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11th International Conference on computer and Communications Management, ICCCM 2023

作者： Lu, Xiqin Funabiki, Nobuo Ueda, Kiyoshi Kao, Wen-Chung Department of Information and Communication Systems Distributed System Design Lab. Okayama University Okayama Japan Department of Computer Science Control Software Lab. Nihon University Koriyama Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9798400707735

Network programming is the act of writing computer codes for communications between programs or processes in different computers across networks. It is crucial for applications and services using networks, including e-commerce, gaming, and social media. However, university courses of educating it are very limited. As a result, it has become important to develop and offer IT tools to support self-studies of network programming by novices. In this paper, we present a study of two types of introductory exercise problems, namely, Grammar-concept Understanding Problem (GUP) and Comment Insertion Problem (CIP), for self-studies of five basic network protocols of TCP/IP, ARP, SMTP, POP3, and SSH with Python. A GUP instance requests to answer the keyword such as a library function in the given source code that corresponds to the grammar concept described in the question. A CIP instance requests to insert the comment at each blank of the code. String matching is adopted to mark any answer at the browser-based answer interface. Through solving them, it is expected to understand the meaning of important words and the code structure for network programming. As preliminary evaluations, we generated six GUP and CIP instances respectively, and assigned them to 10 students in Okayama University, Japan. The results confirmed that the proposal is proper for novices to learn network programming with Python. © 2023 ACM.

关键词： Python

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Method of Tabular Implementation of the Arithmetic Operation of Multiplying Two Numbers Represented in the System of Residual Classes 9

Method of Tabular Implementation of the Arithmetic Operation...

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9th IEEE International Conference on Problems of Infocommunications Science and Technology, PIC S and T 2022

作者： Krasnobayev, Victor Yanko, Alina Kovalchuk, Dmytro V. N. Karazin Kharkiv National University Electronics and Control Systems Department Kharkiv Ukraine National University yuri Kondratyuk Poltava Polytechnic Department of Computer and Information Technologies and Systems Poltava Ukraine

ISBN: (纸本)9798350398915

The article discusses the method of tabular implementation of the arithmetic operation of multiplying two numbers represented in the system of residual classes (SRC). The existing methods of tabular implementation of the arithmetic operation of multiplying two numbers in the SRC allow you to implement this operation only in a positive numerical range. This circumstance significantly narrows the area of effective application of tabular arithmetic (TA) in the SRC. In order to further develop the scope of TA, the method of tabular implementation of the arithmetic operation of multiplication in the SRC has been improved in the article due to the additional possibility of simultaneously performing operations of both arithmetic and algebraic multiplication. For this, a mathematical model (MM) of the process of tabular implementation of the operation of modular multiplication in positive and negative numerical ranges is synthesized. The MM developed in the article is the basis for the method of tabular implementation of the arithmetic operation of multiplication. An example of the application of this method of multiplication of two numbers in the SRC in the negative numerical range is given. In the future, MM and an improved method for multiplying two numbers can be used as the basis for a generalized method for fast processing of arithmetic operations of modular addition, subtraction, and multiplication in the SRC. © 2022 IEEE.

关键词： Numbering systems

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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Gender, Age and Number of Participants Effects on Identification Ability of EEG-based Shallow Classifiers

Gender, Age and Number of Participants Effects on Identifica...

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2021 Ural Symposium on Biomedical Engineering, Radioelectronics and information Technology, USBEREIT 2021

作者： Svetlakov, Mikhail Hodashinsky, Ilya Slezkin, Artem Security of Computer Systems Tomsk State University of Control Systems and Radioelectronics Department of Complex Information Tomsk Russia

ISBN: (纸本)9781728176918

Biometric systems have numerous advantages over traditional authentication methods and should be taken into account: uniqueness, fraud-resistance, portability, convenience of use, scalability, possibility of reliable attendance recording. Use of electroencephalography (EEG) data as biometric factor can keep all these benefits and add new ones. This paper provides insight how EEG based biometrics identification ability can be affected by the number of participants, their gender and age. In order to study aforementioned factor's influence Leipzig Study for Mind-Body-Emotion Interactions (LEMON) resting state (both eyes-open state and eyes-closed state) EEG dataset were used. After few preprocessing adjustments and applying discrete wavelet transform as time-frequency domain feature extraction method resulting dataset were used for classification task. The testing was conducted in 5-fold cross-validation mode using the shallow model, Linear Support Vector Classifier. From experiment results following conclusions were made: increased number of participants decreased classifier accuracy, with age increase accuracy steadily improved, gender effects on accuracy are inconclusive. © 2021 IEEE.

