• Korean Journal of Community Nutrition
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• v.10 no.6
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• pp.905-919
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• 2005

This study raised the necessity of developing performance indicators for measuring the management efficiency and effectiveness of school food service, and as a means of helping its implementation, a balanced score card (BSC) approach developed by Norton and Kaplan was adopted. This study established BSC in seven phases through literature: Phase 1 Defining a school food service and the scope of working activities, Phase 2 Establishing the vision of a school food service, Phase 3 Setting strategic goals, Phase 4 Identifying critical success factors (CSFs), Phase 5 Developing Key Performance Indicators (KPIs), Phase 6 Extracting cause and effect relationship, and Phase 7 Completing a preliminary BSC. The preliminary BSC was fumed into a survey, which was administered to food service related people working at the Office of Education and School Food Service including 16 offices,209 dietitians, 48 school administrators both from self-operated and contract-managed, and 9 experts in areas related to school food service. They were asked questions about strategies from 4 different perspectives,12 CSFs, 39 KPls, and the cause and effect relationships among them. As a result, among the CSFs based on 4 different perspectives, all factors other than ' zero sum on profit/loss ' from the financial perspective turned out to be valid. In terms of KPIs, manufacturing cost percentages, casualty loss count/reduction rates, school foodervice participation rates, and sales goal achievement rates were found to be valid from the financial perspective, while student satisfaction index, faculty satisfaction index, leftover ratio, nutrition educational performance count, index of evaluating nutrition education, customer claim count/reduction rate, handling customer claim count/reduction rate, and parent satisfaction index were found to be valid from the customers' perspective. Besides, nutritional requirement sufficient ratio, nutritional management score, food poisoning outbreak count, employee safety accident count, sanitary inspection assessment index, meals per labor hour (productivity index), computerization ratio, operational management index, and purchase management assessment index were also found to be valid from the perspective of internal business processes. From the perspective of innovation and learning, employee turnover ratio/rate of absenteeism, annual education and training count, employee satisfaction index, human resource management assessment index, annual menu-related customer feedback, food service information index for employees and parents/schools were also found to be valid. The significance of this study is to present indices for measuring overall performance of school lunch food service operations without putting any limitation on types of school food service management, and to help correctly assess the contribution of the current types of school food service management to schools and students. (Korean J Community Nutrition 10(6) : $905\∼919$, 2005)

• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.442-449
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• 2019

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of the military operations research society of Korea
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• v.23 no.2
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• pp.15-24
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• 1997

In this paper we suggested a performance evaluation model for future weapon systems. Weapon Performance Index(WPI) model transform the characteristics of alternatives as indices. We can easily obtain WPIs of alternatives with the model. The highest WPI recommended as the best solution. The performance elements in hierachy for future weapon systems are determined by systems engineering procedure. Priorities in hierachy can be determined through survey of experts engineering procedure. Priorities in hierachy can be determined through survey of experts and statistical analysis. Utility function is formulated as a probability model and utility score is predicted on the basis of historical data about the same category of weapon systems in the world. WPI is calculate from sum of product of priorities and utility scores. The model can be applied to trade-off analysis, cost and effectiveness analysis, war game model.

• Quality Improvement in Health Care
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• v.9 no.1
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• pp.66-73
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• 2002

The current health care system demands provisions for patient care in perspectives of a cost-effectiveness and patient satisfaction. Critical pathway implementation facilitates optimal sequencing and intervention timing of patient care, and makes medical team and patients participate in a treatment actively. In this study, a critical pathway was developed and implemented to patients with osmidrosis who undertake operation. Sixty patients were included in the study. The critical pathway was implemented for care of 26 patients while the traditional care was implemented for 34 patients. In the critical pathway implemented group, time needed for charting and unessential working was reduced. Mean time amount of time for patient nursing was increased. The critical pathway implementation is an effective method to utilize time of medical team. Also it increases the satisfaction index of patients and medical team simultaneously.

