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Differentially Private Opinion Dynamics of Influence Networks

IEEE Transactions on control of Network Systems 2025年

作者： Wu, Guanglei Zhang, Wenbing Mao, Shuai Wu, Xiaotai Tang, Yang Yangzhou University School of Mathematical Sciences Jiangsu225002 China Nantong University Department of Electrical Engineering Nantong226019 China Anhui Polytechnic University Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education Wuhu241000 China East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Shanghai200237 China

In this paper, a unified influence networks model incorporating differential privacy mechanisms (DPMs), called the differentially private opinion dynamics (DPODs) model is proposed. In this model, each individual uses protected opinions rather than the private opinions of his/her neighbors to update his/her private opinions, where the protected opinion of an individual is a blend of private opinions and random noise following Laplace distribution. Building on stochastic analysis techniques and matrix theory, we show that the influence network under consideration converges under specific conditions governing individual sensitivities and interaction weights. In addition, the statistical properties related to convergence accuracy are established by utilizing the Markov inequality to estimate a lower bound on the probability of all individuals' final opinions converging to a neighborhood formed by their initial opinions' convex hull. We further conduct a differential privacy analysis to validate the efficacy of the proposed DPMs in safeguarding the private opinions of all individuals. Finally, two examples including one of the Karate-Club networks are provided to shed new light on the effectiveness of the theoretical results. © 2014 IEEE.

关键词： Differential privacy

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A Memory-Greedy Policy With Guaranteed Convergence for Accelerating Reinforcement Learning

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Journal of Autonomous Vehicles and Systems 2021年第1期1卷 011005页

作者： Yu, Xinglin Wu, Yuhu Sun, Xi-Ming Zhou, Wenya Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian116024 China Liaoning Provincial Key Laboratory of Aerospace Advanced Technology School of Aeronautics and Astronautics Dalian University of Technology Dalian116024 China

Balancing the exploration and exploitation in reinforcement learning is a commonly dilemma and time-wasting work. In this paper, a novel exploration policy used in Q-Learning, called Memory-greedy policy, is proposed to accelerate learning. By memory storage and playback, the probability of random action selecting can be effectively dealt with or reduced, which hence speeds up learning. The principle of this policy is analyzed by maze scene, and the theoretical convergence is given according to dynamic programming. Copyright © 2021 by ASME.

关键词： Reinforcement learning

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Obtaining frequency-time diagram from perturbation signal-time diagram 7

Obtaining frequency-time diagram from perturbation signal-ti...

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7th International Conference on Physics, Mathematics and Statistics, ICPMS 2024

作者： Liu, Xiaoyuan Zhang, Yuxin Dai, Runrui Lai, Zongqiang Qiu, Huibin Shen, Chengshuo Weng, Huang Wu, Shengfa Zheng, Wei Zhang, Wei Liang, Wenxiang Han, Qianhao Li, Xiaobin Liu, Lihuan Shi, Chunhui Xu, Ting Zhang, Haotian Fan, Jiayu Yu, Meiping Tao, Jiajun Jiangxi Province Key Laboratory of Fusion and Information Control Department of Physics Nanchang University Nanchang330031 China NCU-ASIPP Magnetic Confinement Fusion Joint Lab Institute of Fusion Energy and Plasma Application Nanchang University Nanchang330031 China International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

In the field of nuclear fusion energy development, magnetic confinement tokamak reactors, which use strong magnetic fields to confine high-temperature plasmas, are crucial for sustainable and clean fusion energy. Disruption caused by plasma instability can terminate the fusion reaction and even cause irreparable damage to the equipment. Therefore, a thorough understanding of the underlying physical mechanisms is essential for predicting and preventing plasma disruption. The recently proposed analytical theory of plasma disruption may predict and prevent plasma disruption, but its validation requires the frequency-time signals. The Heavy Ion Beam Probe (HIBP) can provide such signals, however, because its scarcity in the world, particularly in China, it is necessary to develop more suitable methods. Here we show a new approach that employs Fast Fourier Transform (FFT) on magnetic perturbation signals to obtain frequency-time diagrams. We successfully applied this algorithm to acquire the frequency-time diagram of the J-TEXT-1054560 discharge. This new method offers an effective tool for validating the plasma disruption theory, overcoming the limitations of traditional techniques and providing a crucial methodological basis for understanding and predicting plasma disruption. This new analytical approach will accelerate the advancement of fusion technology. © Published under licence by IOP Publishing Ltd.

关键词： Heavy ions

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RBFNN-Based ADRC Design for Continuous-Time Systems with Unknown Nonlinear Dynamics Subject to Time-Varying Disturbance

RBFNN-Based ADRC Design for Continuous-Time Systems with Unk...

