• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.457-480
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• 2017

We develop a random partition procedure based on a Dirichlet process prior with Laplace distribution. Gibbs sampling of a Laplace mixture of linear mixed regressions with a Dirichlet process is implemented as a random partition model when the number of clusters is unknown. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities, unlike its counterparts. A full Gibbs-sampling algorithm is developed for an efficient Markov chain Monte Carlo posterior computation. The proposed method is illustrated with simulated data and one real data of the energy efficiency of Tsanas and Xifara (Energy and Buildings, 49, 560-567, 2012).

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1722-1725
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• 1997

This paper presents image analysis method using a Markov random field(MRF) model. Particulary, image esgmentation is to partition the given image into regions. This scheme is first segmented into regions, and the obtained domain knowledge is used to obtain the improved segmented image by a Markov random field model. The method is a maximum a posteriori(MAP) estimation with the MRF model and its associated Gibbs distribution. MAP estimation method is applied to capture the natural image by TMS320C80(MVP) and to realize the segmented image by a MRF model.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.175-177
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• 2018

This paper proposes an algorithm to speed up block structure partition of quad tree plus binary tree (QTBT) in Joint Exploration Test Model (JEM) encoder. The proposed fast encoding of QTBT block partition employs three spatially neighbor coded blocks, such as left, top-left, and top of current block, to early terminate QTBT block structure pruning. The propose algorithm is organized based on statistical similarity of those spatially neighboring blocks, such as block depths and coded block types, which are coded with overlapped block motion compensation (OBMC) and adaptive multi transform (AMT). The experimental results demonstrate about 30% encoding time reduction with 1.3% BD-rate loss on average compared to the anchor JEM-7.1 software under random access configuration.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.237-239
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• 2007

In the paper, we analyze the performances of random access channels in multi-service multi-user OFDMA systems. The resource of the random access channels in OFDMA systems is the nubmer of available sub-channels and PN-codes. For given available sub-channels and PN-codes. we analyze the performances of the random access channels of OFDMA systems according to three resource allocation methods (resource full sharing, resource partial sharing, resource partition) in tenus of the access success probability, the blocking probability, the access delay and the throughput of each service class. Further, we find the feasible region of the access probability of each service class in which the allowable minimum access success probability, the allowable maximum blocking probability and the allowable maximum access delay are satisfied. The results also can be utilized to find proper region of the access probabilities of each service class for differentiated quality of service(QoS)s, and for the system operations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4A
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• pp.330-335
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• 2007

This paper proposes a multichannel random access channel allocation scheme for multihop cellular networks to guarantee the stable throughput of a random access. The fundamental contribution is a mathematical formula for an optimal partition ratio of shared random access channels between a base station and a relay station. In addition, the proposed scheme controls the retransmission probability of random access packets under heavy load condition. Simulation results show that the proposed scheme can guarantee the required random access channel utilization and packet transmission delay even if the a random access packet arrival rate is higher than 0.1.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.77-82
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• 2024

The test set of machine learning consists of the remaining data that did not participate as training data. It is quantitative division and it is setting aside a certain amount of data which has the same effect as random selection. However from a software testing perspective, test cases sufficient to catch errors are selected as a test set rather than a random selection. This is called the adequacy of the test case, and the higher the adequacy, the better the test case is selected. We want to examine whether the test cases used in machine learning are sufficient from this perspective by comparing them with the equivalence split method of software testing. If higher sufficiency is guaranteed when applying a software test design technique, that is, equivalence splitting, high effectiveness can be achieved with a small number of test sets. This reduces the size of the test set, thereby increasing the size of the training data set and ultimately securing more data to learn. It can be expected that more sophisticated models can be built with larger training data sets.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.401-410
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• 2004

We address the problem of parameter estimation in multivariate distributions under ignorable non-monotone missing data. The factoring likelihood method for monotone missing data, termed by Rubin (1974), is extended to a more general case of non-monotone missing data. The proposed method is algebraically equivalent to the Newton-Raphson method for the observed likelihood, but avoids the burden of computing the first and the second partial derivatives of the observed likelihood. Instead, the maximum likelihood estimates and their information matrices for each partition of the data set are computed separately and combined naturally using the generalized least squares method.

• ETRI Journal
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• v.39 no.4
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• pp.546-557
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• 2017

In a long term evolution-advanced (LTE-A) system, the traffic overload of machine type communication devices is a challenge because too many devices attempt to access a base station (BS) simultaneously in a short period of time. We discuss the challenge of the gap between the theoretical maximum throughput and the actual throughput. A gap occurs when the BS cannot change the number of preambles for a random access channel (RACH) until multiple numbers of RACHs are completed. In addition, a preamble partition approach is proposed in this paper that uses two groups of preambles to reduce this gap. A performance evaluation shows that the proposed approach increases the average throughput. For 100,000 devices in a cell, the throughput is increased by 29.7% to 114.4% and 23.0% to 91.3% with uniform and Beta-distributed arrivals of devices, respectively.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.31-39
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• 2015

This paper deals with a method for estimating variance components on the basis of projections under the assumption of random effects model. It discusses how to use projections for getting sums of squares to estimate variance components. The use of projections makes the vector subspace generated by the model matrix to be decomposed into subspaces that are orthogonal each other. To partition the vector space by the model matrix stepwise procedure is used. It is shown that the suggested method is useful for obtaining Type I sum of squares requisite for the ANOVA method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.75-82
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• 2006

본 연구에서 조합 최적화(Combinatorial Optimization) 이론에 바탕을 두고 있는 네스티드 분할(Nested Partition, 이하 NP) 방법을 이용한 최적화 기탄 요소선택 방법(Feature Selection)을 제안한다. 이 새로운 방법은 좋은 요소 부분집합을 찾는 휴리스틱 탐색 절차를 채용하고 있으며 데이터의 인스턴스(Instances 또는 Records)의 무작위 추출(Random Sampling)을 이용하여 이 요소선택 방법의 처리시간 관점에서의 성능을 항상 시키고자 한다. 이 새로운 접근 방법은 처리시간 향상을 위해 2단계 샘플링 방법을 채용하여 근접 최적해로의 수렴(Convergence)을 보장하는 샘플 사이즈를 결정한다. 이는 앨고리듬이 유한한 시간내에 끝이날 때 최종 요소 부분집합 해의 질(Qualtiy)에 관한 정확한 설명을 할 수 있는 이론적인 배경을 제시한다. 중요 결과를 예시하기 위해서 다양한 형태의 다섯 개의 데이터 셋을 이용하였으며 다섯 번의 반복 실험을 통한 실험 결과가 제시되며, 이 새로운 접근 방법이 기존의 단순 네스티드 분할 방법 기반의 요소선택 방법보다 처리시간 관점에서 더욱 효율적임을 보여준다.