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International Conference on Informatics and Computing (ICIC)

作者： Somantri Muhamad Ikhsan Thohir Alun Sujjada Deni Setiawan Ilham Komara Fikri Ardiansyah Efendi Muhamad Muslih Zubaile Bin Abdullah Mohd Zainuri Saringat Department of Software Engineering University Tun Hussein Onn Malaysia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering University Tun Hussein Onn Malaysia Sukabumi Indonesia Department of Information System Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Bahru Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system’s reliability and functionality.

关键词： Accuracy Noise Sensor phenomena and characterization Sensor systems Real-time systems Kalman filters Internet of Things Reliability Leak detection Gas detectors

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High-Performance Chip Design With Parallel Architecture for Magnetic Field Imaging system

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IEEE Transactions on Instrumentation and Measurement 2024年 73卷 1-13页

作者： Lin, Ming-Yi Hsieh, Sheng-Hsien Chen, Ching-Han Lin, Chun-Hung Yuan Ze University Department of Computer Science and Engineering Taoyuan320315 Taiwan National Pingtung University of Science and Technology Program of Artificial Intelligence and Mechatronics Pingtung912301 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan32001 Taiwan International Games System Company Ltd. New Taipei City248 Taiwan

To address the limitations of typical coil detection systems and enhance the performance of traditional magnetic field imaging (MFI) systems, we propose a MFI system that uses a 4×4 array of anisotropic magnetoresistance (AMR) sensors. The core controller of the proposed system was designed using a pipelined parallelization algorithm and implemented on a high-speed field-programmable gate array (FPGA) in a fully hardware-based architecture. The designed MFI system can achieve parallel acquisition of magnetic field components from the 16-channel AMR sensor array, parallel data processing, parallel data transmission, and parallel computations in a single cycle of only 4 ms. Compared with typical detection systems, the proposed solution not only exhibits superior performance but can also provide dynamic visual representations of sensed information. © 1963-2012 IEEE.

关键词： Parallel architectures

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A Development of Object Detection system based on Deep Learning Approach to Support the Laparoscope Manipulating Robot (LMR)

A Development of Object Detection System based on Deep Learn...

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2022 International Electrical engineering Congress, iEECON 2022

作者： Sonsilphong, Suphachoke Sonsilphong, Amornthep Hormdee, Daranee Khampitak, Kovit Khon Kaen University Data Science Health Informatics Program Academic Affairs Faculty of Medicine Khon Kaen Thailand Rajamangala University of Technology Isan Department of Electrical Engineering Khon Kean Thailand Khon Kaen University Embedded System RD Group Department of Computer Engineering Faculty of Engineering Khon Kean Thailand Khon Kean University Faculty of Medicine Department of Obstetrics and Gynecology Khon Kean Thailand

ISBN: (纸本)9781665402064

This paper presents the development of an object detection system based on the deep learning approach of computer vision to support the laparoscopic surgical robotic position control system. The system comprises two main phases, the training phase, and the real-time operating phase. In the training phase, a modified YOLOv4 algorithm is proposed to be adopted within the Darknet framework for training the weights of detection models. The best detection model is then selected to be assigned to the object detector in the real-time operating phase. The validation result of the detection model across the experiment datasets is 90% accuracy on average during the training phase. To validate the viability of real-time object detection, the proposed system was implemented in two test-cases of gynecologic surgery with the soft-body cadavers. © 2022 IEEE.

关键词： Laparoscopy

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A Study on Giant Magnetostrictive Actuator for Interior Sound Control system of the Ultra-Compact Mobility: Fundamental Consideration on Formation of the Giant Magnetostrictive Phases Depending on Heat Treatment Condition

A Study on Giant Magnetostrictive Actuator for Interior Soun...

