• Journal of Distribution Research
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• v.10 no.2
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• pp.1-26
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• 2005

The purpose of this study is to identify the consumer factors influencing the channel choice in the brokerage transaction. The 14 consumer factors were extracted from the two perspectives: first, the off-line channel is based on the relational benefits offered by the salespeople, second, the on-line stock exchange is a transaction mediated by the internet technology. The results shows that the channel choice is significantly influenced by the 8 out of 14 consumer factors hypothesized: interpersonal orientation, risk-taking tendency, amount of money invested, price consciousness, time length of internet usage, innovativeness, information privacy sensitivity, intrinsic desire for information. And the results of discriminant analysis shows that three consumer factors-risk-taking tendency, time length of internet usage, and information privacy sensitivity-are most valuable in discriminating off-line vs. on-line customers.

• Information Systems Review
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• v.6 no.1
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• pp.19-36
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• 2004

The importance of Internet banking has been emphasized due to the spread of Internet and the potential advantages of Internet banking. In case of Korea, Internet banking users have been risen dramatically since 1999 after its introduction. The purpose of this study is to draw the antecedent variables that affect the adoption of Internet banking and show their effects empirically. In this paper, influencing factors are proposed to distinguishing Internet banking users from non-users. And nine hypotheses are empirically tested using logistic regression. The results show that most of hypotheses are supported except for complexity and IT innovativeness. And compatibility was found to have a strong positive effect on the adoption of Internet banking. Implications of these findings are discussed for researchers and practitioners.

• Convergence Security Journal
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• v.14 no.4
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• pp.61-71
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• 2014

In recent years, malwares in Android smartphones are becoming increased explosively. Since a great deal of appsare deployed day after day, detecting the malwares requires commercial anti-virus companies to spend much time and resources. Such a situation causes malwares to be detected after they have become already spread. We propose a scheme called TAU that dynamically tracks application behaviors to specify apps with potential security risks. TAU keeps track of how a user's interactions to smartphones incurs the app installation, the route of app spread, and the behavior of app execution. This tracking specifies apps that have the possibility of attacking the smartphones using the drive-by download and update attack schemes. Moreover, the tracked behaviors are used to decide whether apps are repackaged or not. Therefore, TAU allows anti-virus companies to detect malwares efficiently and rapidly by guiding to preferentially analyze apps with potential security risks.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.169-174
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• 2018

There are many CCTV cameras connected to the video surveillance and monitoring center for the safety of citizens, and it is difficult for a few monitoring agents to monitor many channels of videos. In this paper, we propose an intelligent video surveillance system utilizing a safety map to efficiently monitor many channels of CCTV camera videos. The safety map establishes the frequency of crime occurrence as a database, expresses the degree of crime risk and makes it possible for agents of the video surveillance center to pay attention when a woman enters the crime risk area. The proposed gender classification method is processed in the order of pedestrian detection, tracking and classification with deep training. The pedestrian detection and tracking uses Adaboost algorithm and probabilistic data association filter, respectively. In order to classify the gender of the pedestrian, relatively simple AlexNet is applied to determine gender. Experimental results show that the proposed gender classification method is more effective than the conventional algorithm. In addition, the results of implementation of intelligent video security system combined with safety map are introduced.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.16-16
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• 2015

오늘날 전 세계적으로 가속화되는 기상이변에 따라 돌발성, 국지성 폭우 및 폭설의 빈도가 급격히 증가하는 추세이다. 이 같은 돌발기상 현상은 고층 건물과 인구의 과밀화로 인해 열섬효과가 자주 발생하는 도심지에서 특히 높은 발생률을 보이며, 그로 인한 막대한 인명 및 재산상의 피해가 발생하고 있는 실정이다. 하지만 이러한 돌발성 강수 현상들은 주로 저고도에서 생성 및 발달되며, 그 수명은 2~3시간에 불과하기 때문에 현재의 국내 기상관측 시스템으로는 예측 및 예보에 많은 어려움을 겪고 있다. 현재, 이러한 문제점을 해결하기 위해 국내 관련 기관들에서는 도심지를 중심으로 한 저층 기상 관측을 위한 소형 레이더 네트워크 구축을 계획하고 있다. 그와 함께, 본 논문에서는 향후 도입될 소형 레이더 네트워크의 활용성을 증대시키고, 기상재해의 피해를 줄이는 방법으로써, 구글 어스의 지도 서비스를 기반으로 한 기상 레이더 자료 활용 실시간 돌발성 기상재해 감시/추적 및 경보 시스템 플랫폼을 제안한다. 제안하는 플랫폼은 전 세계적으로 통용되는 GIS 엔진으로서, 높은 확장성이 장점인 구글어스 플랫폼을 바탕으로 하며, 레이더 자료 분석 도구, 위험도 판별 도구 및 자료 표출/경보 도구 등으로 크게 세 가지의 기술도구 집단으로 구성된다. 제안한 플렛폼 상에서 시뮬레이션을 통해 구글어스 기반에서 레이더 누적강수량의 실시간 처리와 3차원 GIS 기반에서의 직관적인 경보 메시지 표출을 구현하였으며, 향후 각 기술 도구들 상의 기법들을 연구 및 개선함으로써 국토관측센서 네트워크 및 기상 재해 예 경보 체계를 위해 활용되어질 수 있을 것으로 기대한다.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.359-372
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• 2019

