• Journal of Information Technology Applications and Management
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• 제27권2호
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• pp.37-50
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• 2020

This paper is very exploratory and addresses the issue 'Is a general quality model of software possible?'. If possible, how specific can/should it be?' ISO 25000 Series SQuaRE is generally regarded as a general quality model which can be applied to most kinds of software. Usability is one of the 8 characteristics of SQuaRE's Product Quality Model. It is the main issue associated with SQuaRE's Quality in Use Model too. it is the most important concept associated software quality since using is the only ultimate goal of software products. Playability, however, is generally regarded as a special type of usability, which can be applied to game software. This common idea contradicts with the idea that SQuaRE is valid for most kinds, at least many kinds, of software. The empirical evidences of this paper show that SQuaRE is too specific to be a general quality model of software.

• 한국물환경학회지
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• 제20권4호
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• pp.321-326
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• 2004

The objective of this study is to test the applicability of neural network models to forecast water quality at Naesa and Pyongchang river. Water quality data devided into rainy day and non-rainy day to find characteristics of them. The mean and maximum data of rainy day show higher than those of non-rainy day. And discharge correlate with TOC at Pyongchang river. Neural network model is trained to the correlation of discharge with water quality. As a result, it is convinced that the proposed neural network model can apply to the analysis of real time water quality monitoring.

• 한국IT서비스학회지
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• 제10권2호
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• pp.235-245
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• 2011

Early criticality prediction models that determine whether a design entity is fault-prone or not are becoming more and more important as software development projects are getting larger. Effective predictions can reduce the system development cost and improve software quality by identifying trouble-spots at early phases and proper allocation of effort and resources. Many prediction models have been proposed using statistical and machine learning methods. This paper builds a prediction model using Support Vector Machine(SVM) which is one of the most popular modern classification methods and compares its prediction performance with a well-known prediction model, BackPropagation neural network Model(BPM). SVM is known to generalize well even in high dimensional spaces under small training data conditions. In prediction performance evaluation experiments, dimensionality reduction techniques for data set are not used because the dimension of input data is too small. Experimental results show that the prediction performance of SVM model is slightly better than that of BPM and polynomial kernel function achieves better performance than other SVM kernel functions.

• 대한간호학회지
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• 제27권3호
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• pp.614-626
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• 1997

The main purposes of the study were to develop and test a model which explains the dynamic relationship among factors reported as affecting to the quality of life of individuals with rheumatoid arthritis and to examine the relationship between self-help response and quaility of life. Data for the study were collected from March 1996 to December 1996 from 153 female patients who regularly visited a clinic for people with rheumatism. The patients were introduced to the investigators by nurses who worked at that clinic, and then the investigator interviewed the patients for 30 to 40 minutes to collect the data. Instruments used in the study were modified self-report questionaires from the ones which were already developed in previous studies or from related literature. Data analysis were performed using LISREL(Lineal Structural Relations) 8 program to test whether the proposed hypothesized model fit the collected data. To test the fitablity of the hypothesized model both a general fit measure and a detailed fit measure were used. Based on the test results from the various fit measures, the hypothesized model was found to be well suited to the real data. As characteristics related to illness becomes severe, the feasibility for these characteristics leading to the perception of uncertainty about the illness tend to increase, but, the direct effects from the illness characteristics(such as level of physical symptoms, sense of social-psychologic change, limitations of action) as they are related to the other intrinsic variables (self-efficacy or self-help behavior and quality of life), were found to be not significant. It was found that uncertainty had a direct effect on self-efficacy but did not have a direct effect on self-help behavior or quality of life. Also, it is noted that self-efficacy had a positive effect on self-help behavior and quality of life and there was a bilateral relationship between self-efficacy and self-help behavior. Lastly, the hypothesis proposed from the theoretical model in this study was supported basis of the results that self-help behavior provides both direct and positive effects to quality of life. Particularity, since a bilateral relationship was also found between self-help behavior and quality of life in the modified model, as self-help behavior increased, so did quality of life. And, reversely, as quality of life increased, so did self-help behavior. In conclusion, the results of this study suggest that focusing on both acquirement and reinforcement of adjustment factors or self-help behavior is more efficient than focusing on the characteristics of illness in establishing the stategies for improving quality of life of individuals with rheumatoid arthritis.

• 음성과학
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• 제7권1호
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• pp.17-29
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• 2000

The STC(Sinusoidal Transform Coding) is a vocoding technique that uses a sinusoidal speech model to obtain high- quality speech at low data rate. It models and synthesizes the speech signal with fundamental frequency and its harmonic elements in frequency domain. To reduce the data rate, it is necessary to represent the sinusoidal amplitudes and phases with as small number of peaks as possible while maintaining the speech quality. As a basic research to develop a low-rate speech coding algorithm using the sinusoidal model, in this paper, we investigate the speech quality depending on the number of sinusoids. By varying the number of spectral peaks from 5 to 40 speech signals are reconstructed, and then their qualities are evaluated using spectral envelope distortion measure and MOS(Mean Opinion Score). Two approaches are used to obtain the spectral peaks: one is a conventional STFT (Short-Time Fourier Transform), and the other is a multiresolutional analysis method.

