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Concentrated differentially private average consensus algorithm for a discrete-time network with heterogeneous dynamics

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JOURNAL OF THE FRANKLIN INSTITUTE 2022年第4期359卷 1655-1676页

作者： Liu, Xiao-Kang Wang, Yan-Wu Xiao, Jiang-Wen Chi, Ming Liu, Zhi-Wei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

This paper presents a privacy-preserving average consensus algorithm for a discrete-time network with heterogeneous dynamic nodes in the presence of Gaussian privacy noises. Renyi divergence is used to measure the privacy, and a distributed algorithm is proposed for each node in the network to protect the initial output state and ensure consensus almost surely. The convergence rate of the proposed algorithm relates to the communication topology, dynamics of systems, and decaying rates of privacy noises. Moreover, by increasing neighbors of nodes in the network, the proposed algorithm can strengthen preservation. To demonstrate the theoretical results, a numerical example is carried out on a network of one hundred nodes. (C) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： Computer networks

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Input-to-state stability of switched linear systems with unstabilizable modes under DoS attacks

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AUTOMATICA 2022年 146卷

作者： Wang, Yan-Wu Zeng, Ze-Hong Liu, Xiao-Kang Liu, Zhi-Wei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

In this paper, the input-to-state stability (ISS) problem of switched linear system with unstabilizable modes is investigated under denial-of-service (DoS) attacks and external disturbance. In contrast to earlier results which consider only stabilizable modes and usually impose certain constraints on the switching behavior and the DoS attacks, the switched system with unstabilizable modes is studied and a flexible description is proposed to specify the unstabilizable modes. Besides, a more general situation is considered that it allows switching during the active period of DoS attack and the attacks can take place arbitrarily independent of switching. Thus, it leads to a better description of switched system under DoS attacks and a more general result. Moreover, under the sampling scheme and DoS attacks, the asynchronous problem arises. Despite these added complications, a sufficient condition concerning DoS attacks frequency and duration is derived to ensure the ISS of the system under DoS attacks, and the result reveals the trade-off between the average time proportion of unstabilizable modes and the resilience of the system against DoS attacks. Finally, a numerical example and a comparative study are given to show the effectiveness of the proposed method. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Networked control system Switched linear system DoS attack Unstabilizable mode Asynchronous switching controller

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TMMM: Transformer in Multimodal Sentiment Analysis under Missing Modalities

TMMM: Transformer in Multimodal Sentiment Analysis under Mis...

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International Joint Conference on Neural Networks (IJCNN)

作者： Huanyu Wu Siyang Li Dongrui Wu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The cross-task gap presents a significant challenge for multimodal models because of the differences in input-output workflows. For instance, multimodal pre-trained transformers may encounter uni-modal data during testing. To mitigate the gap, this paper introduces a Transformer in Multimodal Sentiment Analysis under Missing Modalities (TMMM) aims to perform well using missing-modal data during testing. TMMM uses a missing multimodal training approach to prevent accuracy degradation in testing. At the same time, a new network architecture allows the model to reconstruct missing modalities during testing. Classification token fusion and Mixture-of-Experts structures further enhance the model’s performance. A pre-training method utilizing contrastive learning, which can construct negative samples with positive samples, is proposed to overcome insufficient labeled data. Our experiments demonstrated the effectiveness of TMMM on two datasets with no modalities missing, i.e., it consistently achieved the highest classification accuracy and Macro-F1, which outperformed the best state-of-the-art baseline on each dataset by about 2% and 2.5%. Additionally, TMMM usually performs better than other baselines on datasets with missing modalities during testing.

关键词： Training Sentiment analysis Accuracy Contrastive learning Network architecture Transformers Brain modeling

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An efficient and economical storage and energy sharing model for multiple multi-energy microgrids

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ENERGY 2022年第PartB期244卷 123124-123124页

作者： Cao, Wenzhi Xiao, Jiang-Wen Cui, Shi-Chang Liu, Xiao-Kang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Multi-energy microgrids are facing a dilemma that realizing high local energy efficiency requires large-capacity ESS with hefty investment costs. To address the dilemma, an efficient and economic hybrid storage and energy sharing model for multiple microgrids is proposed. Specificly, a hybrid energy storage system (HESS) is introduced, which contains an electrical battery and a heat storage tank and is able to realize energy conversion. The multiple microgrids can share energy through the HESS in a collaborative way. An energy optimization problem is formulated to minimize the overall energy costs including energy purchase cost and HESS operating cost. ADMM algorithm is used to solve the problem in a distributed manner to avoid privacy concerns. The storage and energy sharing benefits of the microgrids and the HESS are determined by Nash bargaining solution. Simulation results show that the model can effectively improve the utilization of the renewable energy, and lead to considerable economic benefits for both the microgrids and the HESS.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Multi-energy microgrids Storage sharing Energy sharing Optimization Nash bargaining

