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6th International Conference on Communications, information System and Computer Engineering, CISCE 2024

作者： Miao, Weiwei Sun, Yong Zeng, Zeng Hong, Tao Xiao, Maoran State Grid Jiangsu Electric Power Co. Ltd. Information & Telecommunication Branch Nanjing China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China

ISBN: (纸本)9798350353174

The built-in elastic autoscaling strategy of Kubernetes, a container cloud orchestration and management system, balances the relationship between application service quality and cluster resource usage by regularly monitoring a single resource and comparing it with a set threshold to determine whether to scale up or down. However, the built-in strategy has the problems of incomplete evaluation of a single resource index, response delay and scaling jitter. To address the above problems, this paper uses an adaptive weighting algorithm to obtain a comprehensive prediction index based on long-range time multi-resource prediction sequences. This index is not only more comprehensive than a single index, but also measures the resource usage in the future time period, so that the adaptive comprehensive prediction index has the ability to more comprehensively measure the changes in application workload in the future time period. The experimental results show that the autoscaling strategy based on adaptive integrated resource forecast can effectively solve the problems of regulation lag and scaling jitter. © 2024 IEEE.

关键词： Prediction models

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Interference Utilization with Bandwidth Allocation in DFRC systems

Interference Utilization with Bandwidth Allocation in DFRC S...

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International Conference on Computers, Communications, and systems (ICCCS)

作者： Zhan Xu Shengfeng Kang Xiaolong Yang Key Laboratory of Information and Communication Systems Ministry of Information Industry Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Dual-functional Radar and Communication (DFRC) has become one of the key technologies of advanced wire-less systems. This is because of the continuous development of mobile communication, the scarcity of spectrum and the potential network applications. In this paper, we propose a symbol-level precoding optimization scheme for Multi-Input-Multi-Output (MIMO) radar and multi-user Multiple-Input-Single-Output (MU-MISO) communication systems. More specif-ically, the weight sum of communication rate and radar detection probability are maximized by jointly optimizing the precoding vector and bandwidth allocation. Meanwhile, it includes the condition of ensuring the receiving signal-to-interference-plus-noise ratio (SINR) at receivers, the interference to the radar and the transmission power budget at the base station (BS). Com-pared with the traditional beamforming based on Semidefinite Relaxation (SDR), the feasible area of the optimization problem is extended. Based on the concept of constructive interference, the harmful multi-user interference (MUI) is converted into a beneficial signal. So the communication rate is ensured and the system performance is improved. Numerical simulations show that our proposed method achieves a better result of the object function compared to benchmark methods, while obtaining a favorable bandwidth allocation.

关键词： Base stations Precoding System performance Radar detection Interference Radar Channel allocation

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Kernel-Guided Texture-Aware Blind Super-Resolution via Uncertainty Learning 13

Kernel-Guided Texture-Aware Blind Super-Resolution via Uncer...

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13th International Conference on information Technology in Medicine and Education, ITME 2023

作者： Zhao, Tingting Li, Zuoyong Quan, Li Zeng, Kun Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Computer Science and Big Data Fuzhou University Fuzhou350121 China

ISBN: (纸本)9798350319156

The accuracy of kernel estimation is critical to the performance of blind super-resolution (SR). Traditional kernel estimation methods usually use L1 or L2 loss function to minimize the difference between the estimated kernel and the ground-truth kernel. This tends to make the estimated kernel converge towards the average of all possible kernels, deviating from the ground-truth kernel. To improve the performance of kernel estimation, this paper proposes an uncertainty loss for training a kernel estimation network, focusing on regions with high uncertainty (variance) in the kernel. In addition, a texture-aware SR network is proposed that utilizes the Gumbel Softmax trick to pay more attention to the complex regions of the image texture, thus improving the SR performance. Extensive experiments on synthetic datasets show that our approach achieves promising performance. © 2023 IEEE.

关键词： Textures

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A Multi-Kernel Principal Component Analysis Method for Quality-Related Fault Detection 37

A Multi-Kernel Principal Component Analysis Method for Quali...

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Mu, Lingxia Lei, Biyu Liu, Ding Xi'An University of Technology School of Automation and Information Engineering Xi'an China Xi'An University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

ISBN: (纸本)9781665465366

In this paper, a multi-kernel principal component analysis (MKPCA) method for quality-related fault detection is proposed. The initial space is firstly mapped to a new space. The correlated information between the new space and output quality is then obtained by the kernel function. Meanwhile, with consideration of the advantage of global function and local function, a weight factor which combines them together is introduced to construct a multi-kernel function. In this way, the algorithm achieves better learning ability. The new space is projected to two mutually orthogonal subspaces, i.e., quality-related part and quality-unrelated part. In each subspace, fault information is expressed by different statistical indicators. The numerical example is presented to evaluate the performance of the MKPCA. The results show better reliability and high fault detection rate through proper spatial decomposition and kernel function construction. © 2022 IEEE.