• 품질경영학회지
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• 제47권1호
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• pp.163-186
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• 2019

Purpose: The purpose of this study was to present a methodology for assessing the efficiency of supply chain quality management considering characteristics of defense industries to provide academic and policy implications for strengthening quality competitiveness of military supplies. Methods: Using the defense industry's empirical data, conduct an efficiency evaluation by utilizing a multi-stage DEA/Entropy Model for defense industries subject to the quality level survey of military goods manufacturers in 2017. Results: The results of this study are as follows; the first step of the multi-stage DEA model, Quality Management Performance Efficiency Analysis, shows that the CCR model and the BCC model are more efficient than the parent company. the second stage of the multi-stage DEA model showed that the CCR model was slightly more efficient than the parent company and the BCC model was more efficient than the parent.the overall efficiency value of the multistage DEA model, calculated by multipointing the efficiency value of the first stage by the second stage, was more efficient than the parent. Conclusion: The results of this study show that the efficiency of supply chian quality management performance and profitability in the defense industry can be analyzed for the first time using the multistage DEA/Entropy model to identify specific inefficiencies and support objective decision making.

• 한국환경과학회지
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• 제14권11호
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• pp.1049-1056
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• 2005

[ $NO_2$ ] concentration characteristics of Busan metropolitan city was analysed by statistical method using hourly $NO_2$ concentration data$(1998\~2000)$ collected from air quality monitoring sites of the metropolitan city. 4 representative regions were selected among air quality monitoring sites of Ministry of environment. Concentration data of $NO_2$, 5 air pollutants, and data collected at AWS was used. Both Stepwise Multiple Regression model and ARIMA model for prediction of $NO_2$ concentrations were adopted, and then their results were compared with observed concentration. While ARIMA model was useful for the prediction of daily variation of the concentration, it was not satisfactory for the prediction of both rapid variation and seasonal variation of the concentration. Multiple Regression model was better estimated than ARIMA model for prediction of $NO_2$ concentration.

• 품질경영학회지
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• 제29권1호
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• pp.11-23
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• 2001

This paper is concerned with suggesting a Bayesian method for variable selection in multinomial logit model. It is based upon an optimal rule suggested by use of Bayes rule which minimizes a risk induced by selecting the multinomial logit model. The rule is to find a subset of variables that maximizes the marginal likelihood of the model. We also propose a Laplace-Metropolis algorithm intended to suggest a simple method forestimating the marginal likelihood of the model. Based upon two examples, artificial data and empirical data examples, the Bayesian method is illustrated and its efficiency is examined.

• 한국수자원학회논문집
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• 제38권7호
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• pp.565-574
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• 2005

평창강 수질자동측정망 실시간 자료를 이용하여 강우시와 무강우시로 구분하여 분석하였다. 강우시에 측정된 TOC 자료는 무강우시 측정된 자료에 비해 평균값, 최대값, 표준편차가 크게 나타났으며, 강우시의 DO 자료는 무강우시에 측정된 자료보다 낮아 유량이 수질변화에 영향을 미치는 것으로 분석되었다. 신경망 모형과 뉴로-퍼지 모형으로 수질예측 모형을 구성하고, 적용하였다. LMNN, MDNN, ANFIS 모형은 TOC 모의에서 DO 예측에서는 LMNN, MDNN 모형이 ANFIS 모형보다 좋은 결과를 보였으며, 정량적 자료에 정성적 자료인 시간을 학습한 MDNN 모형이 가장 작은 오차를 보였다. 하천의 실시간적 관리를 위해서는 유량과 수질의 측정이 동일한 지점에서 동시간적으로 이루어져야 보다 효과적이다. 그러나 수질자동측정망 지점과 T/M 수위관측소가 원거리에 위치한 경우들이 있으며, 평창강 수질자동측정망 지점이 그 중 하나이다. 연구에서는 평창강 수질자동측정망 지점의 유출예측을 위한 신경망 모형을 구성하여 수질예측 모형과 연계하였으며, 연계된 모형은 수질예측에 개선된 결과를 보였다.

• 한국물환경학회지
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• 제28권2호
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• pp.247-254
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• 2012

Soil and Water Assessment Tool (SWAT) model has been widely used in estimation of flow and water quality at various watersheds worldwide, and it has an auto-calibration tool that could calibrate the flow and water quality data automatically from thousands of simulations. However, only continuous measured day flow/water quality data could be used in the current SWAT auto-calibration tool. Therefore, 8-day interval flow and water quality data measured nationwide by Korean Ministry of Environment (MOE) could not be used in SWAT auto-calibration even though long-term flow and water quality data in the Korean Total Maximum Daily Load (TMDL) watersheds available. In this study, current SWAT auto-calibration was modified to calibrate flow and water quality using 8-day interval flow and water quality data. As a result of this study, the Nash and Sutcliffe Efficiency (NSE) values for flow estimation using auto-calibration are 0.77 (calibration period) and 0.68 (validation period), and NSE value for water quality (T-P load) estimation (using the 8-day interval water quality data) is 0.80. The enhanced SWAT auto-calibration could be used in the estimation of continuous flow and water quality data at the outlet of TMDL watersheds and ungaged point of watersheds. In the next study, the enhanced SWAT auto-calibration will be integrated with Web based Load Duration Curve (LDC) system, and it could be suggested as methods of appraisal of TMDL in South Korea.