• Journal of Korea Game Society
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• v.16 no.2
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• pp.7-16
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• 2016

In this paper, we propose a method for effectively detecting specific behavior. The proposed method detects outlying behavior based on the game players' characteristics. These characteristics are captured non-invasively in a general game environment and add keystroke based on repeated pattern. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players was used to analyze high-dimensional game-player data for a detection effect of repeated behaviour pattern. A support vector machine was used to efficiently detect outlying behaviors. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. In addition, Repeated behaviour pattern can be analysed possible. The proposed method can also be used for feedback and quantification about analysis of various interactive content provided in PC environments.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.105-110
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• 2009

현재 NASA에서 제공되는 MODIS 지표반사율자료(MOD09)는 MODIS영상을 이용한 각종 주제자료들의 중요한 입력 자료로 사용되고 있으며, MODIS 지표반사율 자료에 대한 객관적인 검증연구가 필요한 실정이다. 따라서 본 연구에서는 MOD09의 검증관련 초기 연구로서, 남한에 분포하는 불변성 타겟(invariant target)을 대상으로 2006년 일별 250m MODIS 지표반사율자료(MOD09GQK)자료의 객관적 검증을 시도하였다. 우선, MOD09 QA(Quality Assurance)자료를 이용하여 구름의 영향을 받은 화소를 제거한 후, 수치지도와 토지피복도를 이용하여 정의한 불변성 타겟에 해당되는 MOD09영상의 화소값을 추출하였다. 이와 같이 추출된 시계열 MOD09GHK영상의 화소값에 1차 회귀분석을 적용하여 이상 반사율 값을 탐지하고, 그 원인을 분석하였다. 검증 결과 나지지역에 대해서 0.0186의 RMSE값이 나타났으며, 인공물의 경우 0.2891의 RMSE값을 보였다. 발생된 이상 화소를 살펴보면, 구름, 그림자, 눈에 영향에 의해 발생한 것도 있으며, 원인을 알 수 없는 이상 화소들도 분포하였다. 향후 연구에서는 한반도 전역의 MODIS 시계열 반사율영상을 대상으로 MODIS 대기보정알고리즘과 입력인자의 적합성을 판단하기 위한 연구를 진행할 예정이다.

• Spatial Information Research
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• v.20 no.4
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• pp.47-55
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• 2012

Normalized Difference Vegetation Index (NDVI) has been used to measure and monitor plant growth, vegetation cover, and biomass from multispectral satellite data. It is also a valuable index in forest applications, providing forest resource information. In this research, an approach for monitoring forest change using MODIS NDVI time series data is explored. NDVI difference-based approaches for a specific point in time have possible accuracy problems and are lacking in monitoring long-term forest cover change. It means that a multi-time NDVI pattern change needs to be considered. In this study, an efficient methodology to consider long-term NDVI pattern is suggested using a harmonic model. The suggested method reconstructs MODIS NDVI time series data through application of the harmonic model, which corrects missing and erroneous data. Then NDVI pattern is analyzed based on estimated values of the harmonic model. The suggested method was applied to 49 NDVI time series data from Aug. 21, 2009 to Sep. 6, 2011 and its usefulness was shown through an experiment.

• Geophysics and Geophysical Exploration
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• v.19 no.4
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• pp.200-211
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• 2016

To support cross potential array and direct potential array, the array for leakage detection of all kinds of water facilities is proposed and it is named as the D-Lux array. The D-Lux array data are arranged to a coloured matrix and it is called the D-Lux view. Low potential difference of anomalous zone shown in D-Lux view implies the indication of leakage zone. Furthermore, for an intuitive interpretation of D-Lux array, equipotential distribution map is made by using D-Lux and direct potential array data. Equipotential distribution map makes us possible to predict import point, export point and the path of water leakage that we could have not anticipated in D-Lux view and the graphs. The water tank experiment and numerical analysis were carried out as preparatory experiment and the field explorations were conducted at a concrete weir and a fill dam. As a result, effective and specific detection of leakage path was possible for the concrete weir and the fill dam.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1235-1243
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• 2023

This study proposes the detection algorithm for the cold water mass (CWM) along the eastern coast of the Korean Peninsula using sea surface temperature (SST) data provided by the Korea Institute of Ocean Science and Technology (KIOST). Considering the occurrence and distribution of the CWM, the eastern coast of the Korean Peninsula is classified into 3 regions("Goseong-Uljin", "Samcheok-Guryongpo", "Pohang-Gijang"), and the K-means clustering is first applied to SST field of each region. Three groups, K-means clusters are used to determine CWM through applying a double threshold filter predetermined using the standard deviation and the difference of average SST for the 3 groups. The estimated sea area is judged by the CWM if the standard deviation in the sea area is 0.6℃ or higher and the average water temperature difference is 2℃ or higher. As a result of the CWM detection in 2022, the number of CWM occurrences in "Pohang-Gijang" was the most frequent on 77 days and performance indicators of the confusion matrix were calculated for quantitative evaluation. The accuracy of the three regions was 0.83 or higher, and the F1 score recorded a maximum of 0.95 in "Pohang-Gijang". The detection algorithm proposed in this study has been applied to the KIOST SST system providing a CWM map by email.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.157-170
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• 2014

