• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.380-385
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• 2019

This paper is concerned with departure passengers' dwell time analysis using real system data. Previous researches emphasize the importance of dwell time analysis from perspective of airport terminal planning and non-aeronautical revenue. However, short-term airport operation using passengers' dwell time is considered impossible due to absence of passengers' behavior data. Recently, in accordance with the wave of smart airport, world leading airports are systematically collecting passenger data. So there is high possibility of analyzing passengers' dwell time with the data stacked in the airport database. We conducted dwell time analysis using data from Incheon Int'l airport. In order to handle passenger data, we adapted clustering algorithm which is one of data mining techniques. As a clustering result, passengers are divided into 3 clusters. One is the cluster for passengers whose dwell time is relatively short and who tend to spend longer time in the airside. Another is the cluster for passengers who have near 3 hours dwell time. The other is the cluster for passengers whose total dwell time is extremely long.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.37-46
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• 2017

Recently, disaster caused by social factors is frequently occurring in Korea. Prediction about what crisis could happen is difficult, raising the citizen's concern. In this study, we developed a program to acquire tweet data by applying Python language based Tweepy plug-in, regarding social disasters such as 'Nonspecific motive crimes' and 'Oxy' products. These data were used to evaluate psychological trauma and anxiety of citizens through the text clustering analysis and the opinion mining analysis of the R Studio program after natural language processing. In the analysis of the 'Oxy' case, the accident of Sewol ferry, the continual sale of Oxy products of the Oxy had the highest similarity and 'Nonspecific motive crimes', the coping measures of the government against unexpected incidents such as the 'incident' of the screen door, the accident of Sewol ferry and 'Nonspecific motive crime' due to misogyny in Busan, had the highest similarity. In addition, the average index of the Citizens sentiment score in Nonspecific motive crimes was more negative than that in the Oxy case by 11.61%p. Therefore, it is expected that the findings will be utilized to predict the mental health of citizens to prevent future accidents.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.2
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• pp.277-284
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• 2016

Various network attacks such as DDoS(Distributed Denial of service) and orm are one of the biggest problems in the modern society. These attacks reduce the quality of internet service and caused the cyber crime. To solve the above problem, signature based IDS(Intrusion Detection System) has been developed by network vendors. It has a high detection rate by using database of previous attack signatures or known malicious traffic pattern. However, signature based IDS have the fatal weakness that the new types of attacks can not be detected. The reason is signature depend on previous attack signatures. In this paper, we propose a k-means clustering based malicious traffic detection method to complement the problem of signature IDS. In order to demonstrate efficiency of the proposed method, we apply the bayesian theorem.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1778-1781
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• 2015

본 논문에서는 최근 이슈가 되고 있는 마인드 컨트롤 기술을 이용하여 RC 카를 움직이는 기술에 대하여 설명한다. 사회적으로 주의력 결핍 과잉행동 장애(ADHD)를 갖는 사람이 늘어나고 있으며, ADHD는 여러 가지 치료 기술이 있지만 어릴수록 놀이 치료 등을 사용하는 것이 좋다. 본 논문에서 뇌파측정기를 이용하여 뇌파를 모은 뒤 K-평균 알고리즘과 재 클러스터링을 통하여 방향 뇌파를 데이터화하고, 그 이후에 데이터화 된 방향 뇌파들을 토대로 RC카를 움직이는 기술 제안하였다. 개발된 기술의 경우 성능이 높게 나타나지 않았지만, 앞으로 이 기술이 더 발전한다면, 아동의 놀이 등에 활용하여 ADHD도 효과적으로 치료할 수 있을 뿐만 아니라, 실제 자동차 시스템에 적용하는 등 다양한 응용이 가능할 것으로 생각된다.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.33-41
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• 2022

In this article, we proposed to predict natural gas (NG) leakage levels through feature selection based on a factor analysis (FA) of the integrating the Korean Meteorological Agency data and natural gas leakage data for considering complex factors. The paper has been divided into three modules. First, we filled missing data based on the linear interpolation method on the integrated data set, and selected essential features using FA with OrdinalEncoder (OE)-based normalization. The dataset is labeled by K-means clustering. The final module uses four algorithms, K-nearest neighbors (KNN), decision tree (DT), random forest (RF), Naive Bayes (NB), to predict gas leakage levels. The proposed method is evaluated by the accuracy, area under the ROC curve (AUC), and mean standard error (MSE). The test results indicate that the OrdinalEncoder-Factor analysis (OE-F)-based classification method has improved successfully. Moreover, OE-F-based KNN (OE-F-KNN) showed the best performance by giving 95.20% accuracy, an AUC of 96.13%, and an MSE of 0.031.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.577-580
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• 2017

