• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.4
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• pp.91-99
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• 2006

A POP system, which collects manufacturing data from the shop floors and supply them to higher level systems, should be maintained and upgraded according to the change of production environment such as new product introduction. This situation leads to the need of a cost-effective system development methodology. In this paper, a methodology based on the classification and the similarity comparison of manufacturing processes is proposed. In this, a new product is classified according to the similarity of its manufacturing processes, which enables recycling of existing system modules. The proposed methodology has been tested in the case of an electronics parts manufacturing company, where a POP system is implemented. The result shows that the proposed methodology can save time and efforts for system implementation.

• Science of Emotion and Sensibility
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• v.23 no.2
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• pp.75-88
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• 2020

Given the development of information technology, various methods for efficient information delivery have been constructed as the method of delivering product information moves from offline and 2D to online and 3D. These attempts not only are about delivering product information in an online space where no real product exists but also play a crucial role in diversifying and revitalizing online shopping by providing virtual experiences to consumers. 360 product image is a photorealistic VR that allows a subject to be rotated and photographed to view objects in three dimensions. 360 product image has also attracted considerable attention considering that it can deliver richer information about an object compared with the existing still image photography. 360 product image is influenced by divergent production factors, and accordingly, a difference emerges in the responses of users. However, as the history of technology is short, related research is also insufficient. Therefore, this study aimed to grasp the responses of users, which vary depending on the type of products and the number of source images in the 360 product image process. To this end, a representative product among the product groups that can be frequently found in online shopping malls was selected to produce a 360 product image and experiment with 75 users. The emotional responses to the 360 product image were analyzed through an experimental questionnaire to which the semantic classification method was applied. The results of this study could be used as basic data to understand and grasp the sensitivity of consumers to 360 product image.

• Korean Chemical Engineering Research
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• v.48 no.4
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• pp.483-489
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• 2010

An image analysis-based soft sensor is designed and applied to automatic quality classification of product appearance with color-textural characteristics. In this work, multiresolutional multivariate image analysis (MR-MIA) is used in order to analyze product images with color as well as texture. Fisher's discriminant analysis (FDA) is also used as a supervised learning method for automatic classification. The use of FDA, one of latent variable methods, enables us not only to classify products appearance into distinct classes, but also to numerically and consistently estimate product appearance with continuous variations and to analyze characteristics of appearance. This approach is successfully applied to automatic quality classification of intermediate and final products in industrial manufacturing of engineered stone countertops.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.16-30
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• 2023

The elderly population is increasing owing to a low fertility rate and an aging population. In addition, life expectancy is increasing, and the advancement of medicine has increased the importance of health to most people. Therefore, government and companies are developing and supporting smart healthcare, which is a health-related product or industry, and providing related services. Moreover, with the development of the Internet, many people are managing their health through online searches. The most convenient way to achieve such management is by consuming nutritional supplements or seasonal foods to prevent a nutrient deficiency. However, before implementing such methods, knowing the nutrient status of the individual is difficult, and even if a test method is developed, the cost of the test will be a burden. To solve this problem, we developed a questionnaire related to nutrient classification twice, based upon which an adaptive algorithm was designed. This algorithm was designed as a machine learning based algorithm for nutrient classification and its accuracy was much better than the other machine learning algorithm.

• Journal of the Korea Fashion and Costume Design Association
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• v.11 no.3
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• pp.87-99
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• 2009

The purpose of this study was to find out criteria for classifying fashion brand from the consumer point of view. This was compared with the viewpoint of fashion business practice in order to develop strategy of fashion brands and to manage brand effectively and systematically, and to suggest theoretical frame for application of these criteria. This study was researched as the succeeding study of a model of criteria for classifying fashion brands from the viewpoint of fashion business practice. Survey was used as a research method. The subjects were 422 women who were 20-30 years old and living in and near Seoul. Questionnaires were developed based on 37 fashion brands' classification criteria by means of pre-survey, and SPSS package and LISREL program were used to analyze the data. As a result of factor analysis considering 37 classification criteria, 8 factors were identified as classification criteria. They were as follows; the level of brand form, the level of product concept, the level of management item, the level of brand sales ability, the level of customer management, the level of brand advertising and awareness, the level of brand value, and the level of product lead ability. All of criteria were correlated to each other. The effective method to classify fashion brands was proposed by establishing the model of the relationship of the values of 7 criteria and by proving it with the structure equation model analysis. The model of criteria for classifying fashion brands that was suggested on this study was proved by the structure equation model analysis. In this study, from a consumer's point of view we suggested a theoretical framework describing which criteria would be selected to classify and utilize fashion brand market. This model can be used to select the most efficient classification criteria and classify them hierarchically instead of selecting only one among some factors that complex and interactional and classifying.

