• 한국컴퓨터정보학회논문지
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• 제21권5호
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• pp.79-89
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• 2016

In this paper, we proposed the object classification method using genetic and dynamic random forest consisting of optimal combination of unit tree. The random forest can ensure good generalization performance in combination of large amount of trees by assigning the randomization to the training samples and feature selection, etc. allocated to the decision tree as an ensemble classification model which combines with the unit decision tree based on the bagging. However, the random forest is composed of unit trees randomly, so it can show the excellent classification performance only when the sufficient amounts of trees are combined. There is no quantitative measurement method for the number of trees, and there is no choice but to repeat random tree structure continuously. The proposed algorithm is composed of random forest with a combination of optimal tree while maintaining the generalization performance of random forest. To achieve this, the problem of improving the classification performance was assigned to the optimization problem which found the optimal tree combination. For this end, the genetic algorithm methodology was applied. As a result of experiment, we had found out that the proposed algorithm could improve about 3~5% of classification performance in specific cases like common database and self infrared database compare with the existing random forest. In addition, we had shown that the optimal tree combination was decided at 55~60% level from the maximum trees.

• 지식경영연구
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• 제20권3호
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• pp.155-171
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• 2019

In the entertainment industry which has great uncertainty, it is essential to predict public preference first. Thanks to various mass media channels such as cable TV and internet-based streaming services, the reality audition program has been getting big attention every day and it is being used as a new window to new entertainers' debut. This phenomenon means that it is changing from a closed selection process to an open selection process, which delegates selection rights to the public. This is characterized by the popularity of the public being reflected in the selection process. Therefore, this study aims to implement a machine learning model which predicts the winner of , which has recently been popular in South Korea. By doing so, this study is to extend the research method in the cultural industry and to suggest practical implications. We collected the data of winners from the 1st, 2nd, and 3rd seasons of the Produce 101 and implemented the predictive model through the machine learning method with the accumulated data. We tried to develop the best predictive model that can predict winners of by using four machine learning methods such as Random Forest, Decision Tree, Support Vector Machine (SVM), and Neural Network. This study found that the audience voting and the amount of internet news articles on each participant were the main variables for predicting the winner and extended the discussion by analyzing the precision of prediction.

• 한국가정과교육학회지
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• 제10권1호
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• pp.41-57
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• 1998

The purpose of this study was to make a selection criteria for Home Economics textbooks in the middle school. For this purpose, first, the criteria were out by collecting, analyzing and synthesizing the literature. Second, questionnaire survey of the 6 selection criteria was performed. Questionnaire sent to Home Economics teachers of 401 middle school selected by systematic random sapling, 233 questionnaire were received and 220 questionnaire were analyzed for this study. As a statistical tool, SPSSWIN was used to analyze frequency, mean, standard deviation, and factor analysis. The research findings were as follows ; 1. Now for kinds of Home Economics textbooks are mainly used. At that time when textbooks were selected, these selections were made upon deliberation with the teachers in charge and in future this method will be desirable. Most home economics teachers realize that the selection criteria is needed to improve the objectivity of textbook selection. 2. As a result of making factor analysis, the selection criteria were revised that 52 items in 7 categories were chosen as textbook criteria plan. They consist of 5 items related to the outward form of textbook, 5 items related to the learning materials in textbook, 10 items related to the composition of textbook units, 11 items related to the guiding contents of textbook, 7 items related to the subject of experiment.practice, 9 items related to the composition of picture, photograph and diagram. and 7 items related to the use of instructional-learning method.

• 한국양식학회:학술대회논문집
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• 한국양식학회 2003년도 추계학술발표대회 논문요약집
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• pp.34-34
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• 2003

한국산 선발계통 및 일본산 양식계통과 이들 두 계통간 잡종 참돔 집단의 유전적 특성을 분석하기 위하여, RAPD (Random Amplified Polymorphic DNA) marker를 탐색하였다. 10개의 염기로 이루어진 200개의 random primer 분석을 통하여 polymorphic pattern을 나타내는 23개의 random primer를 선발하였으며, 각 primer의 재현성을 확인하였다. 이들 중 OPA-11 primer는 크기가 각각 600 bp, 650 bp 및 750 bp 인 3개의 DNA 단편에 의하여 4개의 genotype을 나타냈으며, 각 genotype의 빈도는 집단간차이를 보였고, 한국산 선발계통 집단에서는 4개의 genotype이 모두 발견되는 반면, 일본산 양식계통 및 일본산 양식계통을 포함한 교배집단에서는 특정 genotype만 발견되었다. OPA-11 primer 유래의 polymorphic DNA 단편을 cloning하고 염기서열을 결정하였으며, SCAR (Sequence Characterized Amplified Region) primer를 제작하고 분석하였다. 본 연구는 참돔집단의 유전적 특성 파악 및 집단 구별에 RAPD marker를 활용하였으며, 참돔 육종시 형질 및 기능관련 DNA marker 탐색에 적용하기 위하여, 이후의 연구에서는 SCAR과 RFLP 분석에 RAPD marker를 이용하여 100% 정확도를 갖는 RFLP maker를 찾고, MAS (Marker-Assisted Selection)에 적용하고자 한다.

