• 전력전자학회논문지
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• 제9권5호
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• pp.430-437
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• 2004

본 논문에서는 고주파 스위칭 시 스위칭 손실의 감소, 구현의 용이성 및 인버터 제어를 위하여 요구되는 연산시간 감소 등 다양한 장점을 가진 4-Switch 인버터를 대상으로, 새로운 2상 스위칭 패턴(Pattern)과 이에 적용된 새로운 SRP-PWM(Separately Random Pulse Position PWM)기법을 제안한다. 본 논문에서는 고속운전 영역에서의 인버터 출력전류의 고조파 스펙트럼을 넓은 주파수 영역으로 즉, 특정주파수의 side-band로 고루 분산시키는 결과로부터 제안한 스위칭 패턴과 이에 적용된 새로운 SRP-PWM기법의 고조파 저감효과를 확인하고자 한다. 따라서 DSP를 이용한 IGBT인버터에 의한 실험을 수행하고, 이로부터 얻은 결과를 MATLAB/SIMULINK를 이용한 시뮬레이션 결과와 비교ㆍ분석하여 제안된 기법의 타당성을 검증하고자 한다.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• Archives of Plastic Surgery
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• 제42권1호
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• pp.52-58
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• 2015

Background The erbium:yttrium scandium gallium garnet (Er:YSGG) laser differs from other laser techniques by having a faster and higher cure rate. Since the Er:YSGG laser causes an appropriate proportion of ablation and coagulation, it has advantages over the conventional carbon dioxide ($CO_2$) laser and the erbium-doped yttrium aluminum garnet (Er:YAG) laser, including heating tendencies and explosive vaporization. This research was conducted to explore the effects and safety of the Er:YSGG laser. Methods Twenty patients participated in the pilot study of a resurfacing system using a 2,790-nm Er:YSGG laser. All patients received facial treatment by the 2,790-nm Er:YSGG laser system (Cutera) twice with a 4-week interval. Wrinkle reduction, reduction in pigment inhomogeneity, and improvement in tone and texture were measured. Results Study subjects included 15 women and five men. Re-epithelization occurred in all subjects 3 to 4 days after treatment, and wrinkle reduction, reduction in pigment inhomogeneity, and improvement in tone and texture within 6 months of treatment. Conclusions The 2,790-nm YSGG laser technique had fewer complications and was effective in the improvement of scars, pores, wrinkles, and skin tone and color with one or two treatments. We expect this method to be effective for people with acne scars, pore scars, deep wrinkles, and uneven skin texture and color.

• 전산구조공학
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• 제29권1호
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• pp.46-48
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• 2016

• 한국가스학회지
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• 제7권2호
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• pp.7-13
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• 2003

본 연구에서는 국내 휘발성 유기화합물질의 주요 취급시설에 대한 조사를 통하여 배출원별 배출메카니즘을 정립하고, 새로운 배출량산정모델을 제안하였다. 또한 각 배출원에 적용가능한 배출저감기술과 각 기술의 경제성 평가기법을 제안하였다. 여기에 배출원 DB, 화학물질물성치 DB, 기상정보 DB, 배출계수 DB 등의 정보를 연계하여 휘발성유기화합물질 배출량산정 및 관리 소프트웨어 VEER(VOCs Emission Estimation and Reduction)를 개발하였다. 결국 본 연구를 통해 개발된 VEER를 이용하여, 휘발성 유기화합물질 취급업체, 관리기관, 연구기관 등에서 쉽고 정확하게 배출원 인벤토리를 구축하고, 배출량을 산정하며, 계산된 결과를 바탕으로 각 배출원의 배출유량과 농도에 적합한 휘발성 유기화합물질 배출 저감기술을 선택하고, 여러 저감기술들에 대한 경제성을 평가함으로써, 저비용의 배출량 저감 및 배출원 관리기술을 선택하고, 설계할 수 있을 것으로 기대된다.

