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Auto-Adaptive Stator Ground Fault Protection for Synchronous Generators in Diesel-Electric Locomotives

Auto-Adaptive Stator Ground Fault Protection for Synchronous...

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IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)

作者： Kumar Mahtani José M. Guerrero Luis F. Beites Carlos A. Platero Automation Electrical and Electronic Engineering and Industrial Informatics Dept. Universidad Politécnica de Madrid Madrid Spain Electronics and Computer Science Dept. Mondragon Unibertsitatea Arrasate-Mondragon Spain Automation Electrical and Electronic Engineering and Industrial Informatics Department Universidad Politécnica de Madrid Madrid Spain

Diesel-electric locomotives are equipped with one or more synchronous generators driven by diesel engines, that provide the necessary electrical energy for their operation. These generators are usually provided with some protection relays, depending on their rated power and their type of service. Standard stator-ground fault protection has a setting for rated conditions. In case of variable speed and voltage operation, the protection does not cover all the operation range of the machine. In addition, unwanted tripping may occur due to incorrect filtering of the third harmonic. In this paper, an auto-adaptative stator ground protection function is presented, for the operation of synchronous machines at variable voltage and frequency. A specific case study is tackled, focused on the main generators of a diesel-electric locomotive. Numerous simulations are carried out to verify the proposed protection method. The obtained results are promising, and they can be used in other synchronous machines operating at variable speed.

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Motion Sickness Alleviation Based on a Mindfulness Brain-computer Interface

Motion Sickness Alleviation Based on a Mindfulness Brain-Com...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Jiawei Zhu Xiaoyu Bao Qiyun Huang Tao Wang Li Huang Yupeng Han Kendi Li Di Chen Kailin Xu Zijian Wang Ya Jiang Yuanqing Li Dept. School of Automation Science and Engineering South China University of Technology Dept. Research Center for Brain–Computer Interface Pazhou Laboratory Guangzhou China Huizhou Desay SV Automotive Co. Ltd. Huizhou China Dept. Research Center for Brain–Computer Interface Pazhou Laboratory South China Brain-Computer Interface Technology Co. Ltd. Guangzhou China

ISBN: (数字)9798350308501

ISBN: (纸本)9798350308518

Motion sickness is a common affliction that affects nearly half of the global population and poses challenges to comfortable travel experiences, necessitating diverse intervention strategies. Pharmacological interventions have shown promise but present limitations due to side effects. To date, there is no non- pharmacological alleviation method for motion sickness that has been proven effective in a large population. In this study, we developed a novel mindfulness brain-computer interface (mindfulness BCI) for alleviation of motion sickness. Thirty-one subjects who were susceptible to motion sickness participated in our experiments in a real-world car motion environment. The experimental results demonstrated that the proposed mindfulness BCI can effectively alleviate motion sickness symptoms, where more than 90% of subjects reported the effectiveness of the mindfulness BCI system and a significant reduction of Motion Sickness Susceptibility Questionnaire (MISC) scores was obtained when comparing the BCI-based meditation state with the resting state for the subjects (t 30 =4.968, P<0.001). To the best of our knowledge, our mindfulness BCI is the first effective non- pharmacological alleviation method for motion sickness as demonstrated by a strict test.

关键词： Training Sociology Motion sickness Motors Brain-computer interfaces Automobiles Statistics

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Evaluation of Deep Training and Adapting Algorithms in Medical with De-Noising EEG 3

Evaluation of Deep Training and Adapting Algorithms in Medic...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Abdulwahid, Ali Hadi Karetla, Girija Rani Ganesh, K.V.B. Kumar, A Suresh Radha, K. Shivakumar, K. Southern Technical University Engineering Technical College Control and Automation Engineering Department Basra Iraq Western Sydney University School of Computer Data and Mathematical Sciences Australia Koneru Lakshmaiah Education Foundation KL Business School Guntur Andhra Pradesh India Graphic Era Deemed to Be University Department of CSE India St. Martin's Engineering College Dept. of IT Secunderabad Telangana India SR University School of Computer Science and Artificial Intelligence Warangal Telangana India

ISBN: (纸本)9798350399264

An electroencephalogram that was captured with electrodes placed can easily get contaminated with a variety of artifacts. Here is a comparative of various electroencephalogram (EEG) de-noising techniques. Three alternative techniques are used to eliminate five different kinds of noise, and also the outcomes are evaluated. These three methods are just the Least Mean Square (LMS) technique, the Connected Neural Network, and also the Recursive Least-square (RLS) adaptable algorithm (FCNN). Period charts of the actual EEG signal, the noise EEG signal, and thus the predicted EEG signal are used to display the results. RRMSE and signal-to-noise ratio were utilized to compare the effectiveness of the 3 de-noising strategies in this study. Investigating the model parameters in this case, we discover the FCNN forecasts a better outcome compared to the other two methods. © 2023 IEEE.

