• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.175-188
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• 2015

This study is about estimation methods for the population pharmacokinetic and pharmacodymic model. This is a nonlinear mixed effect model, and it is difficult to find estimates of parameters because of nonlinearity. In this study, we examined theoretical background of various estimation methods provided by NONMEM, which is the most widely used software in the pharmacometrics area. We focused on estimation methods using a stochastic sampling approach - IMP, IMPMAP, SAEM and BAYES. The SAEM method showed the best performance among methods, and IMPMAP and BAYES methods showed slightly less performance than SAEM. The major obstacle to a stochastic sampling approach is the running time to find solution. We propose new approach to find more precise initial values using an ITS method to shorten the running time.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.537-542
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• 2017

Aggressive driving is a major cause of car accidents. Previous studies have mainly analyzed young driver's aggressive driving tendency, yet they were only done through pure clustering or classification technique of machine learning. However, since elderly people have different driving habits due to their fragile physical conditions, it is necessary to develop a new method such as enhancing the characteristics of driving data to properly analyze aggressive driving of elderly drivers. In this study, acceleration data collected from a smartphone of a driving vehicle is analyzed by a newly proposed ECA(Enhanced Clustering method for Acceleration data) technique, coupled with a conventional clustering technique (K-means Clustering, Expectation-maximization algorithm). ECA selects high-intensity data among the data of the cluster group detected through K-means and EM in all of the subjects' data and models the characteristic data through the scaled value. Using this method, the aggressive driving data of all youth and elderly experiment participants were collected, unlike the pure clustering method. We further found that the K-means clustering has higher detection efficiency than EM method. Also, the results of K-means clustering demonstrate that a young driver has a driving strength 1.29 times higher than that of an elderly driver. In conclusion, the proposed method of our research is able to detect aggressive driving maneuvers from data of the elderly having low operating intensity. The proposed method is able to construct a customized safe driving system for the elderly driver. In the future, it will be possible to detect abnormal driving conditions and to use the collected data for early warning to drivers.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.307-309
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• 2007

SVDD의 scale prob1em을 해결하기 위하여, 학습 데이터를 sub-groupings하여 group 단위로 SVDD를 통해 학습함으로써 학습 시간을 줄이는, K-means clustering을 이용한 SVDD 방범(KMSVDD)이 제안되었다. 하지만 KMSVDD는 K-means clustering 알고리즘의 본질상 최적의 K값을 정하기 힘들다는 문제와, 동일한 데이터를 학습할지라도 clustered group이 램덤하게 형성되기 때문에 매번 학습의 결과가 달라지는 문제점이 있었다. 또한 데이터의 분포 상태와 관계없이 무조건 타원(dlliptic) 형태의 K개의 cluster로 나누기 때문에 각각의 나눠진 cluster들은 데이터 분포에 대한 특징을 나타내기 힘들게 된다. 이러한 문제점을 해결하기 위하여 본 논문에서는 데이터 분포에서 mode를 먼저 찾은 후 이 mode를 기준으로 clustering하는 Mean Shift clustering 방법을 이용한 SVDD를 제안하고자 한다. 제안된 알고리즘은 KMSVDD와 비교해 데이터 학습 속도에서는 큰 차이가 없으면서도 데이터의 분포 상태를 고려한 형태로 clustering 한 sub-group을 학습하므로 학습의 정확도가 일정하게 되며, 각각의 cluster는 데이터 분표의 특징을 포함하는 효과가 있다. 또한 Mean Shift Kernel의 bandwidth의 결정은 K-Means의 K와는 달리 어느 정도 여유를 갖고 결정되어도 학습 결과에는 차이가 없다. 다양한 데이터들을 이용한 모의실험을 통하여 위의 내용들을 검증하도록 한다.

• The KIPS Transactions:PartC
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• v.12C no.4 s.100
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• pp.571-580
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• 2005

In fast long-distance networks, TCP's congestion control algorithm has the problem of utilizing bandwidth effectively. Several window-based congestion control protocols for high-speed and large delay networks have been proposed to solve this problem. These protocols deliberate mainly three properties : scalability, TCP-friendliness, and RTT-fairness. These protocols, however, cannot satisfy above three properties at the same time because of the trade-off among them This paper presents a new window-based congestion control algorithm, called EM (Exponential Increase/ Multiplicative Decrease), that simultaneously supports all four properties including fast convergence, which is another important constraint for fast long-distance networks; it can support scalability by increasing congestion window exponentially proportional to the time elapsed since a packet loss; it can support RTT-fairness and TCP-friendliness by considering RTT in its response function; it can support last fair-share convergence by increasing congestion window inversely proportional to the congestion window just before packet loss. We evaluate the performance of EIMD and other algorithms by extensive computer simulations.

