• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.5
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• pp.430-437
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• 2004

This thesis proposes novel SRP-PWM(Separately Random Pulse Position PWM) techniques and novel two-phase switching pattern applied to four-switch inverter, having various advantages such as operation time decrease that is required for decrease of switching damage, easy of implementation and inverter control at high frequency switching. In this thesis, we wish to confirm that SRP-PWM techniques disperse harmonic spectrum of inverter output current evenly into wide frequency area, that is, side-band of specification frequency. And we confirm the harmonic reduction effect of proposed techniques. Therefore, we will achieve an experiment by IGBT inverter using DSP and will verify the validity of proposed techniques compared with simulation results that use MATLAB/SIMULINK.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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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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• v.42 no.1
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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.

• Computational Structural Engineering
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• v.29 no.1
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• pp.46-48
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• 2016

• Journal of the Korean Institute of Gas
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• v.7 no.2 s.19
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• pp.7-13
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• 2003

Abstract This paper presents new emission mechanism and emission estimation model in volatile organic compounds(VOCs) emission sources. Also classifies applicable emission reduction techniques and presents new economical evaluation method for each techniques. We ultimately developed VEER(VOCs Emission Estimation and Reduction) software, which is backed by above mentioned model, emission source DB, Chemical properties DB, meteorological DB, and emission factor DB. With VEER, users in enterprise, central government and local self-governing body can get reliable emission results easily, and choose suitable emission reduction techniques.

• Convergence Security Journal
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• v.23 no.1
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• pp.117-123
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• 2023

To detect advanced ransomware attacks with machine learning-based models, the classification model must train learning data with high-dimensional feature space. And in this case, a 'curse of dimension' phenomenon is likely to occur. Therefore, dimensionality reduction of features must be preceded in order to increase the accuracy of the learning model and improve the execution speed while avoiding the 'curse of dimension' phenomenon. In this paper, we conducted classification of ransomware by applying three machine learning models and two feature extraction techniques to two datasets with extremely different dimensions of feature space. As a result of the experiment, the feature dimensionality reduction techniques did not significantly affect the performance improvement in binary classification, and it was the same even when the dimension of featurespace was small in multi-class clasification. However, when the dataset had high-dimensional feature space, LDA(Linear Discriminant Analysis) showed quite excellent performance.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.8 no.4
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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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• v.7 no.1
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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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• v.12 no.1
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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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• v.31 no.2
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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.