Metabolic syndrome is deeply related with risks of chronic disease and mortality. Furthermore, the metabolic syndrome disorder in childhood and adolescence usually tends to lead to a number of cardiovascular disease in the stage of adulthood. In this sense, metabolic syndrome needs to be investigated seriously. The purpose of this study is therefore to prove the relationship between dietary habits(high carbohydrate, high calorie) and metabolic syndrome among adolescents. We also aim to clarify the relationship between emotional factors perceived by adolescents and metabolic syndrome. Our empirical results based on KNHANES dataset significantly demonstrates that the HDL-cholesterol is a decisive factor for the diagnosis of metabolic syndrome disorder. Additionally, emotional experience factors such as stress also show significant effects in difference of adolescents' metabolic syndrome.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.45-48
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• 2019

Detection of crosswalk is an important issue for the blind to walk without the help of others. There is a braille block on the sidewalk, which helps the blind to walk. On the other hand, crosswalk is more dangerous due to the moving vehicles. However, there is no appropriate means to induce the blind. In this paper, we propose a method to detect crosswalk in front of a blind and estimate its direction using an image sensor. We adopt multi-ROIs and make their binary versions. In order to determine whether it is a crosswalk, two features are extracted; one is the number of crossing in the binary image and the other is the ratio of white area. We can also estimate the direction of the crosswalk through the slope of the projection data. We evaluated the performance using experimental dataset and the proposed algorithm showed 80% accuracy of detection.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.263-263
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• 2023

블랙아이스는 눈에 쉽게 구분되지 않아 많은 교통사고를 초래하고 있다. 한국교통연구원 교통사고분석시스템에 따르면, 2017년부터 2021년까지 5년간의 서리/결빙으로 인한 교통사고 사망자는 122명, 적설로 인한 교통사고 사망자는 40명으로, 블랙아이스는 적설에 비해 위험성이 높은 것으로 나타난다. 과거의 다양한 연구에서 블랙아이스 생성조건을 기압과 한기 축적등의 조건에서 예측해왔지만, 이러한 기상학적 모델은 봄철 해빙기의 일교차로 인한 눈의 해동과 재냉각과 같은 다양한 기상 조건에서의 블랙아이스 탐지가 어렵다는 한계가 있어 최근에는 이미지 판별과 딥러닝모델(YOLO 등)을 기반으로 한 센서가 제시되고 있다. 그러나, 이러한 방법은 충분한 컴퓨팅 자원이 뒷받침되어야 하며, 블랙아이스 탐지까지 걸리는 속도가 빠르지 못한 편으로, 블랙아이스 초입 구간에서의 제동에 취약하다는 잠재적인 약점을 가지고 있다. 그러므로 본 연구에서는 블랙아이스의 주 원인인 서리나 어는비가 발생하기 위해서 주변 공기가 이슬점 온도 이하, 노면온도와 이슬점이 어는점보다 낮아야 함을 이용, IoT 센서 모듈을 통해 Magnus 방정식으로 계산한 이슬점 온도와 노면 온도를 사용하는 이동식 블랙아이스 추정 장치를 제시한다. 본 장치는 대기압, 온도, 습도로부터 계산된 이슬점 온도와 노면 온도를 통한 서리발생 가능성과 대기 온도, 노면 온도를 통해 어는비의 발생환경 여부를 계산한다. 본 연구 결과를 통해 블랙아이스 추정과 기상정보 생산을 동시에 가능케 하며, 추정 결과를 통합 수집서버에 전송함으로서 운전자에게 전방 블랙아이스 위험 구간을 조기에 전달하는 시스템과 이를 관리하기 위한 인프라를 구축하여 운전 시 결빙 미끄러짐 사고를 저감하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.491-493
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• 2022

Flood damage due to torrential rains and typhoons is occurring in many parts of the world. In this paper, we propose a water level prediction model using water level, precipitation, and humidity data, which are key parameters for flood prediction, as input data. Based on the LSTM and GRU models, which have already proven time-series data prediction performance in many research fields, different input datasets were constructed using the ASOS(Automated Synoptic Observing System) data and AWS(Automatic Weather System) data provided by the Korea Meteorological Administration, and performance comparison experiments were conducted. As a result, the best results were obtained when using ASOS data. Through this paper, a performance comparison experiment was conducted according to the input data, and as a future study, it is thought that it can be used as an initial study to develop a system that can make an evacuation decision in advance in connection with the flood risk determination model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.216-218
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• 2022

Natural disasters caused by abnormal climates are continuously increasing, and the types of natural disasters that cause the most damage are flood damage caused by heavy rains and typhoons. Therefore, in order to reduce flood damage, this paper proposes a system that can predict the water level, a major parameter of flood, in real time using LSTM and GRU. The input data used for flood prediction are upstream and downstream water levels, temperature, humidity, and precipitation, and real-time prediction is performed through the pre-trained LSTM-GRU model. The input data uses data from the past 20 hours to predict the water level for the next 3 hours. Through the system proposed in this paper, if the risk determination function can be added and an evacuation order can be issued to the people exposed to the flood, it is thought that a lot of damage caused by the flood can be reduced.