• 수산해양기술연구
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• 제56권4호
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• pp.374-383
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• 2020

At ship design stage, the maneuverability is generally estimated based on the empirical formula or the computational fluid dynamic (CFD), which is one of the numerical simulation methods. Using the hydrodynamic derivatives derived through these methods can quantitatively estimate the maneuverability of target vessels and evaluate indirect maneuverability. Nevertheless, research on estimating maneuverability is insufficient for ships not subject to IMO maneuverability standard, especially fishing vessels, and even at the design stage, the empirical formula developed for merchant ships is applied without modification. An estimation error may occur due to the empirical formula derived from the regression analysis results of a model test if the empirical formula developed for merchant ships with different hull shapes is applied to fishing vessels without any modification. In this study, the modified empirical formula that can more accurately estimate the fishing vessel's maneuverability was derived by including the hull shape parameter of target fishing trawlers in the regression analysis process that derives Kijima et al. (1990) formula. As a result, the modified empirical formula showed an average estimation error of 6%, and the result improved the average error of 49% of Kijima et al. (1990) formula developed for merchant ships.

• 한국의류학회지
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• 제34권10호
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• pp.1671-1686
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• 2010

This research presents a fashion adoption process model based on collective selection theory and examines the differences in the consumer adoption process of merchandising types at each stage of the fashion cycle. A questionnaire survey of 472 adult women was conducted for the purpose of empirical analysis of the fashion adoption process. The results show that fashionability and popularity (the primary attributes of fashion goods) have direct effects on resistance and adoption as well as indirectly through social compatibility and personal compatibility in the evaluation stage. In conclusion, on the theoretical side, this study verified the fashion process model according to consumer participation in the adoption process of fashion goods existing at different stage of the fashion cycle, internally through negotiating with individual tastes, and externally through interacting with others. On the practical side, this study presented an empirical result that can apply to merchandizing strategy centered on merchandizing type by connecting consumer adoption for the fashion goods released by actual companies.

• 산업융합연구
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• 제20권1호
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• pp.23-33
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• 2022

본 연구는 제조기업의 디지털 전환(DX)을 위한 실행모형을 개발하기 위해 진행하였다. 이를 위하여 경영혁신과 디지털전환 과정에 대한 선행연구를 검토하였다. 인터넷 비즈니스의 혁신과정에서 적용된 NEBIC이론과 혁신이론을 기반으로 DX과정모형을 도출하였다. 여기에 최고경영층(TMT)의 추진의지 요인을 포함한 연구모형을 구축하고 이를 검증하기 위해 험자료를 통해 확인하였다. 디지털 전환을 추진하고 있는 중견 제조기업의 구성원을 대상으로 수집된 자료를 기반으로 연구가설을 검증하였다. 회귀분석을 통해 연구모형(기술지식 → 기회인지 → 기대성과 → 실행의지)의 각 단계의 영향관계를 확인하였다. DX과정에 최고경영층의 DX추진의지에 대한 구성원의 인식의 매개효과를 파악하기 위해 위계적 회귀분석을 실시였다. Sobel test 확인결과, 각 단계별 관계에 경영층의 DX추진에 대한 인식이 부분적으로 매개하는 사실을 확인하였다. 본 연구는 중견 제조업의 디지털 전환에 적용 가능한 모형을 제시했다는 점에서 의미가 있다. 또한 혁신연구 및 NEBIC 확장에 대한 경험적 근거를 제공한 것에 가치를 지닌다. 동적특성을 지닌 과정모형에 대한 실증자료의 한계를 극복하기 위해서 종단연구가 필요하며 연구결과의 일반화를 위해 제조업 이외 다양한 분야에서 확장된 실증연구가 요구된다.

• 대한수학회지
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• 제38권1호
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• pp.25-35
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• 2001

In this paper we consider an age dependent branching process whose particles move according to a Markov process with continuous state space. The Markov process is assumed to the stationary with independent increments and positive recurrent. We find some sufficient conditions for he Markov motion process such that the empirical distribution of the positions converges to the limiting distribution of the motion process.

