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Forecasting-Aided Graphical Learning for Robust State Estimation of Distribution System 1

8th Purple Mountain Forum on Smart Grid Protection and control, PMF 2023

作者： Di, Cao Zhao, Junbo Hu, Jiaxiang Huang, Yuehui Huang, Qi Chen, Zhe Hu, Weihao University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing China Department of Energy Technology Aalborg University Pontoppidanstraede 111 Aalborg Denmark

ISBN: (数字)9789819992515

ISBN: (纸本)9789819992508

The forecasting of of pseudo-measurements play an important role in distribution system state estimation (DSSE). This paper proposes robust DSSE method based on forecasting-aided graphical learning method. The nodal power consumption models are first built to produce pseudo-measurements based on deep neural network. Then, the pseudo-measurements and real-time measurements are represented as a graph according to the topology of the distribution network, which are further processed by a graph attention network to capture the mapping relationship between the graphical measurements and the state variables based on the error modeling of pseudo-measurements. The robustness against anomalous measurements is achieved through the embedding of structural information of DN. The modeling of pseudo-measurements further enhance its robustness by guiding the formulation of edge weights of the graph neural network. Comparative tests are carried out on a IEEE 119-node system to demonstrate the effectiveness and robustness of the proposed method. © 2024, State Grid Electric Power.

关键词： Electric power distribution

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Adaptively temporal graph convolution model for epidemic prediction of multiple age groups

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Fundamental Research 2022年第2期2卷 311-320页

作者： Yuejiao Wang Dajun Daniel Zeng Qingpeng Zhang Pengfei Zhao Xiaoli Wang Quanyi Wang Yin Luo Zhidong Cao The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina Shenzhen Artificial Intelligence and Data Science Institute(Longhua) ShenzhenChina School of Data Science City University of Hong Kong-Hong Kong SARChina Institute for Infectious Disease and Endemic Disease Control Beijing Center for Disease Prevention and Control BeijingChina

Introduction:Multivariate time series prediction of infectious diseases is significant to public health,and the deep learning method has attracted increasing attention in this research *** and methods:An adaptively temporal graph convolution(ATGCN)model,which leams the contact patterns of multiple age groups in a graph-based approach,was proposed for COVID-19 and influenza *** compared ATGCN with autoregressive models,deep sequence learning models,and experience-based ATGCN models in short-term and long-term prediction ***:Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term(12.5%and 10%improvements on RMSE)and longterm(12.4%and 5%improvements on RMSE)prediction *** the RMSE of ATGCN predictions fluctuated least in different age groups of COVID-19(0.029±0.003)and influenza(0.059±0.008).Compared with the Ones-ATGCN model or the Pre-ATGCN model,the ATGCN model was more robust in performance,with RMSE of 0.0293 and 0.06 on two datasets when horizon is ***:Our research indicates a broad application prospect of deep learning in the field of infectious disease *** characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature ***:The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups,indicating its great potentials for exploring the implicit interactions of multivariate variables.

关键词： Graph convolution model Infectious disease prediction Multiple age group Multivariate time series Public health

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DSNRNet: A Network of LiDAR Data Denoising by Differential Stability in Underground Mine

DSNRNet: A Network of LiDAR Data Denoising by Differential S...

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International Conference on Intelligent Transportation

作者： Dejiang Meng Bin Tian Ziyu Pan Junhui Wang Long Chen The Waytous Inc. Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Data and Computer Science Sun Yat-sen University China

Some sprinklers in underground mines spray water mist to lessen the amount of dust in the air. But water mist causes LiDAR to provide inaccurate point measurements, which are referred to as water mist noise that undermines the effectiveness of LiDAR-based localization and object recognition. Therefore, in order to reduce water mist noise, we have developed a new noise segmentation network that can operate on a CPU in real time-differential stability noise removal network (DSNRNet). This network consists of two sub-networks. The first sub-network is aimed at extracting differential stability features, so we named it the differential stability feature extraction network (sub-network 1). The second sub-network: a fully connected neural network (sub-network 2), is used to segment noise. To evaluate the DSNRNet's performance, we built a LiDAR semantic segmentation dataset of underground mines and run the DSNRNet in an Intel i7-11800H CPU. The experimental results demonstrate that this method is able to strike a better balance between speed (26.3 milliseconds) and accuracy (97.8 % ) compared with the other two most possible methods-DSOR (24.2 milliseconds, 3.2 % ) and WeatherNet (436.8ms, 98.5 % ).

