• International Journal of Quality Innovation
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• v.1 no.1
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• pp.24-31
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• 2000

QS-9000 was established by Chrysler, Ford, General Motors and some Truck Manufactures to solve problems of efforts to prepare original standards for each company. When we compare the requirements in QS-9000 with those in ISO 9000, we may observe that those of QS-9000 are more specific, since the user of the standard is limited to the automobile industry. In particular, the requirements on statistical techniques are described in more detail. In this paper, we present the requirements on statistical techniques in QS-9000. Specifically, we review the contents of QS-9000 and explore the philosophy and the minimum requirement on statistical techniques behind the description.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.371-381
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• 2017

The performance of the statistical context-sensitive spelling error correction depends on the quality and quantity of the data for statistical language model. In general, the size and quality of data in a statistical language model are proportional. However, as the amount of data increases, the processing speed becomes slower and storage space also takes up a lot. We suggest the improved statistical language model to solve this problem. And we propose an effective spelling error candidate generation method based on a new statistical language model. The proposed statistical model and the correction method based on it improve the performance of the spelling error correction and processing speed.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.633-658
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• 2021

Statistical engineering contains design of experiments, quality control/ management, and reliability engineering. Python is a free software environment for machine learning, data science, and graphics. Python package has many functions and libraries for statistical engineering. We can use Python package as a useful tool for statistical engineering. This paper shows applications of Python package for statistical engineering and suggests a total Python projects for statistical engineering.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.651-656
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• 2006

Wu and Clements-Croome (2005) investigate the optimization problem of PM policies for situations where the quality of PM is a random variable with a certain probability distribution. However, they assume that the cost of preventive maintenance is constant, not depending on the quality of PM. Thus, this paper considers a periodic PM model when PM cost depends on the quality of PM activity. The optimal PM policy are presented for the extended PM model and the numerical examples are presented for illustrative purpose.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.1
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• pp.7-16
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• 2003

At present, company has to produce a product that consumer like with a competitive price, a good quality, and a fitting time to supply. Process control and quality control are very important to supply with a product uniformly and inexpensively. Process control is given much weight in the quality control in manufacturing system. Statistical process controls(SPC) that are used in process generally have major impact on manufacturing, product design activities, and process development potentially. Control charts in statistical process control method can be interpreted the data from quality characteristics in production process and discriminated between chance variation and assignable variation in process. In addition, control chart can be used to monitor the process output and detect when changes in the inputs are required to bring the process back to an in-control state. The models that relate the influential inputs to process outputs help determine the nature and magnitude of the adjustments required. In this paper, the characteristic of product quality is monitored by control chart during the machining process and construction of quality control cycle is considered to divide into two types in this case that different assignable causes lead to shifts having different magnitudes. Then we are intended to find a process shift magnitude which has economical quality cost policy and are considered to quality cost functions to find a process shift magnitude. Those costs are categorized into the well-known categories of prevention, appraisal, and internal failure and external failure. This paper ends with numerical examples that demonstrate the usefulness of the model.

• Journal of Korean Society on Water Environment
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• v.37 no.2
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• pp.114-127
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• 2021

Water pollution in the lower reaches of the Namhan River is getting worse due to drought and a decrease in water quantity due to climatic changes and hence is affecting the water quality of Paldang Lake. Accordingly, we have used a water quality index (WQI) and statistical analysis in this study to identify the characteristics of the water quality in the lower reaches of the Namhan River, the main causes of water pollution, and tributaries that need priority management. Typically, 10 items (WT, pH, EC, DO, BOD, COD, SS, T-N, T-P, and TOC) were used as the water quality factors for the statistical analysis, and the matrix of data was set as 324 × 10·1. The correlation analysis demonstrated a strong correlation between Chemical Oxygen Demand (COD) and T-P with a high statistical significance (r=0.700, p<0.01). Furthermore, the result of principal component analysis (PCA) revealed that the main factors affecting the change in water quality were T-P and organic substances introduced into the water by rainfall. Based on the Mann-Kendall test, a statistically significant increase in pH was observed in SH-1, DL, SH-2, CM, and BH, along with an increase in WQI in SH-2 and SM. BH was identified as a tributary that needs priority management in the lower reaches of the Namhan River, with a "Somewhat poor" (IV) grade in T-P, "Fair" grade in WQI, and "Marginal" grade in summer.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.79-90
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• 2007

The image quality measure is an important issue in the image processing. Several methods which measure the image quality have been proposed and these are based on the mathematical point of view. However, there is difference between the mathematicalmeasure and the measure based on the human visual system and a new measure has to be proposed because the final target of the image is a human visual system In this paper, a statistical image quality measure which is considered the human visual feature was suggested. The human visual system is using the global quality of the image and the local quality of the image and the local quality is more important to human visual system. In this paper, the image divided into several segments and the image qualities were calculated respectively. After then, the statistical method using scoring was applied to the image qualities. The result of the image quality measure was similar to the result of measure based on the human visual system.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.205-210
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• 2006

Nowaday manufacture technology and manufacture environment are changing rapidly. By development of computer and enlargement of technique, most of manufacture field are computerized. It is measured automatically do much quality characteristics thereby and great many data happen in a day. corporations is important if have gotten fast information that are useful from wide data to go first in international competition according to these change. Statistical process control(SPC) techniques are used as a problem solution tool at manufacturing process until present. However, this statistical methods is not applied more extensively because have much restrictions in realistic problem. In this paper, wish to develop more realistic and scientific new statistical design techniques doing to integrate data mining(DM) and statistical methods by the alternative to cope these problem. First step selects significant factor using DM techniques from datas of manufacturing process including much factors and second step wish to find optimum of process after get the estimated response function through response surf ace methodology(RSM) that is statistical techniques.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.116.3-116
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• 2001

A diagnosis methodology for thickness quality in hot finishing mill is proposed based on multivariate statistical analysis. The thickness of hot strip is a key quality factor that is measured by x-ray thickness gauge. Currently, the thickness quality is guaranteed by upper and lower limit of thickness deviation from target thickness. But if any over-limit is occurred, there is no in-line method to identify the causes. In this paper, many parameters are extracted from the thickness deviation signal such as mean deviation(top, middle, tail), rms deviation(top, middle, tail) and peak deviation(top, middle, tail) as time domain parameters ...

• International Journal of Quality Innovation
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• v.2 no.2
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• pp.127-135
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• 2001

Personnel engaged in the medical field have implemented continual improvement by team activities in an effort to construct a system that reduces the risks involved in medical care. Knowledge in total quality management (TQM), especially statistical quality control (SQC) developed for industry, seems to be applicable to medical care. This paper describes the application of SQC to continual improvement in medical care.