• 한국멀티미디어학회논문지
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• 제22권8호
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• pp.860-873
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• 2019

LCD backlight boosting and the integration of boosting and dimming are proposed based on image analysis to maximize perceptual image qualities and to reduce display system power. Based on the histogram of the image data, methods for selecting an image suitable for boosting and for adjusting the optimum backlight brightness are proposed. A comprehensive combined optimization method of LCD backlight boosting, dimming and bypass based on image characteristics is also described. Perceptual image quality enhancement and power consumption are evaluated based on well known image databases. Average subjective image quality is improved by 24.8%, RMS contrast is improved more than 20%, and average power consumption is reduced by 15.94% compared to conventional uniform boosting.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2012년도 한국컴퓨터종합학술대회논문집 Vol.39 No.1(B)
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• pp.516-518
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• 2012

본 논문은 Particle Filtering과 계층적인 Boosting 알고리즘을 이용한 다중 객체 추적 기법을 제안한다. Particle Filtering을 이용하여 각 객체를 단일 객체로 추적하고 Boosting 기반의 데이터 연관 알고리즘을 사용하여 영상에서 움직이는 물체들을 추적한다. 본 제안한 알고리즘에서는 객체들의 이동경로 정확한 감지를 위해 Particle Filtering을 통해 각 객체가 움직이는 예측 정보를 이용하고, Boosting 알고리즘을 계측적인 형태로 설계함에 따라 데이터 물체의 추적 정확도를 높일 수 있도록 하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 F
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• pp.1940-1943
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• 1998

A new PWM rectifier which offers a unity power factor is proposed. The circuit has same inductance as the conventional boosting type PWM rectifier in powering mode, but the inductance is splitted to 2 part in freewheeling mode. So the period of freewheeling mode is shorter than that of conventional boosting type PWM rectifier, and discontinuous input current is obtained in wide duty range. Therefore the proposed PWM rectifier accomplishs a unity power factor in wide range of duty ratio and boosting factor. And the conventional boosting type PWM rectifier has poor power factor near the unity boosting ratio, the proposed PWM rectifier improves this problem. The mathmatical analysis are presented and experimental results are given.

• 상하수도학회지
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• 제35권6호
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• pp.507-516
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• 2021

In this study, the machine learning which has been widely used in prediction algorithms recently was used. the research point was the CD(chudong) point which was a representative point of Daecheong Lake. Chlorophyll-a(Chl-a) concentration was used as a target variable for algae prediction. to predict the Chl-a concentration, a data set of water quality and quantity factors was consisted. we performed algorithms about random forest and gradient boosting with Python. to perform the algorithms, at first the correlation analysis between Chl-a and water quality and quantity data was studied. we extracted ten factors of high importance for water quality and quantity data. as a result of the algorithm performance index, the gradient boosting showed that RMSE was 2.72 mg/m³ and MSE was 7.40 mg/m³ and R² was 0.66. as a result of the residual analysis, the analysis result of gradient boosting was excellent. as a result of the algorithm execution, the gradient boosting algorithm was excellent. the gradient boosting algorithm was also excellent with 2.44 mg/m³ of RMSE in the machine learning hyperparameter adjustment result.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 V
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• pp.2935-2938
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• 2003

A new Valley-fill circuit for improving PF(power factor) is proposed in this paper. The proposed topology combines Valley-fill rectifier and an additional inductor for boosting. In the proposed circuit, a shapc of input current is related to the PWM duty cycle. The boosting inductor makes improve PF by the electric charge transfer action. The operation principle and the shape of input current arc analyzed as applied the boosting inductor. The optimum value of boosting inductor is determined. A 100W single-stage converter has been designed and tested. Experimental results are presented to verify the validity of the proposed converter.

• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.55-64
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• 2002

In this paper, we introduce SVM(support vector machine) developed to solve the problem of generalization of neural networks. We also introduce boosting algorithm which is a general method to improve accuracy of some given learning algorithm. We propose a new algorithm combining SVM and boosting to solve classification problem. Through the experiment with real and simulated data sets, we can obtain better performance of the proposed algorithm.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.999-1012
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• 2005

We compare two popular algorithms in current machine learning and statistical learning areas, boosting method represented by AdaBoost and kernel based SVM (Support Vector Machine) using 13 real data sets. This comparative study shows that boosting method has smaller prediction error in data with heavy noise, whereas SVM has smaller prediction error in the data with little noise.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2016년도 추계 학술논문 발표대회
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• pp.73-74
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• 2016

Although various research is being carried out to prevent the construction accidents, the number of victims of construction site is increasing continuously. Therefore, the purpose of this study is construction accidents prediction applying the boosting algorithm to the construction domains. Boosting algorithm was applied to construct construction accident prediction model and application of the model was examined using actual accident cases. It is possible to support safety manager to manage and prevent accidents in priority using the model.

• 디지털융복합연구
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• 제11권2호
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• pp.243-248
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• 2013

본 논문에서는 복잡한 배경에서의 사람의 머리 추적에 있어서 효과적인 Adaptive Boosting에 의한 방법을 제안한다. 하나의 특징 추출 방법은 사람의 머리를 모델링하기에는 부족하다. 따라서 본 연구에서는 여러 가지 특징 추출 방법을 병행하여 정확한 머리 검출을 시도하였다. 머리 영상의 특징 추출은 sub-region과 Haar 웨이블릿 변환(Haar wavelet transform)을 이용하였다. Sub-region은 머리의 지역적인 특징을 나타내고, Haar 웨이블릿 변환은 얼굴의 주파수 특성을 나타내기 때문에 이들을 이용하여 특징을 추출하면 효과적인 모델링이 가능해 진다. 실시간으로 입력되는 영상에서 사람의 머리를 추적하기 위하여 제안하는 방법에서는 3가지 형태의 Harr-wavelet 특징을 AdaBoosting 알고리즘으로 학습한 후 결과를 이용하였다. 원래 AdaBoosting 알고리즘은 학습시간이 매우 길며 학습데이터가 변하면 다시 학습을 수행해야 하는 단점이 존재한다. 이 단점을 극복하기 위하여 제안하는 방법에서는 캐스케이드를 이용한 AdaBoosting의 효율적인 학습방법을 제안한다. 이 방법은 머리 영상에 대한 학습시간은 감소시키며, 학습데이터의 변화에도 효율적으로 대처할 수 있다. 이 방법은 학습과정을 레벨별로 분리한 후 중요도가 높은 학습데이터를 다음 단계에 반복적으로 적용시킨다. 제안하는 방법이 적은 학습 시간과 학습 데이터를 사용해서 우수한 성능을 가지는 분류기를 생성하였다. 또한, 이 방법은 다양한 머리데이터를 가진 실시간 영상데이터에 적용한 결과 다양한 머리를 정확하게 검출 및 추적하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 추계학술발표대회
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• pp.364-366
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• 2012

본 논문은 다중 객체 추적 시스템에 관한 연구로서, Online boosting 을 기반으로 다중 객체 추적 기술이 개발되었다. 기존의 Boosting 기반의 추적 기술과는 다르게 객체들간의 구별을 좀더 명확하게 하기 위하여, 프레임과 프레임간의 객체들끼리의 연결 시 공간적인 제약조건과 시간적 제약 조건을 이용하여 Online Boosting 알고리즘을 설계하였다. 본 시스템에서는 멀리 떨어져있는 객체들간에는 연관성이 낮다는 점을 보다 강력하게 고려하였기에 추적하는 과정에서 물체들끼리의 연관 오류가 줄어들었고, 이는 몇 개의 범용데이터를 이용한 실험을 통해 증명하였다.