• 한국산학기술학회논문지
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• 제17권1호
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• pp.15-20
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• 2016

기존의 다중 모듈러스 기반 자기복원 등화 유형은 등화 초기에 적용되지 못하고 정상상태 성능 개선에 활용되었다. 본 논문에서는 다중 모듈러스를 원하는 응답으로 하는 유형의 자기복원 등화에서, 오차를 증강하여 오차의 역동성을 확장함으로써 초기 수렴 성능을 개선하고, 그 특성을 분석하였다. 제안 방법에서 오차 증강은 등화기 출력에 대한 심볼 판정에 비례하여 이루어진다. 아울러 제안 방법은 오차 역동성의 확장으로 인한 초기 수렴 기능을 갖기 때문에, 초기 수렴속도와 정상상태 오차 레벨에서 우수한 성능을 보인다. 특히 제안 방법은 등화의 전 과정을 하나의 알고리즘으로 진행하므로 기존의 다른 동작 모드로의 전환이나 선택 방법, 또는 다른 알고리즘과의 동시 동작 등이 불필요하다. 다중경로 전파와 부가 잡음이 있는 채널 조건하에서 이루어진 고차 신호점에 대한 자기복원 등화의 성능 분석 시뮬레이션을 통해 제안 방법의 유용성을 검증하였다.

• Parasites, Hosts and Diseases
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• 제47권3호
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• pp.235-241
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• 2009

The aim of this study was to estimate the benefit from repeated examinations in the diagnosis of enterobiasis in nursery school groups, and to test the effectiveness of individual-based risk predictions using different methods. A total of 604 children were examined using double, and 96 using triple, anal swab examinations. The questionnaires for parents, structured observations, and interviews with supervisors were used to identify factors of possible infection risk. In order to model the risk of enterobiasis at individual level, a similarity-based machine learning and prediction software Constud was compared with data mining methods in the Statistica 8 Data Miner software package. Prevalence according to a single examination was 22.5%; the increase as a result of double examinations was 8.2%. Single swabs resulted in an estimated prevalence of 20.1% among children examined 3 times; double swabs increased this by 10.1%, and triple swabs by 7.3%. Random forest classification, boosting classification trees, and Constud correctly predicted about 2/3 of the results of the second examination. Constud estimated a mean prevalence of 31.5% in groups. Constud was able to yield the highest overall fit of individual-based predictions while boosting classification tree and random forest models were more effective in recognizing Enterobius positive persons. As a rule, the actual prevalence of enterobiasis is higher than indicated by a single examination. We suggest using either the values of the mean increase in prevalence after double examinations compared to single examinations or group estimations deduced from individual-level modelled risk predictions.

• 정보처리학회논문지B
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• 제14B권4호
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• pp.303-310
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• 2007

기계학습에서 분류기틀의 집합으로 구성된 앙상블 분류기는 단일 분류기에 비해 정확도가 높다는 것이 입증되어왔다. 본 논문에서는 새로운 앙상블 학습으로서 데이터의 지역 기반 분류기들의 앙상블 학습을 제시하여 기존의 앙상블 학습과의 비교를 통해 성능을 검증하고자 한다. 지역 기반 분류기의 앙상블 학습은 데이터의 분포가 지역에 따라 다르다는 점에 착안하여 학습 데이터를 분할하여 해당하는 지역에 기반을 둔 분류기들을 만들어 나간다. 이렇게 만들어진 분류기들로부터 지역에 따라 가중치를 둔 투표를 적용하여 앙상블 방법을 이끌어낸다. 본 논문에서 제시한 앙상블 분류기의 성능평가를 위해 단일 분류기와 기존의 앙상블 분류기인 배깅과 부스팅 등을 UCI Machine Learning Repository에 있는 11개의 데이터 셋으로 정확도 비교를 하였다. 그 결과 새로운 앙상블 방법이 기본 분류기로 나이브 베이즈와 SVM을 사용했을 때 다른 방법보다 좋은 성능을 보이는 것을 알 수 있었다.

• 스마트미디어저널
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• 제2권2호
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• pp.8-13
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• 2013

Nowadays, the saliency region detection has become a popular research topic because of its uses for many applications like object recognition and object segmentation. Some of recent methods apply color distinctiveness based on an analysis of statistics of color image derivatives in order to boosting color saliency can produce the good saliency maps. However, if the salient regions comprise more than half the pixels of the image or the background is complex, it may cause bad results. In this paper, we introduce the method to handle these problems by using maximum symmetric surround. The results show that our method outperforms the previous algorithms. We also show the segmentation results by using Otsu's method.