• Bulletin of the Korean Chemical Society
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• 제32권4호
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• pp.1237-1240
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• 2011

In this study, we have developed a ligand-based in-silico prediction model to classify chemical structures into hERG blockers using Bayesian and random forest modeling methods. These models were built based on patch clamp experimental results. The findings presented in this work indicate that Laplacian-modified naive Bayesian classification with diverse selection is useful for predicting hERG inhibitors when a large data set is not obtained.

• Structural Engineering and Mechanics
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• 제34권4호
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• pp.417-447
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• 2010

The uncertainty often observed in experimental strengths of masonry constituents makes critical the selection of the appropriate inputs in finite element analysis of complex masonry buildings, as well as requires modelling the building ultimate load as a random variable. On the other hand, the utilization of expensive Monte Carlo simulations to estimate collapse load probability distributions may become computationally impractical when a single analysis of a complex building requires hours of computer calculations. To reduce the computational cost of Monte Carlo simulations, direct computer calculations can be replaced with inexpensive Response Surface (RS) models. This work investigates the use of RS models in Monte Carlo analysis of complex masonry buildings with random input parameters. The accuracy of the estimated RS models, as well as the good estimations of the collapse load cumulative distributions obtained via polynomial RS models, show how the proposed approach could be a useful tool in problems of technical interest.

• JSTS:Journal of Semiconductor Technology and Science
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• 제2권3호
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• pp.197-204
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• 2002

DRAM, SRAM, and FLASH memory are three major memory devices currently used in most electronic applications. But, they have very distinct attributes, therefore, each memory could be used only for limited applications. MRAM (Magneto-resistive Random Access Memory) is a promising candidate for a universal memory that meets all application needs with non-volatile, fast operational speed, and low power consumption. The simplest architecture of MRAM cell is a series of MTJ (Magnetic Tunnel Junction) as a data storage part and MOS transistor as a data selection part. To be a commercially competitive memory device, scalability is an important factor as well. This paper is testing the actual electrical parameters and the scaling factors to limit MRAM technology in the semiconductor based memory device by an actual integration of MRAM core cell. Electrical tuning of MOS/MTJ, and control of resistance are important factors for data sensing, and control of magnetic switching for data writing.

• Journal of information and communication convergence engineering
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• 제11권3호
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• pp.173-178
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• 2013

To discover objects of interest in unstructured peer-to-peer networks, the peers rely on flooding query messages which create incredible network traffic. This article evaluates the performance of an unstructured Gnutella-like protocol over mobile ad-hoc networks and proposes modifications to improve its performance. This paper offers an enhanced mechanism for an unstructured Gnutella-like network with improved peer features to better meet the mobility requirement of ad-hoc networks. The proposed system introduces a novel caching optimization technique and enhanced ultrapeer selection scheme to make communication more efficient between peers and ultrapeers. The paper also describes an enhanced query mechanism for efficient searching by applying multiple walker random walks with a jump and replication technique. According to the simulation results, the proposed system yields better performance than Gnutella, XL-Gnutella, and random walk in terms of the query success rate, query response time, network load, and overhead.

• Communications for Statistical Applications and Methods
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• 제31권1호
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• pp.65-85
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• 2024

The tail probability of a function of a multivariate random variable is not easy to estimate by the crude Monte Carlo simulation. When the occurrence of the function value over a threshold is rare, the accurate estimation of the corresponding probability requires a huge number of samples. When the explicit form of the cumulative distribution function of each component of the variable is known, the inverse transform likelihood ratio method is directly applicable scheme to estimate the tail probability efficiently. The method is a type of the importance sampling and its efficiency depends on the selection of the importance sampling distribution. When the cumulative distribution of the multivariate random variable is represented by a copula and its marginal distributions, we develop an iterative algorithm to find the optimal importance sampling distribution, and show the convergence of the algorithm. The performance of the proposed scheme is compared with the crude Monte Carlo simulation numerically.

• 방송공학회논문지
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• 제21권1호
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• pp.105-108
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• 2016

본 논문에서는 다수의 중계기와 다수의 도청자가 존재하는 협력 재밍 네트워크를 다룬다. 다수의 중계기 중 하나의 중계기가 선택되어 증폭 후 재전송 기법으로 총 두 단계를 통해 수신기에 신호를 전송한다. 도청자의 신호 수신을 방해하기 위해 첫 번째 단계에서 수신기가 재밍 신호를 전송하며, 두 번째 단계에서 송신기가 재밍 신호를 전송한다. 이러한 시스템의 보안 전송률을 수식적으로 분석하며, 사용가능한 채널 정보에 따라 최적의 중계기 선택 기법을 각각 제시한다. 모의실험을 통해 제시한 중계기 선택 기법의 성능이 임의의 중계기 선택 기법에 비해 향상됨을 확인하였다.