• Journal of Information Technology Services
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• v.17 no.1
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• pp.135-153
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• 2018

E-Card Service for Child Nutrition Program is one of the main public policy services nowadays. In case of inconvenience during the use of the e-cards, it is recommended to cooperate with related organizations in order to promptly handle and provide guidance, and thoroughly manage child feeding service such as hygiene, nutrition and kindness etc. To do so, it is very important to provide food service that meets local actual conditions and children's needs in a cost effective manner for the underage who are worried about the poorly-fed by understanding the pattern of child feeding e-card service. Hence. this paper aims to investigate how child feeding e-card service efficiently provides meals according to the local situation and children's needs through big data analysis and to propose a method of identifying welfare conditions according to the purpose of service with actual application examples. The results suggest that, first of all, this study is able to judge appropriateness of public institution's policy in a timely and repetitive manner through non-standard data analysis such as Naver News and transaction data. Secondly, this paper proposes a multi-layered analysis framework, which performs online open data analysis to detect policy issues, visualizes retrieval and preprocessing of real data, and performs abnormal pattern recognition. These will be worthy of reference to other similar projects.

• Journal of the Korean Society of Propulsion Engineers
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• v.21 no.5
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• pp.61-70
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• 2017

Robust thrust distribution method of solid DACS is researched. For the case of the system which has higher number of actuation nozzles than the degree of freedom of thrust to be controlled, the robust thrust allocation law which accommodate the abnormal operation is suggested. Assuming the situation that some nozzles are uncontrollable, the error between nozzle throat area command and response can be calculated. The error is used for realtime reshaping of weighting matrix. From the weighting effect, the nozzle which operated abnormally has low responsibility for the command then, the thrust error is reduced. The suggested algorithm is verified by the simulation of abnormal operation condition of DCS and ACS nozzle respectively.

• Korean Journal of Adult Nursing
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• v.26 no.5
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• pp.573-583
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• 2014

Purpose: Cardiac arrest has multiple characteristics that need to be approached as an integrated method according to the various changes in the body system. This study was performed to develop a useful guideline for early detection of cardiac arrest by revealing the attributes of cardiac arrest through a concept analysis. Methods: This study was conducted according to the Walker and Avant's concept analysis method. Systematic literature review and in-depth interview with nurses who experienced cardiac arrest situation were conducted. Based on the literature reviews and in-depth interviews with nurses, the attributes and the empirical referents of the concept of cardiac arrest were elicited. Results: The definable attributes of cardiac arrest were 1) loss of consciousness, 2) abnormal respiratory condition, 3) abnormal cardiovascular signs. Cardiac arrest was found to occur by several antecedents such as cardiac problem, non-cardiac problem, or general problem, whereas ischemia and re-perfusion injury, which can lead to multiple organ failure and death, were derived as consequences. Conclusion: In this study, the concept analysis eliciting attributes and empirical referents is found to be useful as a guideline for understanding and managing cardiac arrest. Based on these findings, clinical providers are expected to make a precise and rapid decision on cardiac arrest and respond quickly, which may increase survival rate of the patients underwent the arrest event.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.369-376
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• 2018

This paper presents an android-based implementation of real-time remote monitoring system for industrial gas turbines. The use of remote monitoring techniques can be beneficial in terms of not only the reduction of monitoring cost but also the earlier detection of abnormal status. In order to achieve the ability of protecting sensitive information from unauthorized persons, the proposed system supports secure transmissions using the RSA(Rivest Shamir Adleman) algorithm. In the event of abnormal situation on the gas turbine, the remote monitoring system generates an alarm to attract the user's attention by exploiting a push-message technique. The proposed system has been verified through a series of experiments with an experimental setup including a virtual data generator.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.131-137
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• 2020

