• Journal of the Korean Society of Safety
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• v.33 no.5
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• pp.141-149
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• 2018

In a PCB factory, there is a corrosive chemical substance supply system that can causes major leakage accidents. These accidents can give rise to shut down the factory and do residents damage that cause enormous loss of properties. To mitigate these risks, it is necessary to provide a chemical disaster prevention system. Moreover, after considering the situation and environment of the production site, it is of great importance to build an optimal chemical accident prevention system by reflecting risk reduction measures from the point of process design and by assessing quantitative risk based on reliability data. However, because there was no established database of the reliability about facilities and equipment that can be used in the domestic, the business site and consulting organization had being used the reliability data such as USA CCPS(Center for Chemical Process Safety). In these days, Korean institutes are studying on reliability data utilization method of quantitative risk assessment for preventing chemical accidents and domestic utilization algorithms and storage bed of reliability data. This study presents samples of reliability database about the chemical substance supply system that constructed from the history data such as failure, maintenance for 10 years at a PCB factory. Also, this work proposes the safety design criteria for supply facilities of corrosive chemical substance by assessing quantitative risk on the basis of the reliability data.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.321-343
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• 2016

This study aimed to design a semi-automatic web-based pilot system 1) to build a Korean novel geo-name, 2) to update the database using automatic geo-name extraction for a scalable database, and 3) to retrieve/visualize the usage of an old geo-name on the map. In particular, the problem of extracting novel geo-names, which are currently obsolete, is difficult to solve because obtaining a corpus used for training dataset is burden. To build a corpus for training data, an admin tool, HTML crawler and parser in Python, crawled geo-names and usages from a vocabulary dictionary for Korean New Novel enough to train a named entity tagger for extracting even novel geo-names not shown up in a training corpus. By means of a training corpus and an automatic extraction tool, the geo-name database was made scalable. In addition, the system can visualize the geo-name on the map. The work of study also designed, implemented the prototype and empirically verified the validity of the pilot system. Lastly, items to be improved have also been addressed.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.555-564
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• 2015

Since the size of the construction project has become massive, complicated and specialized, the use for a substantial amount of information provided from diverse participants is considered important. Schedule information obtained from a variety of sources is key during the construction project. Misunderstanding of schedule management information causes delay of construction period and low quality of construction. Currently, interest in BIM (Building Information Modeling) that produces the necessary data for the entire life cycle of the building process is rising. The study is to construct the BIM-base-information system in order to systematically manage schedule information of the construction work. For this purpose, the study established a BIM-base-schedule-management-business process and drew a classification system for the work for schedule-information construction. The study also drew information that can be extracted from the BIM model among properties required to build certain information. The schedule is made upon consideration of information needed for schedule management, and information required to schedule a timeline of the construction project by process is established.

• Journal of Korean Academic Society of Home Health Care Nursing
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• v.29 no.1
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• pp.40-49
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• 2022

Purpose: This study aimed to analyze the effects of fatigue, resilience, and self-leadership on nursing service quality of local medical center nurses who nursed COVID-19 patients. Methods: The participants were 135 nurses who worked at regional public hospitals located in H-gun, G, and C-city in province C. The collected data were analyzed using descriptive statistics, t-test, analysis of variance (ANOVA), Pearson's correlation coefficients, and stepwise multiple regression using IBM SPSS Statistics version 25. Results: The participants' nursing service quality showed significant positive correlation with resilience (r=.53, p<.001), and self-leadership (r=.60, p<.001). The factors affecting participants' nursing service quality were commitment to self-leadership (β=.57, p<.001) and work position (chief nursing officer) (β=.26, p<.001), which explained 42% of the participants' nursing service quality. Conclusion: During a crisis such as the COVID-19 pandemic, it is necessary to help nurses enhance their self-leadership skills and build their career continuously by developing relevant policies, systems, and nursing intervention programs. Future studies could expand the knowledge base by including more participants to explore other ways to improve nursing service quality during the COVID-19 pandemic.

