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International Conference on Applied System Innovation (ICASI)

作者： Fu-Nie Loo Tung-Shou Chen Chia-Wei Tsai Jason Lin Chun-Wei Yang Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung City Taiwan Department of Computer Science and Engineering National Chung Hsing University Taichung City Taiwan Master Program for Digital Health Innovation China Medical University Taichung City Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

Industrial Control systems (ICS) automate industrial processes but also introduces cybersecurity threats. Intrusion Detection System (IDS) are crucial for detecting cyber-attacks on ICS, yet zero-day attacks are often inefficiently detecting with supervised learning. This study employs semi-supervised learning using one-class SVM, isolation forest, and Local Outlier Factor (LOF), to train IDS models. Utilizing dataset collected from a self-build virtual ICS environment, the study demonstrates the feasibility of these models in detecting common attack like Injection, ARP, and Man-in-the-Middle.

关键词： Training Support vector machines Technological innovation Supervised learning Intrusion detection Forestry Semisupervised learning

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Evaluation model of students learning outcome using k-means algorithm 2

Evaluation model of students learning outcome using k-means ...

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2nd International Conference on Computer, science, Engineering, and technology, ICComSET 2019

作者： Henderi Sunarya, Abas Zakaria Nurmika, Sri Linggawati Asmainah, Nur Master of Information Technology Study Program Faculty of Science and Technology Raharja University Tangerang Indonesia

The progress of students learning outcomes have to evaluated and make monitor. Student learning outcomes were indicated by score of the exam. Educational institutions can determine the ability of students' knowledge and competences based on student exam scores. For this reason, the goals of this research is develops an evaluation model of student learning outcomes. The model was developed using k-means algorithm and it has functions to analyze the student learning outcomes. The model was tested by conducted using 50 data exam of students, rapid manner software, and has implemented by the Python 3.0 programming language. The testing the evaluation model of student learning outcomes that were developed resulted: there are 3 clusters of student learning outcomes that are good, satisfying, and lacking. The tested results also showed that 40% of students had good learning outcomes, 44% of students were satisfactory, and 16% of other students had poor learning outcomes. The results of testing the student learning evaluation model can be used by the parties concerned to make decisions related to work programs or formulate steps that need to be taken to improve student learning outcomes. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Overview of Bearing Fault Diagnosis Based on Deep Learning

Overview of Bearing Fault Diagnosis Based on Deep Learning

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Xue-Ling Zou Kai-Xu Han Wei Chien Xin-Yan Gan Liu-Yue Shi School of Electric and Information Engineering Beibu Gulf University Qinzhou China School of Information Management and Master’s Program Lunghwa University of Science and Technology Taoyuan City Taiwan Guangxi University of Chinese Medicine Nanning China

Deep learning has excellent learning and feature classification abilities, extracting features in massive, multi-source, and high-dimensional measurement data. It also diagnoses and generalizes models without relying on manual intervention. Thus, it is widely used in equipment fault diagnosis. We introduce the common models of deep learning: deep confidence network, convolutional neural network, and automatic encoder. Using deep learning, the fault diagnosis of rolling bearings is executed, and the result provides the basis for future development.

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Inappropriate Benefits and Identification of ChatGPT Misuse in programming Tests: A Controlled Experiment

arXiv

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arXiv 2023年

作者： Toba, Hapnes Karnalim, Oscar Johan, Meliana Christianti Tada, Terutoshi Djajalaksana, Yenni Merlin Vivaldy, Tristan Master of Computer Science Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Bachelor of Informatics Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Media Culture Department Faculty of Information Sciences and Arts Toyo University Kawagoe Japan EC-Council TampaFL United States

While ChatGPT may help students to learn to program, it can be misused to do plagiarism, a breach of academic integrity. Students can ask ChatGPT to complete a programming task, generating a solution from other people’s work without proper acknowledgment of the source(s). To help address this new kind of plagiarism, we performed a controlled experiment measuring the inappropriate benefits of using ChatGPT in terms of completion time and programming performance. We also reported how to manually identify programs aided with ChatGPT (via student behavior while using ChatGPT) and student perspective of ChatGPT (via a survey). Seventeen students participated in the experiment. They were asked to complete two programming tests. They were divided into two groups per the test: one group should complete the test without help while the other group should complete it with ChatGPT. Our study shows that students with ChatGPT complete programming tests two times faster than those without ChatGPT, though their programming performance is comparable. The generated code is highly efficient and uses complex data structures like lists and dictionaries. Based on the survey results, ChatGPT is recommended to be used as an assistant to complete programming tasks and other general assignments. ChatGPT will be beneficial as a reference as other search engines do. Logical and critical thinking are needed to validate the result presented by ChatGPT. © 2023, CC BY.

