• Korean Journal of Construction Engineering and Management
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• v.9 no.1
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• pp.126-133
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• 2008

Current changing to the customer-oriented market naturally causes suppliers to meet an age of competition on the quality. In order to plan housing meeting this quality competitiveness era, I set up the type classification system of quality differentiation for the unit of apartment housing by executing differentiation cases of unit quality and type analysis of the object. The system is consist of 3 classification systems by quality element, user convenience element and product element as follows: First element is to classify quality element on the basis of plane and interior elements, architectural elements and second one is user convenience element relating facility to classify environment-oriented, safety, energy saving and convenience. The other one is the product element to classify furniture, installing product and convenient product. I believe that this classification system will be useful to determine any classification elements of product for product positioning and product planning in the stage of marketing planning of apartment housing in the future.

• International Journal of Quality Innovation
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• v.2 no.2
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• pp.58-68
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• 2001

This paper proposes a method for quantifying the types of quality elements proposed by Kano; namely: attractive quality, one-dimensional quality, and must-be quality. Kano's classification of required quality has helped us improve our thinking in product development. However, his classification is conceptual rather than quantitative, and the conventional techniques of questionnaire and group interview cannot provide quantification of the relationship between the degree of customer satisfaction and the degree of sufficiency of required qualities. This paper describes how a quality element under Kano's quality model can be expressed as a utility function, and describes an application to quality design of a cellular phone.

• The Journal of Bigdata
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• v.3 no.2
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• pp.11-18
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• 2018

This study is a study on domain automatic classification for domain - based quality diagnosis which is a key element of big data quality diagnosis. With the increase of the value and utilization of Big Data and the rise of the Fourth Industrial Revolution, the world is making efforts to create new value by utilizing big data in various fields converged with IT such as law, medical, and finance. However, analysis based on low-reliability data results in critical problems in both the process and the result, and it is also difficult to believe that judgments based on the analysis results. Although the need of highly reliable data has also increased, research on the quality of data and its results have been insufficient. The purpose of this study is to shorten the work time to automizing the domain classification work which was performed from manually to using machine learning in the domain - based quality diagnosis, which is a key element of diagnostic evaluation for improving data quality. Extracts information about the characteristics of the data that is stored in the database and identifies the domain, and then featurize it, and automizes the domain classification using machine learning. We will use it for big data quality diagnosis and contribute to quality improvement.

• Korea Journal of Hospital Management
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• v.14 no.4
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• pp.88-102
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• 2009

This study aims at providing necessary informations to decide what services would be conducted preferentially in the hospital by limited resources. So this study revalued the customer's perception about the qualities of the hospital services by the Kano Model and examined the customer satisfaction coefficients suggested by Timko. The researcher conducted a survey from the patients of the 4 university hospitals in Incheon and southern Gyeonggi Province In 2008. The results of this study can be summarized as follows; It was found that the total 31 items are could be classified into 7 attractive quality elements, 22 one-dimensional quality elements and 2 indifferent quality elements, while the natural quality element wasn't found. The highest score element of the customer's satisfaction coefficients was identified as easy parking(0.69) and the lowest score item was the offer of the hospital newsletter and information about medical care(0.47). When the hospital service was not sufficient to the customer, the highest score element of the customer's dissatisfaction coefficients was proved the convenient ward and facilities(-0.75) and the lowest score item was the buses running to the entrance of the hospital(-0.32). Also it was found that the attractive quality elements appraised by the preceding study were revalued the one-dimensional quality elements. The reason was because the customer's expectation on the services was changed high, as time went by.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.690-706
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• 2021

Classification is a substantial operation in data mining, and each element is distributed taking into account its feature values in the corresponding class. Metaheuristics have been widely used in attempts to solve satellite image classification problems. This article proposes a hybrid approach, the flower pigeons-inspired optimization algorithm (FPIO), and the local search method of the flower pollination algorithm is integrated into the pigeon-inspired algorithm. The efficiency and power of the proposed FPIO approach are displayed with a series of images, supported by computational results that demonstrate the cogency of the proposed classification method on satellite imagery. For this work, the Davies-Bouldin Index is used as an objective function. FPIO is applied to different types of images (synthetic, Alsat-2B, and Sentinel-2). Moreover, a comparative experiment between FPIO and the genetic algorithm genetic algorithm is conducted. Experimental results showed that GA outperformed FPIO in matters of time computing. However, FPIO provided better quality results with less confusion. The overall experimental results demonstrate that the proposed approach is an efficient method for satellite imagery classification.

