• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.723-732
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• 2022

The success of enterprise information system(EIS) is crucial to align with corporate strategies and eventually attain corporate goals. Since one of the factors to information system success is system use, managerial efforts to measure the level of EIS utilization is vital. In this paper, the EIS utilization level is analyzed using system access log data. In particular, process sequence patterns and clustering of similar functions are identified in more detail based on a process mining method, in addition to basic access log statistics. The result of this research can be used to improve existing information system design by finding real IS usage sequences and function clusters.

• Management & Information Systems Review
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• v.34 no.5
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• pp.23-47
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• 2015

In the Korea stock market, individual investors have perceived stock as short arbitrage investment, not long-term investment strategy. In order to reinforce stock market transparency and soundness, it is important to enforce the measures for stock market management. Especially, stock market event caused by financial policy can be given individual investors negative information regarding a stock trading. Thus, it is a need for investigating whether comprehensive review of listing eligibility is influenced on individual investors' responses and stock behaviors in respect of effectiveness. The purpose of this study to examine the relations between such stock market management and transitional aspect of individual investors' trading types and response on the based of pre- and post-event occurrence. Using an dataset of user's text messages on 9 firms posted on the firm-based social media (i.e., Naver, Daum, Paxnet) over the period 2009 to 2014. And we performed text-clustering and topic modeling according to keywords for classifying into investors group and non-investors groups and two types of investors were categorized depending on main topic transition by event windows in Comprehensive review of listing eligibility. The results indicated that a variety of stockholders existed in the stock. And the ratio of non-investors group was on the decrease, on the other hand, the proportion of investors group veer onto the side of pre-pattern after comprehensive review of listing eligibility. A distinctive feature of our study is to explain the influence of stock market management on response changes of individual investors as well as to categorize in accordance with time progression. Implications an suggestions for future research were also discussed.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.137-145
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• 2008

In general, a large-scale wireless sensor network(WSNs) is composed of hundreds of or thousands of sensor nodes. In this large-scale wireless sensor networks, it is required to maintain and manage the networks to lower management cost and obtain high energy efficiency. Users should be provided with sensing service at the level of quality for users through an efficient system. In evaluating the result data quality provided from this network to users, the number of sensors related to event detection has an important role. Accordingly, the network protocol which can provide proper QoS at the level of users demanding quality should be designed in a way such that the overall system function has not to be influenced even if some sensor nodes are in error. The energy consumption is minimized at the same time. The protocol suggested in this article is based on the LEACH protocol and is a role-based self-Organization one that is appropriate for large-scale networks which need constant monitoring.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1131-1141
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• 2015

In this paper, we proposed a new approach of machine learning based method for detecting game-bots from normal players in MMORPG by inspecting the player's action log data especially in-game money increasing/decreasing event log data. DBSCAN (Density Based Spatial Clustering of Applications with Noise), an one of density based clustering algorithms, is used to extract the attributes of spatial characteristics of each players such as a number of clusters, a ratio of core points, member points and noise points. Most of all, even game-bot developers know principles of this detection system, they cannot avoid the system because moving a wide area to hunt the monster is very inefficient and unproductive. As the result, game-bots show definite differences from normal players in spatial characteristics such as very low ratio, less than 5%, of noise points while normal player's ratio of noise points is high. In experiments on real action log data of MMORPG, our game-bot detection system shows a good performance with high game-bot detection accuracy.

• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.237-247
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• 2016

This study analyzed quantitative effects of ISMS certification. To measure the company value change the stock data was used and the methodology of event study was also applied. Event study methodology is a method of analyzing the effects of information or public announcement about certain events on the stock market through abnormal return of stock price. First, ISMS certification was acquired followed by the measurement of abnormal excess return of company. Based on the increase or decrease of abnormal excess return, the group was classified. There are 3 types of groups("Increase", "Reduce", "Maintain"). Next, the cluster analysis was performed for each group. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups(clusters). The purpose of this study is to have a quantitative measurement of performance of ISMS certification. So, the result of this study will be promoted a company's ISMS certification acquisition. And it would further be beneficial to your company's information security activities.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.1
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• pp.25-33
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• 2020