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Chinese control Conference (CCC)

作者： Shuai Yan Shoulin Hao Haichen Yu Tao Liu Bin Yan Yihui Gong Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China Institute of Advanced Control Technology Dalian University of Technology Dalian China

In this paper, a radial basis function neural network (RBFNN) based active disturbance rejection control (ADRC) scheme is proposed for continuous-time systems with unknown nonlinear dynamics and time-varying disturbance. By using RBFNN to online approximate the unknown nonlinear dynamics, a novel nonlinear extended state observer (ESO) is firstly designed to estimate the total disturbance consisting of time-varying external disturbance and RBFNN approximation error. Then, an anti-disturbance feedback control law together with a tracking differentiator is designed to counteract the total disturbance in a feedforward manner. The bounded convergence of the closed-loop system and ESO as well as the estimation errors of the weighting vector are rigorously analyzed based on the Lyapunov stability theory. A case study is carried out to demonstrate the effectiveness and merit of the proposed design, in contrast to the conventional ADRC.

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Semi-Supervised Multiview Fuzzy Broad Learning

SSRN

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SSRN 2023年

作者： Xi, Chao Peng, Cheng Fan, Zizhu Liu, Qiang Wang, Hui Key Laboratory of Advanced Control and Optimization of Jiangxi Province East China Jiaotong University Nanchang330013 China Department of Engineering King’s College London London4616 United Kingdom State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning110819 China

Semi-supervised learning models often rely on the restricted assumptions, and can easily suffer from corvariate shift or noise. Few studies have explored the use of fuzzy rule-based methods in the semi-supervised discipline. In this paper, we first build a semi-supervised fuzzy broad learning model called MT-FBLS to address these issues. Further, a trusted multiview semisupervised classification method, termed TMSSC, is proposed by integrating the MT-FBLS with a multiview fusion network to enhance the robustness of the model. Under the Mean-Teacher framework, MT-FBLS involves the Takagi-Sugeno-Kang(TSK) fuzzy model which can effectively deal with imprecision and uncertainty, and broad learning system(BLS) which has strong rule learning ability and high computational efficiency. TMSSC utilizes a trusted mechanism to blend multiple views, so as to enhance its learning ability in the semi-supervised *** on the benchmark datasets demonstrate that the proposed methods have better anti-noise ability, competitive classification accuracy, as well as fast running speed. © 2023, The Authors. All rights reserved.

关键词： Learning systems

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Cellulose nanocrystals intercalated clay biocomposite for rapid Cr(VI) removal

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Environmental Science and Pollution Research 2024年第20期31卷 29719-29729页

作者： Deng, Zilong Wu, Zixuan Wu, Qin Yu, Junlei Zou, Chenglong Deng, Huali Jin, Pingliang Fang, Donglu State Key Laboratory for Pollution Control School of Environmental Science and Engineering Shanghai Institute of Pollution Control and Ecological Security Tongji University 1239 Siping Road Shanghai200092 China Co-Innovation Center for Sustainable Forestry in Southern China College of Forestry Nanjing Forestry University Jiangsu Nanjing210037 China School of Civil Engineering and Architecture East China Jiaotong University Nanchang330013 China Shanghai Dongfang Guochuang Advanced Textile Innovation Center Co. Ltd Shanghai Textile Science Research Institute Co. Ltd Shanghai200082 China Food Inspection and Testing Research Institute of Jiangxi General Institute of Testing and Certification Jiangxi Nanchang330046 China

The application of bentonite (Bt) as an adsorbent for heavy metals has been limited due to its hydrophobicity and insufficient surface area. Herein, we present cellulose nanocrystal (CNC) modified Bt composite (CNC@Bt) with enhanced efficiency for Cr(VI) removal. CNC@Bt exhibited an increased specific surface area and a porous structure, while maintaining the original crystal structure of Bt. This was achieved through a synergistic function of ion exchange, hydrogen bonding, electrostatic interactions, and steric hindrance. The adsorption of Cr(VI) by CNC@Bt followed the pseudo-second-order kinetic and Langmuir isotherm adsorption model. Moreover, the process was endothermic and spontaneous. At an initial Cr(VI) concentration of 20 mg/L and pH = 4.0, 10 g/L CNC@Bt achieved a removal rate of 92.7%, and the adsorption capacity was 1.85 mg/g, significantly higher than bare Bt (37.9% and 0.76 mg/g). The removal efficiency remained consistently above 80% over a wide pH range, indicating the potential practical applicability of CNC@Bt. With its fast adsorption rate, pH adaptability, and stable performance, CNC@Bt presents promising prospects for the rapid treatment of Cr-contaminated wastewater. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Adsorption

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Optimal design of extractive dividing-wall column using an efficient equation-oriented approach

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Frontiers of Chemical Science and Engineering 2021年第1期15卷 72-89页

作者： Yingjie Ma Nan Zhang Jie Li Cuiwen Cao Centre for Process Integration Department of Chemical Engineering and Analytical ScienceSchool of EngineeringThe University of ManchesterManchester M139PLUK Key Laboratory of Advanced Control and Optimization for Chemical Processes(Ministry of Education) East China University of Science and TechnologyShanghai 200237China

