Among service industries of knowledge based economic era, the roles of educational service field are becoming more important and standard of educational service makes a direct effect on economic development and social growth. Therefore, accurate measurement of service quality is the most important assignment and the measurement of the service quality remains difficult assignment. So, this researcher classified quality attributes applying weighted value and found potential satisfaction level(PSL) and potential customer demand improvement index(PCDI) for trainees participating in national manpower business so as to suggest measurement of service quality and easiness of use and then, calculated satisfaction position and opportunity cost by quality factor with Taguchi’s loss fraction. And, improvable satisfaction level was measured, opportunity cost by degree of customer dissatisfaction was quantitatively measured, and a model that can indicate with economic factors was suggested. In addition, methodology of measuring quality cost that can be reduced by quality improvement and direction of strategic decision-making for deciding items to be improved preferentially were suggested with qualitative index that can indicate the degree of customers’ dissatisfaction by loss.

Logistics companies are worrying about securing of differential competitiveness so as to be competitive companies in keen logistics market. The ground is how users are satisfied by sell-established service system to respond not only economic feasibility of logistics costs but also diversity and advancement of logistics needs. The competitiveness of logistics companies is also caused by customer satisfaction of service and only companies finding and satisfying customer needs continuously may be more competitive. For the competitiveness, it’s the most important to analyze demands of current and potential customers and their pursuing value properly. Therefore, this researcher grasped PSL for online logistics service users with 5-point Likert-scale and quality-level decision method that consider the weighted value based on Kano model, measured customer’s potential Demand for service through PCDI, and suggested methodology for deciding the priority of the improvement with loss function of Taguchi.

PURPOSES : The purpose of this study is to compare applicability, explanation power, and flexibility of traffic accident models between estimating model using the statistical method and the machine learning method. METHODS: In order to compare and analyze traffic accident models between model estimated using the statistical method and machine learning method, data acquisition was conducted, and traffic accident models were estimated using statistical methods such as negative binomial regression model, and machine learning methods such as a generalized regression neural network (GRNN). Then, the fitness of model as R2, root mean square error (RMSE), mean absolute percentage error (MAPE), accuracy, etc., were determined to compare the traffic accident models. RESULTS: The results showed that the annual average daily traffic (AADT), speed limits, number of lanes, land usage, exclusive right turn lanes, and front signals were significant for both traffic accident models. The GRNN model of total traffic accidents had been better statistical significant with R2: 0.829, RMSE: 2.495, MAPE: 32.158, and Accuracy: 66.761 compared with the negative binomial regression model with R2: 0.363, RMSE: 9.033, MAPE: 68.987, and Accuracy: 8.807. The GRNN model of injury traffic accidents also showed similar results of model’s statistical significance. CONCLUSIONS: Traffic accident models estimated with GRNN had better statistical significance compared with models estimated with statistical methods such as negative binomial regression model.

The characteristics of the turbulent MHD channel flows are analysed within the elliptic blending    model. The evaluation of additional terms representing the MDH interactions in the transport equation for the turbulent kinetic energy and dissipation rate is carried out firstly. And then, the improvement of model coefficients for the additional terms is achieved. Regardless of the magnetic field orientation, perpendicular and parrel to the main flow, the model coefficients for the additional terms are not changed to maintain the generality of the present model. The prediction results are directly compared to the DNS data to assess the performance of the present model.

