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Smith predictor-based nonlinear internal model control for water temperature regulation of combi-boiler systems

Smith predictor-based nonlinear internal model control for w...

引用

2023 Jiangsu Annual Conference on Automation, JACA 2023

作者： Liu, Ruihao Cao, Mengying Li, Shihua Yang, Jun The Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China BDR THERMEA HVAC Co. Ltd Jiaxing314000 China The Department of Aeronautical and Automotive Engineering Loughborough University LoughboroughLE11 3TU United Kingdom

ISBN: (纸本)9781839539848

This paper proposes a reliable and practical water temperature control scheme for combi-boiler systems working in domestic hot water mode. In practice, the water temperature regulation process of a combi-boiler is strongly affected by the nonlinearities of actuators, the variation of system parameters caused by the fluctuation of water flow rate, external disturbances, and large, variable time delays. Although there are some results that can address one or two of these factors, a comprehensive and concise control scheme with consideration of all these factors is lacking. This paper aims to change this situation. First, based on the modeling and identification of real combi-boiler systems, the feedback linearization method is adopted in the controller synthesis to solve the effects of actuator nonlinearity. Next, a Smith predictor-based control structure is designed to eliminate system time delays. Finally, based on the internal model control (IMC) theory, an IMC-based parameter tuning method is proposed for the parameter selection of feedback controllers, in which the model parameters used in the controller synthesis are calculated in real-Time based on the system identification results. As a result, the influence of model uncertainties and external disturbances can be effectively attenuated, and only one parameter is required to be tuned for the desired performance. The feasibility and effectiveness of the proposed control scheme are verified by simulations. © The Institution of engineering & Technology 2023.

关键词： Water temperature

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Research and Suggestions for Evaluation Metrics of On-board Video Desensitization

Research and Suggestions for Evaluation Metrics of On-board ...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Xiaolong Huang Te Hu Haojie Ji Yupeng Chen Wangdong Lu Peng He Guowei Sun Jingyan Wang State Key Laboratory of Intelligent Vehicle Safety Technology Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China China Automotive Engineering Research Institute Co. Ltd Chongqing China Zhongtong Bus Holding Co. Ltd Liaocheng China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

Sensitive data leakage has become an urgent problem to be solved as more images based functionalities are being developed in vehicles. However, there is a scarcity of evaluation for on-board videos data desensitization. This research analyzes on-board video desensitization process including the image pre-processing stage, sensitive area localization stage and sensitive area desensitization stage. Considering that, this paper presents several evaluation methods and metrics of sensitive target detection performance, privacy-utility evaluation of video file metadata and image, so as to provide reference for the related research on the desensitization evaluation of on-board video.

关键词： measurement Location awareness Information science Target recognition Current measurement Distributed databases Object detection

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Enabling few-shot learning with PID control: a layer adaptive optimizer 24

Enabling few-shot learning with PID control: a layer adaptiv...

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Proceedings of the 41st International Conference on Machine Learning

作者： Le Yu Xinde Li Pengfei Zhang Zhentong Zhang Fir Dunkin School of Automation Southeast University Nanjing China and Nanjing Center for Applied Mathematics Nanjing China and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China School of Automation Southeast University Nanjing China and Nanjing Center for Applied Mathematics Nanjing China and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China and Southeast University Shenzhen Research Institute Shenzhen China School of Automation Southeast University Nanjing China and Nanjing Center for Applied Mathematics Nanjing China

Model-Agnostic Meta-Learning (MAML) and its variants have shown remarkable performance in scenarios characterized by a scarcity of labeled data during the training phase of machine learning models. Despite these successes, MAMLbased approaches encounter significant challenges when there is a substantial discrepancy in the distribution of training and testing tasks, resulting in inefficient learning and limited generalization across domains. Inspired by classical proportional-integral-derivative (PID) control theory, this study introduces a Layer-Adaptive PID (LA-PID) Optimizer, a MAML-based optimizer that employs efficient parameter optimization methods to dynamically adjust task-specific PID control gains at each layer of the network, conducting a first-principles analysis of optimal convergence conditions. A series of experiments conducted on four standard benchmark datasets demonstrate the efficacy of the LA-PID optimizer, indicating that LA-PID achieves state-of-the-art performance in few-shot classification and cross-domain tasks, accomplishing these objectives with fewer training steps. Code is available on https://***/yuguopin/LA-PID.

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Path Optimization of complex and Large-Scale Automated Antenna Assembly Lines

Path Optimization of Complex and Large-Scale Automated Anten...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Ma, Zeyu Wang, Hongjun Zhang, Mingliang Jiang, Fan Li, Bin School of Mechanical Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Beijing International Science Cooperation Base of High-End Equipment Intelligent Perception Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100192 China Beijing Institute of Radio Measurement Beijing100854 China School of Information Management Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9783031494208

Considering issues such as low assembly efficiency and the inability of the assembly process index and product characteristics to be fully reflected in the traditional construction mechanical arm assembly process, the concept of a digital twin was introduced. The study involved the modeling and simulation technology of the construction mechanical arm assembly process based on the digital twin, and included some path optimization. Furthermore, according to the virtual model, a genetic algorithm was employed to optimize the path in Matlab. Additionally, the Hamiltonian cycle algorithm was utilized to enhance the efficiency of the robot and decrease the working time. Taking the assembly line of a screw arm as an example, it was demonstrated that the assembly simulation based on digital twinning can monitor the assembly process in real time. It can also optimize its configuration to improve assembly efficiency and implement an effective closed-loop feedback mechanism to enhance production efficiency, improve production quality, and reduce production cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cyber Physical System

