• Journal of Korean Society for Quality Management
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• v.48 no.1
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• pp.51-68
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• 2020

Purpose: The primary objective of this research is to develop the optimal operating conditions as well as their associated design spaces for a Cryogenic Submerged Arc Welding(SAW) process by improving its quality and productivity simultaneously. Methods: In order to investigate functional relationships among quality characteristics and their associated control factors of an SAW process, a stepwise design of experiment(DoE) method is proposed in this paper. Based on the DoE results, not only a multi-dimensional design space but also a safe operating space and normal acceptable range(NAR) by integrating statistical confidence intervals were demonstrated. In addition, the optimal operating conditions within the proposed NAR can be obtained by a robust optimal design method. Results: This study provides a customized stepwise DoE method (i.e., a sequential set of DoE such as a factorial design and a central composite design) for Cryogenic SAW process and its statistical analysis results. DoE results can then provide both the main and interaction effects of input control factors and the functional relationships between the input factors and their associated output responses. Maximizing both the product quality with high impact strength and the productivity with minimum processing times simultaneously in a case study, we proposed a design space which can provide both acceptable productivity and quality levels and NARs of input control factors. In order to confirm the optimal factor settings and the proposed NARs, validation experiments were performed. Conclusion: This research may provide significant contributions and applications to many SAW problems by preparing a standardization of the functional relationship between the input factors and their associated output response. Moreover, the proposed design space based on DoE and NAR methods can simultaneously consider a number of quality characteristics including tradeoff between productivity and quality levels.

• Journal of Korea Trade
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• v.27 no.5
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• pp.113-136
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• 2023

Purpose - Global production chains and their impacts on economic growth have drawn extensive attention from researchers. Close relationships among global production chains, export and economic growth have been illuminated, as evidenced by the fast and stable economic growth of East Asian economies. These economies perform various roles within global production chains using offshoring, in which the impact of import on domestic gross output is as strong as that of export. The impact of import on economic growth would depend on whether imported inputs substitute or complement domestic inputs production, which is likely to vary according to individual countries' functions within global production chains. The economic growth of concerned countries would also be diverse. However, little attention has been paid to the impact brought by imports compared to its significance. Design/methodology - The principal methodology used in this paper is structural decomposition analysis (SDA), widely chosen to elucidate the impact of various factors on domestic gross output using input-output tables. This paper extracts trade data of six Asian economies from the World Input-Output Database (WIOD) 2016 release that covers 43 countries for the period 2000-2014. The extracted data is then categorised into 37 sectors. First, this paper calculates the Feenstra-Hanson Offshoring Index (OSI) of each country. It then applies SDA to measure the changes in each economy's gross output, export, import input coefficients, and domestic input coefficients. Finally, after taking the first difference from pooled time-series data, it estimates the correlations between imported input coefficients and OSI using the ordinary least square (OLS) method. Findings - The main findings of this paper can be summarised as follows. Firstly, all six countries have increasingly engaged in global production chains, as evidenced by the growing size of OSI. Secondly, there are negative correlations in five countries except Japan, with sectoral differences. Thirdly, changes in import input coefficients are not negative in all six countries, indicating that offshoring does not necessarily substitute for domestic inputs production but does complement it and, therefore, fosters their economic growth. This is observed in China, Indonesia, Korea and Taiwan. Offshoring has led to an increase in the use of imported inputs, which has, in turn, stimulated domestic inputs production in these countries. Originality/value - While existing studies focus on the role of export in evaluating the impact of participating global production chains, this paper explicitly examines the unexplored impact of import on domestic gross output by considering both the substitution and the complementary effect, using the WIOD. The findings of this paper suggest that Asian economies have achieved fast and stable economic growth not only through successful export management but also through effective import management within global production chains. This paper recommends that the Korean government and enterprises carefully choose offshoring strategies to minimise disruption to domestic production chains or foster them.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.197-200
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• 1997

Each company is more interested in the productivity to achieve cost reduction and profit maximization through productivity improvement. With this trend, we show the method to measure productivity using TPM(Total Productivity Model) which considers all the input factors of the company instead of using partial productivity such as labor and/or capital productivity We also examine the relation of productivity versus output, profit versus output and profit versus productivity of the case company by actually applying the TPM and suggest the optimal level of profit and output for this company.