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Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models

arXiv

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arXiv 2023年

作者： Moradi, Sarvin Jaensson, Nick Tóth, Roland Schoukens, Maarten Control System group Eindhoven University of Technology Eindhoven Netherlands Eindhoven Artificial Intelligence Systems Institute Eindhoven Netherlands Processing and Performance of Materials Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In order to make data-driven models of physical systems interpretable and reliable, it is essential to include prior physical knowledge in the modeling framework. Hamiltonian Neural Networks (HNNs) implement Hamiltonian theory in deep learning and form a comprehensive framework for modeling autonomous energy-conservative systems. Despite being suitable to estimate a wide range of physical system behavior from data, classical HNNs are restricted to systems without inputs and require noiseless state measurements and information on the derivative of the state to be available. To address these challenges, this paper introduces an Output Error Hamiltonian Neural Network (OE-HNN) modeling approach to address the modeling of physical systems with inputs and noisy state measurements. Furthermore, it does not require the state derivatives to be known. Instead, the OE-HNN utilizes an ODE-solver embedded in the training process, which enables the OE-HNN to learn the dynamics from noisy state measurements. In addition, extending HNNs based on the generalized Hamiltonian theory enables to include external inputs into the framework which are important for engineering applications. We demonstrate via simulation examples that the proposed OE-HNNs results in superior modeling performance compared to classical HNNs. © 2023, CC BY-NC-SA.

关键词： Hamiltonians

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Software Complex and Mathematical Models for Technological Design of Ozone-safe Chladones Synthesis Processes

Software Complex and Mathematical Models for Technological D...

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International Conference on control systems, Mathematical Modelling, automation and Energy Efficiency (SUMMA)

作者： Tamara Balabekovna Chistyakova Inna Vasilievna Novozhilova Dmitriy Vsevolodovich Doctorov Anastasia Valerievna Egorova Olga Evgenievna Bezdudnaya Computer-Aided Design and Control Systems Department World-class Laboratory Saint-Petersburg State Institute of Technology Saint-Petersburg Russia Russian Research Center Applied Chemistry (GIPH) JSC Design Institute Saint-Petersburg Russia World-class Laboratory Saint-Petersburg State Institute of Technology Saint-Petersburg Russia Computer-Aided Design and Control Systems Department Saint-Petersburg State Institute of Technology Saint-Petersburg Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The functional structure of the problem-oriented software complex for solving the problem of designing a technological scheme for the multi-product processes for the synthesis of ozone-safe chladones is presented in the article. The inclusion of a customizable relational database of chladone characteristics in the software complex allows selecting equipment (working volumes of reactors) to form a production flow chart based on specified criteria (productivity, energy consumption, output concentrations of the target and by-products, etc.). The mathematical models library is the basis of the software complex. Mathematical models allow performing verification calculations of various reactors for the synthesis of a wide range of chladones in accordance with the technical specification. The advantage of the software complex is the ability to analyze the effect of contact time and process temperature on the output concentrations of by-products and target products with different compositions of the initial reaction mixture. The use of the software complex allows increasing the efficiency of designing chladones synthesis processes by reducing the time spent on selecting the hardware design of processes that ensure high conversion of raw materials and the required selectivity of processes for the target component.

关键词： Productivity Relational databases Software Mathematical models Raw materials Libraries Hardware Energy efficiency Kinetic theory Inductors

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PCB Surface Defect Detection Based on TPBAS-YOLOv5 3

PCB Surface Defect Detection Based on TPBAS-YOLOv5

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3rd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Luo, Changyu Chen, Shichao Wang, Kunfeng Hu, Xiangyu Wang, Kaiyuan Han, Shuangshuang Sichuan University The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Electrical Engineering Chengdu China Chinese Academy of Sciences Beijing Huairou Academy of Pararllel Sensing The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China Beijing University of Chemical Technology School of Information Science and Technology Beijing China Beijing Technology and Business University School of Artificial Intelligence Beijing China Sichuan University School of Electrical Engineering Chengdu China Technology Beijing School of Computer and Communication Engineering and Technology Beijing Beijing China

ISBN: (纸本)9798350318470

The use of machine vision for industrial defect detection in industrial manufacturing can effectively improve the production efficiency and production quality. Due to the complex defect background, similar defects and small defect targets of printed circuit boards(PCB), the methods of using machine vision to detect PCB defects not work well. To solve these problems, this paper proposes an algorithm model TPBAS-YOLOv5, which is based on YOLOv5 and can effectively improve the accuracy and efficiency of PCB defect detection. Firstly, to improve the accuracy of the small defect detection, a transformer module is imported into the model for obtaining the information of global variables. Secondly, the P2 detection layer is added to the neck of the model layer to obtain the more feature information of the detection target, which can also improve the detection of small targets. Thirdly, to solve the problem of missing key information of detection targets , the weighted bi-directional feature pyramid network , namely BiFPN , is imported into the new network architecture, which can realize the multi-level fusion of feature information. Furthermore, an adaptive feature fusion layer ASFF is added to the model to learn the spatial weight of feature map fusion at various scales, which can achieve better trade-off efficiency for detection *** the model fuses different parts, the size of the model has not changed almost. It can still work on the edge in the industrial field. Experimental results show that TPBAS-YOLOv5 can effectively improve recall rates to 96.1%, map@0.5 to 96.6% and map0.5:0.95 to 70.3% on open-source PCB dataset. Compared with YOLOv5, it increased by 0.9%, 1.6%, and 1.6%, respectively, and the FPS reached ***, it can realize the real-time detection of irregular defects and small target defects with high accuracy, and meet the reliability and real-time requirements of PCB defect detection in industrial scenarios. © 2023 IEEE.

