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Collaborative Learning in Bilateral Academic Training on Robotics Design and Development

Collaborative Learning in Bilateral Academic Training on Rob...

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STEM Education Conference (iSTEM-Ed), International

作者： Patinya Sang-Aroon Sethakarn Prongnuch Tadchanon Chuman Aphirak Thitinaruemit Kalaphath Kounlaxay Chanida Niyomtum Dept. of Industrial Design and Packaging Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Robotics Engineering Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Electrical Technology Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Computer Engineering Faculty of Engineering Souphanouvong University Luangprabang City Lao PDR Education Automation System Co. Ltd. Bangplee Samutprakran Thailand

ISBN: (数字)9798350365979

ISBN: (纸本)9798350365986

This paper presents how collaborative learning can facilitate academic training in robotics design and development, with the objective of establishing a foundation in robotics structure design and microcontroller programming for controlling robots, particularly for bilateral undergraduate students. The students comprise 20 engineering students and 20 architecture students, who are divided into groups of two, consisting of an engineering students and an architecture students each. Group activities focusing on robot structure design and robot Development are provided for the students. The results demonstrate that collaborative learning can effectively facilitate the training activities.

关键词： Training Educational robots Federated learning Microcontrollers Focusing engineering students Programming profession

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An Intelligent Ground Station Selection Algorithm in Satellite Optical Communications via Deep Learning

An Intelligent Ground Station Selection Algorithm in Satelli...

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Mediterranean Conference on Control and automation (MED)

作者： Andrea Wrona Emanuele De Santis Francesco Delli Priscoli Francesco Giacinto Lavacca Dept. of Computer Control and Management Engineering (DIAG) University of Rome “La Sapienza” Rome Italy Consortium for the Research in Automation and Telecommunication (CRAT) Rome Italy Dept. of Information Engineering Electronics and Telecommunications (DIET) University of Rome “La Sapienza” Rome Italy

Among the most common issues to be faced in optical satellite communications, weather conditions play a fundamental role for a correct transmission of the information. In the case of heavy rain, hailstorm snow, or even dense clouds, a communication channel between the satellite and a optical ground station (OGS) may suffer significant interference, causing errors in delivering information. Since satellite transmissions cover in general very widespread areas, it usually happens that different zones are characterized by different weather conditions. This property is exploited by the site diversity technique, that tries to limit bad weather effects on the overall availability of the communication channel. When implementing such a site diversity technique, the satellite should be able to switch between the OGSs, by evaluating the rain events probability either through direct measurement campaigns or exploiting statistical models. The setup studied in this work involves a geostationary satellite equipped with two laser communication terminals (LCTs). In order to dynamically decide the OGSs to be pointed by those LCTs, a Deep-Learning based proactive control algorithm for site diversity, performing weather forecasting and consequent preventive LCT switching on the basis of current and past weather conditions has been developed. Simulative results show the ability of our proposed algorithm in achieving the maximum possible link availability, which is bounded by the weather conditions of all the OGSs.

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Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods

Forecasting Breakdowns of Electric Motors of a Sugar Factory...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Nataliia Zaiets Nataliia Lutska Lidiia Vlasenko Andrii Zhyltsov dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of electromagnetic systems The Institute of Electrodynamics of the National Academy of Sciences of Ukraine Kyiv Ukraine

the paper demonstrates the process of developing mathematical models for identifying breakdowns of electric motors using machine learning methods. The authors have developed three mathematical models for identifying breakdowns of electric drives with a power of 55 kW, 1500 rpm on the example of sugar production: model for quadratic discriminant analysis; binary classification decision tree; feed-forward neural network model. The best structures and parameters of the models were determined by machine learning methods. The performance of the models on the training and test sets was more than 96% by F-score. It was proposed to use all three models independently for diagnosing breakdowns of electric drives, while the decision maker makes the final decision on the replacement or repair of electrical equipment. This approach is justified and effective because of the diversity of models. The developed models, except for assessing the state of the engine, can also be used for simulation modelling, forecasting, and serve as information support for process operators in organizing equipment maintenance. The availability of real-time information about the state of equipment in production will allow timely repair or replacement of equipment, thereby reducing the risks of stopping production and increasing the resource efficiency of technological processes.

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Pulling Figure of Dielectric Resonator for 3D Filament Defectoscopy

Pulling Figure of Dielectric Resonator for 3D Filament Defec...