• Korean Management Science Review
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• v.29 no.3
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• pp.81-89
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• 2012

Since introduced by Vining and Myers in 1990, the concept of dual response approach based on response surface methodology has widely been investigated and adopted for the purpose of robust design. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum settings of input factors. Explicitly assuming functional relationship between responses and input factors, however, it may not work well enough especially when the behavior of responses are poorly represented. A sufficient number of experimentations are required to improve the precision of estimations. This study proposes an alternative to dual response approach in which additional experiments are not required. An artificial neural network has been applied to model relationships between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Training, validating, and testing a neural network with empirical process data, an artificial data based on the neural network may be generated and used to estimate response functions without performing real experimentations. A drug formulation example from pharmaceutical industry has been investigated to demonstrate the procedures and applicability of the proposed approach.

• Journal of Information Technology Services
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• v.12 no.4
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• pp.255-265
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• 2013

This paper proposes a DEA-AR model for the efficiency evaluation of the iron ore brands in an integrated steel mill. The input factor is defined as unit cost of each brand based on CIF and two output factors are chosen as Fe and Al which are the important ingredients of iron ore. The relative importance between two output factors is determined by several experts using AHP model. The efficiency of each brand is determined using DEA and DEA-AR models. The negative correlation between the DEA-AR efficiency and the unit cost (CIF) is shown as significant whereas no significant correlation exist between the efficiency and the output factors. Also, the Kruskal Wallis rank sum test shows that there exist efficiency differences among the iron ore types whereas no difference is shown among the countries. The result could be utilized in selecting good brands of iron ores based on the DEA-AR efficiency in an integrated steel mill.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1512-1515
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• 1997

One of the advantages of master-slave teleoperation is scaling concept such as position scaling, force scaling Meanuhile, lots of quantization effects are generated from position and force sensors in the master and slave manipulator. In this paper, to show the output error caused by the quantizaion effects from the position sensor and position scaling factor, simulation is done for free motion without contact in slave side. Transfer functiion model in which the quantization effect is assumed to be a disturbance input to the system is derived. Model shows that Jacobian, scaling factors, and controller affect the output by quantization effects form esnsors. One dof master and slave are used for simulation. In our study, the higher sensor resolution decreases the output error form quantization. Scaling factors can amplify the quantizatiion effects form the sensors in master and slave manipulators.

• Korean Management Science Review
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• v.34 no.1
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• pp.15-25
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• 2017

We evaluated industry growth and employment effects of every possible pairs of 22 manufacturing sectors and 16 regions (i.e, 352 region-sectors). We used annual data of manufacturing sectors from 2008 to 2014 for the evaluation. The evaluation comprises of two steps; We first find several region-sectors that outperform others with respect to the effects of industry growth and employment, which are measured by location quotient analysis, shift share method, employment to GDP ratio and employment elasticity. In addition, cross-efficiency analysis follows to classify region-sector pairs into two sub-categories : efficient region-sectors that deserve to hold the current level of investments and inefficient region-sectors where we should consider efficiency improvements. To examine the efficiency, R&D investment, employment size, and capital investment were used as input factors and production volume, added value, changes in employment size, changes in annual salary per capita were used as output factors. For region-sector pairs that have outstanding growth and employment effects but are inefficient, we employed a CCR DEA model and analyzed how much to adjust the values of input and output factors to improve the efficiency scores. The analysis results showed that inefficiency is mainly due to several factors such as R&D investment, changes in employment size and changes in annual salary per capita.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.1487-1490
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• 2006

One of the factors deteriorating PDP image quality is the nonlinearity and nonuniform of output luminance as a function of input gray level. A novel method using modified ADS driving scheme is proposed to decrease this nonlinearity. It optimizes the reset pulse and adjusts the subfields, makes the relation of output luminance and input gray level almost linear. This method can be applied to general commercial PDPs.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.47-53
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• 2001

In this paper, we compare the classification performances of both ensemble and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. In view of the unknown relationship between input and output function, we use a Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: When the level of the variance is medium, Bagging & Parameter Combining performs worse than Logistic Regression, Variable Selection Bagging and Clustering. However, classification performances of Logistic Regression, Variable Selection Bagging, Bagging and Clustering are not significantly different when the variance of input data is either small or large. When there is strong correlation in input variables, Variable Selection Bagging outperforms both Logistic Regression and Parameter combining. In general, Parameter Combining algorithm appears to be the worst at our disappointment.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.58-60
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• 2017

In the offshore crane system, the requirements on the operating safety are extremely high due to many external factors. This paper describes a model for studying the dynamic behavior of the offshore crane system. The obtained model allows to evaluate the fluctuations of the load arising from the elasticity of the rope. Especially, in this paper, the authors design control system in which just winch rotation angle and rope tension are used without load position information. The controller design based on input-output feedback linearization theory is presented which can handle the effect of the elasticity of the rope and track the load target trajectory input. Besides that, a full order observer is designed to estimate unknown states. Finally, By the experiment results, the effectiveness of proposed control method is evaluated and verified.