• Communications for Statistical Applications and Methods
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• 제17권4호
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• pp.561-574
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• 2010

본 논문은 8가지 방법의 데이터 마이닝 알고리즘(CART, QUEST, CRUISE, 로지스틱 회귀분석, 선형판별분석, 이차판별분석, 신경망분석, 서포트 벡터 머신) 기법과 단일 알고리즘에 2가지 앙상블기법(배깅, 부스팅)을 적용한 16가지 방법을 바탕으로 총 24가지의 방법을 비교하였다. 알고리즘의 성능 비교를 위하여 13개의 이항반응변수로 구성된 데이터를 사용하였다. 비교 기준은 민감도, 특이도 및 오분류율을 사용하여 데이터 마이닝 기법의 성능향상에 대해 평가하였다.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1203-1210
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• 2017

Switched-inductor (SL) Z-source three-level inverter is a novel high power topology. The SL based impedance network can boost the input dc voltage to a higher value than the single LC impedance network. However, as all the neutral-point-clamped (NPC) inverters, the SL Z-source three-level inverter has to balance the neutral-point (NP) potential too. The principle of the inverter is introduced and then the effects of NP potential unbalance are analyzed. A NP balancing method is proposed. Other than the methods for conventional NPC inverter without Z-source impedance network, the upper and lower shoot-through durations are corrected by the feedforward compensation factors. With the proposed method, the NP potential is balanced and the voltage boosting ability of the Z-source network is not affected obviously. Simulations are conducted to verify the proposed method.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2010년도 추계학술대회
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• pp.196-197
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• 2010

In same cases of grid connected system using photovoltaic modules, high boosting ratio is required for the converters. Four topologies based on conventional boost converters are implemented according to the voltage doubler and cascade methods. The topologies are analyzed and compared according to its boosting ratio and configurations. Consequently, the suitability of four topologies for MIC application is considered by simulation results.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.89-95
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• 2023

Analyzing breast cancer patient files is becoming an exciting area of medical information analysis, especially with the increasing number of patient files. In this paper, breast cancer data is collected from Khartoum state hospital, and the dataset is classified into recurrence and no recurrence. The data is imbalanced, meaning that one of the two classes have more sample than the other. Many pre-processing techniques are applied to classify this imbalanced data, resampling, attribute selection, and handling missing values, and then different classifiers models are built. In the first experiment, five classifiers (ANN, REP TREE, SVM, and J48) are used, and in the second experiment, meta-learning algorithms (Bagging, Boosting, and Random subspace). Finally, the ensemble model is used. The best result was obtained from the ensemble model (Boosting with J48) with the highest accuracy 95.2797% among all the algorithms, followed by Bagging with J48(90.559%) and random subspace with J48(84.2657%). The breast cancer imbalanced dataset was classified into recurrence, and no recurrence with different classified algorithms and the best result was obtained from the ensemble model.

• 아태비즈니스연구
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• 제15권1호
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• pp.145-157
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• 2024

Purpose - In this study, we propose an empirical model for predicting seasoned equity offering (SEO here after) using machine learning methods. Design/methodology/approach - The models utilize the random forest method based on decision trees that considers non-linear relationships, as well as the gradient boosting tree model. SEOs incur significant direct and indirect costs. Therefore, CEOs' decisions of seasoned equity issuances are made only when the benefits outweigh the costs, which leads to a non-linear relationship between SEOs and a determinant of them. Particularly, a variable related to market timing effectively exhibit such non-linear relations. Findings - To account for these non-linear relationships, we hypothesize that decision tree-based random forest and gradient boosting tree models are more suitable than the linear methodologies due to the non-linear relations. The results of this study support this hypothesis. Research implications or Originality - We expect that our findings can provide meaningful information to investors and policy makers by classifying companies to undergo SEOs.

• Communications for Statistical Applications and Methods
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• 제15권1호
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• pp.27-41
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• 2008

그림으로 일반화 선형모형의 적합성을 진단하는 방법을 제안한다. 이 그림은 일반화 선형모형에서 연결함수를 설명변수들의 선형결합으로 표현할 수 있다는 가정을 진단할 때 유용하다. 이 그림에서 연결함수와 설명변수들의 관계를 비모수적으로 추정하는 작업이 필요한데, 이를 위해 여러 가능한 기법중에서 부스팅 기법을 적용하였다. 정규분포와 이항분포 자료로 모의실험을 실시하여 새로이 제안한 진단그림의 효과성을 보였다. 그리고 진단그림의 한계와 기술적 세부사항들을 설명하였다.