关键词： Printed circuit boards

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Data-Based Online Optimal control for Multi-player Nonlinear Non-zero-Sum Games Using Recursive Least Squares

Data-Based Online Optimal Control for Multi-player Nonlinear...

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2021 China automation Congress, CAC 2021

作者： Yang, Gaofu Song, Ruizhuo Wei, Qinglai School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426473

In this paper, optimal control of nonlinear systems for non-zero-sum games is solved with a data-based and recursive least square. The new adjustment law is similar to experience replay algorithm which refer history data and the covariance matrix is introduced for storing historical data, which can avoid the waste of space and time caused by storing a large amount of historical data and collecting data. In tuning process, we use a discount factor which shows how trust to the collected data to guide adjustment law to update the weight. Meanwhile, discount factor can avoid the covariance matrix tending to zero due to accumulation of data. After that, the convergence of the weight is analyzed. Finally, using a simulation to verify the effectiveness of the tuning law. © 2021 IEEE

关键词： Covariance matrix

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Attentive online sequential extreme learning machine for nonlinear dynamic process monitoring

Attentive online sequential extreme learning machine for non...

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第34届中国过程控制会议

作者： Jingxin Zhang Yuying Cao Hai Zhou Department of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education Department of Automation Southeast University New Energy Research Center China Electric Power Research Institute

This paper provides a new monitoring method based on the attention mechanism and online sequential extreme learning machines(OS-ELM) for nonlinear dynamic process,referred as attentive ***-ELM provides the prediction of outputs and the update of parameters by sequential *** cope with nonlinearity,the attention mechanism is utilized to map data to a high-dimensional feature *** effect of the attention mechanism and the single hidden layer feedforward network trained with OS-ELM work collaboratively in monitoring the real-time process data to detect possible *** addition,fault detection rate and false alarm rate are adopted to evaluate the monitoring *** effectiveness of the proposed method is illustrated by a typically complicated industrial system.

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Towards a Test Framework for Reactive (Type 2) Asset Administration Shell Implementations

Towards a Test Framework for Reactive (Type 2) Asset Adminis...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Torben Miny Sebastian Heppner Igor Garmaev Marko Ristin Björn Otto Nico Braunisch Michael Jacoby Tobias Kleinert Hans Wernher van de Venn Martin Wollschlaeger Chair of Information and Automation Systems RWTH Aachen University Aachen Germany Institute of Mechatronic Systems Zurich University of Applied Sciences (ZHAW) Zurich Switzerland Institute for Automation and Communication Otto von Guericke University Magdeburg Germany Institute of Applied Computer Science TU Dresden Dresden Germany Department Information Management and Production Control (ILT) Fraunhofer IOSB Karlsruhe Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

As Industrie 4.0 (I4.0) technologies continue to advance, ensuring the reliability and robustness of emerging standards like Asset Administration Shells (AAS) becomes paramount. This paper introduces a comprehensive testing approach for reactive (a.k.a. Type 2) AAS implementations, addressing the challenges posed by their intricate and dynamic nature. Our methodology adopts a design-by-contract approach to formalize API behavior. We design the client as a test oracle, where code contracts extensively validate the AAS operations, their interactions and effects. The related test cases cover various operation chains, including superpaths, service specifications, and serialization modifiers. The objective of this approach is to enhance the reliability and effectiveness of testing in the evolving landscape of AAS Type 2 implementations.

关键词： Industries Codes Robustness Standards Manufacturing automation Contracts Testing

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Construction Methods Based on Minimum Weight Distribution for Polar Codes with Successive Cancellation List Decoding

arXiv

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arXiv 2023年

作者： Piao, Jinnan Li, Dong Liu, Jindi Yu, Xueting Li, Zhibo Yang, Ming Zeng, Peng The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China

Minimum weight distribution (MWD) is an important metric to calculate the first term of union bound called minimum weight union bound (MWUB). In this paper, we first prove the maximum likelihood (ML) performance approaches MWUB as signal-to-noise ratio (SNR) goes to infinity and provide the deviation when MWD and SNR are given. Then, we propose a nested reliability sequence, namely MWD sequence, to construct polar codes independently of channel information. In the sequence, synthetic channels are sorted by partial MWD which is used to evaluate the influence of information bit on MWD and we prove the MWD sequence is the optimum sequence evaluated by MWUB for polar codes obeying partial order. Finally, we introduce an entropy constraint to establish a relationship between list size and MWUB and propose a heuristic construction method named entropy constraint bit-swapping (ECBS) algorithm, where we initialize information set by the MWD sequence and gradually swap information bit and frozen bit to satisfy the entropy constraint. The simulation results show the MWD sequence is more suitable for constructing polar codes with short code length than the polar sequence in 5G and the ECBS algorithm can improve MWD to show better performance as list size increases. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal to noise ratio

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