关键词： Fault detection

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A New Hybrid Adaptive Self-Loading Fxlms and Cnn-Gru Net for Active Time-Varying Noise control

SSRN

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SSRN 2024年

作者： Zhu, Wenzhao Luo, Lei Key Laboratory of Intelligent Control and Neural Information Processing Education Ministry of China Yanshan University Qinhuangdao066004 China Key Laboratory of Optoelectronic Technology and Systems Education Ministry of China Chongqing University Chongqing400044 China

Active noise control (ANC) provides a lightweight and quick-to-deploy noise attenuation solution compared to traditional passive noise control. However, time-varying noise caused by the changing acoustic environment usually occurs in moving spaces, such as a car cabin, which further deteriorates the robustness of the adaptive filter and even deep networks in the ANC system. To use a deep ANC neural network with great denoising performance in a complex time-varying noise environment and reduce the interference of uncertain noise on the stability of the network, a new hybrid adaptive self-loading filtered-x least mean square (ASL-FxLMS) and convolutional neural network-gate recurrent unit (CNN-GRU) network is proposed. First, based on the CNN-LSTM structure, an improved CNN-GRU network with a multi-head attention mechanism is proposed to enhance attenuation performance for complicated periodical components in the time-varying noise and decrease the computing load as much as possible. In addition, an improved time-delay estimator is applied to improve the real-time ANC performance of CNN-GRU. Meanwhile, an adaptive self-loading FxLMS algorithm has been developed to deal with the uncertain components of time-varying noise. Moreover, to achieve balance attenuation, robustness, and tracking performance, the ASL-FxLMS and CNN-GRU are connected by a convex combination structure. Furthermore, theoretical analysis and simulations are also conducted to show the effectiveness of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Active noise control

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Solo Adaptation for Distributed Synchronization With Application to Event-Triggered Linear Multi-Agent systems

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IEEE Transactions on Automatic control 2025年第6期70卷 4225-4232页

作者： Chen, Ci Zheng, Zhiyang Lewis, Frank L. Xie, Kan Xie, Shengli Guangdong University of Technology Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education School of Automation Guangzhou510006 China Guangdong University of Technology School of Automation Guangdong Guangzhou510006 China The University of Texas at Arlington UTA Research Institute Fort Worth76118 United States Guangdong University of Technology 111 Center for Intelligent Batch Manufacturing Based on IoT Technology School of Automation Guangzhou China Guangdong University of Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of Education School of Automation Guangzhou510006 China

We study a solo adaptation-based protocol design problem for distributed synchronization of event-triggered linear multi-agent systems (MAS) over general directed graphs. We show that a solo adaptive gain, a locally static control gain matrix, and a distributed state measurement are sufficient for obtaining adaptive distributed synchronization of linear MAS over directed graphs without any prior knowledge of the global communication network. We then extend the solo adaptation structure to formulate an event-triggered synchronization control protocol. The main feature of our result is that the time for triggering a subsequent event is now computed using the distributed event-triggered state measurement, rather than the continuous-time state measurement. © 1963-2012 IEEE.

关键词： Continuous time systems

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FATA: Focal-Adjusted Test-Time Adaptation Under Data Imbalance

FATA: Focal-Adjusted Test-Time Adaptation Under Data Imbalan...

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International Conference on Big Data and information Analytics (BigDIA)

作者： Jinshen Jiang Hao Yang Yun Zhou National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China National Key Laboratory on Blind Signal Processing Chengdu China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Robust models should exhibit strong performance even in the presence of data imbalance problems. Solutions such as test-time adaptation(TTA) are currently proposed to address the challenge of inconsistent distribution of training and test data. However, most existing TTA methods do not consider the model's performance in the presence of data imbalance. In this paper, we propose a method of optimizing the loss function using focal loss while using a predictive entropy minimization optimization strategy for updating the parameters of the BN-layer affine transform in a cycle, i.e.,Focal-Adjusted Test-Time Adaptation (FATA), to alleviate the problem of data class imbal-ance. We selected the CIFAR10-C and CIFAR100-C datasets as experimental objects and used them to create class imbalance data. Among them, the CIFAR10-C dataset contains ten classes, and we set different data volume shares according to the class labels; the CIFAR100-C dataset contains ten major classes and 100 minor classes, and we set different data volume shares for each major class. The experimental results show that compared with the source pre-trained model and the traditional cross-entropy loss, the optimized method significantly improves the generalization performance and robustness of the model when dealing with the data imbalance problem. The research in this paper provides new ideas and techniques for solving the data imbalance problem, which is of great significance for further improving the performance of deep learning models in practical applications.

