan province Xiangtan China School of Mathematics and Computational Science Hunan University of Science and Technology Hunan Engineering Research Center for Big Data and Intelligent Decision-making of Industrial Development Hunan province Xiangtan China

ISBN: (纸本)9781510666665

In this paper, we propose a new MRMRKA-SVR-GARCH model to predict the volatility of time series. This model is a combination of the MRMRKA-SVR model and the GARCH model. Since SVR-GARCH model has been successfully employed to forecast volatility. Empirical results show that the multi-kernel support vector regression based on minimal redundancy maximal correlation criteria and kernel target alignment (MRMRKA-SVR) tend to beat single-kernel SVR models in terms of forecasting accuracy. First,we use the MRMRKA algorithm to select a set of kernels to establish a multi-kernel, and establish the MRMRKA-SVR model. Then we use MRMRKA-SVR to estimate the volatility equation of GARCH model, and establish MRMRKA-SVR-GARCH model. Furthermore, the models’ predictive ability was evaluated using three empirical analysises on China Unicom’s stock price data and stock price data of ICBC. Under different basic kermel numbers m, compare the prediction errors between the proposed model and the SVR-GARCH model based on the mixed Gaussian kernels in the two stock data, the results show that the prediction accuracy of the MRMRKA-SVR-GARCH model fluctuates slightly with the change of m, and the MAE and RMSE of the MRMRKASVR- GARCH model are lower than the mixed-Gaussian-kernel SVR-GARCH model . Description MRMRKA-SVRGARCH model managed to outperform the mixed-Gaussian-kernel SVR-GARCH model. Threrfore, the MRMRKASVR- GARCH model can provide a certain degree of reference in analyzing the trend and prediction of volatility, and has high feasibility and effectiveness. © 2023 SPIE.

关键词： Time series

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MetaCity: data-driven sustainable development of complex cities

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The Innovation 2025年第2期6卷 88-103,87页

作者： Yunke Zhang Yuming Lin Guanjie Zheng Yu Liu Nicholas Sukiennik Fengli Xu Yongjun Xu Feng Lu Qi Wang Yuan Lai Li Tian Nan Li Dongping Fang Fei Wang Tao Zhou Yong Li Yu Zheng Zhiqiang Wu Huadong Guo Department of Electronic Engineering Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijing 100084China John Hopcroft Center for Computer Science Shanghai Jiao Tong UniversityShanghai 200240China University of Chinese Academy of Sciences Beijing 100049China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Civil and Environmental Engineering Northeastern UniversityBostonMA 02115USA School of Architecture Tsinghua UniversityBeijing 100084China School of Civil Engineering Tsinghua UniversityBeijing 100084China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China JD iCity JD Technology&JD Intelligent Cities ResearchBeijing 100176China College of Architecture and Urban Planning Tongji UniversityShanghai 200292China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China These authors contributed equally

Cities are complex systems that develop under complicated interactions among their human and environmental *** generates substantial outcomes and opportunities while raising challenges including congestion,air pollution,inequality,etc.,calling for efficient and reasonable solutions to sustainable ***,booming technologies generate large-scale data of complex cities,providing a chance to propose data-driven solutions for sustainable urban *** paper provides a comprehensive overview of data-driven urban sustainability *** this review article,we conceptualize MetaCity,a general framework for optimizing resource usage and allocation problems in complex cities with data-driven *** this framework,we decompose specific urban sustainable goals,e.g.,efficiency and resilience,review practical urban problems under these goals,and explore the probability of using data-driven technologies as potential solutions to the challenge of *** the basis of extensive urban data,we integrate urban problem discovery,operation of urban systems simulation,and complex decision-making problem solving into an entire cohesive framework to achieve sustainable development goals by optimizing resource allocation problems in complex cities.

关键词： resource usage sustainable development urbanization resilience data driven urban systems simulation complex cities complexity

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Attenuated niacin-induced skin flush response in individuals with clinical high risk for psychosis

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General Psychiatry 2022年第2期35卷 92-100页

作者： Ranpiao Gan Yanyan Wei Guisen Wu Jiahui Zeng Yegang Hu Lihua Xu Xiaochen Tang Xiaohua Liu Haichun Liu Tao Chen Jijun Wang Tianhong Zhang Shanghai Intelligent Psychological Evaluation and Intervention Engineering Technology Research Center Shanghai Key Laboratory of Psychotic DisordersShanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina Department of Automation Shanghai Jiao Tong UniversityShanghaiChina Big Data Research Lab University of WaterlooWaterlooOntarioCanada Center for Excellence in Brain Science and Itelligence Technology(CEBSIT) Chinese Academy of ScienceShanghaiChina Brain Science and Technology Research Center Shanghai Jiao Tong UniversityShanghaiChina

