• Transactions of the Korean Society of Mechanical Engineers B
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• v.41 no.11
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• pp.735-742
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• 2017

In this study, an effective model-based sensor fault detection methodology that can detect and isolate PEM temperature sensors fault is introduced. In fuel cell vehicle operation process, the stack temperature affects durability of a fuel cell. Thus, it is important for fault algorithm to detect the fault signals. The major objective of sensor fault detection is to guarantee the healthy operations of the fuel cell system and to prevent the stack from high temperature and low temperature. For the residual implementation, parity equation based on the state space is used to detect the sensors fault as stack temperature and coolant inlet temperature, and residual is compared with the healthy temperature signals. Then the residuals are evaluated by various fault scenarios that detect the presence of the sensor fault. In the result, the designed in this study fault algorithm can detect the fault signal.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.8
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• pp.944-949
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• 2007

The profiles of on-line sensors such as DO, ORP and pH can provide useful information about pollutant removal reaction in sequencing batch reactor. For detection of denitrification completion, the nitrate hee point from ORP profile has been considered as a main indicator of denitrification completion. However, many researchers pointed out that the nitrate knee usually disappeared been the progress of denitrification is so fast and it makes the fault at detection of denitrification completion. In this paper, dynamic time warping(DTW) method and discriminant analysis were used to detect and isolate the profiles of two cases, denitrification completed and uncompleted. As the results, proposed methods can detect state of denitrification successfully.

• Journal of the Korean Magnetics Society
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• v.23 no.3
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• pp.104-109
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• 2013

Clip-type pulsimeter equipped with Hall device and a minute permanent magnet as sensing the minute movement of a radial artery was developed. The clinical data of the 120 number of subject acquisited through the clip-type pulsimeter did treated with a typical statistical logistic regression analysis. The prediction algorithm for the replete pulse and vacuous pulse was studied. The reflective peak time and the notch peak time were major parameters to discern the replete pulse and vacuous pulse. The discrimination rate was 65%. It suggests that the logistic regression equations are possible to use the diagnosis index to predict and discern the oriental pulse wave.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.705-711
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• 2013

QRS detection of ECG is the most popular and easy way to detect cardiac-disease. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, the design of algorithm that exactly detects QRS wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, efficient QRS detection and PVC classification based on profiling method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. Also, we applied profiling method to classify each patient's normal cardiac behavior through hash function. The performance of R wave detection, normal beat and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.77% in R wave detection and the rate of 0.65% in normal beat classification error and 93.29% in PVC classification.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.4
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• pp.91-98
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• 2018

As the importance of computing technology is emphasized, Korea has revised the educational curriculum to teach SW education compulsory at the elementary and secondary school level. For successful SW education, it is very important not only to require an educational environment and educational materials, but also to obtain the capacity of the teachers who are responsible for SW education. However, due to the lack of research on specifically examining the present state of teachers' SW competencies, there are many deficiencies in establishing a concrete teacher's training and a support plan for SW education. This study is to develop test tools and apply to measure a common sense about a computer, the latest IT technology algorithm design and a programming ability for the purpose of evaluating the SW competency of current teachers. As a result of the study, the understanding of common sense about a computer and the latest IT technology is very high, on the other hand the algorithm design and programming ability were analyzed as low. Therefore, the implications for SW education teacher's training and a process of prospective teachers' training are derived.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.433-443
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• 2022

Purpose: In this paper, we propose a study of deep learning algorithms that estimate and predict sarcopenia by exploiting the high penetration rate of smart devices. Method: To utilize deep learning techniques, experimental data were collected by using the inertial sensor embedded in the smart device. We implemented a smart device application for data collection. The data are collected by labeling normal and abnormal gait and five states of running, falling and squat posture. Result: The accuracy was analyzed by comparative analysis of LSTM, CNN, and RNN models, and binary classification accuracy of 99.87% and multiple classification accuracy of 92.30% were obtained using the CNN-LSTM fusion algorithm. Conclusion: A study was conducted using a smart sensoring device, focusing on the fact that gait abnormalities occur for people with sarcopenia. It is expected that this study can contribute to strengthening the safety issues caused by sarcopenia.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.59-61
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• 2021

The electric propulsion development is underway to minimize pollutants and greenhous gas emissions during the operation of ships / offshore installation vessels. The importance of the use and efficient management of batteries, which is an ESS system in ships / offshore installation vessels, is increasing. Generally, in ESS where battery is applied, cell balancing and life span are monitored in real time by BMS. Ships / offshore installation vessel are equipped with several ESS rooms, and ESS rooms with ESS systems of the same specification are being constructed due to the recent demand for electric propulsion development. In this paper, we propose an algorithm to additionally predict and diagnose battery pack and cell balancing failures by comparing BMS data for each rooms. The proposed algorithm compares the BMS data of each ESS Room according to the environmental change of the ship / offshore installation vessels, measures accurate status information, and reliably monitors it to prevent accidents in advance.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.89-96
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• 2020

Solar lamp systems may not operate normally in the event of some system or controller failure due to internal or external factors, in which case secondary problems occur, which may cost the system recovery. Thus, when these errors occur, a technology is needed to recover to the state it was in before the failure occurred and to enable re-execution. This paper designs and implements a system that can recover the state of the system to the state prior to the time of the error by using the Watchdog Timer within the controller if a software error has occurred inside the system, and it also proposes a technology to reset and re-execution the system through a separate reset circuit in the event of hardware failure. The proposed system provides stable operation, maintenance cost reduction and reliability of the solar lamp system by enabling the system to operate semi-permanently without external support by utilizing the automatic recovery and automatic reset function for errors that occur in the operation of the solar lamp system. In addition, it can be applied to maintain the system's constancy by utilizing the self-operation, diagnosis and recovery functions required in various high reliability applications.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.248-253
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• 2019

Early diagnosis of mild cognitive impairment (MCI) can reduce the incidence of dementia. This study developed the MCI prediction model for the elderly in Korea. The subjects of this study were 3,240 elderly (1,502 men, 1,738 women) aged 65 and over who participated in the Korean Longitudinal Survey of Aging (KLoSA) in 2012. Outcome variables were defined as MCI prevalence. Explanatory variables were age, marital status, education level, income level, smoking, drinking, regular exercise more than once a week, average participation time of social activities, subjective health, hypertension, diabetes Respectively. The prediction model was developed using Restricted Boltzmann Machine (RBM) neural network. As a result, age, sex, final education, subjective health, marital status, income level, smoking, drinking, regular exercise were significant predictors of MCI prediction model of rural elderly people in Korea using RBM neural network. Based on these results, it is required to develop a customized dementia prevention program considering the characteristics of high risk group of MCI.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.83-89
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• 2022

Internet of Things technology is rapidly developing due to the recent development of information and communication technology. IoT technology utilizes various sensors to generate unique data from each sensor, enabling diagnosis of system status. However, the equipment management system currently in effect is a post-preservation concept in which administrators must deal with the problem after the problem occurs, which could mean system reliability and availability problems due to system errors, and could result in economic losses due to negative productivity disruptions. Therefore, this study confirmed that edge controller control decision algorithms for more efficient operation of rectifiers in the factory by applying intelligent IoT (AIoT) technology and domain knowledge-based modeling for each sensor data collected based on this, outputting appropriate status messages for each scenario.