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International Scientific Symposium on Metrology and Metrology Assurance (MMA)

作者： Maryna Miroshnyk Olga Zaichenko Anatolii Miroshnyk Nataliia Hapon Yurii Pakhomov Liubov Klymenko dept. of Theoretical and Applied System Engineering V. N. Karazin Kharkiv National University Kharkov Ukraine dept. of Design and Operation of Electronic Devices Kharkiv National University of Radioelectronics Kharkiv Ukraine dept. of Design Automation Kharkiv National University of Radioelectronics) Kharkiv Ukraine dept. of Computer Science and Information Technologies O.M. Beketov National University of Urban Economy in Kharkiv Kharkiv Ukraine dept. of Specialized Computer Systems Ukrainian State University of Railway Transport Kharkiv Ukraine

ISBN: (数字)9798350352832

ISBN: (纸本)9798350352849

The research aim is to derive connection between the parameters of dielectric resonators frequency shift and the warp defect of 3D printing filament. Object of investigation are the processes of interaction of electromagnetic wave of a dielectric resonator with filament for 3D printing. The proposed model describes the connection between the frequency shift and defect warp of filament frequency pulling depending on the load.

关键词： Electromagnetic scattering Resonant frequency Estimation Metrology Three-dimensional printing Resonance Dielectrics Frequency measurement Resonators Load modeling

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Analysis of the Influence of Students’ Identity on Academic Performance

Analysis of the Influence of Students’ Identity on Academic...

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IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics engineering, SIBIRCON

作者： Natalya V. Pustovalova Tatiana V. Avdeenko Dept. Automation and Computer Engineering Novosibirsk State Techinal University Novosibirsk Russian Federation Dept. Applied Mathematics and Computer Science Novosibirsk State Techinal University Novosibirsk Russian Federation

ISBN: (纸本)9781665464819

This paper presents the results of ongoing work. Today, personalization is one of the main trends in education. Universities collect and analyze data about the students' identity and their needs in order to develop specialized systems that support the personalization of the educational process. The purpose of the current work is to verify the applicability of the "Learner model" that was designed previously. This model is one of the main component of universities' intelligent learning system architecture. This model is based on the results of psychometric test. We increased the sample of students by including new observations and new predictors. Having performed preliminary data preparation, we obtained linear regression models. The results of psychometric tests were used as independent predictors (regressors). We tested students from the 2nd to 4th year of the Faculty of Applied Mathematics and Informatics. The dependent variable is the general ECTS score, as well as the average score for the groups of disciplines (mathematical, work-oriented, humanitarian). We compared the resulting models with those obtained using a smaller sample. In general, our conclusion was confirmed.

关键词： Learning systems Humanities Linear regression Knowledge based systems Predictive models Ontologies Multivariate regression

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Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information 6

Contrastive Learning for Context-aware Neural Machine Transl...

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6th Conference on Machine Translation, WMT 2021

作者： Hwang, Yongkeun Yun, Hyungu Jung, Kyomin Dept. of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Automation and Systems Research Institute Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781954085947

Context-aware neural machine translation (NMT) incorporates contextual information of surrounding texts, that can improve the translation quality of document-level machine translation. Many existing works on context-aware NMT have focused on developing new model architectures for incorporating additional contexts and have shown some promising results. However, most existing works rely on cross-entropy loss, resulting in limited use of contextual information. In this paper, we propose CorefCL, a novel data augmentation and contrastive learning scheme based on coreference between the source and contextual sentences. By corrupting automatically detected coreference mentions in the contextual sentence, CorefCL can train the model to be sensitive to coreference inconsistency. We experimented with our method on common context-aware NMT models and two document-level translation tasks. In the experiments, our method consistently improved BLEU of compared models on English-German and English-Korean tasks. We also show that our method significantly improves coreference resolution in the English-German contrastive test suite. © 2021 Association for Computational Linguistics

关键词： Neural machine translation

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University’s Educational Environment Personalization Based on the Ontological Models

University’s Educational Environment Personalization Based ...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Natalya V. Pustovalova Tatiana V. Avdeenko Alexandra V. Pustovalova dept. Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russian Federation dept. Applied Mathematics and Computer Science Novosibirsk State Technical University Novosibirsk Russian Federation

ISBN: (数字)9781665498043

ISBN: (纸本)9781665498050

The paper presents a work in progress. Personalization of the educational environment allows improving the quality of the educational process, raising it to a new level. Our development is intended to diagnose and recommend educational content, depending on the students’ personality. This article describes a system of ontological models (Learner Model, educational content model and cognitive functions model) designed to diagnose students’ characteristics and the subsequent issuance of educational content, taking into account these characteristics. The learner model, implemented as a Protege ontology, is based not only on human professional experience but also on student testing results. This approach, based on the use of various IQ tests, allows reflecting in the ontology the dependencies identified due to linear regression methods, as well as other types of analysis. By this way we will reduce the dependence of the model on the human expert’s personality and make it more objective. Using the Protege tools, we will combine three ontologies in the architecture of a personalized environment through rules and axioms.