Background Im paired sesitwity of he soin Mlush response bo miacin is one of the most rpicated findngs in patents with schizoprenia *** studies have usaly focused on postonset psychusis,and ll is knowm about the dinical high-risk(CHR)phase of niacin senstity in psychosis Aims To proftle and compare the miacin flush responsge among CHR individuals(converters and non-coverters)patients with frstepso schinophrenia(FES)and healty controls(HCs).Methods Sensivily 1o ftour concentralions (0.1-0001M)of aqueous methylnicotinate was tested in 105 CHR individuals,57 patients with FES and 52 *** individuals were further grouped as converters and non converters according to the 2-year follow-up *** flush response scores were rated on a 4-point *** Of the 105 CHR individuals,21 individuals were lost during the study,leaving 84 CHR individuals;16(19.0%)converted to full psychosis at 2 years of *** response scores identifed in the CHR samples were characterised as modest degree levels,intermediate between those of HC individuals and patients with *** flush responses in the CHR group mimicked the responses observed in the FES group at higher concentrations(0.01 M,0.1 M)and longer time points(15 min,20min);however,these became comparable vith the responses in the HC group at the shorter time points and at lower concentr *** converters exhibited lower mean flush response scores than the *** Attenuated niacin-induced flushing emerged during the early phase of *** devices should be developed and verified for objective quantification of skin responses in the CHR population.

关键词： Skin high shorter

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Detection Method of River Floating Objects based on Edge Computing 2

Detection Method of River Floating Objects based on Edge Com...

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2nd International Conference on Robotics, Automation and intelligent Control, ICRAIC 2022

作者： Li, He Jia, Huimei Zhang, Rui Yang, Shuaipeng Qi, Qinglei Liu, Tianyang Henan Costar Group Co. Ltd Nanyang China College of Information Engineering Henan University of Science and Technology Luoyang China Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China Sydney Business School Shanghai University Shanghai China

For the detection of objects floating in the river, most of the traditional intelligent video monitoring methods are used to monitor through manual viewing. During the traditional video data transmission and processing, the massive amount of data will put pressure on the background such as network and computing power. The new effective method is to crop and optimize YOLOV5s, add a specific image pre-processing module, and deploy it by edge computing, embed a SOC (System on Chip) chip in the web camera for real-time processing of video data. Simulation experiments prove that the above method can improve the accuracy and speed of the target detection network. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

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Multi-scale fusion clustering algorithm for urban travel features 3

Multi-scale fusion clustering algorithm for urban travel fea...

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2023 3rd International Conference on intelligent Traffic systems and Smart City, ITSSC 2023

作者： Zhang, Zixiang Zhao, Zhilong Deng, Jiaming Zhao, Yingying Xu, Tao Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China School of Computer and Information Engineering Henan University Kaifeng475004 China International Education College Henan University Zhengzhou450046 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

ISBN: (纸本)9781510672963

Urban travel data typically encompass various temporal and spatial scales and features. Nonetheless, existing traffic travel feature analysis often focuses on a single spatial scale, which may overlook crucial information and hinder features at different scales. Consequently, this could reduce the accuracy of travel pattern recognition. To solve this problem, we introduce a multi-scale fusion transformation algorithm. In our approach, we first establish a multi-level taxi travel data grid system. Then, we apply the I-Clique clustering algorithm to perform grid clustering at each level of the system to identify taxi travel feature regions of different scales. Subsequently, a multi-scale fusion pyramid model is constructed utilizing the acquired travel feature regions at various scales. Finally, we determine the transformation relationships between scales in the pyramid model, enabling the identification of travel feature areas across both known and unknown scales. This approach facilitates the recognition of travel features within cities at any scale. Experimental results demonstrate that the multi-scale fusion transformation algorithm enhances our understanding of spatial aggregation patterns and the hierarchical structure of geographical entities, offering a novel approach for multi-scale urban traffic analysis. © 2024 SPIE. All rights reserved.

关键词： Clustering algorithms

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Robust Network Optimization by Deep Generative Models and Stochastic Optimization

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IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Shutao Xue, Ye Tang, Zhiwei Wang, Hao Shen, Chao Shi, Qingjiang Chang, Tsung-Hui The Chinese University of Hon g Kong School of Science and Engineering Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hon g Kong School of Data Science Shenzhen China Shenzhen Research Institute of Big Data Shenzhen International Center for Industrial and Applied Mathematics China Guangdong Provincial Engineering Technology Research Center for Intelligent Industrial Digital Twin and Optimization Shenzhen518172 China Tongji University School of Software Engineering Shanghai China

Wireless network optimization is essential for improving the network performance in mobile communications. However, due to the stochastic nature of wireless networks, existing schemes based on analytical models and deterministic optimization are less reliable. To this end, we design a framework for robust network optimization based on deep generative models and stochastic optimization. Inspired by the powerful diffusion process, we propose a deep generative simulator to capture the statistical distribution of the network performance. By sampling from the deep generative simulator, we can alleviate the inherent uncertainty related to the network performance and devise an innovative expectation-quantile-based stochastic objective function. The inner expectation is designed for the temporal statistics, while the outer quantile is developed for the spatial statistics. This designated two-tier objective function is capable of mitigating temporal fluctuations and ensuring satisfactory network performance across most geographical grids, thereby achieving robustness. To solve this stochastic optimization problem, a smooth zeroth-order approach is introduced by taking advantage of the unique structure of quantile functions. Through theoretical performance analysis and simulation experiments with real-world datasets, we demonstrate the superiority of our approach over other baseline schemes, highlighting its practical utility in robust network optimization. © 2002-2012 IEEE.

关键词： Generative diffusion model quantile function robust approach wireless network optimization

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