• The KIPS Transactions:PartD
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• v.13D no.1 s.104
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• pp.133-138
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• 2006

Electric commerce of surpassing the traditional one appeared before the public and has currently led the change in the management of enterprises. To establish and maintain good relations with customers, electric commerce has various channels for customers that understand what they want to and suggest it to them. The bulletin board and e-mail among em are inbound information that enterprises can directly listen to customers' opinions and are different from other channels in characters. Enterprises can effectively manage the bulletin board and e-mail by understanding customers' ideas as many as possible and provide them with optimum answers. It is one of the important factors to improve the reliability of the notice board and e-mail as well as the whole electric commerce. Therefore this thesis researches into methods to classify various kinds of documents automatically in electric commerce; they are possible to solve existing problems of the bulletin board and e-mail, to operate effectively and to manage systematically. Moreover, it researches what the most suitable algorithm is in the automatic classification of Q&A documents by experiment the classifying performance of Naive Bayesian, TFIDF, Neural Network, k-NN

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.111-122
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• 2015

This study purposed on introducing a new automated solution for detecting railway tracks and reconstructing track models from the mobile laser scanning data. The proposed solution completes following procedures; the study initiated with detecting a potential railway region, called Region Of Interest (ROI), and approximating the orientation of railway track trajectory with the raw data. At next, the knowledge-based detection of railway tracks was performed for localizing track candidates in the first strip. In here, a strip -referring the local track search region- is generated in the orthogonal direction to the orientation of track trajectory. Lastly, an initial track model generated over the candidate points, which were detected by GMM-EM (Gaussian Mixture Model-Expectation & Maximization) -based clustering strip- wisely grows to capture all track points of interest and thus converted into geometric track model in the tracking by detection framework. Therefore, the proposed railway track tracking process includes following key features; it is able to reduce the complexity in detecting track points by using a hypothetical track model. Also, it enhances the efficiency of track modeling process by simultaneously capturing track points and modeling tracks that resulted in the minimization of data processing time and cost. The proposed method was developed using the C++ program language and was evaluated by the LiDAR data, which was acquired from MMS over an urban railway track area with a complex railway scene as well.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3B
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• pp.138-143
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• 2005

This paper aims to optimize an access probe algorithm for the CDMA 2000 system. The incremental value of PWR_STEP increases as NUM_STEP as 1dBm±0.2 for access probe of the area with good receiving sensitivity when the mobile station transmits via access channel and does not receive any ACK message. However, for the area with weak receiving sensitivity, according to the algorithm of open-loop power control, the transmitting power amplifier becomes saturated and PWR_STEP incremental value keeps performing access probe to 0dBm±0.2. Therefore interference and battery consumption increases according to the transmission of the mobile station. We have optimized the access probe algorithm according to the receiving sensitivity. We transmit the incremental value of access probe power, with delaying as much RT slot value as indicated by IS-95C standard in case of good receiving sensitivity and with delaying RT+l slot value in case of weak receiving sensitivity. Simulation results showed that the proposed algorithm contributes to decrease of the interference and battery consumption according to the transmitting power of the mobile station and improves the call duration.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.297-299
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• 2006

최근 들어 전력 계통은 점차 복잡해지고 계통 규모 역시 빠른 속도로 성장하고 있다. 이러한 환경 하에서 전력계통의 안정적, 경제적 운영을 담당하고 있는 한국전력거래소는 EMS(Energy Management System)를 통해 실시간 전력계통에 대한 정확한 판단을 기반으로 전력계통의 안정성과 경제성 확보에 주력하고 있다. EMS의 다양한 기능 중 스카다(SCADA) 기능은 단순히 취득 데이터를 처리하는 기능뿐 아니라 인텔리젼트한 기능을 탑재하고 있는데 이중 대표적 기능이 스카다 토폴로지(SCADA Topology)라 할 수 있다. 스카다 토폴로지는 그 이름에서 알 수 있듯이 스카다에서 실시간 취득, 처리되는 아날로그와 스테이터스 데이터를 기반으로 전력계통에서 운용하는 각종 전력설비(발전기, 송전선로, 변압기, 조상설비 등)에 대한 가압 또는 무압여부를 스캔주기(2초)내에 결정하는 기능을 말한다. 현재 전력거래소는 100인치 화면 16장을 연견한 Rear Projector에 전국전력계통도 화면을 제작하여, 한눈에 전력계통의 가압 또는 무안상태를 표시 실시간 감시에 활용하고 있다. 그럼에도 국내에서는 스카다 토폴로지에 대해 소개된 논문이나 소개가 부재한 형편임을 감안하여, 본고는 스카다 토폴로지의 기본 알고리즘을 분석하여 국내 스카다 기능의 선진화에 기여하고자 한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.5
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• pp.195-200
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• 2009

CRT-based RSA algorithm, which was implemented on smartcard, microcontroller and so on, leakages secret primes p and q by fault attacks using laser injection, EM radiation, ion beam injection, voltage glitch injection and so on. Among the many fault injection methods, voltage glitch can be injected to target device without any modification, so more practical. In this paper, we made an experiment on the fault injection attack using abnormal source voltage. As a result, CRT-RSA's secret prime p and q are disclosed by fault attack with voltage glitch injection which was introduced by several previous papers, and also succeed the fault attack with source voltage blocking for proper period.

• The Journal of the Acoustical Society of Korea
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• v.23 no.1
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• pp.47-53
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• 2004

We propose a new speech enhancement algorithm based on left-right Hidden Markov Model (HMM) with state decision using Log-likelihood Ratio Test (LRT). Since the conventional HMM-based speech enhancement methods try to improve speech quality for all states, they introduce huge computational loads inappropriate to real-time implementation. In the left-right HMM, only the current and the next state are considered for a possible state transition so to reduce the computational complexity. In this paper, we propose a method to decide the current state by using the LRT on the previous state. Experimental results show that the proposed method improves the speed up to 60% with 0.2∼0.4 dB degradation of speech quality compared to the conventional method.