The extractive dividing-wall column(EDWC)is one of the most efficient technologies for separation of azeotropic or close boiling-point mixtures,but its design is fairly *** this paper we extend the hybrid feasible path optimisation algorithm(Ma Y,McLaughlan M,Zhang N,Li ***&Chemical Engineering,2020,143:107058)for such optimal *** tolerances-relaxation integration method is refined to allow for long enough integration time that can ensure the solution of the pseudo-transient continuation simulation close to the steady state before the required tolerance is *** ensure the gradient and Jacobian information available for optimisation,we allow a relaxed tolerance for the simulation in the sensitivity analysis mode when the simulation diverges under small *** addition,valid lower bounds on purity of the recycled entrainer and the vapour flow rate in column sections are imposed to improve computational *** computational results demonstrate that the extended hybrid algorithm can achieve better design of the EDWC compared to those in *** energy consumption can be reduced by more than 20%compared with existing literature *** addition,the optimal design of the heat pump assisted EDWC is achieved using the improved hybrid algorithm for the first time.

关键词： design extractive dividing-wall column equation-oriented optimisation pseudo-transient continuation model hybrid algorithm

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Wavelet Function Based Spectral Model Calibration for Measuring Crystallization Solution via ATR-FTIR Spectroscopy 11

Wavelet Function Based Spectral Model Calibration for Measur...

引用

11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Pei, Xiaojing Liu, Tao Liu, Jingxiang Hao, Shoulin Yang, Siwei Dalian University of Technology Ministry of Education Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Dalian116024 China Institute of Advanced Control Technology Dalian University of Technology Dalian116024 China School of Marine Electrical Engineering Dalian Maritime University Dalian116026 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

For measuring the solution concentration of crystallization process by ATR-FTIR spectroscopy, this paper proposes an improved spectral model calibration method to guarantee in-situ measurement accuracy, based on wavelet function regression. The spectral model structure is composed of wavelet basis functions, which could address the nonlinear properties and high-dimensional problem of infrared spectral variables. Moreover, the mean centralization strategy is taken to reduce the influence from measurement noise. In addition, a comparative study on measuring the solution concentration of the L-glutamic acid (LGA) crystallization process is conducted in terms of using different modeling methods and spectral variables. It is found that the wavelet function regression with all spectral variables could obtain good measurement accuracy compared to the traditional partial least-squares (PLS) method. Experimental results well demonstrate the measurement accuracy of the proposed spectral calibration method. © 2022 IEEE.

关键词： Fourier transform infrared spectroscopy

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Optimizing Bounding Box Regression in Complex Backgrounds

Optimizing Bounding Box Regression in Complex Backgrounds

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2024 China Automation Congress, CAC 2024

作者： Chen, Jun Zhang, Haiyan Yu, Anjun Wang, Yiwei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Jiangxi Ganyue Expressway Co.Ltd. Nanchang China School of Automation China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798350368604

In the context of electrical power operation sites, the automated and accurate detection of whether workers are properly equipped with safety gear, such as safety clothing, helmets, and safety ropes for high-altitude operations, is crucial for ensuring the safety of personnel. Traditional object detection models often struggle to accurately identify these critical pieces of safety equipment in complex environments, due to variable object sizes, overlapping objects, and substantial background noise. To specifically address these challenges, we propose a novel loss function-Enhanced Alignment Intersection over Union (EAIoU) loss. This loss function is designed to enhance the model’s ability to discriminate between closely spaced and overlapping safety gear in real-time, significantly improving robustness against scale variations and complex backgrounds. In experiments based on the YOLOv8s architecture, our model achieved a mean Average Precision (mAP) of 92.1% on a dataset specific to electrical power operations, markedly outperforming traditional IoU loss functions. Additionally, EAIoU demonstrated improved performance on the COCO dataset, indicating its generalization capability across various complex scenarios. This research not only enhances the accuracy and real-time performance of safety equipment detection at electric power operation sites but also opens new avenues for advanced object detection technology in complex, multi-object environments. © 2024 IEEE.

关键词： Safety devices

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Using deep learning to denoise and detect gravitational waves

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Physical Review D 2024年第6期110卷 063010页

作者： CunLiang Ma ShuoQiu Li Zhoujian Cao Mingzhen Jia School of Information Engineering Jiangxi Province Key Laboratory of Multidimensional Intelligent Perception and Control Ganzhou 341000 China Institute of Applied Mathematics School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study

We have upgraded the MSNRnet framework to MSNRnet-2 by refining the training strategy, drawing inspiration from generative adversarial networks for data generation. The astrophysical discrimination network enforces constraints on the denoising output, ensuring that the “signal+noise” case conforms to an astrophysical-origin shape, while limiting the denoised output of the “noise” case to a nonastrophysical origin shape. This improvement has led to enhanced denoising and astrophysical discrimination performance in most scenarios. For the analysis of confident events, we observed a 1.3% increase in the detection rate. Remarkably, no false triggers were generated during the analysis of the data jointly processed by the Hanford and Livingston interferometers in O3b (about five months of data). These findings suggest that the denoising-discrimination-matched filtering framework holds promising potential for gravitational wave searches in the future.

关键词： Classical black holes General relativity Gravitational waves

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