• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3268-3283
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• 2018

With the advent of the Information Age, the source identification of digital images, as a part of digital image forensics, has attracted increasing attention. Therefore, an effective technique to identify the source of digital images is urgently needed at this stage. In this paper, first, we study and implement some previous work on image source identification based on sensor pattern noise, such as the Lukas method, principal component analysis method and the random subspace method. Second, to extract a purer sensor pattern noise, we propose a sample selection method to improve the random subspace method. By analyzing the image texture feature, we select a patch with less complexity to extract more reliable sensor pattern noise, which improves the accuracy of identification. Finally, experiment results reveal that the proposed sample selection method can extract a purer sensor pattern noise, which further improves the accuracy of image source identification. At the same time, this approach is less complicated than the deep learning models and is close to the most advanced performance.

• 응용통계연구
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• 제23권2호
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• pp.357-366
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• 2010

Many tree algorithms have been developed for regression problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy when there are many noise variables. To handle this problem, we propose the multi-step GUIDE, which is a regression tree algorithm with a variable selection process. The multi-step GUIDE performs better than some of the well-known algorithms such as Random Forest and MARS. The results based on simulation study shows that the multi-step GUIDE outperforms other algorithms in terms of variable selection and prediction accuracy. It generally selects the important variables correctly with relatively few noise variables and eventually gives good prediction accuracy.

• 의학교육논단
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• 제12권1호
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• pp.3-8
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• 2010

This study is designed to evaluate the effectiveness of teaching or studying programs, and thus to overcome the selectionbias in studies. Selection-bias derived from unobservable characteristics in the course of participants selection of the teaching or studying programs, in the case of cross-section data instrumental variable(IV) method and two stage least square estimation were suggested as an analysis tool. Panel data were analyzed by using both fixed effect in which individual effects are captured by intercept terms and random effect estimation where an unobserved effect can be characterized as being randomly drawn from a given distribution.

• 한국융합학회논문지
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• 제11권6호
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• pp.227-235
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• 2020

본 연구는 주로 의사결정과제를 통해 검증되어왔던 무작위적 리더 선출 방법의 우수성을 실제 수행과제로 확장시켰고, 리더 선출 방법과 집단 효능감의 집단수행에 대한 상호작용효과를 살펴보았다. 2(리더 선출 방법: 공식적/무작위적) × 2(집단 효능감: 저/고) 피험자간 실험설계를 통해 집단수행에 대한 영향을 검증했다. 연구 결과, 리더 선출 방법의 주효과가 유의하여 공식적으로 리더가 선출된 집단보다 리더가 무작위적으로 선출된 집단의 수행이 유의하게 높았다. 또한, 두 변인 간의 상호작용이 유의하였는데, 집단 효능감이 낮을 때는 리더가 공식적으로 선출된 집단과 무작위적으로 선출된 집단 간의 수행차이가 유의하지 않았으나, 집단 효능감이 높을 때는 리더가 공식적으로 선출된 집단보다 무작위적으로 선출된 집단의 수행이 유의하게 높았다. 본 연구의 결과는 높은 집단 효능감이 집단의 수행에 긍정적인 영향을 주기 위해서는 리더가 집단의 사회정체성을 손상시키지 않는 방법(예. 리더와 구성원의 차이점을 부각시키기 보다는 동질성에 대한 지각을 높이는 방식)으로 선출되어야 한다는 점을 제안함으로써 조직과 작업 현장에 기여할 수 있다. 향후에 본 연구를 작업 팀으로 확장하여 연구할 필요성을 논의하였다.

• 응용통계연구
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• 제34권2호
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• pp.153-166
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• 2021

약물유전체학 연구의 주요 목표는 고차원의 유전 변수를 기반으로 개인의 약물 반응성을 예측하는 것이다. 변수의 개수가 많기 때문에 변수의 개수를 줄이기 위해서는 변수 선택이 필요하며, 선택된 변수들은 머신러닝 알고리즘을 사용하여 예측 모델을 구축하는데 사용된다. 본 연구에서는 400명의 뇌전증 환자의 차세대 염기서열 분석 데이터에 로지스틱 회귀, ReliefF, TurF, 랜덤 포레스트, LASSO의 조합과 같은 여러 가지 혼합 변수 선택 방법을 적용하였다. 선택된 변수들에 랜덤포레스트, 그래디언트 부스팅, 서포트벡터머신을 포함한 머신러닝 방법들을 적용했고 스태킹을 통해 앙상블 모형을 구축하였다. 본 연구의 결과는 랜덤포레스트와 ReliefF의 혼합 변수 선택 방법을 이용한 스태킹 모형이 다른 모형보다 더 좋은 성능을 보인다는 것을 보여주었다. 5-폴드 교차 검증을 기반으로 하여 적합한 최적 모형의 평균 검증 정확도는 0.727이고 평균 검증 AUC 값은 0.761로 나타났다. 또한, 동일한 변수를 사용할 때 스태킹 모델이 단일 머신러닝 예측 모델보다 성능이 우수한 것으로 나타났다.

