• Korea Journal of Hospital Management
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• v.12 no.1
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• pp.1-30
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• 2007

The study carried out a survey with employees of hospitals located in Daejeon, Chungnam, and Chungbuk from Sep. 12 to Sep. 30, 2005 in order to derive primary elements that affect the improvement of hospital's competitiveness. The study investigated and analyzed the employees' recognition on the change of competitive environment caused by the change of medical environment. The study also analyzed the elements that affect the hospital's competitiveness and the competitive strategies of the hospitals. The conclusion of this study can be summarized as follows. 1. Summary 1) Most of the employees responded that there is a rival in the competitive environment and the competitive is intense. Especially when the employees are married, live in urban areas, have an education level of university graduate or are managers, they tend to think the competitive is very intense. Also, they said that the competitive is based upon the quality of medical service. They mentioned the element that has the biggest effect on the competitiveness is the element of medical consumer and they recognized that the medical services in university and general hospitals have more competitiveness than the one-department hospitals. 2) It was investigated that the medical technique service has the most effect on the hospital's competitiveness. Also, the external service of medical techniques also has a large effect on the hospital's competitiveness. 3) When they were asked for the factors that affect the patients' decision on selecting a hospital, most of them responded "capability and technique of the medical staffs." Also, they said that "sufficient explanation from doctors" and "special center and clinic" are the factors that have big effects on the patients' decision. 4) In the SWOT analysis, most of them responded that the strength is the hospital's characteristics and the weakness is insufficient and obsolete equipment. They said the opportunity is the demands for professional medical service and the risk is the intense competitive among the hospitals. 5) In the SWOT strategy, they emphasized the strategy that uses the opportunity and the strength and the strategy that uses the opportunity while overcoming the weakness. 6) As for the basic competition strategy, most of them thought of the strategy of professionalizing the medical service most importantly. Next, they focused on the strategy of distinct service and the strategy of lower prime cost. 2. Conclusion 1) Because service competition between hospitals is happening seriously, need competitiveness security through right awareness transfer and satisfaction upgrade about medical consumer. 2) For medical technique service upgrade that equip Hospital's competitiveness but affects most, must solidify the countermeasure because professionalizing the medical service and newest medical technique induction should be achieved first, and compose task force for the external service of medical techniques improvement. 3) To improve SWOT of hospital, opportunity and the strength strategy choice that rescue hospital's characteristics heightening professionalizing the medical service level is fancied. 4) As for the basic competition strategy, will have to try in phase triangular position of hospital which is trusted medical level upgrade and excellent manpower security and finance independence through upgrade. The study was only done with hospitals in Daejeon, Chungnam and Chungbuk. Also, it is a study from the side of suppliers of medical service so there are limitations. However, the significance of the study is to present the basic data for improvement of hospital's competitiveness by examining the importance of medical techniques and external service of medical techniques that are the main effects on the improvement of hospital's competitiveness.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• Journal of the military operations research society of Korea
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• v.32 no.2
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• pp.188-211
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• 2006

This study aims to empirically investigate the effects of the individual-level characteristics on their timing decisions for their enlistments even though military services are their duties under a draft system. The individual characteristics considered include five psychological factors, such as attitude, uncertainty, information search level, future expectation, and perceived risk towards army, and other several demographic variables. Measurement scales for these psychological variables are developed and a duration model for individuals' enrollment timing decisions is also proposed. The proposed model is fitted to a survey data set collected from both those who have completed military service and those who have not. The estimation results show that two of five psychological variables, negative attitude and perceived risk, and several demographic variables, including education level, income level, residence area, and the number of family members serving the army, have meaningful impacts on the timing decisions for military service. Specifically, the enlistment timings are found to be more delayed as negative attitude towards army is stronger, perceived risk on army is higher, education level is higher, academic performance is better, income level is either low or high, residence area is either Seoul or big cities, and the proportion of family members enlisted is smaller. Several important managerial implications for alleviating problems resulting from enrollment postponements are also discussed.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of KIISE
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• v.42 no.1
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• pp.54-67
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• 2015

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.1-11
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• 2016

Currently Korean government is rushing the new electronic government system introduced as 'platform e-government' with big data and cloud computing technologies and systems, ultimately intending to provide the public institution services customized from the integrated counter or window for the heterogeneous resident services. In this regard, this study suggested how to design the new metadata information system in which mutual integration of information systems can take place, where heterogeneous services can be shared efficiently at the application and data unit, as a separate application that can provide a single one- stop service for residents' petition at the integrated level in the back-office based on the public data in possession of each of government ministries and related organizations. If this proposed system is implemented, the achievement of customized public service can be advanced one step forward in processing the petitions of the residents by organically connected link between 'Demand Chain' and 'Supply Chain' in the integrated window. In other words, it could be made possible through the unification of both the 'Supply Chain' performed in the office space of the officials at the back-office level and the 'Demand Chain' performed in the living space of the residents at the front-office level.

