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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： En-Ping Chu Kai-Chun Liu Chia-Yeh Hsieh Chih-Ya Chang Yu Tsao Chia-Tai Chan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei Taiwan Research Center for Information Technology Innovation (CITI) at Academia Sinica Taipei Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City Taiwan Department of Physical Medicine and Rehabilitation School of Medicine National Defense Medical Center Tri-Service General Hospital Taipei Taiwan

The assessment of a frozen shoulder (FS) is critical for evaluating outcomes and medical treatment. Analysis of functional shoulder sub-tasks provides more crucial information, but current manual labeling methods are time-consuming and prone to errors. To address this challenge, we propose a deep multi-task learning (MTL) U-Net to provide an automatic and reliable functional shoulder sub-task segmentation (STS) tool for clinical evaluation in FS. The proposed approach contains the main task of STS and the auxiliary task of transition point detection (TPD). For the main STS task, a U-Net architecture including an encoder-decoder with skip connection is presented to perform shoulder sub-task classification for each time point. The auxiliary TPD task uses lightweight convolutional neural networks architecture to detect the boundary between shoulder sub-tasks. A shared structure is implemented between two tasks and their objective functions of them are optimized jointly. The fine-grained transition-related information from the auxiliary TPD task is expected to help the main STS task better detect boundaries between functional shoulder sub-tasks. We conduct the experiments using wearable inertial measurement units to record 815 shoulder task sequences collected from 20 healthy subjects and 43 patients with FS. The experimental results present that the deep MTL U-Net can achieve superior performance compared to using single-task models. It shows the effectiveness of the proposed method for functional shoulder STS. The code has been made publicly available at https://***/RobinChu9890/*** Relevance— This work provides an automatic and reliable functional shoulder sub-task segmentation tool for clinical evaluation in frozen shoulder.

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Entropy and Information Gain Analysis on Low Cost BCI for Motorbike Users to Prevent Accident

Entropy and Information Gain Analysis on Low Cost BCI for Mo...

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International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Rolly Maulana Awangga Syafrial Fachri Pane Dinda Majesty Asep Setiawan Moch Yusuf Asyhari Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia Magister Program of Informatics Engineering Universitas Islam Indonesia Yogyakarta Indonesia

ISBN: (数字)9781728151182

ISBN: (纸本)9781728151199

The phenomenon of mothers giving the wrong vehicle signal lights to turn left and right reminds the effect of giving the right signal lights to other road users. Weak human concentration while driving can cause errors when giving vehicle signal lights or not giving vehicle signal lights. Mistakes when giving a signal light a vehicle has a risk of an accident. The driver's brain wave data will show the results of the rider's brain thinking when turning. This study aims to analyze the concentration of the driver when driving. This research will obtain driver brain wave data when driving and analyze human brain waves while riding a motorcycle, especially when turning left and right, to reduce the risk of accidents while driving. The driver will use brain waves tool while driving, and the computer will detect the driver's brain waves, and then send brainwave data to the computer. Driver brain waves are analyzed using the formula entropy and gain. The results of the driver's brain waves become a decision whether the driver is allowed to drive on the highway based on the detected waves (alpha, beta, gamma, theta, and delta) to reduce the chance of an accident.

关键词： Electroencephalography Entropy Turning Tools Calibration Motorcycles

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Smart Fish Feeder Using Arduino Uno With Fuzzy Logic Controller

Smart Fish Feeder Using Arduino Uno With Fuzzy Logic Control...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Nisa Hanum Harani Anggi Sholihatus Sadiah Anny Nurbasari Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia Economics Maranatha Khristian University Bandung Indonesia

ISBN: (数字)9781728120942

ISBN: (纸本)9781728120959

Smart Fish Feeder is an automatic fish feeder that can be controlled using android smartphone. With this tool, fish owners will easily adjust the feeding schedule according to the recommended feed dose, and provide aquarium cleaning scheduling. In designing a fish feeder, there are some criteria such as temperature and time interval of feeding. The data collection has been done by interviewing ornamental fish traders. The data also has been obtained from literature studies that support problem solving theory. The Laravel framework is used to interpret the system while Firebase as the Database Management System. The Android is as a front end that interacts directly with the user. Automatic fish feeding systems are implemented using arduino micro-controller and prototype feeding devices. This research uses Fuzzy Logic Controller method. With the creation of the prototype smart fish feeder the device functions well in terms of both controller and push data. The results of the calculation of the duration of fish feed using the Fuzzy Sugeno Algorithm have been successfully applied to the smart fish feeder.