• 대한안전경영과학회지
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• 제15권1호
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• pp.77-85
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• 2013

In the location science, environmental effect becomes a new main consideration for site selection. For the unwanted facility location selection, decision makers should consider the cost of resolving the environmental conflict. We introduced the negative influence cost for the facility which was inversely proportional to distance between the facility and residents. An unwanted facility location problem was suggested to minimize the sum of the negative influence cost and the transportation cost. The objective cost function was analyzed as nonlinear type and was neither convex nor concave. Three GRASP (Greedy Randomized adaptive Search Procedure) methods as like Random_GRASP, Epsilon_GRASP and GRID_GRASP were developed to solve the unwanted facility location problem. The Newton's method for nonlinear optimization problem was used for local search in GRASP. Experimental results showed that quality of solution of the GRID_GRASP was better than those of Random_GRASP and Epsilon_GRASP. The calculation time of Random_GRASP and Epsilon_GRASP were faster than that of Grid_GRASP.

• Journal of electromagnetic engineering and science
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• 제11권4호
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• pp.262-268
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• 2011

In this paper, we evaluate the outage performance of cooperative transmission in two-dimensional random networks. Firstly, we derive the joint distributions of the source-relay and the relay-destination links. Secondly, the outage probability for the decode-and-forward relaying system is derived when selection combining (SC) is employed at the destination. Finally, we calculate the average outage probability of the system and then attempt to express it by a simple approximate expression. The simulation results are presented to verify the accuracy of the derivations. Similar to deterministic networks, the cooperative transmission in random networks outperforms direct transmission at a high signal-to-noise ratio (SNR).

• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.397-405
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• 1998

We develop parametric bootstrap model selection criteria in an example to fit a random sample to either a general normal distribution or a normal distribution with prespecified mean. We apply the bootstrap methods in two ways; one considers the direct substitution of estimated parameter for the unknown parameter, and the other focuses on the bias correction. These bootstrap model selection criteria are compared with AIC. We illustrate that all the selection rules reduce to the one sample t-test, where the cutoff points converge to some certain points as the sample size increases.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권4호
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• pp.282-287
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• 2006

This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merits regarding pattern recognition performance. Thus, we developed a method called thee 'Interactive Feature Selection' and the results (selected features) of the IFS were applied to an emotion recognition system (ERS), which was also implemented in this research. The innovative feature selection method was based on a Reinforcement Learning Algorithm and since it required responses from human users, it was denoted an 'Interactive Feature Selection'. By performing an IFS, we were able to obtain three top features and apply them to the ERS. Comparing those results from a random selection and Sequential Forward Selection (SFS) and Genetic Algorithm Feature Selection (GAFS), we verified that the top three features were better than the randomly selected feature set.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 학술대회 논문집 전문대학교육위원
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• pp.154-158
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• 2006

The switching-mode power converter has been widely used because of its features of high efficiency and small weight and size. These features are brought by the ON-OFF operation of semiconductor switching devices. However, this switching operation causes the surge and EMI(Electromagnetic Interference) which deteriorate the reliability of the converter themselves and entire electronic systems. This problem on the surge and noise is one of the most serious difficulties in AC-to-DC converter. Random Pulse Width Modulation (RPWM) is peformed by adding a random perturbation to switching instant while output-voltage regulation of converter is performed. RPWM method for reducing conducted EMI in single switch three phase discontinuous conduction mode boost converter is presented. The more white noise is injected, the more conducted EMI is reduced. But output-voltage is not sufficiently regulated. This is the reason why carrier frequency selection topology is proposed. In the case of carrier frequency selection, output-voltage of steady state and transient state is fully regulated. A RPWM control method was proposed in order to smooth the switching noise spectrum and reduce it's level. Experimental results are verified by converter operating at 300v/1kW with $5%{\sim}30%$ white noise input. Spectrum analysis is performed on the Phase current and the CM noise voltage. The former is measured with Current Probe and the latter is achieved with LISN, which are connected to the spectrum analyzer respectively.