• Journal of the Korean Wood Science and Technology
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• v.33 no.4 s.132
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• pp.23-29
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• 2005

The fancy veneer flooring board is high value-added wooden material. The classification of the flooring boards by the observation with the naked eye causes color difference among fancy veneers. It turned out that this inaccuracy of color difference among fancy veneers increased in case of the identification by metamerism or the flooring construction. Therefore, this study was performed to classify fancy veneers produced from 3 species such as Tilia sp., Betula sp., and Acer sp. which were identified with the naked eye by Light and Dark grade, by using CIELAB. In addition, each specie's threshold on CIERGB was investigated and a range of hue, saturation and intensity by an application of HSI color model were studied. Intensity of the HIS color model could be the best value to be used when color, saturation and intensity of the HSI color model were used for the classification of the flooring board's color. In addition, it seemed that color range of all three criteria lied between $45^{\circ}$ and $55^{\circ}$. In the case of identification by threshold of RGB element, considering only specific color element value is unlikely to lead to accurate classification of fancy veneers of flooring board.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.498-512
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• 2023

The traditional newspaper industry faces the opportunities and challenges of industry transformation and integration with new media. Consequently, the catalogs of newspapers and magazines are also updated. In this study, necessary information on catalogs was obtained and used to analyze the overall development trend of the newspaper industry. A word frequency analysis was then performed on the introduction and product categories of the catalogs, and the content and types of newspapers and magazines were examined. Furthermore, related factors such as price, number of pages, publishing frequency, and best-selling status were analyzed; the correlation among factors affecting best-selling status was also explored. Subsequently, each element and a combination of elements were used to generate a dataset, build three classification models, and analyze the accuracy of predictions of whether newspapers sold well under other circumstances. The experimental results showed that price is the most critical factor affecting the best-selling status of newspapers and magazines. Publishing frequency and the number of pages were also found to be significant indicators that impact people's subscription choices. Finally, a competitive strategy regarding content, price, quality, and positioning was developed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.33-40
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• 2020

In order to satisfy customers, it is important to identify the quality elements that affect customers' satisfaction. The Kano model has been widely used in identifying multi-dimensional quality attributes in this purpose. However, the model suffers from various shortcomings and limitations, especially those related to survey practices such as the data amount, reply attitude and cost. In this research, a model based on the text sentiment analysis is proposed, which aims to substitute the survey-based data gathering process of Kano models with sentiment analysis. In this model, from the set of opinion text, quality elements for the research are extracted using the morpheme analysis. The opinions' polarity attributes are evaluated using text sentiment analysis, and those polarity text items are transformed into equivalent Kano survey questions. Replies for the transformed survey questions are generated based on the total score of the original data. Then, the question-reply set is analyzed using both the original Kano evaluation method and the satisfaction index method. The proposed research model has been tested using a large amount of data of public IT service project evaluations. The result shows that it can replace the existing practice and it promises advantages in terms of quality and cost of data gathering. The authors hope that the proposed model of this research may serve as a new quality analysis model for a wide range of areas.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.633-644
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• 2020

Rice paper is an element of Vietnamese cuisine that can be used to wrap vegetables and meat. Rice and starch are the main ingredients of rice paper and their mixing ratio is important for quality control. In a commercial factory, assessment of food safety and quantitative supply is a challenging issue. A rapid and non-destructive monitoring system is therefore necessary in commercial production systems to ensure the food safety of rice and starch flour for the rice paper wrap. In this study, fluorescence hyperspectral imaging technology was applied to classify grain flours. Using the 3D hyper cube of fluorescence hyperspectral imaging (fHSI, 420 - 730 nm), spectral and spatial data and chemometric methods were applied to detect and classify flours. Eight flours (rice: 4, starch: 4) were prepared and hyperspectral images were acquired in a 5 (L) × 5 (W) × 1.5 (H) cm container. Linear discriminant analysis (LDA), partial least square discriminant analysis (PLSDA), support vector machine (SVM), classification and regression tree (CART), and random forest (RF) with a few preprocessing methods (multivariate scatter correction [MSC], 1^st and 2^nd derivative and moving average) were applied to classify grain flours and the accuracy was compared using a confusion matrix (accuracy and kappa coefficient). LDA with moving average showed the highest accuracy at A = 0.9362 (K = 0.9270). 1D convolutional neural network (CNN) demonstrated a classification result of A = 0.94 and showed improved classification results between mimyeon flour (MF)1 and MF2 of 0.72 and 0.87, respectively. In this study, the potential of non-destructive detection and classification of grain flours using fHSI technology and machine learning methods was demonstrated.

• Journal of Korean Society of Rural Planning
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• v.18 no.3
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• pp.111-122
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• 2012

This study is to evaluate the classification system of rural-villages remodeling components which is provided for improving the quality of life for rural community by improvement of settlement environment. To achieve this, rural-villages remodeling components are classified according to the spatial structure of rural area through analysis of literature, then we have examined the applicability through case studies after modification work which is based on experts's discussion and rearrangement by pilot investigation of researcher. In the classification system of rural-villages remodeling classified productivity area, residential area, community area in first group and this classification is divided into 6 space to production, 4 space to residence, 5 space to community in second group by literature search, pilot investigation of researcher and field survey. The subject elements surveyed a total of 123 through the literature search, additionally, 1 element at a space to production and space to community in field survey for types in zoning cases. As a result, categories and items are decided that it is included 125 target elements.