• 한국주조공학회지
• /
• 제24권2호
• /
• pp.63-70
• /
• 2004

Foundry techniquews for large steel casting depends on the skills of foundrymen considerably. Especially, the problem of reducing casring surface defects is difficult to clear numerically. Statistical analysis by using wuantification theory for hot tear and sand inclusion, and multiple regression analysis for dimensional defects have been shown to be examples of solving this difficulty. Many causes of surface defects can be evaluated by these analyses. These evaluations serve as the base data of defect reduction and contribute to the constant improvement of casting quality and quality enhancement activity. The system to perform quality enhancement activity was developed and it proved very useful for transfering foundry techniques and skills from the old to young generations.

• 한국습지학회지
• /
• 제21권3호
• /
• pp.215-223
• /
• 2019

본 연구에서는 낙동강 주요 지류를 대상으로 상관분석, 주성분 및 요인분석, 군집분석과 같은 통계분석을 통해 수질 특성을 분석하였다. 유기물질과 영양물질은 높은 상관관계를 가지고 있으며 봄철 및 가을철에 높게 나와 해당 계절에 대한 집중적인 수질 관리가 필요한 것으로 나타났다. 주성분 및 요인분석 결과 전체 분산의 82%를 유기물질, 영양물질, 자연, 기상 등 4개의 주성분으로 설명할 수 있으며 BOD, COD, TOC, TP 항목이 주요 영향요인으로 분석되었다. 군집분석 결과 계절별 유기물, 영양물질의 오염도를 고려하여 4개의 군집으로 분류하였으며 금호강 유역은 사계절 높은 오염특성을 나타내고 있었다. 따라서 지류 하천의 효과적인 수질 관리를 위해서는 시공간적 특성을 고려한 대책이 필요하며 다변량 통계기법은 수질 관리 및 정책 수립에서 유용하게 활용 가능할 것으로 분석되었다.

• 대한안전경영과학회:학술대회논문집
• /
• 대한안전경영과학회 2012년 추계학술대회
• /
• pp.461-471
• /
• 2012

Most useful statistical techniques in six sigma DMAIC are hypothesis testing and interval estimation. So this paper reviews and derives sample size formula by considering significance level, power of detectability and effect difference. The quality practioners can effectively interpret the practical and statistical significance with the rational sample sizing.

• 대한안전경영과학회지
• /
• 제15권2호
• /
• pp.243-253
• /
• 2013

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of ${\alpha}$ and ${\beta}$. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.

• 한국근적외분광분석학회:학술대회논문집
• /
• 한국근적외분광분석학회 2001년도 NIR-2001
• /
• pp.1081-1081
• /
• 2001

The constant need for quality improvement and production rationalization in the chemical and related industries has led to the increasing replacement of conservative control procedures by more specific and environmentally compatible analytical techniques. In this respect, vibrational spectroscopy has developed over the last yews - in combination with new instrumental accessories and statistical evaluation procedures - to one of the most important analytical tools for industrial chemical quality control and process monitoring in a wide field of applications. In the present communication this potential is demonstrated in order to further support the implementation of mid-infrared (MIR), near-infrared (NIR) and Raman spectroscopy Primarily as industrial on-line tools. To this end the data of selected feasibility studies will be discussed in terms of the individual strengths of the different techniques for the respective application.

• 품질경영학회지
• /
• 제47권4호
• /
• pp.687-700
• /
• 2019

Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

• 환경영향평가
• /
• 제20권5호
• /
• pp.705-716
• /
• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• Journal of Information Processing Systems
• /
• 제13권4호
• /
• pp.778-804
• /
• 2017

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

• Membrane and Water Treatment
• /
• 제13권5호
• /
• pp.245-257
• /
• 2022

Water quality characteristics and their spatial variations in the Nakdong River were statistically analyzed by multivariate techniques including correlation analysis, CA, and FA/PCA based on water quality parameters for 17 sites over 2017-2019, yielding PI values for primary factors. Site 10 indicated the highest parameter concentrations, and results of pearson's correlation analysis suggest that non-biodegradable organic matter had been distributed on the site. Five clusters were identified in order of descending pollution levels: I (Ib > Ia) > II (IIa > IIb) > III. Spatial variations started from sub-cluster Ib in which Daegu city and Geumho-river are joined. T-P, PO4-P, SS, COD, and TOC corresponded to VF 1 and 2, which were found to be principal components with strong influence on water quality. Sub-cluster Ib was strongly influenced by NO3-N and T-N compared to other clusters. According to the PIs, water quality pollution deteriorated due to non-biodegradable organic matter, nitrogen- and phosphorus-based nutrient salts in the middle and lower reaches, illustrating worsening water pollution due to inflows of anthropogenic sources on the Geumho-river, i.e., sewage and wastewater, discharged from Site 10, at which there is a concentration of urban, agricultural, and industrial areas.

• Journal of Biosystems Engineering
• /
• 제40권2호
• /
• pp.124-136
• /
• 2015

Purpose: Grain quality is a general concept that covers many characteristics, ranging from physical to biochemical and physiochemical properties. Rice aging during storage is currently a challenge in the rice industry, and is a complicated process involving changes in all of the above properties. Spectroscopic techniques can be used to obtain information on the quality of rice samples in a non-destructive manner. Methods: The objective of this review was to highlight the factors that contribute to rice quality and aging, and to describe various spectroscopic modalities, particularly vibrational and hyperspectral imaging, for the assessment of rice quality. Results: Starch and protein are the main components of the rice endosperm, and are therefore key factors contributing to eating and cooking quality. While the overall starch, protein, and lipid content in the rice grain remains essentially unchanged during storage, structural changes do occur. These changes affect pasting and gel properties, and ultimately the flavor of cooked rice. In addition, grain quality is significantly affected by growing and environmental conditions, such as water availability, temperature, fertilizer application, and salinity stress. These properties can be evaluated using spectroscopic techniques, and rice samples can be discriminated by using multivariate statistical analysis methods. Conclusion: Hyperspectral imaging and vibrational spectroscopy techniques have good potential for determining rice quality properties in a non-invasive manner, i.e., not requiring the introduction of instruments into the rice grain.