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Energy, Power and Electrical engineering (EPEE), International Conference on

作者： Jinpeng Kan Yongqing Meng Changyi Li Wenjie Chen Fengjiao Li Yixin Shi School of Electrical Engineering Xi’an Jiaotong University Xi’an China Department of Computer and Electronic Systems Engineering AVIC Xi’an Flight Automatic Control Research Institute Xi’an China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

Power electronic switching devices and pulse width modulation (PWM) not only improves the performance of motor drive systems, but also brings about common-mode voltage (CMV) issues, that challenging the normal operation of the system. This article proposes a zero common-mode voltage modulation strategy based on unified voltage modulation for a 60° Y-connected six-phase permanent magnet synchronous motor system driven by a dual three-phase inverter. The theoretical basis and zero common-mode implementation scheme of unified modulation are analyzed, and the effectiveness of the proposed zero common-mode voltage modulation strategy is finally verified through simulation.

关键词： Pulse width modulation inverters Performance evaluation Simulation Modulation Voltage Switches Permanent magnet motors Inverters Permanent magnets Switching circuits

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Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 74-87页

作者： Bogang Qu Zidong Wang Bo Shen Hongli Dong Hongjian Liu the College of Automation Engineering Shanghai University of Electric PowerShanghai 200090China IEEE the Department of Computer Science Brunel University LondonUxbridgeMiddiesex UB83PHUK the College of Information Science and Technology Donghua UniversityShanghai 200051 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast PetroleumDaqing 163318 the Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of EducationAnhui Polytechnic UniversityWuhu 2410000 the School of Mathematics and Physics Anhui Polytechnic UniversityWuhu 241000China

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

关键词： Decentralized state estimation(SE) measurements with anomalies minimum error entropy unscented Kalman filter wide-area power systems

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Cardiac Ischemia Detection Using Self-Organizing Map Feature Selection Method

Cardiac Ischemia Detection Using Self-Organizing Map Feature...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Ramin Aghili Karampour Amir Arshia Bakhtiar Kiarash Hajizamani Alireza Fallahi Hamid Soltanian Zadeh Biomedical Engineering Department Hamedan University of Technology Hamedan Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529710

ISBN: (纸本)9798331529727

Cardiac ischemia, a prevalent cause of heart failure, remains the leading cause of death in Iran. Early diagnosis of this condition is crucial, and electrocardiogram (ECG) signal processing techniques offer valuable insights. By analyzing ECG signals from ischemic patients and comparing them to those of normal individuals, we can extract relevant features for classification. Our study extracted some features from ECG signals, including statistical, time-based, frequency, and time-frequency features. We employed the self-organizing maps method (SOM) to identify the most informative features. Subsequently, we evaluated the classification accuracy using several machine learning algorithms, including support vector machines (SVM), Naive Bayes, Linear Discriminant Analysis (LDA), and Logistic Regression. The results demonstrated the superiority of the SOM method in selecting the best features compared to PCA, with accuracies of $\mathbf{9 1. 2 \%}, \mathbf{8 4. 3 \%}, \mathbf{7 2. 5 \%}$, and $88.9 \%$ for SVM, LDA, NB, and LR methods, respectively. Our findings contribute to the early detection and management of cardiac ischemia, potentially improving patient outcomes.

关键词： Self-organizing feature maps Support vector machines Time-frequency analysis Logistic regression Accuracy Machine learning algorithms Electrocardiography Signal processing Feature extraction Principal component analysis

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Q-Learning Based control for Swing-Up and Balancing of Inverted Pendulum

Q-Learning Based Control for Swing-Up and Balancing of Inver...

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Intermountain engineering, Technology and Computing (IETC)

作者： Antora Dev Kanan Roy Chowdhury Marco P. Schoen Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Measurement & Control Engineering Research Center (MCERC) Idaho State University Pocatello ID USA

ISBN: (数字)9798350373073

ISBN: (纸本)9798350373080

This study addresses the classic control problem of stabilizing an inverted pendulum on a moving cart, a challenge in control theory and robotics due to its inherent instability and highly nonlinear dynamics. We explore the application of Q-learning, a model-free reinforcement learning algorithm, and its efficacy in deriving an optimal control policy for the system without precise system models. Our approach utilizes Q-learning's capacity for stabilizing a pendulum in an upright position on the top of the horizontally moving cart within a certain boundary. Our strategy adapts to a dynamic environment while showcasing its robustness in developing control policies for complex systems. This research bridges classical control theory with reinforcement learning techniques, contributing to the domain by demonstrating the versatility and potential of machine learning in control tasks.