关键词： Fish Feeds Fuzzy logic Fuzzy sets Temperature sensors Microcontrollers

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Deep learning based signal enhancement of low-resolution accelerometer for fall detection systems

arXiv

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arXiv 2020年

作者： Liu, Kai-Chun Hung, Kuo-Hsuan Hsieh, Chia-Yeh Huang, Hsiang-Yun Chan, Chia-Tai Tsao, Yu Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Department of Electrical Engineering Chung Yuan Christian University Taoyuan32023 Taiwan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei11221 Taiwan Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City24205 Taiwan

In the last two decades, fall detection (FD) systems have been developed as a popular assistive technology. To support long-term FD services, various power-saving strategies have been implemented. Among them, a reduced sampling rate is a common approach for an energy-efficient system in the real world. However, the performance of FD systems is diminished flowing to low-resolution (LR) accelerometer signals. To improve the detection accuracy with LR accelerometer signals, several technical challenges must be considered, including mismatch of effective features and the degradation effects. In this work, a deeplearning-based accelerometer signal enhancement (ASE) model is proposed as a front-end processor to help typical LR-FD systems achieve better detection performance. The proposed ASE model based on a deep denoising convolutional autoencoder architecture reconstructs high-resolution (HR) signals from the LR signals by learning the relationship between the LR and HR signals. The results show that the FD system using support vector machine and the proposed ASE model at an extremely low sampling rate (sampling rate Copyright © 2020, The Authors. All rights reserved.

关键词： Wearable sensors

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Beach Topographic Change Analysis Using Multi-temporal UAV Data

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IOP Conference Series: Earth and Environmental Science 2021年第1期799卷

作者： K T Chang H M Fang S S Hsiao C S Li Associate Professor Department of Civil Engineering and Environmental Informatics Minghsin University of Science and Technology Assistant Professor Bachelor's Degree Program in Ocean Engineering and Technology National Taiwan Ocean University Professor Department of Harbor and River Engineering National Taiwan Ocean University Center of Excellence for Ocean Engineering Master Department of Harbor and River Engineering National Taiwan Ocean University

A time-serial topographic change was performed using two kinds of surveying methods, including direct measurement and indirect remote sensing technology for beach topography surveillance. Compared with reference topographic data, the root mean square error was 0.169 m. From May, 2018 to March, 2019, multi-temporal beach topographic change results indicated that the distance between baseline and shoreline varied in the range of −24.82∼9.58 meter, and weekly variates are −3.16∼0.45 m. The UAV technique can be efficiently used to detect topographic change information in the coastal area.

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Comparison Of Multinomial Naive Bayes Algorithm And Logistic Regression For Intent Classification In Chatbot

Comparison Of Multinomial Naive Bayes Algorithm And Logistic...

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International Conference on Applied engineering (ICAE)

作者： Muhammad Yusril Helmi Setyawan Rolly Maulana Awangga Safif Rafi Efendi Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia

ISBN: (纸本)9781538680674

Chatbot is software that communicates using natural language. chatbots such as machine conversation systems, Chatterbot, virtual agents, and dialogue systems. This software enables to simulate human conversations. In this research, the chatbot system that will be created must be able to understand the natural language of what is entered by the user, and the chatbot will answer according to what the user is expecting. The researcher proposes a classification method to identify intent rather than user input or called intent classification on the chatbot system; the researcher also wants to know the level of accuracy, precision, and recall on the evaluation results of both methods. The classification method applied in this research is the Naive Bayes method and compared with the Logistic Regression method to determine the class intention. The evaluation results show the level of accuracy precision and recall in the Logistic Regression model is higher than the Naive Bayes model.