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International Conference on Electrical Machines and systems (ICEMS)

作者： Taro Kato Ryusei Naganuma Koki Bando Kentaro Sawada Wu Wenbao Ikkei Kobayashi Jumpei Kuroda Daigo Uchino Kazuki Ogawa Keigo Ikeda Ayato Endo Hideaki Kato Takayoshi Narita Mitusaki Furui Department of Mechanical Engineering Tokyo University of Technology Hachioji-shi Tokyo Japan Sustainable Engineering Program Graduate School of Engineering Tokyo University of Technology Hachioji-shi Tokyo Japan Course of Mechanical Engineering Tokai University Hiratsuka-shi Kanagawa Japan Course of Science and Technology Tokai University Hiratsuka-shi Kanagawa Japan Department of Mechanical Engineering National Institute of Technology (KOSEN) Numazu College Numazu-shi Shizuoka Japan Department of Electronic Robotics Aichi University of Technology Gamagori-shi Aichi Japan Department of Mechanical Engineering Hokkaido University of Science Sapporo Hokkaido Japan Department of Electrical Engineering Fukuoka Institute of Technology Fukuoka-shi Fukuoka Japan Department of Mechanical System Engineering Tokai University Hiratsuka-shi Kanagawa Japan

ISBN: (数字)9784886864406

ISBN: (纸本)9798350379105

An active noise control system using giant magnetostrictive actuator has been proposed as a countermeasure for interior noise of the ultra-compact mobilities, which is a new transportation tools. In this paper, as basic research toward the development of the proposed giant magnetostrictive actuator for active noise control, we conduct experiments on the material properties when terbium, dysprosium, and iron powder, which make up the giant magnetostrictive material, are mixed and the heat treatment conditions are changed. In this study, the heat treatment temperature was varied from $800^{\circ}\mathrm{C}$ to $1100^{\circ}\mathrm{C}$ in $100^{\circ}\mathrm{C}$ incrementsbased on the equilibrium state temperature of each material, and the heat treatment time was set in 30-minute increments from 1 hour to 3 hours. As a result, TbFe2 was confirmed after heat treatment at an electric furnace temperature of $1000^{\circ}\mathrm{C}$ for 3 hours, although its strength was insufficient.

关键词： Actuators Heat treatment Powders Temperature Magnetostriction Noise Terbium Iron Resistance heating Material properties

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Relation of Determinant Factors and Their Effect on Improving The Online Learning : A systematic literature review

Relation of Determinant Factors and Their Effect on Improvin...

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Advancement in Data Science, E-learning and Information systems (ICADEIS), International Conference on

作者： Ida Bagus Ketut Widiartha Junseok Hwang Ryan Adhitya Nugraha Dept. of Informatics Engineering University of Mataram Mataram Indonesia Technology Management Economic Policy Program Seoul National University Seoul South Korea Dept. of Information System Telkom University Bandung Indonesia

This study introduces the Harmony Model, which examines factors influencing the success of online learning and the workload of lecturers. Drawing upon the Theory of Action Reason, it explores the impact of student satisfaction and lecturers' willingness to prepare course materials. The model emphasizes the challenge faced by lecturers in creating high-quality content and highlights the importance of balancing supporting factors and alleviating the associated burden. Strategies to enhance online learning effectiveness should address both lecturers' motivation and student satisfaction. Identifying determinant factors and analyzing their impact on material development can increase lecturers' willingness to provide appropriate course materials. Knowledge extraction processes and satisfaction-related factors play crucial roles. This study offers insights to enhance the online learning experience, optimize learning outcomes, and improve overall effectiveness.

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Performance Comparison Study of Cherry Tomato Ripeness Detection Using Multiple YOLO Models

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Journal of Institute of Control, Robotics and systems 2024年第11期30卷 1214-1220页

作者： Yang, Dayeon Son, Hyoung Il Ju, Chanyoung Department of Convergence Biosystems Engineering Chonnam National University Korea Republic of Interdisciplinary Program in IT-Bio Convergence System Chonnam National University Korea Republic of Research Center for Biological Cybernetics Chonnam National University Korea Republic of Purpose Built Mobility Group Korea Institute of Industrial Technology Korea Republic of

Millions of tons of cherry tomatoes are produced annually, with the harvesting process being crucial. This paper presents a deep learning-based approach to distinguish the ripeness of cherry tomatoes during the harvesting process. It specifically analyzes the performance of the YOLO (You Only Look Once)v5 and YOLOv8 models, which are designed to rapidly detect the ripeness of cherry tomatoes in real time. The proposed system is expected to resolve rural issues caused by population decline and enhance productivity in cherry tomato harvesting environments. Future research will aim to apply this technology to harvesting robots for the automatic recognition and harvesting of cherry tomatoes. © 2024, Institute of Control, Robotics and systems. All rights reserved.

