• Journal of Korean Association for Spatial Structures
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• v.24 no.3
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• pp.69-77
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• 2024

The purpose of this study is to present a plan for reducing noise between floors of apartment houses in Korea and to examine the method for evaluating noise blocking performance rating between floors. The definition of floor noise and classification method of floor noise can be described, and floor noise can be distinguished into lightweight impact sound and heavy impact sound. The wall-type structure, which is mainly adopted in domestic apartments, relatively transmits vibration caused by impact sources rather than using columns and beams, so noise problems between floors are relatively higher than systems using columns and beams. Three representative methods for reducing and blocking floor noise are described, and criteria for evaluating the effectiveness of floor noise reduction by each method are described. In addition, the method for noise reduction and blocking grades for each construction method currently applied in Korea was described, and as a result, it was judged that the domestic rating evaluation method was not suitable for the current domestic situation, and a new evaluation method and standard were needed.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.659-669
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• 2021

Multivariate regression often appears in longitudinal or functional data analysis. Since multivariate regression involves multi-dimensional response variables, it is more strongly affected by the so-called curse of dimension that univariate regression. To overcome this issue, Yoo (2018) and Yoo (2019a) proposed three model-based response dimension reduction methodologies. According to various numerical studies in Yoo (2019a), the default method suggested in Yoo (2019a) is least sensitive to the simulated models, but it is not the best one. To release this issue, the paper proposes an selection algorithm by comparing the other two methods with the default one. This approach is called principal selected response reduction. Various simulation studies show that the proposed method provides more accurate estimation results than the default one by Yoo (2019a), and it confirms practical and empirical usefulness of the propose method over the default one by Yoo (2019a).

• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.277-284
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• 2024

This study proposes a method to predict noise reduction based on noise-reduction measures, using harmonic response analysis, for transformer design. The dynamic elastic coefficients of the components comprising the actual transformer were determined by manufacturing the materials of the transformer components into simple-shaped specimens, followed by a comparison of the modes between the experiments and the analyses. A finite element model of the transformer was implemented, and harmonic response analysis was performed by deriving the exciting force of the transformer. Subsequently, the theoretical sound power level of the transformer was derived from the results of the harmonic response analysis. Finally, noise reduction measures were established, and the noise reduction amounts were compared between the experiments and the analyses, before and after applying the measures. Through the comparison and analyses of the noise reduction measures, it was confirmed that the trends in the experiments and analyses matched.

• Archives of Plastic Surgery
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• v.37 no.2
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• pp.195-198
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• 2010

Purpose: The zygoma is a key element which composes the facial contour. Zygomatic fracture induces facial asymmetry. We use radiologic evaluation or inspections mainly for identification of symmetry after reduction depressed zygomatic fracture. But the disadvantages of such methods are time-consuming and complicated process. So we tried to develop a new testing method with a ruler and a level. Methods: In unilateral depressed zygomatic fracture patient, parallel to the patient's head to make sure lay horizontaly. Put the leg of a ruler on the malar eminence so that it is at the same distance from the facial midline. Then take the level of malar eminence as put the level above the ruler. This process was performed before and after the reduction. Results: We were able to fix with plate and screw after checking the results of reduction fast and easily. Good results were obtained at post-operative radiologic evaluation. Conclusion: We can easily get the ruler and level around life. This method is not only simple but also shorttime process compared with other method-radiologic evaluation or inspection. And the operator can explain the results to the patients easily and objectively. Authors obtained the good results with this new method, and would introduce it for another method of identifying the result of reduction in depressed zygomatic fractures.