• 융합보안논문지
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• 제23권1호
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• pp.117-123
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• 2023

점점 더 고도화되고 있는 랜섬웨어 공격을 기계학습 기반 모델로 탐지하기 위해서는, 분류 모델이 고차원의 특성을 가지는 학습데이터를 훈련해야 한다. 그리고 이 경우 '차원의 저주' 현상이 발생하기 쉽다. 따라서 차원의 저주 현상을 회피하면서 학습모델의 정확성을 높이고 실행 속도를 향상하기 위해 특성의 차원 축소가 반드시 선행되어야 한다. 본 논문에서는 특성의 차원이 극단적으로 다른 2종의 데이터세트를 대상으로 3종의 기계학습 모델과 2종의 특성 추출기법을 적용하여 랜섬웨어 분류를 수행하였다. 실험 결과, 이진 분류에서는 특성 차원 축소기법이 성능 향상에 큰 영향을 미치지 않았으며, 다중 분류에서도 데이터세트의 특성 차원이 작을 경우에는 동일하였다. 그러나 학습데이터가 고차원의 특성을 가지는 상황에서 다중 분류를 시도했을 경우 LDA(Linear Discriminant Analysis)가 우수한 성능을 나타냈다.

• 한국환경복원기술학회지
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• 제8권4호
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• pp.68-80
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• 2005

This study intends to analysis problems and propose of EIA(Environmental Impact Assessment) and PER(Pre-Environmental Review) especially on division of landscape impacts. The problems of EIA and PER are first, on analysis of existing conditions side, insufficiency of the list of landscape elements and their descriptions and presentations, and the number of viewpoints and each validity second, on estimation of landscape impacts, the methods and techniques of estimation and simulation, and the process of impact estimation, third, on suggestion of reduction plans, reduction devices covering impacts, the lack of influence reduction forecasting devices, the deficiency of execution power of reduction plans, finally, the systematic connection of impact estimation with existing conditions analysis and reduction plans. Therefore, on each step from existing condition analysis to reduction plan suggestion, the solutions to each problem are proposed.

• Journal of information and communication convergence engineering
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• 제7권1호
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• pp.35-40
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• 2009

Many researchers strive to research on ways on how to improve the performance of RFID system and many papers were written to solve one of the major drawbacks of potent technology related with data management. As RFID system captures billions of data, problems arising from dirty data and large volume of data causes uproar in the RFID community those researchers are finding ways on how to address this issue. Especially, effective data management is important to manage large volume of data. Data reduction techniques in attempts to address the issues on data are also presented in this paper. This paper introduces readers to a new data reduction algorithm that might be an alternative to reduce data in RFID Systems. A process on how to extract data from the reduced database is also presented. Performance study is conducted to analyze the new data reduction algorithm. Our performance analysis shows the utility and feasibility of our categorization reduction algorithms.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권1호
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• pp.1-5
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• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• Communications for Statistical Applications and Methods
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• 제31권2호
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• pp.179-189
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• 2024

In multivariate regression with a high-dimensional response Y ∈ ℝ^r and a relatively low-dimensional predictor X ∈ ℝ^p (where r ≥ 2), the statistical analysis of such data presents significant challenges due to the exponential increase in the number of parameters as the dimension of the response grows. Most existing dimension reduction techniques primarily focus on reducing the dimension of the predictors (X), not the dimension of the response variable (Y). Yoo and Cook (2008) introduced a response dimension reduction method that preserves information about the conditional mean E(Y | X). Building upon this foundational work, Yoo (2018) proposed two semi-parametric methods, principal response reduction (PRR) and principal fitted response reduction (PFRR), then expanded these methods to unstructured principal fitted response reduction (UPFRR) (Yoo, 2019). This paper reviews these four response dimension reduction methodologies mentioned above. In addition, it introduces the implementation of the mbrdr package in R. The mbrdr is a unique tool in the R community, as it is specifically designed for response dimension reduction, setting it apart from existing dimension reduction packages that focus solely on predictors.