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IEEE Conference on Global Communications (GLOBECOM)

作者： Chao Fang Haizhen Luo Xiaoping Yang Haofei Xie Fangqing Tan Shu-Ming Tseng Mianxiong Dong Faculty of Information Technology Beijing University of Technology Beijing China Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin China Key Laboratory of Industrial Internet of Things & Networked Control Chongqing University of Posts and Telecommunications Chongqing China Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan

To satisfy the differentiated service requirements of delay-sensitive and computing-intensive tasks, it is urgent to efficiently allocate limited network resources to improve content distribution in cloud-edge environments. In this paper, we proposes a task partition-based intelligent offloading scheme to optimize resource allocation in cache-assisted cloud-edge cooperation environments. Specifically, we formulate the task partition-based optimal computation offloading problem as a latency minimization model in the cache-aided cloud-edge collaboration system. A new deep reinforcement learning (DRL) algorithm is designed to make optimal subtask offloading and resource allocation decisions based on current network state information, improving resource utilization and network delay. Simulation results demonstrate that the proposed model achieves lower-latency content delivery than the existing popular models in cache-enabled cloud-edge cooperation networks, and fast converges.

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Source-free Unsupervised Domain Adaptation for Privacy-Preserving Intelligent Fault Diagnosis

Source-free Unsupervised Domain Adaptation for Privacy-Prese...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Mengliang Zhu Xiangyu Zeng Jie Liu Kaibo Zhou MOE Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Huazhong University of Science and Technology Wuhan P. R. China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan P. R. China

Rolling bearing is of vital significance in industrial applications and Intelligent fault diagnosis (IFD) have been widely exploited in this field. However, cross-machine variations hinder the model performance across different machines in varying working conditions. To solve this issue, many unsupervised domain adaptation (UDA) approaches have been proposed recently, requiring direct access to the fully labeled source domain. However, privacy concerns are raised in traditional UDA scenario, since raw signals contain various private information. To address this issue, the source-free unsupervised domain adaptation (SFUDA) scenario is considered, where only the pre-trained source model is required, instead of the fully labeled source domain. In this paper, a SFUDA approach for intelligent fault diagnosis (IFD) is proposed. It consists of two steps: 1) source model generalization, where virtual adversarial training, R-Drop and label smoothing techniques are adopted to improve the generalization ability of the source model; and 2) target model adaptation, where information maximization is used for cluster assumption and mean teacher training paradigm is utilized to alleviate the catastrophic forgetting phenomenon. The proposed approach is verified on the Case Western Reserve University (CWRU) dataset. Experimental results on the show that the proposed SFUDA approach outperforms several typical UDA approaches, and its effectiveness is verified.

关键词： Fault diagnosis Training Employee welfare Adaptation models Privacy Smoothing methods Data analysis

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A New Anomaly Detector of Flare Pilot Based on Siamese Network

A New Anomaly Detector of Flare Pilot Based on Siamese Netwo...

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Chinese Automation Congress (CAC)

作者： Xiaotian Xie Ke Gu Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The flare pilot is the key to ensure that the flare system is ignited at any time. At present, domestic and foreign companies mainly use thermocouples, flame ionization detectors and other sensors to detect the working condition of the flare pilot. These electronic components have the hysteretic and vulnerable problems due to extreme heat, thermal shock and vibration, which may well further lead to the failure to ignite the flare gas with flare pilot in time and cause production accidents. In view of the above problems, this paper proposes an anomaly detector of flare pilot based on deep learning technology. First of all, we made an anomaly detection database for flare pilot, which was shot in a domestic petrochemical company. Then, we designed a specific siamese network based on the triplet loss function for learning the similarity between the samples, in order to apply to the curing scene of the flare pilot. Finally, it is determined whether the flare pilot is abnormal according to the similarity between the input picture and the positive and negative samples. Experimental results demonstrate that the proposed method can be effectively used for the anomaly detection of flare pilot.

关键词： Databases Anomaly detection Petrochemicals Feature extraction Employee welfare Detectors Combustion

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A New Method to Estimate the Severity of Interturn Short-Circuit Fault for PMSM

A New Method to Estimate the Severity of Interturn Short-Cir...

