• Asia pacific journal of information systems
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• v.10 no.1
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• pp.19-35
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• 2000

Data Envelopment Analysis(DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications, some features of DEA remain bothersome. DEA offers no guidelines to where relatively inefficient DMU(Decision Making Unit) improve since a reference set of an inefficient DMU consists of several efficient DMUs and it doesn't provide a stepwise path for improving the efficiency of each inefficient DMU considering the difference of efficiency. We aim to show that DEA can be used to evaluate the efficiency of System Integration Projects and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with machine learning.

• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.249-258
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• 2013

Data Envelopment Analysis (DEA) is a useful tool to analyze the relative efficiency of decision making units (DMU) characterized by multiple inputs and multiple outputs. This method has been popularly used as an analytical tool to suggest some strategic improvement. To do this, the results of DEA provide decision makers with a single efficiency score, efficient frontier, return to scale, benchmarking decision making units, etc. The purpose of this paper is to evaluate research performance of 38 universities and provide an inefficient university with the way of organizational changes to be an efficient university by using DEA. Various input and output variables are used to identify technical and scale inefficiency. Additionally, we analyze how an inefficient DMU could be changed an efficient DMU based on a case university. This result will give an insight of constructive directions for increasing of research performance to university decision makers.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.77-88
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• 2006

Data Envelopment Analysis (DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications, some features of DEA remain bothersome. DEA offers no guidelines to where relatively inefficient DMU(Decision Making Unit) improve since a reference set of an inefficient DMU consists of several efficient DMUs and it doesn't provide a stepwise path for improving the efficiency of each inefficient DMU considering the difference of efficiency. We aim to show that DEA can be used to evaluate the efficiency of System Integration Projects and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with machine learning.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.1-6
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• 2006

This study proposes a new approach which combines Data Envelopment Analysis(DEA) and the Analytic Hierarchy Process(AHP) techniques to effectively evaluate Decision Making Units(DMUs). While DEA evaluates a quantitative data set, employs linear programming to obtain input and output weights and ranks the performance of DMUs, AHP evaluates the qualitative data retrieved from expert opinions and other managerial information in specifying weights. The objective of this research is to design a decision support process for managers to incorporate positive aspects of DEA's absolute numerical evaluations and AHP's human preference structure values. It is believed that a pragmatic manager will be more receptive to the results that include subjective opinions incorporated into the evaluation of the efficiency of each DMU efficiency. The WPDEA method provides better discrimination than the DEA method by reducing the number of efficient units.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.365-373
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• 2000

Data Envelopment Analysis (DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications and merits, some features of DEA remain bothersome. DEA offers no guideline about to which direction relatively inefficient DMUs improve since a reference set of an inefficient DMU, several efficient DMUs, hardly provides a stepwise path for improving the efficiency of the inefficient DMU.In this paper, we aim to show that DEA can be used to evaluate the efficiency of life insurance companies while overcoming its limitation with the aids of machine learning methods.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.211-217
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• 2007

The Diesel Multiple Units (DMU) is a successful mass transportation system, that is being, continuously, on demand by train operators and railway authorities around the globe. One of its advantages is the fact that a diesel engine, along with the correct propulsion and control equipment. This paper describes a study on the Power Pack developed for Ireland DMU to help comprehensive concept of Diesel Propulsion System.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.597-614
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• 2001

Level 0: The Marekting Buzzword : □Confusion between DMU and Visualization □Having a Mobile Data Viewer/Analyser is Anyway a Prerequisite Level 1: Digital Pre-Assembly (DPA): □Building Digital Prototype before Physical Build □Usually a job for Packaging or Prototype Teams □Usually no time Left to take Feed-back into account before Actual Build Level 2: Design in Context: □All Designers within Car Maker do Local DMU before DPA Level 3: Design in Extended Context □Design in Context Expanded to Suppliers(omitted)

• CDE review
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• v.9 no.1
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• pp.26-30
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• 2003

• Journal of the Korean Society of Clothing and Textiles
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• v.11 no.3 s.25
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• pp.57-65
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• 1987

The purpose of this study was to investigate the optimum treatment [condition for the Durable press finish of viscose rayon fabrics. Three types of commercial N-methylol crosslinking agents were applied to the fabric utilizing the pad-dry-cure technique. Changes in physical properties were evaluated for the various resin and catalyst concentrations. For DMU, the effect of different catalysts, $MgCl_2$ and $NH_4Cl$, were also compared. DMU treated fabrics showed in crease recovery angle, tensile strength and tearing strength but drastic decrease in abrasion resistance. DMDHEU and MDMDHEU treated fabrics were similar in most physical properties. However, DMDHEU treated fabrics were better in crease recovery angle and stiffness, and MDMDHEU treated fabrics were better in tensile strength, tearing strength and abrasion resistance. For a given resin system, crease recovery angle, tensile strength and stiffness increased with a increase in resin concentration. Tearing strength showed very little change, while abrasion resistance was decreased significantly as the crease recovery angle was increased. For the treatment of DMU, $MgCl_2$ catalyst was much better than $NH_4Cl$ in all physical properties. When $NH_4Cl$ catalyst was used, strength reduction and discoloration were observed. As the catalyst concentration increased, crease recovery angle, stiffness were increased. Tensile strength and tearing strength were increcased than control but at high catalyst concentration, the strength were decreased and abrasion resistance was significantly lowered. DMDHEU and MDMDHEU were more sensitive to catalyst concentrations than DMU.

• Journal of Pharmaceutical Investigation
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• v.21 no.1
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• pp.33-41
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• 1991

A high-performance liquid chromatographic (HPLC) method was developed for the simultaneous determination of theophylline(TP) and its metabolites, 1-methyluric acid (1-MU) and 1,3-dimethyluric acid (1,3-DMU), in rat plasma and urine. An $100\;{\mu}l$ aliquot of a plasma or urine sample was mixed with $250\;{\mu}l$ of acetonitrite and vortexed. After centrifugation, $200\;{\mu}l$ (plasma) or $20\;{\mu}l$ (urine) aliquot of the supernatant was dried by $N_2$ stream and redissolved in $100\;{\mu}l$ (plasma) or $200\;{\mu}l$ (urine) of the mobile phase. A $20\;{\mu}l$ of the mobile phase solution was injected onto a $C_{18}$ reversed-phase column. The column was maintained at $45^{\circ}C$ by the aid of electric heating jacket. The mobile phase was a 3%(v/v) methanol solution in deionized water which contains sodium acetate (100 mM) and tetrabutyl ammonium hydroxide (4 mM). pH of the mobile phase was adjusted 4.5 by the addition of acetic acid. Detection limits for TP, 1-MU, and 1,3-DMU in plasma were 0.2, 0.1 and $0.1\;{\mu}/ml$, respectively and the corresponding values in urine were all $5\;{\mu}g/ml$. Inter- and intra-day variability of the assay for all compounds in the plasma samples was less than 5.5 and 3.8%, respectively. The retention times for 1-MU, 1,3-DMU, and TP were approximately 7, 8.5 and 18 min, respectively. Sample preparation procedure used in this method was simple, rapid and reproducible. Renal clearance of TP and its metabolites in rats showed plasma concentration dependency indicating renal tubular secretion and reabsorption of them.