关键词： Adaptation models Q-learning Heuristic algorithms Optimal control Robustness control theory Nonlinear dynamical systems

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Estimating the job's pending time on a high-performance computing cluster through a hierarchical data-driven methodology 23

Estimating the job's pending time on a high-performance comp...

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23rd International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data, DOLAP 2021

作者： Carfí, Fabio Capitelli, Enrica Nosenzo, Vladi Massimo Cerquitelli, Tania Department of Control and Computer Engineering Politecnico di Torino Turin Italy CNH Industrial Turin Italy

Nowadays, manufacturing industries have to shorten the time to market to satisfy customers' needs and survive in globally competitive contexts. For these reasons, all the steps required to create new products need to be optimized as much as possible to minimize the overall execution time. Technologies like CAD (computer-Aided Design) and CAE (computer-Aided engineering) are useful in this process. At the same time, Finite Element Methods are fundamental because they allow performing simulations, not only of mechanical components but also of an entire manufacturing process. Doing so, there is the possibility to avoid errors which would entail the need to carry out further operations and, therefore, increase execution time during the product's creation. In the context of heavy simulations performed on High-Performance Computing (HPC) architectures, it is challenging to estimate each job's pending time, i.e., the time between the job submission and the job execution starting time. This information could support the optimization of the resource requests, drastically reducing the waiting times needed to obtain the simulations' results. This paper presents an innovative and hierarchical data-driven methodology to estimate the discretized value of pending time, i.e., estimating the range time that a job will have to wait before the necessary hardware resources are supplied for its execution. A large set of experiments have been performed on a real dataset collected in an Italian industrial context to assess the proposed approach's effectiveness in correctly and accurately predict the pending time's discretized value. This work is a preliminary step towards implementing a scheduling system based on machine learning techniques. © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： computer aided engineering

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Computationally Efficient Implementation of the Weighted Kalman Filter for Quadratic Systems

Computationally Efficient Implementation of the Weighted Kal...

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American control Conference (ACC)

作者： Damiano Rotondo Marcin Witczak Lothar Seybold Department of Electrical and Computer Engineering (IDE) University of Stavanger Norway Institute of Control and Computation Engineering University of Zielona Gora Poland RAFI GmbH & Co. KG Germany

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

The weighted Kalman filter (WKF) can be per- ceived as a variant of the extended Kalman filter (EKF), which incorporates the so-called weighted linearisation. The price behind such an extension is expressed by an increased computational complexity, which aims at improving the overall convergence of the filter. Owing to the increased complexity, the WKF is in general not suitable for high-order nonlinear systems. To overcome this difficulty, the objective of this paper is to show that a closed-form expression of the WKF can be obtained for a specific class of nonlinear systems, i.e., those characterised by a quadratic state equation. This closed-form of the WKF scales well with the order of the system, enabling the application of the WKF to high-order nonlinear quadratic systems, as exemplified in the final part of the paper using a comprehensive Monte Carlo analysis applied to a large number of random systems.

关键词： Closed-form solutions Monte Carlo methods Simulation Integral equations control systems Mathematical models Computational efficiency

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Fractional Order Sliding Mode control with Fuzzy Parameter Tuning Applied to a SCARA Robot

Fractional Order Sliding Mode Control with Fuzzy Parameter T...

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Mechatronic Systems Supervision (IW_MSS), IEEE International Workshop on

作者： Aicha Znidi Fatma Abdelhedi Ahmed Said Nouri Nabil Derbel Lab: Numerical Control of Industrial Processes National Engineering School of Gabes University of Gabes Tunisia Department of Electrical and Computer Engineering College of Engineering King Abdulaziz University Jeddah Saudi Arabia Control & Energy Management Laboratory University of Sfax National Engineering School of Sfax Tunisia

ISBN: (数字)9798350378382

ISBN: (纸本)9798350378399

This paper presents the application of Fractional Order Sliding Mode control (FO-SMC) in order to achieve a robust motion trajectory regulation in dynamic robot systems. The proposed control strategy benefits of both advantages of fractional calculus and sliding mode control to upgrade the robustness and the performances of the controller. Moreover, the paper proposes to use a fuzzy tuning of sliding mode controller in order to satisfy the capabilities of the actuators. The stability of the closed loop system has been demonstrated using the Lyapunov theory. Simulation results demonstrate the efficiency of the proposed method achieving precise trajectory tracking and robust performances, it has been shown that fuzzy tuning of the sliding function parameters significantly reduces control variable peaks, making the control more suitable for actuator application.