关键词： Biometrics

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FUSE: a federated learning and U-shape split learning-based electricity theft detection framework

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Science China(information Sciences) 2024年第4期67卷 339-340页

作者： Xuan LI Naiyu WANG Liehuang ZHU Shuai YUAN Zhitao GUAN School of Control and Computer Engineering North China Electric Power University School of Cyberspace Science and Technology Beijing Institute of Technology Department of Finance Operationsand Information Systems(FOIS)Brock University

In recent years, the data-driven electricity theft detection methods integrated with edge cloud computing [1, 2] have not only demonstrated superior detection accuracy but also improved efficiency, making them viable alternatives to indoor inspections. Energy service providers(ESPs) typically manage regions by dividing them into various transformer districts(TDs). The detection of electricity theft in a particular region is performed by the associated TD,

关键词： Sensitive data

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Learning-based control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy 49

Learning-based Control for PMSM Using Distributed Gaussian P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Yin, Zhenxiao Dai, Xiaobing Yang, Zewen Shen, Yang Hattab, Georges Zhao, Hang Robotics and Autonomous Systems Thrust Guangzhou China Technical University of Munich Chair of Information-oriented Control Munich Germany Berlin Germany Freie Universität Berlin Department of Mathematics and Computer Science Berlin Germany The Hong Kong University of Science and Technology Department of Electronic & Computer Engineering Hong Kong

ISBN: (纸本)9798350331820

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to its flexibility of continuous system modeling and its guaranteed performance. For practical implementation, distributed GPR is adopted to alleviate the high computational complexity. However, the study of distributed GPR from a control perspective remains an open problem. In this paper, a control-aware optimal aggregation strategy of distributed GPR for PMSMs is proposed based on the Lyapunov stability theory. This strategy exclusively leverages the posterior mean, thereby obviating the need for computationally intensive calculations associated with posterior variance in alternative approaches. Moreover, the straightforward calculation process of our proposed strategy lends itself to seamless implementation in high-frequency PMSM control. The effectiveness of the proposed strategy is demonstrated in the simulations. © 2023 IEEE.

关键词： Synchronous motors

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Temperature sensor research as a part of a microprocessor system by statistical analysis methods 3

Temperature sensor research as a part of a microprocessor sy...

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3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Hryhorenko, Ihor Tverytnykova, Elena Hryhorenko, Svitlana Krylova, Viktoria National Technical University 'Kharkiv Polytechnic Institute' Information Measuring Technologies and Systems Department Kharkiv Ukraine National Technical University 'Kharkiv Polytechnic Institute' Department of Computer and Radio-Electronic Control Systems and Diagnostics Kharkiv Ukraine National Technical University 'Kharkiv Polytechnic Institute' Department of Automation and Control in Technical Systems Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

The paper considers the solution of the scientific and practical problem of assessing the accuracy and stability of the sensors working as part of the microprocessor control system for the parameters of the microclimate of an artificial ecosystem by using statistical methods for analyzing measurement results using the example of a temperature sensor. The importance of this task lies in the fact that the organization of accurate control of the parameters of the microclimate will ensure optimal climatic conditions for the development of plants and fish, and create an environment as similar as possible to the natural one by timely control of parameters and a quick reaction to the output of parameters at critical values. For the analysis, three series of measurement results of the control indicator (water temperature) were used. The analysis confirmed the hypothesis that there are no violations of the stability of the temperature sensor, which makes it possible to recognize the metrological sensor as reliable. It is proved that the problems of increasing the likelihood of controlling objects with stochastic parameters, and controlling their metrological reliability are relevant and are important for the development of the theory and practice of non-dismantling control. © 2022 IEEE.

关键词： Reliability theory

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Integration of ANN for Accurate Estimation and control in Wastewater Treatment 28

Integration of ANN for Accurate Estimation and Control in Wa...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ţiru, Andreea Elena Vasiliev, Iulian Diaconu, Larisa Vilanova, Ramon Voipan, Daniel Ratnaweera, Harsha Dunǎrea de Jos University of Galaţi Department of Automatic Control and Electrical Engineering Galaţi Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain Dunǎrea de Jos University of Galaţi Department of Computer Science and Information Technology Galaţi Romania Ås Norway

ISBN: (纸本)9798350339918

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so. © 2023 IEEE.

关键词： Neural networks

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On the Stabilization of a Quasilinear Discrete SDC System with control Constraints 16

On the Stabilization of a Quasilinear Discrete SDC System wi...

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16th International Conference on Stability and Oscillations of Nonlinear control systems (Pyatnitskiy's Conference), STAB 2022

作者： Danik, Yulia Dmitriev, Mikhail Murzabekov, Zainel Federal Research Center Computer Science and Control of the Russian Academy of Sciences Department 71 Moscow Russia Federal Research Center Computer Science and Control of the Russian Academy of Sciences Department 81 Moscow Russia Al-Farabi Kazakh National University Department of Information Systems Almaty Kazakhstan

ISBN: (纸本)9781665465861

We consider the stabilization problem for a quasilinear discrete system with state-dependent coefficients and control constraints. The Krotov extension principle is used to take into account the constraints and the control is based on the Discrete State-dependent Riccati equation approach (D-SDRE). The results of numerical experiments are presented. © 2022 IEEE.

关键词： Stabilization

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