• The Journal of Industrial Distribution & Business
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• v.11 no.8
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• pp.31-38
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• 2020

Purpose: This study uses 'Autoregressive Integrated Moving Average Model' to predict the impact of a sharp drop in the base rate due to COVID-19 at the present time when government policies for stabilizing house prices are in progress. The purpose of this study is to predict implications for the direction of the government's house policy by predicting changes in house transaction prices and house rental prices after a sharp cut in the base rate. Research design, data, and methodology: The ARIMA intervention model can build a model without additional information with just one time series. Therefore, it is a time-series analysis method frequently used for short-term prediction. After the subprime mortgage, which had shocked since the global financial crisis in April 2007, the bank's interest rate in 2020 is set at a time point close to zero at 0.75%. After that, the model was estimated using the interest rate fluctuations for the Bank of Korea base interest rate, the house transaction price index, and the house rental price index as event variables. Results: In predicting the change in house transaction price due to interest rate intervention, the house transaction price index due to the fall in interest rates was predicted to change after 3 months. As a result, it was 102.47 in April 2020, 102.87 in May 2020, and 103.21 in June 2020. It was expected to rise in the short term. In forecasting the change in house rental price due to interest rate intervention, the house rental price index due to the drop in interest rate was predicted to change after 3 months. As a result, it was 97.76 in April 2020, 97.85 in May 2020, and 97.97 in June 2020. It was expected to rise in the short term. Conclusions: If low interest rates continue to stimulate the contracted economy caused by COVID-19, it seems that there is ample room for house transaction and rental prices to rise amid low growth. Therefore, In order to stabilize the house price due to the low interest rate situation, it is considered that additional measures are needed to suppress speculative demand.

• IE interfaces
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• v.24 no.2
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• pp.139-150
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• 2011

The EPCglobal Network is a computer network used to share product data among trading partners. It provides the supply chain with improved visibility and traceability by using Electronic Product Code (EPC), which is stored on an RFID tag. Although this network model is widely accepted as a global standard and the growth of EPCglobal-subscriber base is considerable, the EPC technology adoption process is still in its infancy. This is because some of the critical issues on this model still remain to be verified such as scalability, data management, security, privacy and the economic value of data sharing. In this paper, we focus on scalability issue among the challenges to overcome and we regard performance of the EPCglobal Network only as a track and trace query-processing cost in the network. We developed performance models consisting of three elements of the EPCglobal Network : Discovery Services (DS), EPC Information Services (EPCIS), Object Naming Services (ONS). Then we abstracted out the track and trace query execution model to evaluate performance of the overall EPCglobal Network. Finally using the proposed models, we carried out simulation analysis based on an RFID-based inbound logistics process of automobile parts. This work is an important step towards the EPC technology diffusion and provides guidelines for businesses looking to buy or build the EPCglobal Network-based systems.

• Journal of KIISE:Information Networking
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• v.30 no.3
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• pp.397-406
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• 2003

As the information technology grows interests in the intrusion detection system (IDS), which detects unauthorized usage, misuse by a local user and modification of important data, has been raised. In the field of anomaly-based IDS several artificial intelligence techniques such as hidden Markov model (HMM), artificial neural network, statistical techniques and expert systems are used to model network rackets, system call audit data, etc. However, there are undetectable intrusion types for each measure and modeling method because each intrusion type makes anomalies at individual measure. To overcome this drawback of single-measure anomaly detector, this paper proposes a multiple-measure intrusion detection method. We measure normal behavior by systems calls, resource usage and file access events and build up profiles for normal behavior with hidden Markov model, statistical method and rule-base method, which are integrated with a rule-based approach. Experimental results with real data clearly demonstrate the effectiveness of the proposed method that has significantly low false-positive error rate against various types of intrusion.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.565-579
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• 2016

