• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.1-8
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• 2024

For measuring the performance of passive sonars, we usually consider the maximum Detection Range (DR) under the environment and system parameters in operation. In shallow water, where sound waves inevitably interacts with sea surface or bottom, detection generally maintains up to the maximum range. In deep water, however, sound waves may not interact with sea surface or/and bottom, and thus there may exist shadow zones where sound waves can hardly reach. In this situation, DR alone may not completely define the performance of each sonar. For complete description of sonar performance, we employ the concept 'Robustness Of Detection (ROD)'. In the coastal region of the East Sea, the spatial variations of water masses have close relations with DR and ROD, where the two parameters show reverse spatial variations in general. The spatial and temporal analysis of the temperature by employing the Empirical Orthogonal Function (EOF) shows that the 1-st mode represents typical pattern of seasonal variation and the 2-nd mode represents strength variations of mixed layers and currents. The two modes are estimated to explain about 92 % of the variations. Assuming two types of targets located at the depths of 5 m (shallow) and 100 m (deep), the passive sonar performance (DR) gives high negative correlations (about -0.9) with the first two modes. Most of temporal variations of temperature occur from the surface up to 200 m in the water column so that when we assume a target at 100 m, we can expect detection performance of little seasonal variations with passive sonars below 100 m.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.2
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• pp.1-9
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• 2004

This study aims to examine the relationship between total quality management(TQM) practices and operational performance perceived by employees who are in different functional organization types, R&D function and Non-R&D function organization in manufacturing companies. Operational performance is defined as the degree of operation efficiency in the perspective of quality, delivery, and cost. Our study showed that the significant elements of TQM practice were different depending on functional organization types. In case of R&D organization, the categories of customer focus and process management were the strongest significant predictors of operational performance. Therefore, the optimal TQM practices for R&D organization can be obtained in consideration of customer focus and process management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5955-5977
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• 2018

Performance of the interpolation algorithm used in the technique of bi-dimensional empirical mode decomposition directly affects its popularization and application, so that the researchers pay more attention to the algorithm reasonable, accurate and fast. However, it has been a lack of an adaptive interpolation algorithm that is relatively satisfactory for the bi-dimensional empirical mode decomposition (BEMD) and is derived from the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. All parameters involved in the interpolation process are determined by using the particle swarm optimization algorithm. The presented interpolation algorithm can solve those problems of low efficiency and poor precision in the interpolation operation of bi-dimensional empirical mode decomposition and can also result in accurate and reliable bi-dimensional intrinsic modal functions with higher speed in the decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.

• International Journal of Highway Engineering
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• v.15 no.4
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• pp.135-146
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• 2013

PURPOSES : This study aimed to analyze the impact the operation of pre-signals at 4-leg signalized intersections and present primary environmental factors of roads that need to be considered in the installation of pre-signals. METHODS : Shift of proportions safety effectiveness evaluation method which assesses shifts in proportions of target collision types to determine safety effectiveness was applied to analyze traffic crash by types. Also, Empirical Bayes before/after safety effectiveness evaluation method was adapted to analyze the impact pre-signal installation. Negative binomial regression was conducted to determine SPF(safety performance function). RESULTS : Pre-signals are effective in reducing the number of head on, right angle and sideswipe collisions and both the total number of personal injury crashes and severe crashes. Also, it is deemed that each factor used as an independent variable for the SPF model has strong correlation with the total number of personal injury crashes and severe crashes, and impacts general traffic crashes as a whole. CONCLUSIONS: This study suggests the following should be considered in pre-signal installation on intersections. 1) U-turns allowed in the front and rear 2) A high number of roads that connect to the intersection 3) Many right-turn traffic flows 4) Crosswalks installed in the front and rear 5) Insufficient left-turn lanes compared to left-turn traffic flows or no left-turn-only lane.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.6
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• pp.497-504
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• 2001