• Journal of Navigation and Port Research
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• v.29 no.8 s.104
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• pp.755-761
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• 2005

In this paper, we propose a high-speed adaptive PTS method which eliminates high PAR (Peak-to-Average Power Ratio) and we compare the proposed method with other conventional methods. In addition, we have designed a combined type SLM-PTS scheme to reduce PAR and evaluate the performance. The system used for evaluating PAR performance can be constructed as COFDM (Coded Orthogonal Frequency Division Multiplexing) applying ETD(Enhabced Time Diversity)-Turbo coding scheme. All the analyses in this paper are focused on the system characteristics according to IFFT's point and modulation method and the performance evaluation are based on the PAR reduction rates. As a result, the SLM-PTS combination method reveals good PAR reduction rate and remarkable reduction in the amount of calculations. Especially, in the case of combine-3 scheme, we can achieve approximately $3.7\~3.9$ dB PAR reduction on a basis of 10-5 BER level. Moreover, we can eliminate the side information in COFDM system because of its adaptive characteristics in evaluating PAR reduction rate, so that the additional errors can be omitted.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.34 no.1
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• pp.56-62
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• 2021

In this study, we created a DBD plasma device and a MnO2 catalyst mesh filter for evaluating ozone reduction of devices via the catalyst method. The DBD plasma device was manufactured by applying Ag paste to soda lime glass via the screen-printing method. The MnO2 catalyst mesh filter was manufactured by mixing MnO2 powder with binder with a 10% difference in concentration from 10% to 50% and then applying it using the dip-coating method. Finally, we sintered a MnO2 catalyst mesh filter in an electric furnace. We evaluated the characteristics of ozone generation according to the Ar gas flow of DBD plasma devices, the opening ratio, and ozone reduction performance of the MnO2 catalyst filters. Ozone reduction performance was approximately 20.4% at MnO2 10 wt%, 37.8% at MnO2 30 wt% and 50% at MnO2 50 wt%.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.323-335
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• 2023

In causal analysis of high dimensional data, it is important to reduce the dimension of covariates and transform them appropriately to control confounders that affect treatment and potential outcomes. The augmented inverse probability weighting (AIPW) method is mainly used for estimation of average treatment effect (ATE). AIPW estimator can be obtained by using estimated propensity score and outcome model. ATE estimator can be inconsistent or have large asymptotic variance when using estimated propensity score and outcome model obtained by parametric methods that includes all covariates, especially for high dimensional data. For this reason, an ATE estimation using an appropriate dimension reduction method and semiparametric model for high dimensional data is attracting attention. Semiparametric method or sparse sufficient dimensionality reduction method can be uesd for dimension reduction for the estimation of propensity score and outcome model. Recently, another method has been proposed that does not use propensity score and outcome regression. After reducing dimension of covariates, ATE estimation can be performed using matching. Among the studies on ATE estimation methods for high dimensional data, four recently proposed studies will be introduced, and how to interpret the estimated ATE will be discussed.

• 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.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.454-462
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• 2014

Retention of possible churning customer is one of the most important issues in customer relationship management, so companies try to predict churn customers using their large-scale high-dimensional data. This study focuses on dealing with large data sets by reducing the dimensionality. By using six different dimension reduction methods-Principal Component Analysis (PCA), factor analysis (FA), locally linear embedding (LLE), local tangent space alignment (LTSA), locally preserving projections (LPP), and deep auto-encoder-our experiments apply each dimension reduction method to the training data, build a classification model using the mapped data and then measure the performance using hit rate to compare the dimension reduction methods. In the result, PCA shows good performance despite its simplicity, and the deep auto-encoder gives the best overall performance. These results can be explained by the characteristics of the churn prediction data that is highly correlated and overlapped over the classes. We also proposed a simple out-of-sample extension method for the nonlinear dimension reduction methods, LLE and LTSA, utilizing the characteristic of the data.

• Journal of Welding and Joining
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• v.28 no.2
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• pp.74-78
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• 2010

In this paper, a non-contact method of evaluating the thickness reduction in an aluminum sheet caused by corrosion and friction using SH-EMAT (shear horizontal, electromagnetic acoustic transducer) is described. Since this method is based on the measurement of the time-of-flight and amplitude change of guided waves caused from the thickness reduction, it provides information on the thinning defects. Information was obtained on the changes of the various wave features, such as their time-of-flight and amplitude, and their correlations with the thickness reduction were investigated. The interesting features in the dispersive behavior of selected guided modes were used for the detection of thinning defects. The measurements of these features using SH waves were performed on aluminum specimens with regions thinned by 7.2% to 29.5% of the total thickness. It is shown that the time-of-flight measurement provides an estimation of the thickness reduction and length of the thinning defects.