In this paper a novel cancer subtype's classifier based on Hybrid Samples Balanced Genetic Algorithm with Extreme Learning Machine (hSBGA-ELM) is presented. Proposed cancer subtype's classifier uses genes' expression data of 16063 genes from open Global Cancer Map (GCM) data base for accurate cancer subtype's classification. Proposed method efficiently classifies 14 subtypes of cancer (breast, prostate, lung, colorectal, lymphoma, bladder, melanoma, uterus, leukemia, renal, pancreas, ovary, mesothelioma and CNS). Proposed hSBGA-ELM unifies genes' selection procedure and cancer subtype's classification into one framework. Proposed Hybrid Samples Balanced Genetic Algorithm searches a reduced robust set of genes responsible for cancer subtype's classification from 16063 genes available in GCM data base. Selected reduced set of genes is used to build cancer subtype's classifier using Extreme Learning Machine (ELM). As a result, reduced set of robust genes guarantees stable generalization performance of the proposed cancer subtype's classifier. Proposed hSBGA-ELM discovers 95 genes probably responsible for cancer. Comparison with existing cancer subtype's classifiers clear indicates efficiency of the proposed method.

• Journal of Environmental Science International
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• v.20 no.6
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• pp.783-788
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• 2011

Different from the main land of South Korea, Jeju Island has been in difficulties for measuring discharge. Due to high infiltration rate, most of streams in Jeju Island are usually in the dried state except six streams with the steady base flow, and the unique geological characteristics such as steep slope and short traveling distance of runoff have forced rainfall runoff usually to occur during very short period of time like one or two days. While discharge observations in Jeju Island have been conducted only for 16 sites with fixed electromagnetic surface velocimetry, effective analysis and validation of observed discharge data and operation of the monitoring sites still have been limited due to very few professions to maintain such jobs. This research is sponsored by Ministry of Land, Transport and Maritime Affairs to build water cycle monitoring and management system of Jeju Island. Specifically, the research focuses on optimizing discharge measurement techniques adjusted for Jeju Island, expanding the monitoring sites, and validating the existing discharge data. First of all, we attempted to conduct discharge measurements in streams with steady base flow, by utilizing various recent discharge monitoring techniques, such as ADCP, LSPIV, Magnetic Velocimetry, and Electromagnetic Wave Surface Velocimetry. ADCP has been known to be the most accurate in terms of discharge measurement compared with other techniques, thus that the discharge measurement taken by ADCP could be used as a benchmark data for validation of others. However, there are still concerns of using ADCP in flood seasons; thereby LSPIV would be able to be applied for replacing ADCP in such flooded situation in the stream. In addition, sort of practical approaches such as Magnetic Velocimetry, and Electromagnetic Wave Surface Velocimetry would also be validated, which usually measure velocity in the designated parts of stream and assume the measured velocity to be representative for whole cross-section or profile at any specified location. The result of the comparison and analysis will be used for correcting existing discharge measurement by Electromagnetic Wave Surface Velocimetry and finding the most optimized discharge techniques in the future.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.291-303
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• 2021

In the modern society, the number of people in disaster vulnerable groups is rapidly increasing such as the elderly, the disabled, foreigners, and children. The common characteristics of the groups vulnerable to disasters are that they live in residence types that are exposed to disasters because they are impoverished and if they are exposed to disasters, recovery is a slow process. The purpose of this study is to identify the new risk issues by performing risk issue analysis on the targets of disaster vulnerable group and provide base data for the development of the policies. For the research method, this study centered on the cases of children and pregnant women out of the disaster vulnerable groups and focused on the issue data of social media throughout the past 10 years ('10~'19) and performed social network analysis. As a result, first, the development of the issue showed relevance in the occurrence of specific cases. Second, the awareness about the types, targets, and management method of crisis management was analyzed. Third, an analysis was performed on the sentiment words that considered the solution measures of risk issues or the characteristics of the targets and it was analyzed that there were word that triggered negative emotions. Therefore, it is anticipated for the base data to be used for the government and also for the local government to build an effective crisis management system of the rapidly changing disaster environment on the basis of the sentiment analysis performed on the people of the nation as well as public awareness.