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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Rifki Ramadan Gilang Titah Ramadhan Wei-Cheng Shen Wen-Thong Chang Hsu-Tang Cheng Ben-Yi Liau Yih-Kuen Jan Ardha Ardea Prisilla Chi-Wen Lung Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Creative Product Design Asia University Taichung Taiwan Division of Plastic Surgery Asia University Hospital Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Kinesiology and Community Health University of Illinois Urbana USA Department of Fashion Design Sekolah Tinggi Desain LaSalle Jakarta Indonesia

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Negative pressure has been utilized in medical practice, exemplified by methods like cupping therapy. This study aimed to determine the effect of pressure and time duration of cupping therapy on the stratum corneum (SC) and deeper epidermis layer (ED) thickness. Three volunteer participants with healthy palms and no scars or wounds were allocated. Optical Coherence Tomography (OCT) records the thickness of skin layers using 105 mmHg and 145 mmHg negative pressure with 5 and 10 minutes duration. We analyzed the paired T-test for the SC thickness changes before and after cupping therapy. We found that there is a significant increase in SC with negative pressure 105 mmHg and 5 minutes duration (0.032 ± 0.005 to 0.036 ± 0.005, P < 0.05), and with negative pressure 145 mmHg and 10 minutes duration (0.037 ± 0.005 to 0.038 ± 0.005, P < 0.05). This indicates that cupping therapy has a discernible impact on the thickness of SC.

关键词： Optical coherence tomography Medical treatment Coherence Wounds Epidermis Consumer electronics

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Development of Methodology for System Identification of Non-linear System Using Radial Basis Function Neural Network

Development of Methodology for System Identification of Non-...

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2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020

作者： Naing, Nyi Nyi Min Khaing, Zaw Naing, Zaw Myo Defense Services Acadamy Pyin Oo Lwin Township Mandalay Division Computer Department Myanmar National Research University of Electronic Technology Department of Automatic Control System and Control Zelenograd Moscow Russia

ISBN: (纸本)9781728157610

In the past few years, there has been significant growth in the development and use of artificial neural networks (ANNs). At present ANN technologies are used in such areas of science as pattern recognition, medicine, speech recognition and synthesis, image processing, robotics and control systems. The use of ANN technologies in the control systems refers to develop a complex structure that can be used as intelligence control system and the identification of controlled object. To design intelligence control system and identification models based on ANN, it is necessary to determine the structure and parameters of ANN. One of the promising approaches to solving this problem is the use of evolutionary modeling methods, namely genetic algorithms, for training and structural optimization of ANN. In this paper a synthesis of a Radial Basis Function (RBF) neural network using genetic algorithm which is uses to determine the number of neurons in hidden layer and the parameters of RBF neural network for system identification process of non-linear system was presented. © 2020 IEEE.

关键词： Genetic algorithms

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Development of high performance computing systems 27th

Development of high performance computing systems

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27th International Conference on computer Networks, CN 2020

作者： Kozielski, Stanislaw Mrozek, Dariusz Department of Applied Informatics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9783030507183

The aim of the work is to present the development trends of high performance computers. The analysis focused on system architecture, processors and computing accelerators used. Particular attention was paid to interconnection networks, connecting system nodes. The problem of energy saving in the construction of large systems was also discussed. The data from the list of 500 largest computer systems was used in the analysis. © Springer Nature Switzerland AG 2020.

关键词： Energy conservation

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Parallel kinematic construction robot for aec industry 37

Parallel kinematic construction robot for aec industry

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37th International Symposium on Automation and Robotics in Construction: From Demonstration to Practical Use - To New Stage of Construction Robot, ISARC 2020

作者： Klöckner, M. Haage, M. Nilsson, K. Robertsson, A. Andersson, R. Department of Computer Science Lund University Sweden Cognibotics Ab Lund Sweden Department of Automatic Control Lund University Sweden AChoice Ab Malmö Sweden

ISBN: (纸本)9789529436347

This article reports work-in-progress of a parallel kinematic robot development for construction with main focus on the concept phase. We assume the weight distribution of the proposed structure enables integration of robotic components into construction equipment while enabling tailoring of important characteristics such as accuracy, stiffness and workspace toward application needs. We describe challenges as well as kinematics, simulation and an experimental setup for evaluating performance of the proposed concept in a construction experiment using a concrete build system. © 2020 Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot. All rights reserved.