• Journal of Distribution Research
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• v.3 no.1
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• pp.145-158
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• 1998

The selection of proposed sites for the general logistic center of agriculture products would be made the most suitable place by considering the spread of population as real consumers, the prospect of the demand, the expansion of traffic system, the regional, hourly and carring traffic volume and the use of land based urban planning, etc. As the preconsideration, the possible occupant companies have to be selected on the category of business and the district. After posing questions and having interview, several selected regions would be compared and analysed for deciding the most suitable place. The model for the general logistic center of agricultural products must be selected taking key factors approach for choosing key factors at first and referring to many documentary records. And the more, cooperating with the specialists for location selection and making objective questions to concerned companies, the most suitable place is selected by marking high score for the moderate land cost, the low traffic jam, the connection with the back cities and the possible expansion as the general logistic center of agriculture products.

• Journal of Power System Engineering
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• v.17 no.1
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• pp.19-26
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• 2013

The purpose of the study is carried out volume optimization of a combined system consisting of an LNT and SCR catalysts from the standpoint of its economic feasibility and de-NOx performance. Under the rich air-fuel ratio conditions for 5s (${\Phi}$=1.1), CO, $H_2$ and THC were generated at levels of 4%, 1.2% and $110ppmC_1$, respectively. The NOx conversion of the 1+1 combination was 5% lower than that of the 1.5+0.5 combination, however the reduced volume of the LNT catalyst decreased the total cost by about 6%. Therefore, the optimal volume ratio of the LNT and SCR catalysts was found to be the 1+1 catalyst combination, which has the highest total score in the terms of an economic feasibility and the NOx performance.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.310-316
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• 2006

Since 'Act on Private Participation in Infrastructure' were changed in January 2005, BTL(Build-Transfer-Lease) projects of Private Participation in Infrastructure was introduced in the domestic construction market for the purpose of providing public facilities with the public. Selecting the most qualified Promoter should be considered discreetly in BTL projects because BTL projects is for projects with approximately thirty year project life cycle, plan, construction, operating, maintenance and obviously the success of the project totally depends on the capability and role of Promoter. However, score for cost is likely to influence selecting Promoter to take the project. Accordingly, low bid contract with too much competition could decrease the quality of the construction plan and operating plan. Thus, this study did preliminary research and documents on problems of evaluation criteria for selecting Promoter in BTL projects, and proposed improvement factors by doing questionnaire over specialists of each field.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.20-25
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• 2015

Interface management problems inherent in construction projects hamper their successful delivery. Therefore, this study aimed at determining the most important project interfaces in construction works in Nigeria in terms of most significant potential impacts, so that management attention are objectively focused on potential highest impacting project interfaces. From a review of literature, 28 project interfaces management issues were identified and categorized. Structured questionnaires were used to collect data concerning the impact (estimated losses to the project in terms of cost) and probability of occurrence of the identified interfaces. The interfaces were ranked using their computed Matrix Scores (MS). The results reveal that "project-workers interfaces problem manifested in use of inappropriate mixes" is the highest impacting. A ranking of the interface categories also reveal that the interfaces at the execution phase of a project (MS = 1226.79) are those that could result in the highest losses to the project.

• Speech Sciences
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• v.13 no.4
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• pp.177-186
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• 2006

In an effort to enhance the quality of feature vector classification and thereby reduce the recognition error rate of the speaker-independent speech recognition, we employ the Mahalanobis distance in the calculation of the similarity measure between feature vectors. It is assumed that the metric matrix of the Mahalanobis distance be diagonal for the sake of cost reduction in memory and time of calculation. We propose that the diagonal elements be given in terms of the variations of the feature vector components. Geometrically, this prescription tends to redistribute the set of data in the shape of a hypersphere in the feature vector space. The idea is applied to the speech recognition by hidden Markov model with fuzzy vector quantization. The result shows that the recognition is improved by an appropriate choice of the relevant adjustable parameter. The Viterbi score difference of the two winners in the recognition test shows that the general behavior is in accord with that of the recognition error rate.