关键词： Students

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Integration of the Use of Conventional technology Towards Smart Home technology for People in the City of Jakarta

Integration of the Use of Conventional Technology Towards Sm...

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International Conference on ICT For Smart Society (ICISS)

作者： Tanty Oktavia Febian Wijaya Lintang Gerald Christian Ongko Griselda Valerie Michelle Benedicta Vieri Aska Juneio Sembiring Zaldy Raditya Ananta Takaaki Hosoda Ford Lumban Gaol Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Smart Home technology offers technological concepts that can be controlled through smart devices to make it easier for the community. Therefore, smart home technology is starting to seize the home electronics market in Indonesia, especially in the city of Jakarta. This study aims to determine the integration of conventional technology into smart home technology for people in Jakarta. The research model in this study uses UTAUT2 which analyzes nine variables, namely Perceived Usefulness, Perceived Cost, Perceived Ease of Use, Social Influence, Perceived Compatibility, Trust, Perceived Pleasure, and Effort Expectations, Intention to Use Smart Home technology. After the responses were received, data analysis was carried out using the PLS-SEM approach with the SmartPLS application. The results of this study show that there are many people who live in Jakarta who are helped by the presence of smart home technology. However, there are some people who don't really know how to use a smart home because of a lack of knowledge about this technology.

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A Survey on Recent Applications of Artificial Intelligence and Optimization for Smart Grids in Smart Manufacturing

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Energies 2023年第22期16卷 7660-7660页

作者： Hsu, Chao-Chung Jiang, Bi-Hai Lin, Chun-Cheng Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program in Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan

To enable highly automated manufacturing and net-zero carbon emissions, manufacturers have invested heavily in smart manufacturing. Sustainable and smart manufacturing involves improving the efficiency and environmental sustainability of various manufacturing operations such as resource allocation, data collecting and monitoring, and process control. Recently, a lot of artificial intelligence and optimization applications based on smart grid systems have improved the energy usage efficiency in various manufacturing operations. Therefore, this survey collects recent works on applications of artificial intelligence and optimization for smart grids in smart manufacturing and analyzes their features, requirements, and challenges. In addition, potential trends and further challenges for the integration of smart grids with renewable energies for smart manufacturing, applications of 5G and B5G (beyond 5G) technologies in the SG system, and next-generation smart manufacturing systems are discussed to provide references for further research. © 2023 by the authors.

关键词： Smart power grids

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Attribute estimation using pedestrian clothing and autonomous mobile robot navigation based on it 2

Attribute estimation using pedestrian clothing and autonomou...

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2nd International Workshop on Intelligent Systems, IWIS 2022

作者： Takahashi, Hidenao Takino, Nami Hashimoto, Hiroki Chugo, Daisuke Muramatsu, Satoshi Yokota, Sho She, Jin-Hua Hashimoto, Hiroshi Graduate School of Sceince and Technology Kwansei Gakuin University Hyogo Sanda Japan School of Sceince and Technology Kwansei Gakuin University Hyogo Sanda Japan School of Information Science and Technology Tokai University Kanagawa Hiratsuka Japan Toyo University Faculty of Science and Engineering Saitama Kawagoe Japan School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Shinagawa Tokyo Japan

ISBN: (数字)9798350398939

ISBN: (纸本)9798350398939

This paper proposes a new method for estimating the attributes of a pedestrian, e.g. an office worker or a student, from the pedestrian's clothing and, based on the estimation results, a method for efficiently navigating an autonomous mobile robot while safely avoiding pedestrians. The robot can estimate the organization the pedestrians belong to by learning in advance the clothing of pedestrians that frequently appear in the city in which the is travelling, e.g. the uniforms of certain high school students. Furthermore, the robot is given the direction in which the person in that organization is likely to be heading at the current time, so it uses this information to estimate the future walking trajectory of that pedestrian and design a running path that is less likely to prevent that pedestrian from walking. We use the artificial potential method to design the robot's running trajectory. Even if pedestrians with different attributes are detected in the robot's running path, our proposed robot can efficiently reach its destination while avoiding those pedestrians appropriately in real time. The effectiveness of the proposed method was confirmed by computer simulations and subjects' experiments with our prototype robot. © 2022 IEEE.