关键词： Linear regression Ontologies Electron devices Testing

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Towards a Revolutionary Info-Geometric Control Theory with Potential Applications of Fokker Planck Kolmogorov(FPK) Equation to System Control, Modelling and Simulation 28

Towards a Revolutionary Info-Geometric Control Theory with P...

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28th International Conference on automation and Computing, ICAC 2023

作者： Mageed, Ismail A Zhou, Yuyang Liu, Yefeng Zhang, Qichun University of Bradford Dept. of Computer Science BradfordBD7 1DP United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom School of Automation and Electrical Shenyang Institute of Technology Liaoning Key Lab of Information Physical Fusion and Intelligent Cnc Machine Tool Manufacturing Fushun China

ISBN: (纸本)9798350335859

An exposition of a ground-breaking Info-Geometric approach to FPK probability density function (PDF) is undertaken in this paper. The results extend to finding the FPK Geodesic equations (GEs) of motion. Exact analytic solutions of these GES are derived. the Some potential applications of FPK to system control, modelling and simulation are provided. Challenging Open problems are given combined with conclusion and next phase research. © 2023 IEEE.

关键词： Probability density function

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Hybrid Water Quality Prediction with Graph Attention and Spatio-Temporal Fusion

Hybrid Water Quality Prediction with Graph Attention and Spa...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Lin, Yongze Qiao, Junfei Bi, Jing Yuan, Haitao Gao, Han Zhou, MengChu Beijing University of Technology Faculty of Information Technology Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Chinese Academy of Environmental Planning Beijing100012 China New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Spatio-temporal prediction has a wide range of applications in many fields, e.g., air pollution, weather forecasting, and traffic forecasting. Water quality prediction is also one of spatio-temporal prediction tasks. However, it faces the following challenges: 1) Water quality in river networks has complex spatial dependencies;2) There are complex nonlinear relations in water quality time series;and 3) It is difficult to realize long-term forecasting. To address these challenges, this work proposes a spatio-temporal prediction model called a Graph Attention-based Spatio-Temporal (GAST) neural network. GAST investigates spatial and temporal dependencies of water quality time series. First, we introduce a temporal attention mechanism to capture time series dependencies, which can effectively handle nonlinear relationships in time series. Second, we adopt a spatial attention mechanism to extract spatial dependencies of river networks and fuse temporal features of spatial nodes. Third, we adopt a temporal convolution residual mechanism based on the spatio-temporal fusion, which improves the accuracy of long-term series prediction. This work adopts two real-world datasets to evaluate the proposed GAST and experiments demonstrate that GAST outperforms several state-of-the-art methods in terms of prediction accuracy. © 2022 IEEE.

关键词： Time series

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Improved Transform Algorithm of Direct Computation of Discrete Cosine for Mel-scale Frequency Cepstral Coefficient

Improved Transform Algorithm of Direct Computation of Discre...

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2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021

作者： Lai, Shin-Chi Hung, Ying-Hsiu Zhu, Yi-Chang Wang, Szu-Ting Sheu, Ming-Hwa Juang, Wen-Ho National Formosa University Dept. of Automation Engineering Yunlin Taiwan National Yunlin University of Science and Technology Dept. of Electronic Engineering Yunlin Taiwan National Formosa University Doctor's Program of Smart Industry Technology Research and Design Yunlin Taiwan Nanhua University Dept. of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9781665437554

In this work, we proposed a direct computation of the DCT algorithm that is improved by utilizing the coefficients of cosine and sine to calculate the DCT bins for Mel-scale frequency cepstral coefficients (MFCC). Instead of computing directly, the cosine coefficient is sharply declined more than 85% at the expense of the amount of additions and multiplications. According to the PSNR comparison results, and the proposed method is similar to Jo et al.'s algorithm. which uses approximate computation, so that the PSNR value is closed to 52 dB. Although the proposed algorithm must pay for some accuracy and extra complexity in computation, it still can greatly reduce more sizes of ROM. Based on the point of interest of simple control and straightforward structure, the algorithm is a selectable design for applications to the design of MFCC. © 2021 IEEE.

关键词： Discrete cosine transforms

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