关键词： Logistics Training data Bayes methods Mathematical model Data models Classification algorithms Natural languages

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An Analysis of Customer Agrotourism Resort Behaviour based on RFM and Mean Shift Clustering

An Analysis of Customer Agrotourism Resort Behaviour based o...

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International Conference on Applied engineering (ICAE)

作者： Muhammad Ruslan Maulani Syafrial Fachri Pane Rolly Maulana Awangga Diana Asri Wijayanti Wahyu Caesarendra Diploma Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781538680674

This paper presents an analysis study of customer reservation behavior in Agrotourism N8 based on recency, frequency and monetary (RFM) method. The historical data from January to December 2016 were collected from Rancabali tour and used for RFM analysis. Prior to the RFM analysis, the data were grouped into frequency and monetary using mean shift clustering. This analysis will provide the guideline to Agrotourism N8 to predict the customer needs in the future based on historical data. The RFM method shows that Rancabali tour always crowded with visitors until the end of each month. The highest number of visitors occurred in January, July, and December. Moreover, The highest revenue earned in December. The mean shift shows the income-based cluster by the frequency of customer.

关键词： Companies Market research Bandwidth informatics Kernel Industries

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National Border Agency Communication Behaviour Clustering Using Centrality and Meanshift

National Border Agency Communication Behaviour Clustering Us...

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International Conference on Applied engineering (ICAE)

作者： Sari Armiati Rolly Maulana Awangga Adji Suradji Muhammad Diploma Program of Informatics Management Politeknik Pos Indonesia Bandung Indonesia Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia Faculty of Social and Political Science Universitas Maritim Raja Ali Haji Tanjungpinang Indonesia

ISBN: (纸本)9781538680674

Indonesia is Island Based Country with 1.905 million kilometers per square area. This territory area watching by National Border Agencies consists of a collaboration of many departments and government organization to make Indonesia strengthening the border. Indonesia has 6 neighboured country, Singapore, Malaysia, Philippine, Australia, Papua New Guinea, and Timor Leste. In Kepulauan Riau, have a direct border with Malaysia, Singapore, Vietnam, and Kamboja. This issue makes Kepulauan Riau have particular National Border Agencies from many Government Institution. In this research, conduct communication analysis between government Institution to run the task as National Border Agencies. The Analysis using Centrality from communication pattern in every institution. The centrality data clustering with meanshift to get a cluster of communication National Border Agencies.

关键词： Bandwidth Government Conferences Clustering algorithms Social network services Ontologies Level measurement

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Real world fracture prediction of fracture risk assessment tool (FRAX), osteoporosis self-assessment tool for Asians (OSTA) and one-minute osteoporosis risk test: An 11-year longitudinal study

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Bone Reports 2024年 20卷 101742页

作者： Sheng, Yueh-Hsuan Wu, Tai-Yin Liaw, Chen-Kun Hsiao, Sheng-Huang Kuo, Kuan-Liang Tsai, Ching-Yao Department of Family Medicine Taipei City Hospital Renai Branch No. 10 Sec. 4 Renai Rd. Daan Dist. Taipei City Taiwan Department of Family Medicine Taipei City Hospital Zhongxing Branch No. 145 Zhengzhou Rd. Datong Dist. Taipei City Taiwan Institute of Epidemiology and Preventive Medicine National Taiwan University No. 17 Xuzhou Rd. Zhongzheng Dist. Taipei City Taiwan Department of Health and Welfare University of Taipei No. 101 Sec. 2 Zhongcheng Rd. Shilin Dist Taipei City Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University No. 250 Wuxing St. Xinyi Dist. Taipei City Taiwan Department of Orthopedics Shuang Ho Hospital Taipei Medical University No. 291 Zhongzheng Rd. Zhonghe Dist. New Taipei City Taiwan Research Center of Biomedical Device College of Biomedical Engineering Graduate Institute of Biomedical Optomechatronics Taipei Medical University No. 250 Wuxing St. Xinyi Dist. Taipei City Taiwan TMU Biodesign Center Taipei Medical University No. 250 Wuxing St. Xinyi Dist. Taipei City Taiwan Department of Neurosurgery Taipei City Hospital Renai Branch No. 10 Sec. 4 Ren'ai Rd. Da'an Dist. Taipei City Taiwan Department of Psychology National Chengchi University No. 64 Sec. 2 Zhinan Rd. Wenshan Dist. Taipei City Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University No. 155 Sec. 2 Linong St. Beitou Dist. Taipei City Taiwan The Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen University No. 510 Zhongzheng Rd. Xinzhuang Dist. New Taipei City Taiwan Department of Ophthalmology Taipei City Hospital No. 145 Zhengzhou Rd. Datong Dist. Taipei City Taiwan Institute of Public Health National Yang Ming Chiao Tung University No. 155 Sec. 2 Linong St. Beitou Dist. Taipei City Taiwan Department of Business Administration Fu Jen Catholic University No. 510 Zhongzheng Rd. Xinzhuang Dist. New Taipei City Taiwan