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SMA-PDPPO: Safe Multiagent Primal-Dual Deep Reinforcement Learning for Industrial Parks Energy Trading

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第3期21卷 2640-2649页

作者： Lu, Renzhi Wu, Ning Yang, Tao Chen, Ying Sun, Mingyang Wang, Dong Peng, Xin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Tsinghua Univ Elect Engn Beijing 100084 Peoples R China Peking Univ Coll Engn Dept Ind Engn & Management Beijing 100091 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

Energy trading in industrial parks has great potential for reducing carbon emissions and lowering energy bills. This article proposes a safe multiagent deep reinforcement learning algorithm for optimizing the energy trading strategy in industrial parks to achieve less reliance on the main grid and save energy costs. Specifically, an industrial park that contains multiple industrial users with both thermal and electrical load requirements is considered, in which the different users can trade energy with each other and with the main grid based on their own strategies. Unlike the existing studies, the time-phased energy trading problem is transformed into a constrained partially observable Markov game, which models the industrial users and objectives of the buyers and sellers. Finally, a novel multiagent primal-dual proximal policy optimization algorithm that guarantees safety is developed to achieve the optimal trading strategies between the main grid and multiple users. Numerical simulations with real-world data demonstrate that the proposed algorithm allows higher total revenue for sellers and lower total costs for buyers in the park, limits each user's bid or offer to a relatively safe range, and increases the amount of electricity traded locally, while reducing trading with the grid.

关键词： Deep reinforcement learning (DRL) electricity market energy management energy trading industrial park Deep reinforcement learning (DRL) electricity market energy management energy trading industrial park

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Scheduling Parallel Real-Time Tasks on Virtual Processors

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第1期34卷 33-47页

作者： Jiang, Xu Liang, Haochun Guan, Nan Tang, Yue Qiao, Lei Wang, Yi Northeastern Univ Key Lab Intelligent Comp Med Image Minist Educ Shenyang 110819 Liaoning Peoples R China City Univ Hong Kong Hong Kong Peoples R China Beijing Inst Control Engn Beijing 100124 Peoples R China Uppsala Univ S-75236 Uppsala Sweden

In many popular parallel programming models, e.g., OpenMP (OpenMP, 2013), applications are usually dispatched into several dedicated scheduling entities (named "threads " in common) for which the processor time of physical platform is provided through the OS schedulers. This behavior requires for a hierarchical scheduling framework, considering each thread as a virtual processor (VP). Moreover, hierarchical scheduling allow separate applications to execute together on a common hardware platform, with each application having the "illusion " of executing on a dedicated component. However, the problem for scheduling parallel real-time tasks on virtual multiprocessor platform has not been addressed yet. An analogous approach to virtual scheduling for parallel real-time tasks is federeted scheudling, where each task exclusively executes on a set of dedicated physical processors. However, federated scheduling suffers significant resource wasting. In this article, we study the scheduling of real-time parallel task on virtual multiprocessors. As a physical processor is shared by virtual processors, tasks effectively share processors with each other. We conduct comprehensive performance evaluation to compare our proposed approach with existing methods of different types. Experiment results show that our approach consistently outperforms existing methods to a considerable extent under a wide range of parameter settings.

关键词： Real-time scheduling vitual processors parallel tasks multiprocessor

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Fast Fixed-Time Output Multi-Formation Tracking of Networked Autonomous Surface Vehicles: A Mathematical Induction Method

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第5期72卷 5769-5781页

作者： Ding, Teng-Fei Xu, Kun-Ting Ge, Ming-Feng Park, Ju H. Liang, Chang-Duo China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Yeungnam Univ Sch Dept Elect Engn Gyongsan 38541 South Korea

In this paper, we aim to exploit an effective way to solve the output multi-formation tracking problem of the networked autonomous surface vehicles (ASVs) in a fast fixed time manner. Specifically, addressing the output multi-formation tracking problem implies that 1) the networked ASVs are divided into multiple interconnected subnetworks with respect to multiple targets;2) for each subnetwork, the outputs of the networked ASVs form a desired geometric formation with exchanging the local interactions. Besides, solving the fast fixed-time tracking problem in this paper implies that 1) the convergence time is independent of the initial conditions;2) the system states are forced to reach the employed nonsingular fixed-time sliding surface in a prescribed time, which thus called fast fixed-time control. Then, based on a time-related function and a nonsingular fixed-time sliding surface, a hierarchical fast fixed-time control algorithm is proposed to solve the aforementioned problem within a fast fixed time being independent of the initial conditions. Furthermore, by employing the Lyapunov argument and mathematical induction, we present the sufficient conditions for fast fixed-time convergence of the tracking errors with respect to multiple targets. Finally, numerous simulation examples are presented to demonstrate the effectiveness of the proposed control algorithm.