• 응용통계연구
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• 제24권6호
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• pp.1197-1211
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• 2011

The actual power performance of historical structural change tests are compared under various alternatives. The tests of interest are F, CUSUM, MOSUM, Moving Estimates and empirical distribution function tests with both recursive and ordinary least-squares residuals. Our comparison of the structural tests involves limiting distributions under the hypothesis, the ability to detect the alternative hypotheses under one or double structural change, and smooth change in parameters. Even though no version is uniformly superior to the other, the knowledge about the properties of those tests and connections between these tests can be used in practical structural change tests and in further research on other change tests.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2006년 추계학술발표대회 개요집
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• pp.271-273
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• 2006

This paper focuses on the developed empirical models for the prediction on top-bead width in GMA(Gas Metal Arc) welding process. Three empirical models have been developed: linear, curvilinear and an intelligent model. Regression analysis was employed fur optimization of the coefficients of linear and curvilinear model, while Genetic Algorithm(GA) was utilized to estimate the coefficients of intelligent model. Not only the fitting of these models were checked, but also the prediction on top-bead width was carried out. ANOVA analysis and contour plots were respectively employed to represent main and interaction effects between process parameters on top-bead width.

• 한국의류학회지
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• 제18권3호
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• pp.395-409
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• 1994

Consumers experience various emotional responses to clothing in consumption process. These emotional responses are important in explaining consumer behavior of clothing. The Purpose of the study was to identify the types of emotion that consumers experience in the process of wearing clothes. A questionnaire consisted of 35 words expressing emotion was developed. 110 female college students were surveyed to find out consumption experience of clothing. The empirical study was conducted in two ways. One is that the subjects were asked to respond six-point unipolar scale of emotional adjectives to identify the various emotions that consumers experienced. The other is that four different wearing situations were described for the survey and then the subjects were asked to respond same questionnaire. The data surveyed for research were analyzed by factor analysis. The conclusion of empirical study was as follows. The types of emotion that consumers experienced at various situstions were: pleasure, arousal, unpleasure, aversion, dominance, sandness, security and tension.

• 오토저널
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• 제10권4호
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• pp.39-56
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• 1988

In this study, the computer program was developed to predict the engine performances and exhaust emissions of a 4-cylinder 4-stroke cycle ignition engine including intake and exhaust system. The simulation models applied to each process were as follows. For the combustion process, two zone model which requires only one empirical constant was applied, and for the gas exchange process, the method of characteristics that allows the calculations of the time variation and spatial variation of properties along the pipes was used. Constant pressure perfect mixing model was applied to take into account of the interaction at manifold branches. To predict exhaust emissions, twelve chemical species were considered to be present in combustion products. These species were calculated through equilibrium thermodynamics and kinetic theory. The empirical constants reduced to least number as possible were determined through the comparison with the experimental indicator diagram of one particular operating condition and these constants were applied to other operating conditions. The predicted performances and emissions were compared with the experimental results over the wide range of operating conditions.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.395-395
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• 2000

Optimization of existing processes becomes more important than the past as environmental problems and concerns about energy savings stand out. When we can model a process mathematically, we can easily optimize it by using the model as constraints. However, modeling is very difficult for most chemical processes as they include numerous units together with their correlation and we can hardly obtain parameters. Therefore, optimization that is based on the process models is, in turn, hard to perform. Especially, f3r unknown processes, such as bioprocess or microelectronics materials process, optimization using mathematical model (first principle model) is nearly impossible, as we cannot understand the inside mechanism. Consequently, we propose a few optimization method using empirical model evolutionarily instead of mathematical model. In this method, firstly, designing experiments is executed fur removing unecessary experiments. D-optimal DOE is the most developed one among DOEs. It calculates design points so as to minimize the parameters variances of empirical model. Experiments must be performed in order to see the causation between input variables and output variables as only correlation structure can be detected in historical data. And then, using data generated by experiments, empirical model, i.e. response surface is built by PLS or MLR. Now, as process model is constructed, it is used as objective function for optimization. As the optimum point is a local one. above procedures are repeated while moving to a new experiment region fur finding the global optimum point. As a result of application to the pulp digester benchmark model, kappa number that is an indication fur impurity contents decreased to very low value, 3.0394 from 29.7091. From the result, we can see that the proposed methodology has sufficient good performance fur optimization, and is also applicable to real processes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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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.