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Model-based Tracking of Magnetic Sensor Gloves in Real Time

Current Directions in Biomedical engineering 2023年第1期9卷 85-88页

作者： Schmidt, Gerhard Boueke, Moritz Hoffmann, Johannes Schmidt, Tobias Bald, Christin Bergholz, Robert Digital Signal Processing and System Theory Department of Electrical and Information Engineering Faculty of Engineering Kiel University Kaiserstr. 2 Kiel 24143 Germany Department of Electrical and Information Engineering Faculty of Engineering Kiel University Kiel Germany Department of Pediatric Surgery Faculty of Medicine Kiel University Kiel Germany

While mechanical tracking is commonly seen in robot-Assisted surgery, contactless gesture control leads to a more intuitive approach. Magnetic localization systems might be able to provide an untethered access to the necessary hand movement data. A model-based system exploiting a priori knowledge to improve the accuracy and robustness can be beneficial in this context. We present a proof of concept, in which the index finger of a digital twin of a hand is tracked with three simulated sensors. Based on the physiological composition, the finger is modeled as a kinematic chain with rotational degrees of freedom between the segments according to the corresponding finger joints. We applied an extended Kalman filter using this description to enhance position and rotation estimates for the sensors on the finger segments. We achieved mean absolute errors © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Degrees of freedom (mechanics)

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Prototype for Current and Voltage Monitoring of Distribution Transformer Based on WiFi

Prototype for Current and Voltage Monitoring of Distribution...

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2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023

作者： Yudianto, Firman Herlambang, Teguh Adinugroho, Mukhtar Susanto, Fajar Annas Wulan, Tri Deviasari Farid, Imam Wahyudi Unusa Faculty of Business Economics and Digital Technology Department of Information System Indonesia Unusa Faculty of Business Economics and Digital Technology Department of Management Indonesia Its Vocational Faculty Department of Electrical Automation Engineering Indonesia

ISBN: (纸本)9798350309225

A transformer is a key component in the electrical power distribution system. To maintain the continuity and reliability of the power system, it is essential to ensure the reliability of the energy distribution system. Factors affecting the quality of electrical energy include current, voltage, and temperature. With an increase in load, the transformer's temperature and current can rise, and the increase in current can be influenced by excessive load, thus raising the transformer's temperature. The temperature rise can also be influenced by the environmental conditions inside the transformer room. Excessive temperature in the transformer room can lead to explosions. Therefore, this thesis research aims to design a prototype monitoring device based on WiFi for parameters such as current, voltage, and temperature, to provide real-time information about the transformer's condition. The results of this research show that the prototype monitoring device for the transformer provides values very close to manual calculations, with the largest error percentages being 2.18% for current measurement, 1.04% for voltage measurement, and 3.3% for temperature measurement. This prototype monitoring device also provides information to the user when the current value in the circuit reaches 2A, making it easier for the user to identify potential transformer faults before they lead to damage. The transformer's efficiency ranges from 99.54% to 99.82%. Maintenance is required for the transformer as it approaches high temperatures to ensure optimal transformer performance. © 2023 IEEE.

关键词： Temperature measurement

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Model-based Tracking of Magnetic Sensor Gloves in Real Time

Model-based Tracking of Magnetic Sensor Gloves in Real Time

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作者： Schmidt, Gerhard Boueke, Moritz Hoffmann, Johannes Schmidt, Tobias Bald, Christin Bergholz, Robert Digital Signal Processing and System Theory Department of Electrical and Information Engineering Faculty of Engineering Kiel University Kaiserstr. 2 Kiel24143 Germany Department of Electrical and Information Engineering Faculty of Engineering Kiel University Kiel Germany Department of Pediatric Surgery Faculty of Medicine Kiel University Kiel Germany

关键词： Degrees of freedom (mechanics)

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A third-party platoon coordination service: Pricing under government subsidies

A third-party platoon coordination service: Pricing under go...

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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of Control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic Control Society.

关键词： Trucks

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system for coronavirus classification based on a new textural descriptor 21

System for coronavirus classification based on a new textura...

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21st IEEE International Conference on Sciences and Techniques of Automatic Control and Computer engineering, STA 2022

作者： Boubakri, Fathia Gargouri, Norhene Masmoudi, Alima Damak Sfax Tunisia Digital Research Center of Sfax Technopole of Sfax Computer Imaging and Electronic System Group Tunisia Sfax Engineering School University of Sfax Computer Imaging and Electronic System Group CEM Laboratory Department of Electrical Engineering P.O. Box 1169 Sfax3038 Tunisia

ISBN: (纸本)9781665482615

The combination of Chest X-Ray imaging and Artificial Intelligence (AI) has proven its efficiency in coronavirus disease (COVID-19) detection [1]. The present paper proposes an efficient COVID-19 detection system based on a new textural features descriptor: Monogenic Local Binary Pattern Variance (MLBPV). An Artificial Neural Network (ANN) model is used for Regions Of Interest (ROIs) classification. Evaluating MLBPV, it outperforms other tested models by achieving an Area Under Curve (A-{z}) of 0.96263 and an accuracy of 99.9805%. Comparing our method with previous ones proves that ours provides the best performance. This model may be implemented in digital X-Ray machine for radiography and help radiologists. © 2022 IEEE.