Introduction: Fractures affect people's quality of life especially in the elders. One of the most important risk factors is osteoporosis. There are many screening tools to predict osteoporosis and fractures. We aimed to compare the predictive validity of three commonly used screening tools: fracture risk assessment tool (FRAX), osteoporosis self-assessment tool for Asians (OSTA) and one-minute osteoporosis risk test. Among them, OSTA and one-minute osteoporosis risk test were originally developed to predict osteoporosis risks and FRAX was to predict fracture risks. Methods: This is an 11-year longitudinal study. We enrolled 708 senior people from health examinees in Taiwan in 2010. A standardized questionnaire and blood tests were provided. Annual telephone interview was conducted to assess the real fracture status. We calculated risk scores of FRAX, OSTA, and one-minute osteoporosis risk test and compared with real-world fracture records. Results: The mean age of the participants were 74.9 (SD 6.4). There were 356 (50.3 %) men. From 2010 to 2020, a total of 105 (14.8 %) persons suffered from fractures. Compared to people without fractures, people with fractures had higher FRAX major osteoporotic fracture risk scores (14.0 % ± 7.6 % vs.11.3 % ± 5.7 %), higher hip fracture risk scores, and higher OSTA risk (5.9 % ± 1.4 % vs. 5.3 % ± 1.3 %). Cox regression analysis showed that hazard ratios for fracture of high FRAX risk was 1.53 (95 % confidence interval (CI) 1.05–2.21), and for high OSTA risk was 1.37 (95 % CI 1.04–1.82). Conclusions: Only OSTA and FRAX scores were satisfactory in predicting 10-year fractures. © 2024

关键词： Fracture risk assessment tool (FRAX) Fractures One-minute osteoporosis risk test Osteoporosis self-assessment tool for Asians (OSTA)

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Ontology Design of Influential People Identification Using Centrality

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Journal of Physics: Conference Series 2018年第1期1007卷

作者： Rolly Maulana Awangga Muhammad Yusril Helmi Setyawan Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Indonesia

Identifying influential people as a node in a graph theory commonly calculated by social network analysis. The social network data has the user as node and edge as relation forming a friend relation graph. This research is conducting different meaning of every nodes relation in the social network. Ontology was perfect match science to describe the social network data as conceptual and domain. Ontology gives essential relationship in a social network more than a current graph. Ontology proposed as a standard for knowledge representation for the semantic web by World Wide Web Consortium. The formal data representation use Resource Description Framework (RDF) and Web Ontology Language (OWL) which is strategic for Open Knowledge-Based website data. Ontology used in the semantic description for a relationship in the social network, it is open to developing semantic based relationship ontology by adding and modifying various and different relationship to have influential people as a conclusion. This research proposes a model using OWL and RDF for influential people identification in the social network. The study use degree centrality, between ness centrality, and closeness centrality measurement for data validation. As a conclusion, influential people identification in Facebook can use proposed Ontology model in the Group, Photos, Photo Tag, Friends, Events and Works data.

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