关键词： Construction equipment

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Application of Machine Learning to Performance Assessment for a class of PID-based control Systems

arXiv

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arXiv 2021年

作者： Grelewicz, Patryk Khuat, Thanh Tung Czeczot, Jacek Nowak, Pawel Klopot, Tomasz Gabrys, Bogdan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Gliwice44100 Poland University of Technology Sydney Faculty of Engineering and IT Advanced Analytics Institute NSW2007 Australia

In this paper, a novel machine learning derived control performance assessment (CPA) classification system is proposed. It is dedicated for a wide class of PID-based control industrial loops with processes exhibiting dynamical properties close to second order plus delay time (SOPDT). The proposed concept is very general and easy to configure to distinguish between acceptable and poor closed loop performance. This approach allows for determining the best (but also robust and practically achievable) closed loop performance based on very popular and intuitive closed loop quality factors. Training set can be automatically derived off-line using a number of different, diverse control performance indices (CPIs) used as discriminative features of the assessed control system. The proposed extended set of CPIs is discussed with comprehensive performance assessment of different machine learning based classification methods and practical application of the suggested solution. As a result, a general-purpose CPA system is derived that can be immediately applied in practice without any preliminary or additional learning stage during normal closed loop operation. It is verified by practical application to assess the control system for a laboratory heat exchange and distribution *** Codes 93 © 2021, CC BY-NC-SA.

关键词： Three term control systems

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Sensor Fusion-Based Localization Framework for Autonomous Vehicles in Rural Forested Environments

Sensor Fusion-Based Localization Framework for Autonomous Ve...

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International Conference on Intelligent Transportation

作者： Jose Matute Mario Rodriguez-Arozamena Joshue Perez Ali Karimoddini Department of Electrical and Computer Engineering North Carolina Agricultural and Technical State University Greensboro NC USA TECNALIA Research & Innovation Basque Research and Technology Alliance (BRTA) Derio Bizkaia Spain Department of Automatic Control and Systems Engineering University of the Basque Country (UPV/EHU) Bilbao Bizkaia Spain Department of Electrical and Computer Engineering North Carolina A&T State University Greensboro NC US

One major hurdle for the deployment of autonomous vehicles in rural environments is achieving accurate localization in areas with tree-canopied roads or outdated point cloud maps. The presence of limited visibility and high variability renders standalone sensor localization unreliable in such situations. To tackle these issues, this paper presents a sensor fusion-based localization framework that integrates data from GNSS, LiDAR, INS, and vehicle odometry. The proposed approach uses a loosely-coupled Extended Kalman Filter for sensor fusion and a weighted gate approach for accurate state estimations. Compared to a state-of-the-art technique, the proposed method achieves a reduction of around 71% in maximum lateral deviations. This method successfully enables a safe and reliable localization in challenging scenarios that are frequently found in the rural and inter-urban sectors.

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Performance evaluation for ranking-based discretisation 24

Performance evaluation for ranking-based discretisation

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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Baron, Grzegorz Stanczyk, Urszula Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Discretisation often constitutes a part of initial data preparation stage. It translates continuous domain of features into granular, by assigning a number of intervals to represent attributes' values by nominal categories. Typically all real-valued features are subjected to transformations, regardless of their characteristics. The paper presents research on discretisation executed with a discerning approach. To all available attributes, feature selection mechanisms were employed, in the form of rankings that order variables based on their importance. Exploiting this discovered knowledge on attributes, discretisation was then driven by a ranking, and either highest or lowest ranking features were selected for transformation. The influence of selective discretisation on the performance of classification systems was studied for three popular inducers. The procedure was employed in the field of stylometry, and a task of authorship recognition, considered as a binary classification with balanced classes. The experiments show that discretisation based on importance of features can lead to better performance than in the case of transformations applied to all attributes. © 2020 The Authors. Published by Elsevier B.V.

关键词： Feature Selection

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical Engineering 2024年 1114 LNEE卷 v页

作者： Lin, Jerry Chun-Wei Shieh, Chin-Shiuh Horng, Mong-Fong Chu, Shu-Chuan Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and IT Devices Silesian University of Technology Gliwice Poland

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

arXiv

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arXiv 2024年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Grau, Antoni Zhang, Fuquan The College of Computer and Big Data Fuzhou University Fuzhou China The Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain The New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Minjiang University Fuzhou China Fujian Polytechnic Normal University Fujian Province University Fuzhou China

Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://***/BryantDom/Driver-Drowsiness-Monitoring. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method

Texture Analysis of Breast US Images Using Morphological Tra...

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Mediterranean Conference on control and Automation (MED)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer.

关键词： Ultrasonic imaging Transforms Feature extraction Time measurement Fractals Ensemble learning Image preprocessing

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