The research investigates the impact of the lockdown period caused by the COVID-19 to the stock market of India. The study examines the extent of the influence of the lockdown on the Indian stock market and whether the market reaction would be the same in pre- and post-lockdown period caused by COVID-19. Market Model Event study methodology is used. A sample of 31 companies listed on Bombay Stock Exchange (BSE) are selected at random for the purpose of the study. The sample period taken for the study is 35 days (24 February-17 April, 2020). An event window of 35 days was taken with 20 days prior to the event and 15 days during the event. The event (t1) being the official announcement of the lockdown. The results indicate that the market reacted positively with significantly positive Average Abnormal Returns during the present lockdown period, and investors anticipated the lockdown and reacted positively, whereas in the pre-lockdown period investors panicked and it was reflected in negative AAR. The study finds evidence of a positive AR around the present lockdown period and confirms that lockdown had a positive impact on the stock market performance of stocks till the situation improves in the Indian context.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.2
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• pp.94-100
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• 2005

Abnormal symptoms on operating conditions of power transformer are monitored by a preventive diagnostic system which prevents the sudden power failure in case of quick progress of abnormal situation. The preventive diagnostic system helps plan the proper maintenance method according to the transformer conditions via accumulated data. KEPCO has adopted the preventive diagnostic system at nine of 345kV substations since 1997. Application techniques of the diagnostic sensors were settled, but diagnostic algorithm and practical use of accumulated data are not yet established. To build up the diagnostic algorithm and effective use of the preventive diagnostic system, the reliability of the data which were accumulated in a server computer is very important. This paper describes the data analysis in the server in order to advance the reliability of the accumulated data of the preventive diagnostic system. The principles and data flows of the diagnostic sensors were analyzed, and the data discrepancy between sensors and server were calibrated.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.05a
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• pp.45-46
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• 2018

S2 module, which is one of the Korean type e-Navigation services, is a service concept that monitors the situation onboard and provides an emergency level determination and response guidance to the ship when an alarm occurs. S2 module is divided into fire/ seakeeping / navigation safety sub-module. In this paper, the concept of S2 service based on actual ship is explained through the response guidance case study of navigation safety module in case of abnormal condition in the passenger ship H.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.756-759
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• 2017

The productivity of cultivated crops in Korea is low compared to the Netherlands, which is an advanced agricultural country. In addition, modernization of facility and complex environmental control technology are needed to overcome poor growth and productivity deterioration caused by shortage of sunshine, abnormal temperature and high temperature due to abnormal climate. On the other hand, domestic facility horticulture complex environmental control is a level of machine automation that can check the internal situation of a green house with a cell phone and remotely operate a sprinkler, heat cover, curtain, ventilator, Therefore, this paper suggests the development of optimum environment control technology for facility horticulture based on the growth model and the cultivation technology knowledge base in order to realize the automation of optimal complex environment control and contribute to improvement of quality and productivity of cultivated crops.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.4
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• pp.64-75
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• 2018

A status of air traffic controller is a government officer and air traffic controllers who work at airport are divided by duty rating and work experience. Abiding by law, rules and regulation, air traffic controllers are working together based on mutual trust. This paper's theoretical background is based on cultural bias theory. The theory divide people group into four groups according to cultural bias such as fatalism, hierarchy, individualism and egalitarianism. A research model was designed how such four cultural bias could affect air traffic controller's risk response in case of emergency or abnormal situation during their work. Depend on empirical research, it was found that air traffic controllers perceived they had been more biased to fatalism than hierarchy. The characteristics of fatalism group are as follows: first of all, they follow rigid rules and regulation. However, they have less self-efficacy compared to other government officers. According to structural equation model, air traffic controller's fatalism had a significant negative effect on organizational royalty. Their royalty, however, had a very significant positive effect on planning response and immediate response.

• Journal of Platform Technology
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• v.11 no.5
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• pp.110-125
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• 2023

In this paper, we utilize time-series aircraft location data measured based on 2019 domestic airports to analyze Go-Around and UOC_D situations during the approach phase of domestic airports. Various clustering-based machine learning techniques are applied to determine the most appropriate analysis method for domestic aviation data through experimentation. The ADS-B sensor is solely employed to measure aircraft positions. We designed a model using clustering algorithms such as K-Means, GMM, and DBSCAN to classify abnormal situations. Among them, the RF model showed the best performance overseas, but through experiments, it was confirmed that the GMM showed the highest classification performance for domestic aviation data by reflecting the aspects specialized in domestic terrain.