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Power systems and Electrical Technology (PSET), IEEE International Conference on

作者： Zhaofei Zeng Man Zhang Luan Chen Qi Huang Ding Sun Key Laboratory of Wi de-area Measurement and Control on Power System of Sichuan Province University of Electron ic Science and Technology of China Chengdu China 1University of Electronic Science and Technology of China Chengdu China Institute of Electronic and Information Engineering of UESTC in Guangdong UESTC in Guangdong Dongguan China Chengdu Univer sity of technology Chengdu China Key Laboratory of W ide-area Measurement and Control on Power System of Sichuan Province University of Electron ic Science and Technology of China Chengdu China

This paper proposes a new method to determine the number of shorted turns for permanent magnet synchronous motor (PMSM). At first, the equivalent circuit model of PMSM under both normal condition and inter-turn short-circuit (ITSC) fault are presented. Then, the procedure to estimate the severity of ITSC in PMSM is introduced. In detailed, the stator winding of PMSM at standstill is excited by a low AC voltage, the corresponding current can be measured including its amplitude and phase, finally, the severity estimation indicator is established based on the electromagnetic parameters, including amplitude and phase of voltage and current, phase inductance and resistance. The effectiveness of the proposed method is verified by some simulations, which illustrates that the proposed method have good accuracy in the number of shorted turns detection in the range from the fault-free state to the full-winding fault state. The method proposed in this paper can be directly implemented without installing complex equipment and additional sensors on the motor, and it has utility value in the maintenance of PMSM.

关键词： Resistance Voltage measurement Phase measurement Power supplies Simulation Stator windings Sensor phenomena and characterization

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Global Finite Time Stabilization of SISO Hurwitz Linear systems Subject to Actuator Saturation: The Case of Real Eigenvalues

Global Finite Time Stabilization of SISO Hurwitz Linear Syst...

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American control Conference (ACC)

作者： Tan Hou Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA U.S.A.

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper considers the global finite time stabilization of linear systems subject to actuator saturation. We show that a controllable SISO system whose eigenvalues are real and negative can be globally stabilized in finite time by a homogeneous feedback law. To obviate numerical issues, this homogeneous feedback law is implemented by discretizing a compact set. Numerical simulation validates the effectiveness of the proposed approach.

关键词： Linear systems Actuators Numerical simulation control systems Eigenvalues and eigenfunctions

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GM-DF: Generalized Multi-Scenario Deepfake Detection

arXiv

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arXiv 2024年

作者： Lai, Yingxin Yu, Zitong Yang, Jing Li, Bin Kang, Xiangui Shen, Linlin The School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Information Security The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510080 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Existing face forgery detection usually follows the paradigm of training models in a single domain, which leads to limited generalization capacity when unseen scenarios and unknown attacks occur. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets. We first find a rapid degradation of detection accuracy when models are directly trained on combined datasets due to the discrepancy across collection scenarios and generation methods. To address the above issue, a Generalized Multi-Scenario Deepfake Detection framework (GM-DF) is proposed to serve multiple real-world scenarios by a unified model. First, we propose a hybrid expert modeling approach for domain-specific real/forgery feature extraction. Besides, as for the commonality representation, we use CLIP to extract the common features for better aligning visual and textual features across domains. Meanwhile, we introduce a masked image reconstruction mechanism to force models to capture rich forged details. Finally, we supervise the models via a domain-aware meta-learning strategy to further enhance their generalization capacities. Specifically, we design a novel domain alignment loss to strongly align the distributions of the meta-test domains and meta-train domains. Thus, the updated models are able to represent both specific and common real/forgery features across multiple datasets. In consideration of the lack of study of multi-dataset training, we establish a new benchmark leveraging multi-source data to fairly evaluate the models' generalization capacity on unseen scenarios. Both qualitative and quantitative experiments on five datasets conducted on traditional protocols as well as the proposed benchmark demonstrate the effectiveness of our approach. The codes will be available on https://***/laiyingxin2/GM-DF. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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Power Quality Disturbances Classification with Imbalanced/Insufficient Samples Based on Wgan-Gp-Sa and Dcnn

SSRN

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SSRN 2024年

作者： Xi, Yanhui Li, Xule Zhou, Feng Hunan Province Higher Education Key Laboratory of Power System Safety Operation and Control Changsha University of Science and Technology Hunan Province Changsha410114 China School of Electronic Information and Electrical Engineering Changsha University Hunan Province Changsha410022 China