关键词： COVID-19

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Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model

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Computer systems Science & engineering 2023年第5期45卷 1679-1694页

作者： Mashael M Asiri Heba G.Mohamed Mohamed K Nour Mesfer Al Duhayyim Amira Sayed A.Aziz Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer Science College of Science&Art atMahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application ***,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication *** traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find *** study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)*** major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT *** the preliminary stage,data pre-processing is carried out to transform the input data into useful *** addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection ***,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of ***,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization *** experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several *** simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.

关键词： Cyberattacks security deep learning internet of things feature selection data classification

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Prototype for Current and Voltage Monitoring of Distribution Transformer Based on WiFi

Prototype for Current and Voltage Monitoring of Distribution...

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International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Firman Yudianto Teguh Herlambang Mukhtar Adinugroho Fajar Annas Susanto Tri Deviasari Wulan Imam Wahyudi Farid Department of Information System Faculty of Business Economics and Digital Technology UNUSA Indonesia Department of Management Faculty of Business Economics and Digital Technology UNUSA Indonesia Department of Electrical Automation Engineering Vocational Faculty ITS Indonesia

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Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

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Computers, Materials & Continua 2022年第12期73卷 6579-6594页

作者： Manar Ahmed Hamza Aisha Hassan Abdalla Hashim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Amal S.Mehanna Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt

Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and *** recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected *** the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful *** and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection system(IMVO-DLIDS)for IoT *** presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT *** proposed IMVO-DLIDS model follows a three-stage *** first,data pre-processing is performed to convert the actual data into useful *** addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature ***,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for ***,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU *** experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct *** extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.

关键词： Internet of everything deep learning feature selection classification intrusion detection cybersecurity

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Telecom Fraud Detection via Imbalanced Graph Learning 22

Telecom Fraud Detection via Imbalanced Graph Learning

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22nd IEEE International Conference on Communication Technology, ICCT 2022

作者： Hu, Xinxin Chen, Haotian Chen, Hongchang Zhang, Shibo Liu, Shuxin Li, Xing National Digital Switching System Engineering and Technological Research Center Zhengzhou450002 China University of Toronto The Edward S. Rogers Sr. Department of Electrical & Computer Engineering TorontoM5S 3G4 Canada

ISBN: (纸本)9781665470674

In recent years, an increasing number of telecom frauds have caused huge losses to people around the world. Graph neural network(GNN) brings new possibilities for telecom fraud detection. However, due to the existence of the imbalance problem in the graph, it is difficult for general GNN models to detect a small number of positive samples. To address this problem, we design a new GNN-based fraud detector. First, we transform the node features with a multilayer perceptron. Subsequently, a reinforcement learning-based neighbor sampling strategy is designed to balance different classes of node neighbors. Then the node features are aggregated using GNN, while the focal loss function is used to measure the model error. We conducted experiments on two real-world telecom fraud datasets, and the results show that the proposed method does have competitive performance, especially for the graph imbalance problem. © 2022 IEEE.

关键词： Graph neural networks

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Component and system Levels Limitations in Power-Hydrogen systems: Analytical Review

SSRN

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SSRN 2024年

作者： Sahraie, Elahe Kamwa, Innocent Moeini, Ali Mohseni-Bonab, Seyed Masoud Electrical Engineering Department College of Engineering Université Laval QuebecQC Canada Hydro-Québec/IREQ System Simulation VarennesQC Canada Hydro-Québec/IREQ Digital Systems VarennesQC Canada

This study conducts an analytical review, covering the necessity for decarbonizing the electric power system (EPS), challenges and potential solutions in electrification and integration of renewable energy sources (RESs), multi-carrier energy systems (MCESs), and hydrogen energy systems (HESs). Innovatively, it maps the role of HES in EPS decarbonization and categorizes HES integration challenges into component and system levels. At component level, technological aspects of hydrogen generation, storage, transportation, and refueling are explored, while at the system-level, HES coordination, hydrogen market frameworks and hydrogen adoption challenges are evaluated. Findings highlight research and development (R&D) gaps in EPS decarbonization through HES development, including misalignment between HESs operational targets and their sectors techno-economic development, integration insufficiency, model deficiency, and challenges in operational complexity. © 2024, The Authors. All rights reserved.

关键词： Renewable energy

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