关键词： Electroencephalography

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Young Driver Gaze (YDGaze): Dataset for driver gaze analysis

Young Driver Gaze (YDGaze): Dataset for driver gaze analysis

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2022 International Conference on Artificial Intelligence of Things, ICAIoT 2022

作者： Ceven, Suleyman Albayrak, Ahmet Bayir, Raif Düzce University Düzce Vocational School Dept. of Electronics and Automation Tech. Düzce Turkey Düzce University Faculty of Engineering Department of Computer Engineering Düzce Turkey Karabük University Faculty of Technology Department of Mechatronics Engineering Karabük Turkey

ISBN: (纸本)9798350396768

One of the most important factors in ensuring successful and safe driving in the road traffic network is the driver's sense of sight. The driver can determine the environmental factors in the traffic with his gaze and can predict the next move after evaluating it with the current driving position. In this process, the focus of the driver's gaze should be on the area to which the vehicle is heading. Driver eye movements and gaze analysis is a topic that has been intensively studied in recent years in 'Intelligent Driver Assistance Systems'. In this study, a data set was prepared with a group of 40 participants between the ages of 18-28 in order to determine which points the driver looked at while driving. The dataset was created by having each participant look at the labeled areas on the vehicle in turn for one minute. Experimental studies lasted approximately 11 days. Approximately 350 hours of work was carried out to prepare the obtained data set. © 2022 IEEE.

关键词： Automobile drivers

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Development of CAN and OBD-II Supported Experimental Setup for Vehicle ECU Data Acquisition

Development of CAN and OBD-II Supported Experimental Setup f...

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2022 International Conference on Artificial Intelligence of Things, ICAIoT 2022

ISBN: (纸本)9798350396768

Today, CAN Bus protocol is widely used in various technological fields such as industrial, medical or automotive. Data is provided from ECUs and diagnostic codes through the OBD-II system, which is a standard in vehicles. The aim of this study is to develop an experimental setup to analyze driver driving skills based on vehicle data obtained over CAN Bus protocol with an open-source software. In this study, an experimental setup with a PiCAN 2 module and an LCD touch screen compatible with Raspberry Pi 3 Model B+ has been developed. Thanks to the developed experimental setup, it is connected to the vehicle ECU via an OBD-II connector to obtain real-time ECU data of the vehicle. This article presents validation tests to determine the design and construction phases of the system and its functionality as a data logger. Python programming language CAN software library was used on the experimental setup. © 2022 IEEE.

关键词： Data acquisition

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Multi-Classification Decision Fusion Based on Stacked Sparse Shrink AutoEncoder and GS-Tabnet for Network Intrusion Detection

Multi-Classification Decision Fusion Based on Stacked Sparse...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Ziqi Wang Ziyue Guan Xiangxi Wu Jing Bi Haitao Yuan MengChu Zhou Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

With the rapid development of the Internet, various network invasive behaviors are increasing rapidly. This seriously threatens the economic development of individuals, enterprises, and society. Network intrusion detection is important in network security systems, which can be regarded as a classification problem. It aims to distinguish between the specific categories of various network behaviors and determine whether the behavior belongs to network intrusion. However, network intrusions present a diverse and fast-changing trend, making categorizing difficult. Due to feature redundancy, uneven distribution of sample numbers, and inefficient parameter optimization, traditional rule-based approaches fail to achieve satisfying classification accuracy. This work proposes a multi-classification intrusion detection model based on Stacked Sparse Shrink AutoEncoder (SSSAE), Genetic Simulated annealing-based particle swarm optimization optimized Tabnet classifier (GS-Tabnet), and Decision Fusion (DF), called for SGTD short. Among them, SSSAE extracts multiple feature sets from the input data. Then GS-Tabnet trains a classifier for each feature set. Finally, the decision fusion fuses the results from these classifiers to obtain the final classification result. SGTD is compared with eight multi-classification benchmark models, and its intrusion detection accuracy is superior to its peers.