중합 효소 연쇄 반응 (PCR) 젤 전기영동 이미지에서 DNA 지문을 분석하기 위한 새로운 레인 검출 및 추적 알고리즘이 제안하였다. 이전에 여러 연구 결과가 보고되었지만 갑작스런 배경 밝기 차이와 구부러진 레인이 있는 이미지에서 레인을 정확하게 추출하는 것은 여전히 어려움이 있다. 우리는 평균 레인 폭과 레인 주기를 계산하기 위한 에지 기반 알고리즘을 제안한다. 본 논문에서 제안한 방법은 k-means 클러스터링 알고리즘을 이용하여 상승 에지와 하강 에지를 정확하게 추출하는 부화소(sub-pixel) 알고리즘을 적용하여 레인 폭과 주기를 추정한다. 구부러진 레인을 처리하기 위해 젤 이미지를 정상영역과 비정상영역으로 분할하고, 각 분할 된 이미지의 레인 중심을 추적한다. 우리가 제안한 방법의 성능을 평가하기 위해 534 레인을 포함한 32 개의 젤 이미지가 사용되었다. 실험 결과는 우리의 방법이 전처리 과정 없이 배경 차이와 구부러진 레인을 갖는 이미지에 강인함을 보여 주었다.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.4
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• pp.17-23
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• 1999

This paper described a security system using text-independent speaker recognition algorithm. Security system is based on PIC16F84 and sound card. Speaker recognition algorithm applied a k-means based model and weighted cepstrum for speech features. As the experimental results, recognition rate of the training data is 100%, non-training data is 99%. Also false rejection rate is 1%, false acceptance rate is 0% and verification mean error rate is 0.5% for registered 5 persons.

• Journal of the Korean Institute of Gas
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• v.24 no.1
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• pp.56-65
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• 2020

Forecasting of coming fire accidents is quite a challenging problem cause normally fire accidents occur for a variety of reasons and seem randomness. However, if fire accidents that cause critical losses can be forecasted, it can expect to minimize losses through preemptive action. Classifications using machine learning were determined as appropriate classification criteria for the forecasting cause it classified as a constant damage scale and proportion. In addition, the analysis of the periodicity of a critical fire accident showed a certain pattern, but showed a high deviation. So it seems possible to forecast critical fire accidents using advanced prediction techniques rather than simple prediction techniques.

• Journal of Broadcast Engineering
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• v.11 no.1 s.30
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• pp.15-27
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• 2006

In this paper, a novel approach is addressed to facilitate the browsing of large collection of digital photos associated with specified person(s) in the photos. The goal of the proposed method is to exploit additional person-identity features as incorporating facial regions and peripheral clothes region associated with them. For more effective incorporation of the clothes and facial features, situation-based photo clustering is also proposed. To evaluate the efficacy of the proposed method experiment was performed with 1120 generic home photos. The experiment results showed that the proposed method outperformed the conventional method us El.g only face feature as showing the average performance of about 92% contrary to the average performance of about 70% in the conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.123-129
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• 2017

New mobile phones with high tech-camera and a large size memory have been recently launched and people upload pictures of beautiful scenes or unknown flowers in SNS. This paper develops a flower recognition system that can get information on flowers in the place where mobile communication is not even available. It consists of a registration part for reference flowers and a recognition part based on OpenCV for Android platform. A new color classification method using RGB color channel and K-means clustering is proposed to reduce the recognition processing time. And ORB for feature extraction and Brute-Force Hamming algorithm for matching are used. We use 12 kinds of flowers with four color groups, and 60 images are applied for reference DB design and 60 images for test. Simulation results show that the success rate is 83.3% and the average recognition time is 2.58 s on Huawei ALEUL00 and the proposed system is suitable for a mobile phone without a network.