关键词： Machine design

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jen-Chien Yu Chun-Chieh Yang John Reuben Gilbert Rou-Jun Liu Yen-Jen Oyang Meng-Han Yang Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan ROC Master Program in Statistics National Taiwan University Taipei Taiwan ROC Department of Computer Science & Information Engineering National Taiwan University Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset.

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Drone Application Model for Image Acquisition of Plantation Areas and Oil Palm Trees Counting 7

Drone Application Model for Image Acquisition of Plantation ...

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7th International Conference on information Management and technology, ICIMTech 2022

作者： Sastrohartono, Hermantoro Suryotomo, Andiko Putro Saifullah, Shoffan Suparyanto, Teddy Perbangsa, Anzaludin Samsinga Pardamean, Bens Instiper Yogyakarta Faculty of Agricultural Technology Department of Agricultural Engineering Yogyakarta55281 Indonesia Universitas Pembangunan Nasional Veteran Yogyakarta Department of Informatics Yogyakarta55283 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The area of oil palm plantations in Indonesia increased by 7% from 14 million ha in 2017 to 15 million ha in 2021. The vast land requires the support of effective and efficient management techniques to maintain sustainable productivity. The high-performance computing technologies, Internet of Things (IoT), Big Data, Artificial Intelligence, spatial modeling, and drones are the answers to these needs. This study aims to design an application that can obtain plantation image data and analyze the calculation of the number of oil palm trees. The image of oil palm plantations is obtained from processing photo data from drones through a mosaic and composite process. This study also employed Scrum and UML as a system model development method. The image of the oil palm plantation area is used to build a tree counting model using the Viola-Jones algorithm. The oil palm tree count information generated by this system can then be used by management for fertilizing, harvesting, and monitoring the condition of oil palm trees. © 2022 IEEE.

关键词： Drones

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Analysis of the Musculoskeletal Load Generated when a Small Dog Walks on a Slippery Floor*

Analysis of the Musculoskeletal Load Generated when a Small ...

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IEEE/SICE International Symposium on System Integration

作者： Shijian Li Daisuke Chugo Satoshi Muramatsu Sho Yokota Jin-Hua She Hiroshi Hashimoto Uemura Takashi Hiroaki Kamishina Yoshiharu Hata Takayuki Yamada Takahiro Uchida Graduate School of Science and Technology Kwansei Gakuin University Sanda Japan School of Information Science and Technology Tokai University Hiratsuka Japan Faculty of Science and Engineering Toyo University Kawagoe Japan School of Engineering Tokyo University of Technology Tokyo Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Tokyo Japan KyotoAR Animal Advanced Medical Center Kyoto Japan Rinrei Co. Ltd. Tokyo Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

The objective of this paper is to identify the stresses placed on the musculoskeletal system when small dogs walk on floors with low friction coefficients. In many households, and small dogs in particular are often kept indoors. According to veterinary experience, it is known that small dogs kept indoors that walk with their lower limbs slipping cause a great deal of stress on the musculoskeletal system, and that the incidence of joint disease in the lower limbs of such small dogs is high. In this study, we measured the body movements of small dogs walking on slippery floors using a three-dimensional motion capture system and force plate system. Furthermore, by inputting these measurements into the musculoskeletal simulation model we have developed to simulate the musculoskeletal system of dogs, we derived the load on the musculoskeletal system of small dogs on slippery and non-slippery floors. As a result, it was identified that walking on a slippery floor causes a strong load on the lower limb muscles of small dogs.

关键词： Legged locomotion Limbs Friction Dogs System integration Muscles Object recognition Floors Stress Paints

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