As the increase of transaction using mobile banking continues, threat to the mobile financial security is also increasing. Mobile banking service performs the financial transaction using the dedicate application which is made by financial corporation. It provides the same services as the internet banking service. Personal information such as credit card number, which is stored in the mobile banking application can be used to the additional attack caused by a malicious attack or the loss of the mobile devices. Therefore, in this paper, to cope with the mobile financial accident caused by personal information exposure, we suggest outlier detection method which can judge whether the transaction is conducted by the appropriate user or not. This detection method utilizes the user's input patterns and transaction patterns when a user uses the banking service on the mobile devices. User's input and transaction pattern data involves the information which can be used to discern a certain user. Thus, if these data are utilized appropriately, they can be the information to distinguish abnormal transaction from the transaction done by the appropriate user. In this paper, we collect the data of user's input patterns on a smart phone for the experiment. And we use the experiment data which domestic financial corporation uses to detect outlier as the data of transaction pattern. We verify that our proposal can detect the abnormal transaction efficiently, as a result of detection experiment based on the collected input and transaction pattern data.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.647-656
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• 2020

Recently, there has been an increasing number of cases in which important data (personal information, technology, etc.) of national and public institutions are leaked to the outside world. Surveys show that the largest cause of such leakage accidents is "insiders." Insiders of organization with the most authority can cause more damage than technology leaks caused by external attacks due to the organization. This is due to the characteristics of insiders who have relatively easy access to the organization's major assets. This study aims to present an optimized property selection model for detecting such abnormalities through supervised learning algorithms among machine learning techniques using actual data such as CrossNet data transfer system transmission log, e-mail transmission log, and personnel information, which safely transmits data between separate areas (security area and non-security area) of the business network and the Internet network.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.5
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• pp.285-291
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• 2014

The statistical information of the coastal water temperature monitoring data can be biased because of outliers and missing intervals. Though a number of outlier detection methods have been developed, their applications are very limited to the in-situ monitoring data because of the assumptions of the a prior information of the outliers and no-missing condition, and the excessive computational time for some methods. In this study, the practical robust method is developed that can be efficiently and effectively detect the outliers in case of the big-data. This model is composed of these two parts, one part is the construction part of the approximate components of the monitoring data using the robust smoothing and data re-sampling method, and the other part is the main iterative outlier detection part using the detailed components of the data estimated by the approximate components. This model is tested using the two-years 5-minute interval water temperature data in Lake Saemangeum. It can be estimated that the outlier proportion of the data is about 1.6-3.7%. It shows that most of the outliers in the data are detected and removed with satisfaction by the model. In order to effectively detect and remove the outliers, the outlier detection using the long-span smoothing should be applied earlier than that using the short-span smoothing.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.319-322
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• 2010

본 논문에서는 저가의 비용으로 구축이 가능한 USB 인터페이스용 PC 카메라를 이용한다. 사람의 침입이나 사람의 움직임을 감시할 필요가 있는 장소에 카메라를 설치하여 영상을 계속 감시한다. 감시가 필요한 장소에 설치된 각 카메라의 영상에 변화를 저장하여 기록하는데 있어서, 비교적 적은 비용이 필요하다. 또한 감시가 필요한 장소를 보다 안전하고 정확하게 감시할 수 있는 무인 침입탐지시스템에 영상처리와 영상인식 기술을 이용하여 실시간 감시시스템을 구현한다. 구현한 시스템은 웹을 기반으로 다양한 원격지의 화상 자료의 신속한 전송, 정확성의 구현, 특정 움직임의 캡처 및 선택, 검색, 자동 움직임 감지 등의 장점을 제공한다. 또한 독자적 시스템을 제공하여 다수의 시스템을 영상 입력 서버로 이용이 가능하도록 하였다. 뿐만 아니라, 서버에 C/S 형태의 시스템도 함께 제공하여, 영상인식 모듈을 탑재할 수도 있다. 덧붙여 인터넷을 통한 자료의 전송기술 및 QoS 만족을 위한 자료의 압축 및 품질 향상 기술을 적용하여 원격 출력과 원격 전송이 가능하여 저장 장치의 유지 관리 및 설치면에서 많은 경제적 이점이 있다.