본 연구에서는 건천화된 농촌 소하천의 환경을 개선하기 위해 농업용 저수지의 방류 능력을 평가하고 하천환경용수 방류에 따른 하류하천의 수질변화를 모의하였다. 대상유역은 경상남도 고성군 하이면에 위치한 봉현저수지 및 봉현천을 선정하였으며, 2011년부터 2013년까지 현장조사를 실시하였다. 유량측 정이 용이한 총 5개 지점을 선정하여 수질과 유량자료를 수집하였고, 기상자료는 지리적으로 인접한 진 주 기상관측소의 2000년부터 2013년까지 일 자료를 수집하였다. 저수지 방류조건 전후의 하천수질 (BOD, SS, TN, TP)을 비교, 분석하기 위한 수질모형으로 QUAL2K 모델을 선정하였다. 대상유역에 대 한 기상자료, 하천자료, 수질자료 등의 입력자료를 구축한 후 QUAL2K 모형을 적용하여 실측값과 비교 한 결과, BOD, SS, TN, 그리고 TP 항목에 대한 모형의 평가지표는 다음과 같이 나타났다. R2는 0.8508~0.9913, RMSE 0.005~0.52mg/L, NSE 0.949~0.998로 나타나 비교적 높은 일치도를 나타냈 다. 저수지 물수지 모형을 이용하여 다양한 저수지 방류 시나리오에 대해 분석한 결과, 적정 방류량은 3,000ton/day로 나타났다. 방류 전후의 수질을 비교한 결과 BOD와 SS의 경우 각각 9.2%, 21.0% 증가 하여 오히려 수질이 오염되는 것으로 나타났으며, TN과 TP는 각각 -9.0%, -2.4% 감소하여 수질이 개 선되는 것으로 나타났다. 이는 하천환경용수를 공급을 통해 건천화를 완화하더라도 저수지 수질에 따라 하류하천의 수질이 오염될 수도 있음을 나타내었다.

Ensemble classification involves combining individually trained classifiers to yield more accurate prediction, compared with individual models. Ensemble techniques are very useful for improving the generalization ability of classifiers. The random subspace ensemble technique is a simple but effective method for constructing ensemble classifiers; it involves randomly drawing some of the features from each classifier in the ensemble. The instance selection technique involves selecting critical instances while deleting and removing irrelevant and noisy instances from the original dataset. The instance selection and random subspace methods are both well known in the field of data mining and have proven to be very effective in many applications. However, few studies have focused on integrating the instance selection and random subspace methods. Therefore, this study proposed a new hybrid ensemble model that integrates instance selection and random subspace techniques using genetic algorithms (GAs) to improve the performance of a random subspace ensemble model. GAs are used to select optimal (or near optimal) instances, which are used as input data for the random subspace ensemble model. The proposed model was applied to both Kaggle credit data and corporate credit data, and the results were compared with those of other models to investigate performance in terms of classification accuracy, levels of diversity, and average classification rates of base classifiers in the ensemble. The experimental results demonstrated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.

As film products are increasingly used in a wide range of areas, from producing traditional flexible packaging to high-tech electronic products, a higher level of quality is demanded. Most film products are made in the form of rolled finished goods, therefore, various quality issues related to their shape characteristics must be addressed. The thickness of the film products is one of the most common and important critical-to-quality attributes (CTQs). Particularly, the degree of thickness uniformity is more important than other thickness parameters, because it will be potential causes of many secondary thickness-related quality problems, such as wrinkles or faulty windings. To control the degree of thickness uniformity, the fixed bending region is oneof the most important CTQs to manage. Fixed bending regions are special points in the transverse direction of a rolled product with consistent minute variations of the thickness gap. This paper describes the measurement and analysis of thickness uniformity data, which were performed in a real manufacturing field of biaxial oriented polypropylene (BOPP) film. In previous researches, quality function deployment (QFD) or fault tree analysis were used to find the most critical process attributes out to controlthe CTQ of thickness uniformity. Whereas, this paper uses traditional control charts to find the most critical process attributes out in this problem. In addition, the selection of one of the major critical process attributes (CTPs) that is expected to affect the CTQ of thickness uniformity is also described. The selected critical-to-process attributes are the controlled temperatures along the transverse direction. A dramatic improvement in thickness uniformity was observed when the selected CTPs were controlled.

