• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.1
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• pp.131-138
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• 2001

The performance analysis of an electronic equipment test is very complicate due to the variety o vehicles. In this study, automatic design system for the electronic equipment test has been carried out using the standard load patterns. For the test, standard signal patterns for each item are modeled. The test items can be decided by the user by means of these patterns. Also, engineering software modules are developed and proved to be very efficient for analyzing the test results statistically. Experiments are performed for the test system in the vehicle assembly line. By analyzing the test results, it is found that bad samples can be detected without failure. Also, the engineering software modules provide an analytical tool for the automation of the test process.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.53-54
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• 2009

본 논문은 홈 네트워크 시스템에서 사용자 프로파일을 기반으로 거주자의 행동패턴을 예측하고 분석하는 BPP(Behavior Pattern Prediction) 알고리즘을 제안한다. BPP 알고리즘은 거주자가 어느 방에 자주 방문하고, 어떤 행동을 자주 반복 하는지 파악을 하여 사용자 프로파일을 구축한다. 그리고 사용자가 머물렀던 방에 대한 관심을 객관적으로 측정하기 위해 거주지 사용자의 흥미에 대해서 가중치(weight)를 부여 한다. 사용자의 프로파일로부터 얻어진 데이터에 근거를 둔 가중치가 높을수록 사용자의 행동과 방에 대한 연관성이 높다는 것을 나타낸다. BPP 알고리즘의 특징은 시간대 별로 가중치를 측정하여 사용자의 다음 행동을 예측하고, 객관적으로 사용자의 행동 패턴을 분석한다.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.861-869
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• 2020

We introduce the application that automatically makes several images stored in user's device into one video by using the different climax patterns appearing for each film genre. For the classification of the genre characteristics of movies, a climax pattern model style was created by analyzing the genre of domestic movie drama, action, horror and foreign movie drama, action, and horror. The climax pattern was characterized by the change in shot size, the length of the shot, and the frequency of insert use in a specific scene part of the movie, and the result was visualized. The model visualized by genre developed as a template using Firebase DB. Images stored in the user's device were selected and matched with the climax pattern model developed as a template for each genre. Although it is a short video, it is a feature of the proposed application that it can create an emotional story video that reflects the characteristics of the genre. Recently, platform operators such as YouTube and Naver are upgrading applications that automatically generate video using a picture or video taken by the user directly with a smartphone. However, applications that have genre characteristics like movies or include video-generation technology to show stories are still insufficient. It is predicted that the proposed automatic video editing has the potential to develop into a video editing application capable of transmitting emotions.

• Korean journal of communication and information
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• v.26
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• pp.259-289
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• 2004

In this study, the use of mainstream online news site and alternative online news site were compared in the aspects of users' characteristics, attitude on online news sites, and using pattern. A survey was conducted for 182 mainstream-only users, 46 alternative online news users, and 47 both sites users, Also, their traffic data of online news sites were analyzed during the 16th presidential election. As a result, it was found that both sites users had the highest political interest and the most progressive political position among the user groups. In the aspect of users' attitude, mainstream-only users were most positive to the mainstream online news site and both sires users were most positive and more involved in alternative online news site. But all user groups set higher credibility on alternative online news site than mainstream online news sire. In the comparison of user size, mainstream online news site has larger user size than alternative online site. However, the user royalty, such as time per person, pages per person, and visiting days per person, was lower than that of the latter. These results suggest thar small but differentiated news sires have royal users, and online news users be segmented according to news contents.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.903-910
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• 2016

As smart mobile devices having touchscreens are growingly deployed, a pattern lock system, which is one of the graphical password systems, has become a major authentication mechanism. However, a user's unlocking behaviour leaves smudges on a touchscreen and they are vulnerable to the so-called smudge attacks. Smudges can help an adversary guess a secret pattern correctly. Several advanced pattern lock systems, such as TinyLock, have been developed to resist the smudge attacks. In this paper, we study an automated smudge attack that employs machine learning techniques and its effectiveness in comparison to the human-only smudge attacks. We also compare Android pattern lock and TinyLock schemes in terms of security. Our study shows that the automated smudge attacks are significantly advanced to the human-only attacks with regard to a success ratio, and though the TinyLock system is more secure than the Android pattern lock system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1477-1484
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• 2015