A drought event is characterized by duration, severity and affected area. In general, after calculating a drought index using hydro-meteorological time series at a station, a drought event is defined based on the run theory to identify the beginning and end time. However, this one-dimensional analysis has limitations for analyzing the spatio-temporal occurrence characteristics and movement paths of drought. Therefore, this study is to define a three-dimensional drought event using a simple clustering algorithm and to develop a drought map that can be used to understand the drought severity according to the spatio-temporal expansion of drought. As a result, compared with the two-dimensional monitoring information to show spatial distribution of drought index, a proposed drought map is able to show three-dimensional drought characteristics inclusing drought duration, spatial cumulative severity, and centroid of drought. The analysis of drought map indicated that there was a drought event which had the affected area less than 10 % while on occations while there were 11 drought events (44 %) which had the affected area more a than 90 % of the total area. This means that it is important to understand the relationship between spatial variation of drought affected area and severity corresponding to various drought durations. The development of drought map based on three-dimensional drought analysis is useful to analyze the spatio-temporal occurrence characteristics and propagation patterns of regional drought which can be utilized in developing mitigation measures for future extreme droughts.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.69-70
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• 2015

본 논문에서는 운행중인 차량이 수집한 위치별 교통 이벤트 (지체, 정체, 사고, 노면상태 등)를 다른 운행 차량과 실시간으로 공유하여 안전운행 서비스를 제공하기 위한 방법을 제안한다. 운행중인 차량은 차량내의 스마트 기기나 전용 기기를 이용해 수집한 교통이벤트를 실시간으로 서버로 전송하고 서버는 전송된 교통이벤트를 위치별, 시간별로 색인하고 중복된 교통이벤트를 분류하여 저장한다. 이런 모든 과정은 처리 속도 향상을 위해 Spark의 RDD를 이용해서 인-메모리에서 처리된다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.466-468
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• 2005

신문기사를 대상으로 사건 단위로 문서를 클러스터링 하기 위해서, 기존의 연구에서는 기사의 발행일 또는 기사의 내용만 사용하여 하나의 사건을 다른 사건과 구분하는 방법을 사용해 오고 있다. 하지만 사건의 전개가 시간 차이를 두고 진행되는 경우 또는 비슷한 시간대에 같은 범주에 속하는 사건이 발생하는 경우 기사의 발행일만 사용하여 사건 관련 기사를 구분하는 것은 한계가 있다. 본 연구에서는 한국어 신문기사를 대상으로 신문기사에 나타난 시간정보를 자동 추출하고, 이를 기사의 발행일을 기준으로 정규화 한 후 사용하여 사건단위로 기사를 클러스터링 하는 방법을 개발하였다. 즉 한국어 신문 기사를 대상으로 기사에 나타난 시간 표현을 자동으로 추출한 후, 사건과의 유사도 비교에 사용함으로써 사건 단위 클러스터링의 정확도를 높이기 위한 방법을 제안한다.

• Journal of Korean Neurosurgical Society
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• v.67 no.3
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• pp.299-307
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• 2024

Cerebral cavernous malformation (CCM) is a vascular anomaly commonly found in children and young adults. Common clinical presentations of pediatric patients with CCMs include headache, focal neurological deficits, and seizures. Approximately 40% of pediatric patients are asymptomatic. Understanding the natural history of CCM is crucial and hemorrhagic rates are higher in patients with an initial hemorrhagic presentation, whereas it is low in asymptomatic patients. There is a phenomenon known as temporal clustering in which a higher frequency of symptomatic hemorrhages occurs within a few years following the initial hemorrhagic event. Surgical resection remains the mainstay of treatment for pediatric CCMs. Excision of a hemosiderin-laden rim is controversial regarding its impact on epilepsy outcomes. Stereotactic radiosurgery is an alternative treatment, especially for deep-seated CCMs, but its true efficacy needs to be verified in a clinical trial.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.189-200
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• 2022

Ground subsidence on urban roads is a social issue that can lead to human and property damages. Therefore, it is crucial to detect underground cavities in advance and repair them. Underground cavity detection is mainly performed using ground penetrating radar (GPR) surveys. This process is time-consuming, as a massive amount of GPR data needs to be interpreted, and the results vary depending on the skills and subjectivity of experts. To address these problems, researchers have studied automation and quantification techniques for GPR data interpretation, and recent studies have focused on deep learning-based interpretation techniques. In this study, we described a hyperbolic event detection process based on deep learning for GPR data interpretation. To demonstrate this process, we implemented a series of algorithms introduced in the preexisting research step by step. First, a deep learning-based YOLOv3 object detection model was applied to automatically detect hyperbolic signals. Subsequently, only hyperbolic signals were extracted using the column-connection clustering (C3) algorithm. Finally, the horizontal locations of the underground cavities were determined using regression analysis. The hyperbolic event detection using the YOLOv3 object detection technique achieved 84% precision and a recall score of 92% based on AP50. The predicted horizontal locations of the four underground cavities were approximately 0.12 ~ 0.36 m away from their actual locations. Thus, we confirmed that the existing deep learning-based interpretation technique is reliable with regard to detecting the hyperbolic patterns indicating underground cavities.