关键词： Fruits

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Deep learning-based channel estimation for RIS-assisted UAV-enabled wireless communications system

Deep learning-based channel estimation for RIS-assisted UAV-...

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International Conference on Innovation, Communication and engineering (ICICE 2023)

作者： Belayneh A Tesfaw Hsin-piao Lin Getaneh B Tarekegn Rong-Terng Juang Shiann-Shiun Jeng Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Institute of Aerospace and System Engineering National Taipei University of Technology Taipei 10608 Taiwan Department of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu 30010 Taiwan Department of Electrical Engineering National Dong Hwa University Hualien Taiwan

ISBN: (数字)9781839539954

Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. This problem can be effectively addressed by employing reconfigurable intelligent surfaces (RIS). To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communications system. Simulation results showed that LSTM can effectively improve the channel estimation performance of RIS-assisted UAV-enabled wireless communications.

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling Computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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A Novel Method for the Fabrication of Antibacterial Stainless Steel with Uniform Silver Dispersions by Silver Nanoparticle/Polyethyleneimine Composites

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Materials 2023年第10期16卷 3719-3719页

作者： Chih, Yu-Kun You, Jhu-Lin Lin, Wei-Hsuan Chang, Yen-Hao Tseng, Chun-Chieh Ger, Ming-Der Graduate School of Defense Science Chung Cheng Institute of Technology National Defense University Taoyuan335 Taiwan Department of Chemical and Materials Engineering Chung Cheng Institute of Technology National Defense University Taoyuan335 Taiwan System Engineering and Technology Program National Chiao Tung University Hsinchu300 Taiwan Combination Medical Device Technology Division Medical Devices and Opto-Electronics Equipment Department Metal Industries Research & Development Centre Kaohsiung821 Taiwan

Only a few studies have so far focused on the addition of silver to SS316L alloys by conventional sintering methods. Unfortunately, the metallurgical process of silver-containing antimicrobial SS is greatly limited due to the extremely low solubility of silver in iron and its tendency to precipitate at the grain boundaries, resulting in an inhomogeneous distribution of the antimicrobial phase and loss of antimicrobial properties. In this work, we present a novel approach to fabricate antibacterial stainless steel 316L by functional polyethyleneimine-glutaraldehyde copolymer (PEI-co-GA/Ag catalyst) composites. PEI is a highly branched cationic polymer, which makes it exhibit very good adhesion on the surface of the substrate. Unlike the effect of the conventional silver mirror reaction, the introduction of functional polymers can effectively improve the adhesion and distribution of Ag particles on the surface of 316LSS. It can be seen from the SEM images that a large number of silver particles are retained and well dispersed in 316LSS after sintering. PEI-co-GA/Ag 316LSS exhibits excellent antimicrobial properties and does not release free silver ions to affect the surrounding environment. Furthermore, the probable mechanism for the influence of the functional composites on the enhancement of adhesion is also proposed. The formation of a large number of hydrogen bonds and van der Waals forces, as well as the negative zeta potential of the 316LSS surface, can effectively enable the formation of a tight attraction between the Cu layer and the surface of 316LSS. These results meet our expectations of designing passive antimicrobial properties on the contact surface of medical devices. © 2023 by the authors.

关键词： Silver nanoparticles

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Techno-economic and life-cycle assessment of electrochemical captured CO2 conversion process in monoethanolamine

Techno-economic and life-cycle assessment of electrochemical...

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2022 AIChE Annual Meeting

作者： Lee, Suhyun Choi, Woong Kim, Jae Hyung Hwang, Yun Jeong Na, Jonggeol Ewha Womans University School of Chemical Engineering and Materials Science Korea Republic of Ewha Womans University Graduate program in System Health Science and Engineering Korea Republic of Clean Energy Research Center Korea Institute of Science and Technology Korea Republic of Seoul National University School of Chemistry Korea Republic of Korea Republic of Ewha Womans University Dept. of Chemical Engineering & Material science Advanced Manufacturing Systems Laboratory Korea Republic of

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