With the growth of financial industry with smartphone, interest on user authentication using smartphone has been arisen in these days. There are various type of biometric user authentication techniques, but gait recognition using accelerometer sensor in smartphone does not seem to develop remarkably. This paper suggests the method of user authentication using accelerometer sensor embedded in smartphone. Specifically, calibrate the sensor data from smartphone with 3D-transformation, extract features from transformed data and do principle component analysis, and learn model with using gaussian mixture model. Next, authenticate user data with confidence interval of GMM model. As result, proposed method is capable of user authentication with accelerometer sensor on smartphone as a high degree of accuracy(about 96%) even in the situation that environment control and limitation are minimum on the research.

• Journal of Korean Library and Information Science Society
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• v.51 no.2
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• pp.245-272
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• 2020

The purpose of this study is to analyze the current status of space use in university library and draw up considerations for enhancing space utilization based on the analysis results. For this purpose, the utilization rate of each space was analyzed by categorizing the user's space of the university library, and secondly, the preferences of the major user groups were analyzed according to the characteristics of the users' space. For empirical analysis, we collected and refined users' space usage data (98,282 people's data 433,769 cases) accumulated for one year for the central library of "J" National University. Then we analyzed the usage pattern according to space and period(monthly, hourly). In addition, preferences for each major group of users (undergraduates, graduate students and graduates) in the library were analyzed through the library visitors' data (2,426,553 cases) over the same period. The results of this study can be used as preliminary research for the composition and arrangement of users' space in future university libraries.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.956-961
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• 2006

Keystroke time intervals can be a discriminating feature in the verification and identification of computer users. This paper presents a comparison result obtained using several classification methods including k-NN (k-Nearest Neighbor), back-propagation neural networks, and Bayesian classification for keystroke identification. Performance of k-NN classification was best with small data samples available per user, while Bayesian classification was the most superior to others with large data samples per user. Thus, for web-based on-line identification of users, it seems to be appropriate to selectively use either k-NN or Bayesian method according to the number of keystroke samples accumulated by each user.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.3
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• pp.627-637
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• 2015

As game item trading becomes more popular with the rapid growth of online game market, the market for trading game items by cash has increased up to KRW 1.6 trillion. Thanks to this active market, it has been easy to turn these items and game money into real money. As a result, some malicious users have often attempted to steal other players' rare and valuable game items by using their account. Therefore, this study proposes a detection model through analysis on these account thieves' behavior in the Massive Multiuser Online Role Playing Game(MMORPG). In case of online game identity theft, the thieves engage in economic activities only with a goal of stealing game items and game money. In this pattern are found particular sequences such as item production, item sales and acquisition of game money. Based on this pattern, this study proposes a detection model. This detection model-based classification revealed 86 percent of accuracy. In addition, trading patterns when online game identity was stolen were analyzed in this study.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1365-1373
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• 2019

Meltdown and Spectre are vulnerabilities that exploit out-of-order execution and speculative execution techniques to read memory regions that are not accessible with user privileges. OS patches were released to prevent this attack, but older systems without appropriate patches are still vulnerable. Currently, there are some research to detect Meltdown and Spectre attacks, but most of them proposed dynamic analysis methods. Therefore, this paper proposes a binary signature that can be used to detect Meltdown and Spectre malware without executing them. For this, we collected 13 malicious codes from GitHub and performed binary pattern analysis. Based on this, we proposed a static detection method for Meltdown and Spectre malware. Our results showed that the method identified all the 19 attack files with 0.94% false positive rate when applied to 2,317 normal files.