This research introduces the Financial Effect Measurement (FEM) models which measures both the improvement and the innovation performance of Quality Control Circle (QCC) and activities of Six Sigma. Concepts and principle of Comprehensive Income Statement (CIS), Balanced Scorecard (BSC), Time-Driven Activity Based-Costing (TDABC) and Total Productive Maintenance (TPM) are applied in order to develop the 4 FEM models presented in this paper. First of all, FEM using CIS depicts the improvement effects of production capacity and yield using relationships between demand and supply, and line balancing efficiency between bottleneck process and non-bottleneck processes . Secondly, cause-and-effect relation of Key Performance Indicator (KPI) is used to present Critical Success Factor (CSF) effects for QC Story 15 steps of QCC and DMAIC (Define, Measure, Analyze, Improve, and Control) of Six Sigma. The next is FEM model for service management innovation activities that uses TDABC to calculate the time-driven effect for improving the indirect activities according to the cost object. Lastly, FEM model for TPM activities presents the interpretation of improvement effect model of TPM Capital Expenditure (CAPEX) and Operating Expenditure (OPEX) maintenance using profit, cash and Economic Added Value (EVA) as metrics of enterprise values. To better understand and further investigate FEMs, recent cases on National Quality Circle Contest are used to evaluate new financial effect measurement developed in this paper.

This study is conducted to suggest ways to mitigate the mismatch phenomenon between job seekers who want to find right company for themselves and companies looking for appropriate new employees. For this purpose, this study improves the company assessment framework reflecting job seekers interests by using fuzzy analytic hierarchy process. The improved evaluation framework is a three-level hierarchical structure, where there are 4 groups at the top level, 12 factors at the intermediate level and 36 indexes at the bottom level. For the empirical analysis of the applicants preferences based on the improved model, a survey for F-AHP analysis is carried out to university students and then priorities of components in the evaluation model are calculated. Moreover, the differences of priority of the company assessment framework are analyzed for different genders, college years, and major divisions. The results show that job seekers’ most concerning factors are wages, stability, working environments, and labor deal, which are ranked highly in this order and the differences in preferences for each type of job seekers (genders, college years, and major divisions) are obvious. The results also show that the male prefers wages to environment, on the other hand female does working environment to wages.

An algebraic model for turbulent heat fluxes which is originally suggested by Suga & Abe is modified on the basis of the elliptic blending equation. In order to satisfy the heat transfer characteristics of near-wall region and the flow center region far away from the wall, the model coefficients of the algebraic heat flux model are modified by using the solution of the elliptic blending equation. The predictions of turbulent heat transfer in a plane channel flow have been carried out with constant wall heat flux and constant wall temperature difference boundary conditions respectively. Also, the predictions are performed at various Prandtl numbers to test the applicability of the model. The prediction results show that the distributions of the turbulent heat fluxes and mean temperature are well captured by the modified algebraic heat flux model

온도와 수분의 불균일한 분포로 인하여 콘크리트 슬래브는 컬링과 와핑 거동을 하고 슬래브에는 항상 응력이 도입되어 있다. 따라서 교통하중 외에도 환경하중을 고려해야만 콘크리트 슬래브에 발생하는 응력을 보다 정확하게 예측할 수 있다. 콘크리트 포장에 반복적으로 재하되는 교통하중과 환경하중에 의해 콘크리트 슬래브의 강도는 지속적으로 감소하고 응력 이하로 낮아지게 되면 피로균열이 발생한다. 본 연구에서는 기존에 수행된 연구들로부터 피로실험 결과를 수집하고 피로 회귀모형을 개발하였다. 개발된 모형을 검증하기 위하여 모형개발에 사용되지 않은 실내 휨피로실험 결과를 예측하고 기존 모형으로 예측된 결과와 비교하였다. 콘크리트 포장 누적 피로손상 해석 프로그램을 개발한 후 본 연구에서 제안된 모형과 기존 모형들을 적용하고 예측 결과를 비교 및 평가하였다. 피로모형 별로 슬래브 두께, 줄눈간격, 복합 지지력계수, 그리고 하중전달률에 따른 누적 피로손상의 민감도 분석을 실시하였다. 그 결과 본 연구에서 개발된 모형이 최소-최대응력비 R을 고려하는 기존 모형들의 경향을 개선하여 환경하중을 더욱 합리적으로 반영할 수 있는 것으로 나타났다.