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Research on Eigenvalue of Excitation Signal in Nondestructive Testing of Power Transformer Core 1

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18th Annual Conference of China Electrotechnical Society, ACCES 2023

作者： Qu, Yinghui Jin, Fang Dong, Kaifeng Cai, Shengwei Gong, Yujia Fan, Fan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China China Electric Power Research Institute Co. Ltd. Wuhan430074 China

ISBN: (数字)9789819714285

ISBN: (纸本)9789819714278

Transformer is the most critical equipment in the power system. In recent years, secondary silicon steel sheets have been used in the manufacture of transformer cores, which has caused hidden dangers in the quality of transformers. Currently, the main testing method for transformer core materials is disassembly testing, and there is no effective non-destructive testing method. Because magnetic Buckhausen noise (MBN) signals can reflect the internal defects of ferromagnetic components, this paper uses MBN testing methods to conduct non-destructive testing and analysis the side of transformer core silicon steel sheets. In the study of transformer core MBN signals, it was found that the waveform, frequency, amplitude and other parameters of the excitation signal can affect the eigenvalues of the transformer core MBN signal. The effects of excitation signal waveform, frequency, and amplitude on the characteristic values of MBN signals on the side of transformer core silicon steel sheet were experimentally studied. The research results show that when the excitation signal is a square wave with an amplitude of about 5V, excellent MBN signals can be excited. © Beijing Paike Culture Commu. Co., Ltd. 2024.

关键词： Nondestructive examination

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Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

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17th International Conference on Intelligent Robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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A Noise Filter for Dynamic Vision Sensor Based on Spatiotemporal Correlation and Hot Pixel Detection

A Noise Filter for Dynamic Vision Sensor Based on Spatiotemp...

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International Conference on Autonomous Unmanned Systems, ICAUS 2021

作者： Xu, Ninghui Zhao, Jiajia Ren, Yuan Wang, Lihui Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education School of Instrument Science and Engineering Southeast University Nanjing210096 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789811694912

As a new type of neuromorphic sensor, the events-driven Dynamic Vision Sensor (DVS) has the advantages of low latency and wide dynamic range. The development of DVS overcomes the application limitations of traditional cameras in the field of high speed robots and autonomous driving. However, there are abundant unwanted noise events contained in the output of DVS, which severely affects the subsequent data-processing. To tackle the problem, a novel noise filter for DVS is proposed in this paper. Firstly, since the noise is composed of hot pixel events with high-frequency and random background activities, a time window is constructed to continuously detect hot pixels. Secondly, a spatiotemporal correlation-based denoising approach for each incoming event is conducted, while excluding its surrounding hot pixels, to avoid the negative effect caused by their high temporal correlation on the filter. Finally, the hot pixels are compensated with the events that are most probably to occur depending on the adjacent events, to achieve the completeness and precision of the output event stream. Experiments in different scenes demonstrate that the proposed noise filter can effectively eliminate the noise from the event stream of DVS and obviously outperforms the baseline method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pixels

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Asymptotic Stabilization of Time-Varying Delay System with Actuator Saturation via Multi-dimensional Taylor Network

Asymptotic Stabilization of Time-Varying Delay System with A...

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International Conference on Robotics and Automation engineering (ICRAE)

作者： Guo-Biao Wang Hong-Sen Yan School of Automation Southeast University Nanjing Jiangsu China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing Jiangsu China

This paper addresses the stabilization problem of time-varying delay systems subject to actuator saturation. A novel class of Lyapunov functional with a nonlinear metric is developed to guarantee that there exists a Lyapunov-Razumikhin function that strictly decreases for the controlled system. Such a nonlinear metric is parameterized by a multi-dimensional Taylor network with concise topology, which brings into the high-order dynamic information and guarantees real-time performance. A sum-of-squares programming approach based on the designed Lyapunov functional is formulated to deduce the stability criteria, which maximizes the estimate of the region of attraction and ensures the uniform asymptotic stability of the closed-loop system theoretically. A numerical example demonstrates the effectiveness of the proposed stabilization control scheme.

关键词： measurement Actuators Asymptotic stability Delay systems Stability criteria Programming Real-time systems

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A Multi-view Non-negative Matrix Factorization Single-cell Clustering Algorithm for Missing Data

A Multi-view Non-negative Matrix Factorization Single-cell C...

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2022 International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2022

作者： Yu, Ailian Zhu, Yuan School of Automation China University of Geosciences Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781665454728

ISBN: (纸本)9781665454728

A significant study focus on the field of bioinformatics has been on how to quickly and accurately accomplish the cell clustering task using computational approaches for single-cell RNA sequencing (scRNA-seq) data. Technical issues with scRNA-seq data include missing values, high noise levels, and a large batch impact. While the single-cell data recovery method based on data filling can enhance data quality, it will subjectively alter the distribution of data. To address the above-mentioned issues, we propose a non-negative matrix factorization clustering algorithm (scMVNMF) based on multi-view data of scRNA-seq. Under the assumption of essentially unchanging original data distribution, it can not only identify the typical low-dimensional representation patterns of multi-view data but also produce final explicable clustering findings. The results of the experiments demonstrate that the suggested algorithm has some advantages over the filling approach in terms of clustering tasks and can maintain a certain level of stability when the quality of single-cell data varies. © 2022 IEEE.

关键词： Non-negative matrix factorization

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation 26

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Huang, Nanxin Xu, Chi 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 Wuhan China

ISBN: (纸本)9798331521950

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models. © 2025 IEEE.

关键词： Industrial robots

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