The accurate identification of power quality disturbance signals (PQDs) is of great importance to ensure the safe and stable operation of power systems. Considering that there are insufficient samples and imbalance samples of power quality disturbance signals in real systems, the hybrid classification method based on WGAN-GP-SA (Wasserstein generative adversarial networks with gradient penalty and Squeeze-and-Excitation block with atrous spatial pyramid pooling module) and DCNN (deep convolutional neural network) is proposed for recognition of PQDs. Firstly, time-frequency features are extracted by combining improved Kalman and continuous wavelet transforms (CWT);secondly, the original training time-frequency maps are expanded by using WGAN-GP-SA, which can learn the distribution of real data and generate similar samples, and PSNR, SSIM, FID and other indexes are introduced for evaluation of generated data;finally, classification experiments are conducted on two data sets using DCNN, and simulation results show that the accuracy is improved by 2.82% compared with the original data without augmentation. Furthermore, t-distributed stochastic neighbor embedding algorithm (t-SNE) is introduced for visually analysing of classification performance of various datasets, and the visualization indicates the superiority of the hybrid classification method. © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Adaptive Neural Consensus Tracking control for Nonlinear Multiagent systems with Dead Zone and Sensor Faults

Adaptive Neural Consensus Tracking Control for Nonlinear Mul...

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Chinese control Conference (CCC)

作者： Chengjie Huang Ci Chen Zhi Liu Zhenni Li Guangdong University of Technology Guangzhou P. R. China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou P. R. China Guangdong Key Laboratory of IoT Information Technology Guangzhou P. R. China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou P. R. China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of Education Guangzhou

In this paper, an adaptive neural tracking control design is investigated for uncertain multiagent systems (MASs) with actuator dead zone and sensor faults. The adaptive control technique based on neural networks is employed to handle the uncertainty in the systems, which, however, results in a bounded tracking error from most existing works. To achieve the asymptotic tracking performance, we proposed a direct adaptive strategy via the tuning function method. Technically, a Lyapunov function with a series of smooth functions is designed, which benefits proposing of an asymptotic controller. Then, under the framework of direct adaptive control, an inverse compensation scheme, as well as a fault-tolerant mechanism, are considered for disposing of the actuator dead zone and sensor failures. As a result, user-preset tracking performance is achieved and the system error in the MASs converges to a predefined range. An example in the simulation section illustrates the effectiveness of the proposed method.

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Connectivity-Aware Contract for Incentivizing IoT Devices in Complex Wireless Blockchain

arXiv

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arXiv 2022年

作者： Wang, Weiyi Chen, Jin Jiao, Yutao Kang, Jiawen Dai, Wenting Xu, Yuhua The College of Communications Engineering Army Engineering University of Pla Nanjing210007 China The School of Automation Guangdong University of Technology Guangzhou510006 China The Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China The School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China

Blockchain is considered the critical backbone technology for secure and trusted Internet of Things (IoT) in the future 6G network. However, deploying a blockchain system in a complex wireless IoT network is challenging due to the limited resources, complex wireless environment, and the property of self-interested IoT devices. The existing incentive mechanism of blockchain is not compatible with the wireless IoT network. In this paper, to incentivize IoT devices to join the construction of the wireless blockchain network, we propose a multi-dimensional contract to optimize the blockchain utility while addressing the issues of adverse selection and moral hazard. Specifically, the proposed contract considers the IoT device's hash power and communication cost and especially explores the connectivity of devices from the perspective of complex network theory. We investigate the energy consumption and the block confirmation probability of the wireless blockchain network via simulations under varied network sizes and average link probability. Numerical results demonstrate that our proposed contract mechanism is feasible, achieves 35% more utility than existing approaches, and increases utility by 4 times compared with the original PoW-based incentive mechanism. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Toward Understanding How the Saturation Effect Impacts the Spread of Computer Viruses

Toward Understanding How the Saturation Effect Impacts the S...

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International Conference on Electronic Engineering and Informatics (EEI)

作者： Xianxiu Zhang Yuming Feng Yi Yang Baojie Zhang Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

It is well known that people who know the presence of network viruses will defend against them, and the more computer viruses are detected and the more measures are taken. However, most previous computer virus propagation models have completely ignored the saturation effect. This paper investigates how the saturation effect impacts the transmission of electronic virus. For this purpose, a new transmission model of network virus is proposed, in which the virus is not very prevalent. Under certain conditions, there is a global asymptotical stable equilibrium point with no virus in this model. Finally, we give several numerical examples to exhibit that sufficient saturation effect is greatly conducive to containing network viruses.

关键词： Asymptotic stability Computer viruses Computational modeling Stability analysis Numerical models Informatics Viruses (medical) Numerical stability

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