关键词： Training Adaptation models Uncertainty Training data Transforms Predictive models Minimization Data models Robustness Entropy

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A Novel Power Switching control Method of the Wind Power MPPT System Based on the Switching System Theory 24

A Novel Power Switching Control Method of the Wind Power MPP...

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24th International Electronics Symposium, IES 2022

作者： Guo, Xin Li, Wei-Peng Pan, Yuan Liu, Jun Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China Xi'an University of Technology School of Automation and Information Engineering Xi'an China

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

In order to effectively utilize the wind energy, the max power point tracking (MPPT) control method has been extensively used in the wind power system. The MPPT control in the wind power system is actually to control the power electronic converters connected with the wind turbine. However, due to the existing of the power switching devices, the converters were the typical nonlinear system. The traditional linearization modeling methods of the converters cannot represent the working process of the system accurately and the control strategies based on the linearization model of the converters only guarantee the satisfactory performance of system near the predefined working point. They were not suitable to the wind power MPPT system, which has the variable wind speeds and the variable max power points. This paper proposes a power switching control method based on a novel power switched model of the wind power MPPT system with Boost converter. The main contributions of the work are: (1) The proposed power switched model realizes the accurate description of the converter working process, which directly focus on the power states of the wind power system. (2) The proposed switching control method realizes the direct power switching control of the converter of the wind power MPPT system. The merits of the proposed method are simple control structure, no control parameter, no pulse width modulation block and strong robustness under circuit parameters uncertainty. The simulation results verify the effectiveness and the superiority of the proposed methods. © 2022 IEEE.

关键词： Wind power

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Product Traceability Digital Tool Powered by Mathematical Model for Logistics Digital Platform 15

Product Traceability Digital Tool Powered by Mathematical Mo...

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Kulba, Vladimir Medennikov, Viktor V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Laboratory of Modular Data Processing and Control Systems Moscow Russia Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Information and Computing Systems Department Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

This paper discusses a mathematical model for establishing a single digital logistics platform as a link between all participants in the added-value creation chain, including producers, resource and services suppliers, consumers, and logistics companies. This demonstrates that the platform in question is an opportunity to create digital tools for products and goods traceability throughout the entire production and logistics chain from the field to the end consumer's kitchen. © 2022 IEEE.

关键词： Digital devices

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Research on Robust Measurement Method of Heart Rate Using Remote Photoplethysmography Based on Adversarial Learning Network with High and Low Frequency Features

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IEEE Transactions on Circuits and systems for Video Technology 2025年第6期35卷 5208-5222页

作者： Zhai, Dezhao Chen, Wei Ding, Yinghao Yu, Ming Li, Qinwei Wu, Hang Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China People's Liberation Army Systems Engineering Institute Academy of Military Sciences Tianjin300161 China Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin300300 China Nankai University School of Artificial Intelligence Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, breaking the constraints of contact measurements and offering broad application prospects. However, in real monitoring scenarios, the distance of subjects and facial illumination often vary. This leads to degradation of rPPG signals due to changes in facial resolution and light intensity. This paper presents the High and Low Frequency Feature Adversarial Learning Network (HLFF-AL), which utilizes feature space interpolation and composite feature capture to perform multi-band global and local signal extraction. By adopting an adversarial learning strategy, it enhances the capability to capture semantic information of rPPG under varying resolutions and lighting, bridging the differences in rPPG signal generation due to resolution changes. While maintaining accuracy in HR measuring, the robustness of the network is improved to achieve robust measurement of HR via rPPG. Even under high-resolution conditions, the Mean Absolute Error (MAE) for the three public datasets - unchanging lighting (COHFACE, UBFC-rPPG) and varying lighting (BUAA-MIHR) - reached 1.10 bpm, 1.64 bpm, and 4.41 bpm, respectively, with a mere 0.29 bpm difference in MAE between high and low resolutions on the BUAA-MIHR dataset. This demonstrates that HLFF-AL can predict more robust rPPG signals in various resolutions and lighting scenarios, achieving competitive results compared to state-of-the-art methods. © 1991-2012 IEEE.

关键词： Heart

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