• Health Policy and Management
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• v.24 no.2
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• pp.136-142
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• 2014

Background: This study is to research on how hypertension control is associated with adherence in newly diagnosed hypertension patients. Methods: The study is based on 255,916 patients who were diagnosed with hypertension in 2009 and didn't have any previous medical history of hypertension or associated complication for the past year using data collected by National Health Insurance Corporation. Newly diagnosed hypertension patients are divided into two group by visiting medical center numbers (more than 300 days was adherence group, if not non-adherence group). Patients are considered to have successfully controlled their hypertension based on blood pressure measured by health examination. Chi-square test and logistic regression, repeated measured analysis of variance was used to analyze. Results: The relations between adherence and hypertension control show that 1.12 times of patients in adherence group was able to control their hypertension. The additional analysis proves that adherence group are more decreased level of blood pressure than non-adherence group except for patients who are over 70. Comparison of the average of systolic blood pressure and diastolic blood pressure between adherence and non-adherence groups shows that the blood pressure has been significantly among the adherence group. Conclusion: The study proves that constant treatment for hypertension could control the blood pressure and encourages patients to put more effort for persistent treatment. It also shows that hypertension treatment are more effective in younger patients than the elderly and strategies of approaching are different depending on age.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.961-974
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• 2015

With the advancement of SIEM from ESM, it allows deep correlated analysis using huge amount of data. By collecting software's vulnerabilities from assessment with certain classification measures (e.g., CWE), it can improve detection rate effectively, and respond to software's vulnerabilities by analyzing big data. In the phase of monitoring and vulnerability diagnosis Process, it not only detects predefined threats, but also vulnerabilities of software in each resources could promptly be applied by sharing CCE, CPE, CVE and CVSS information. This abstract proposes a model for effective detection and response of software vulnerabilities and describes effective outcomes of the model application.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.468-481
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• 2018

This study aims to empirically investigate how users' online engagement behaviors predict their uses of paid contents. To this end, the data from the 2016 Korean Media Panel Survey, which has been conducted annually by the Korea Information Society Development Institute(KISDI), were analyzed. Major findings(N=8.313) were as follows. First, the active type of online engagement(e.g., posting, commenting), which contributes to direct creation of online contents, was the most powerful predictor to explain the DV. On the other hand, relatively passive actions of user engagement(e.g., sharing, endorsing, voting) turned out to have no significant effects on the uses of paid contents, just as personality traits and online privacy concerns did. Based on these results, it is recommended that online contents or platform service providers should try to establish clearly-targeted marketing strategies, after thoroughly collecting and analyzing the data of users' various online behaviors.

• Korean Journal of Social Welfare
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• v.66 no.3
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• pp.133-158
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• 2014

Over the past decades, changes in economic, social and demographic structures have pushed the growth of care employment across countries around the world. Women's increasing labor force participation has squeezed the time so far available for unpaid caregiving and led to increased demand for paid care services. Population aging and increasing needs for pre-school education also have contributed to the growth in demand for care services. As a result, care workers now comprise a large and growing segment of the labor force in many countries including South Korea. But, there are not a few problems. Especially, we take underpaid and undervalued care work very seriously. care work has been generally characterized as underpaid and undervalued compared with other work in developed and developing countries alike. This study tries to show current situation of care work and estimate the wage penalty for doing care work in Korea using official employment micro-data and applying propensity matching analysis. Especially, recent expansion of social service is a big step up for Korean Welfare State. But, there are not a few problems. Especially, we take underpaid and undervalued care work very seriously. This presentation tries to show current situation of care work and estimate the wage penalty for doing care work in Korea using official employment micro-data and applying propensity matching analysis.