关键词： Target tracking Convergence Formation control Asymptotic stability Germanium Task analysis Standards Output multi-formation tracking hierarchical fast fixed-time control networked autonomous surface vehicles (ASVs)

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Design and application of a structure-varying coupled chaotic system with high security

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期226卷

作者： Ming, Hao Hu, Hanping Zheng, Jun Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Wuhan 430074 Peoples R China Minist Educ Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China

Chaos-based security applications are facing great challenges with various data analysis and prediction techniques. Focusing on the security risks in chaotic applications, this paper proposes a structure-varying delay -coupled chaotic model (SVDCCM), which has attack immunity and is proven to be chaotic mathematically. To choose suitable coupling structures for variation, the effects of different structures on chaotic performance are investigated in detail. We recommend loop structures for better chaotic behaviors and the structures in which the same coupling terms exist should be avoided to produce synchronous behaviors. Moreover, the general changing principles including random and fast varying are proposed to be the guidance on designing structure -varying systems, and a simple state-driven changing mechanism is given as an example. Experiment results show that SVDCCM has complex chaotic behaviors, good statistical properties, and high unpredictability. Its security when facing various attack challenges is also demonstrated, such as time-frequency attacks, phase space reconstruction attacks, change-point detection, etc. To illustrate its feasibility in the practical security application, a pseudo-random number generator (PRNG) based on SVDCCM is designed and implemented on the hardware platform. Performance tests indicate that the proposed PRNG can produce binary sequences with high reliability of randomness and unpredictability, which can be used in cryptosystems and other potential applications.

关键词： Coupled chaos Structure-varying Security Attack-immunity Pseudo-random number generator (PRNG)

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Stationary Mapping Based Generalized Monitoring Scheme for Industrial Processes With Mixed Operational Stages

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1-13页

作者： Wang, Zhaojing Zheng, Ying Wong, David Shan-Hill Wang, Yang Yang, Weidong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Natl Tsing Hua Univ Dept Chem Engn Hsinchu 300 Taiwan

Frequent operation changes are inevitable to achieve different production aims, which leads to mixed periods of stationarity and nonstationarity in industrial processes and increases the monitoring difficulty. In this article, a generalized monitoring scheme is proposed for industrial processes with stationary and nonstationary operational stages. First, the local average similarity (LAS) and distance average similarity (DAS) are developed on offline training data to divide operational stages and identify repeating stages. The equilibrium relationship between variables in each stage can be guaranteed rather than in the whole process, which is the premise of refined modeling. Then, multiple cointegration analysis (CA) and detrended fluctuation regression (DFR) models are proposed to handle nonstationary variables with different integrated orders, so as to map all stages into stationary space. For online monitoring, the real-time stage identification is performed based on the comprehensive similar index (CSI) that combines LAS and DAS, and the process monitoring is realized by a local static principal component analysis (PCA). Finally, the effectiveness of our proposed method is verified by an extended Tennessee Eastman simulation and batch-fed penicillin fermentation process.

关键词： Microwave integrated circuits Correlation Process monitoring Principal component analysis Color Biological system modeling Fluctuations Operational stage identification process monitoring stationary and nonstationary operational stages

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Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification

Knowledge-Data Fusion Based Source-Free Semi-Supervised Doma...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ruimin Peng Jiayu An Dongrui Wu Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Electroencephalogram (EEG)-based seizure sub-type classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with no source data and limited labeled target data, can be used for privacy-preserving seizure subtype classification. This paper considers two challenges in SF-SSDA for EEG-based seizure subtype classification: 1) How to effectively fuse both raw EEG data and expert knowledge in classifier design? 2) How to align the source and target domain distributions for SF-SSDA? We propose a Knowledge-Data Fusion based SF-SSDA approach, KDF-MutuaISHOT, for EEG-based seizure subtype classification. In source model training, KDF uses Jensen-Shannon Diver-gence to facilitate mutual learning between a feature-driven Decision Tree-based model and a data-driven Transformer-based model. To adapt KDF to a new target dataset, an SF-SSDA algorithm, MutualSHOT, is developed, which features a consistency-based pseudo-label selection strategy. Experiments on the public TUSZ and CHSZ datasets demonstrated that KDF-MutualSHOT outperformed other supervised and source-free domain adaptation approaches in cross-subject seizure subtype classification.

关键词： Training Adaptation models Data privacy Fuses Brain modeling Transformers Data models Electroencephalography Decision trees Cybernetics

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