关键词： Accuracy Fuses Network intrusion detection Simulated annealing Network security Feature extraction Genetics Data mining Particle swarm optimization Optimization

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A Reference Shaping Filter Design Method for Discrete-Time Sliding Mode Control with Decoupled Disturbance Compensator to Inhibit Overshoot

A Reference Shaping Filter Design Method for Discrete-Time S...

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International Conference on Control, automation and Systems ( ICCAS)

作者： Dae-Young Yang Tae-Ho Oh Young-Seok Kim Tae-Hoon Kim Sang-Hoon Lee Dong-Il Dan Cho Dept. of Electrical and Computer Engineering ASRI/ISRC Seoul National University Seoul Korea RS Automation Co. Ltd. Gyeonggi-do Korea

The discrete-time sliding mode control (DSMC) is a highly utilized method in industrial systems, such as manufacturing systems, robot manipulators, and automation systems. The DSMC with decoupled disturbance compensator (DDC) method is one of the robust control methods with high tracking performance and fast responses. Based on error dynamics, a performance-shaping methodology for DSMC with DDC has also been developed to reduce tracking position errors in industrial servo systems. However, the input reference to the output transfer function of the DSMC has a transfer function zero, causing an unwanted overshoot and degrading the performance of the controller. Thus, this overshoot characteristic can be problematic in some systems that have stringent no-overshoot specifications. In this paper, a reference shaping filter design method is proposed that inhibits any overshoot in the response of the DSMC with DDC in a double-integrator system. The presented controller is applied to a linear motor system to illustrate the performance.

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Convolutional Neural Network-based Keyword Classification for Mixer Control

Convolutional Neural Network-based Keyword Classification fo...

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International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Suz-Ting Wang Jeng-Dao Lee Wen-Ho Juang Ming-Hwa Sheu Shin-Chi Lai Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Dept. Doctor’s Program of Smart Industry Technology Research and Design National Formosa University Huwei Taiwan Dept. Doctor’s Program of Smart Industry National Yunlin University of Science and Technology Douliu Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

This study proposed a keyword recognition of microphone feed-in, achieving the effect of allowing users to control the real-time mixer system intelligently. The proposed system structure includes a front-end circuit to capture voice and Mel-scale Frequency Cepstral Coefficients (MFCC) features in conjunction with a Convolutional Neural Network (CNN) algorithm. The testing results show that the lightweight CNN model achieves a keyword recognition accuracy rate of 96.4%.

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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency

Intelligent Operational Decision Support System for Sugar Fa...

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International Conference on Modern Electrical and Energy Systems (MEES)

作者： Nataliia Lutska Nataliia Zaiets Lidiia Vlasenko Stefan Junge Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Packaging Technology Berlin University of Applied Sciences Berlin Germany

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%.

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Constructing Multi-granular Knowledge Graph with Structure Similarity and Hierarchical Clustering 9

Constructing Multi-granular Knowledge Graph with Structure S...

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9th IEEE International Conference on Progress in Informatics and Computing, PIC 2024

作者： Yang, Ke Feng, Jingyun Xi, Zhiyu Zheng, Qiuke Zhang, Huan Deng, Wei China Railway Eryuan Engineering Group Co. Ltd. Chengdu China Southern University of Science and Technology Dept. of Statistics Shenzhen China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China School of Statistics Southwestern University of Finance and Economics Chengdu China Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Chongqing China

ISBN: (纸本)9798350363203

The structure of knowledge graph is basically flat and lack of hierarchies to make knowledge graph too general to deal with concrete reasoning problems. There are few work about hierarchical knowledge graph. In this paper, the entity and relation combined method is proposed to divide the entities into groups based on structure similarity. The hierarchical cluster method is proposed with the entity and relation combined embedding representation to construct a multi-granular knowledge graph. The experimental results show that the hierarchical division relied on combined embedding are significantly better than those based on the existing entity embedding methods in three evaluation indexes. The combined embedding can capture the structure information which is helpful to hierarchical knowledge graph accurately and reasonably. © 2024 IEEE.

关键词： Knowledge graph

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