关键词： Asymptotic stability Actuators Trajectory tracking Simulation Manipulators Stability analysis Closed loop systems Tuning Sliding mode control Fractional calculus

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An α-regret analysis of adversarial bilateral trade

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Artificial Intelligence 2024年 337卷

作者： Azar, Yossi Fiat, Amos Fusco, Federico Tel Aviv University Tel Aviv Israel Sapienza University of Rome Department of Computer Control and Management Engineering "Antonio Ruberti" Via Ariosto 25 Rome00185 Italy

We study sequential bilateral trade where sellers and buyers valuations are completely arbitrary (i.e., determined by an adversary). Sellers and buyers are strategic agents with private valuations for the good and the goal is to design a mechanism that maximizes efficiency (or gain from trade) while being incentive compatible, individually rational and budget balanced. In this paper we consider gain from trade, which is harder to approximate than social welfare. We consider a variety of feedback scenarios and distinguish the cases where the mechanism posts one price and when it can post different prices for buyer and seller. We show several surprising results about the separation between the different scenarios. In particular we show that (a) it is impossible to achieve sublinear α-regret for any α © 2024 The Author(s)

关键词： Adversarial machine learning

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Exploiting Hankel–Toeplitz Structures for Fast Computation of Kernel Precision Matrices

arXiv

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arXiv 2024年

作者： Viset, Frida Kullberg, Anton Wesel, Frederiek Solin, Arno Delft Center for Systems and Control Delft University of Technology Netherlands Department of Electrical Engineering Linköping University Sweden Department of Computer Science Aalto University Finland

The Hilbert–space Gaussian Process (hgp) approach offers a hyperparameter-independent basis function approximation for speeding up Gaussian Process (gp) inference by projecting the gp onto M basis functions. These properties result in a favorable data-independent O(M3) computational complexity during hyperparameter optimization but require a dominating one-time precomputation of the precision matrix costing O(NM2) operations. In this paper, we lower this dominating computational complexity to O(NM) with no additional approximations. We can do this because we realize that the precision matrix can be split into a sum of Hankel–Toeplitz matrices, each having O(M) unique entries. Based on this realization we propose computing only these unique entries at O(NM) costs. Further, we develop two theorems that prescribe sufficient conditions for the complexity reduction to hold generally for a wide range of other approximate gp models, such as the Variational Fourier Feature (vff) approach. The two theorems do this with no assumptions on the data and no additional approximations of the gp models themselves. Thus, our contribution provides a pure speed-up of several existing, widely used, gp approximations, without further approximations. © 2024, CC BY.

关键词： Gaussian distribution

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Towards Named Entity Disambiguation with Graph Embeddings

Towards Named Entity Disambiguation with Graph Embeddings

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Felice Paolo Colliani Giuseppe Futia Giovanni Garifo Antonio Vetrò Juan Carlos De Martin Department of Mathematical Sciences Politecnico di Torino Torino Italy GraphAware Srl Lecce Italy Department of Control and Computer Engineering Politecnico di Torino Torino Italy

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

Extracting structured knowledge from scientific literature is crucial for helping professionals make well-informed decisions. This paper presents an approach to distilling knowledge from biomedical documents within the context of Named Entity Disambiguation (NED). The proposed method leverages a joint representation of biomedical entities, combining pre-trained language models with graph machine learning techniques. A Siamese Neural Network (SNN) is trained to optimize this joint representation by integrating the contextual text embeddings of entity mentions with the graph embeddings of corresponding canonical entities in a biomedical Knowledge Graph (KG). During the inference phase, the SNN model assigns a score to this joint representation to disambiguate the target entity among a set of candidates. To the best of our knowledge, this is the first NED method in the biomedical domain that incorporates graph embeddings using a neural model. We empirically evaluated the effectiveness of our approach against well-known biomedical datasets, such as MedMentions and BC5CDR. The results demonstrate a promising direction in utilizing the relational knowledge captured by graph embeddings for the NED task.

关键词： Biological system modeling Neural networks Machine learning Knowledge graphs Information and communication technology

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