A study to improve the accuracy of a map-based compressor model with experiment was performed. Corrections on the effects of suction gas superheat and heat leakage from a compressor shell are required to apply the compressor amp model based on the empirical performance data(map) of compressor manufacturers to the actual system. So experiments to assess the effects of superheat and hat leakage were performed and the corrected equations were made. Compressors and refrigerant used in the experiment were the high pressure type rotary compressor and R-22, experiments were performed by compressor calorimeter. From the experiment, a volumetric efficiency correction factor$(F_ν)$ showed the value of 0.77, slightly higher than 0.75 proposed by Dabiri and Rice for low pressure type reciprocating compressor, and the heat leakage from the compressor shell turned out to be a factor that influenced the discharged mass flow rate. The relation between heat leakage of compressor shell and the variation of discharged mass flow rate from compressor was considered in compressor map modeling as an empirical function. With this function, the prediction accuracy of compressor model in system conditions was improved.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.393-401
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• 2012

This paper deals with the estimations of the least squares support vector regression when the responses are subject to randomly right censoring. The estimation is performed via two steps - the ordinary least squares support vector regression and the least squares support vector regression with censored data. We use the empirical fact that the estimated regression functions subject to randomly right censoring are close to the true regression functions than the observed failure times subject to randomly right censoring. The hyper-parameters of model which affect the performance of the proposed procedure are selected by a generalized cross validation function. Experimental results are then presented which indicate the performance of the proposed procedure.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.7
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• pp.306-312
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• 2006

Support Vector Machine(SVM), of which the foundations have been developed by Vapnik (1995), is gaining popularity thanks to many attractive features and promising empirical performance. In this paper, we propose a new short-term load forecasting technique based on SVM. We discuss the input vector selection of SVM for load forecasting and analyze the prediction performance for various SVM parameters such as kernel function, cost coefficient C, and $\varepsilon$ (the width of 8 $\varepsilon-tube$). The computer simulation shows that the prediction performance of the proposed method is superior to that of the conventional neural networks.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.15-25
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• 2005

As implied by the terms of IT productivity Paradox, measuring the Information technology contribution to economic performance has been one of the challenging issues to both policy makers and business professionals. As such, diverse attempts with sophisticate analyses have been reported in the literature to analyze the effect of IT contributions. In this paper, we follow Growth Accounting Method to measure the IT contribution effect to manufacturing firm's economic performance in Korea. Various regression methods and statistical analyses are applied with fourteen years of industry Panel data. Using the Cobb-Douglas function, time lag analysis is made to understand IT effect to economic growth. Instead of capturing data from individual firm, industry level data from the National Statistics Bureau is used for IT capital, non-IT capital, and so on. Statistical analysis following the panel unit test and Panel co-integration test was performed to reveal the exact effect of IT contribution to economic performance. Empirical testing results for non-stationary nature of IT investment effect are reported as well as IT contribution to manufacturing industry's economic performance.

• Ocean Systems Engineering
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• v.2 no.2
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• pp.137-145
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• 2012

Recent developments in underwater image recognition methods have received large attention by the ocean engineering researchers. In this paper, an improved bi-dimensional empirical mode decomposition (BEMD) approach is employed to decompose the given underwater image into intrinsic mode functions (IMFs) and residual. We developed a joint algorithm based on BEMD and Canny operator to extract multi-pixel edge features at multiple scales in IMFs sub-images. So the multiple pixel edge extraction is an advantage of our approach; the other contribution of this method is the realization of the bi-dimensional sifting process, which is realized utilizing regional-based operators to detect local extreme points and constructing radial basis function for curve surface interpolation. The performance of the multi-pixel edge extraction algorithm for processing underwater image is demonstrated in the contrast experiment with both the proposed method and the phase congruency edge detection.

• Structural Engineering and Mechanics
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• v.68 no.2
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• pp.247-259
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• 2018

In this study (Part I), an advanced empirical formulation was proposed to predict the ultimate strength of initially deflected steel plate subjected to longitudinal compression. An advanced empirical formulation was proposed by adopting Initial Deflection Index (IDI) concept for plate element which is a function of plate slenderness ratio (${\beta}$) and coefficient of initial deflection. In case of initial deflection, buckling mode shape, which is mostly assumed type in the ships and offshore industry, was adopted. For the numerical simulation by ANSYS nonlinear finite element method (NLFEM), with a total of seven hundred 700 plate scenarios, including the combination of one hundred (100) cases of plate slenderness ratios with seven (7) representative initial deflection coefficients, were selected based on obtained probability density distributions of plate element from collected commercial ships. The obtained empirical formulation showed good agreement ($R^2=0.99$) with numerical simulation results. The obtained outcome with proposed procedure will be very useful in predicting the ultimate strength performance of plate element subjected to longitudinal compression.