• Journal of Applied Reliability
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• v.11 no.2
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• pp.177-186
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• 2011

Traditionally, one uses a method of straight-line recognition to evaluate quality of product or service. One can satisfy with the product or service if their physical requirement of are met some criterions and can not satisfy them if their physical requirement are not met. Kano, et al(1984) introduce two dimensional Quality model to evaluate quality of product or service. They classify Quality Characteristic of product and service to three categories; satisfying quality, attractive quality, expected quality. In this paper, 17 evaluation features in 6 categories of smart-card are obtained from Focus-interview and Brainstorming and classified into 3 categories of quality model by Kano's two dimensional method. This classification is expected to provide a guideline for evaluation of smart-card.

• Management & Information Systems Review
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• v.3
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• pp.15-45
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• 1999

Processing techniques of remote sensed image data using computer have been recognized very necessary techniques to all social fields, such as, environmental observation, land cultivation, resource investigation, military trend grasp and agricultural product estimation, etc. Especially, accurate classification and analysis to remote sensed image da are important elements that can determine reliability of remote sensed image data processing systems, and many researches have been processed to improve these accuracy of classification and analysis. Traditionally, remote sensed image data processing systems have been processed 2 or 3 selected bands in multiple bands, in this time, their selection criterions are statistical separability or wavelength properties. But, it have be bring up the necessity of bands selection method by data distribution characteristics than traditional bands selection by wavelength properties or statistical separability. Because data sensing environments change from multispectral environments to hyperspectral environments. In this paper for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theory is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band using indiscernibility relation of Rough set theory from analysis results. Proposed method is applied to LANDSAT TM data on 2 June 1992. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.

• Journal of Biosystems Engineering
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• v.38 no.3
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• pp.199-207
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• 2013

Purpose: Tomatoes, an important agricultural product in fresh-cut markets, are sometimes a source of foodborne illness, mainly Salmonella spp. Growth cracks on tomatoes can be a pathway for bacteria, so its detection prior to consumption is important for public health. In this study, multispectral Visible/Near-Infrared (NIR) reflectance imaging techniques were used to determine optimal wavebands for the classification of defect tomatoes. Methods: Hyperspectral reflectance images were collected from samples of naturally cracked tomatoes. To classify the resulting images, the selected wavelength bands were subjected to two-band permutations, and a supervised classification method was used. Results: The results showed that two optimal wavelengths, 713.8 nm and 718.6 nm, could be used to identify cracked spots on tomato surfaces with a correct classification rate of 91.1%. The result indicates that multispectral reflectance imaging with optimized wavebands from hyperspectral images is an effective technique for the classification of defective tomatoes. Conclusions: Although it can be susceptible to specular interference, the multispectral reflectance imaging is an appropriate method for commercial applications because it is faster and much less expensive than Near-Infrared or fluorescence imaging techniques.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1031-1040
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• 2004

Electrocardiogram being the recording of the heart's electrical activity provides valuable clinical information about heart's status. Many re-searches have been pursued for heart disease diagnosis using ECG so far. However, electrocardio-graph uses foreign diagnosis algorithm due to inaccuracy of diagnosis results for a heart disease. This paper suggests ECG data collection, data preprocessing and heart disease pattern classification using data mining. This classification technique is the FB(Frequent pattern Bayesian) classifier and is a combination of two data mining problems, naive bayesian and frequent pattern mining. FB uses Product Approximation construction that uses the discovered frequent patterns. Therefore, this method overcomes weakness of naive bayesian which makes the assumption of class conditional independence.

• Journal of Biomedical Engineering Research
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• v.33 no.1
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• pp.15-24
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• 2012

Classification of human thought is an emerging research field that may allow us to understand human brain functions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introduce a new approach to classify various mental states from noninvasive electrophysiological recordings of human brain activity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signals recorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spatiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-related synchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current (input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices. Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For the verification of our approach, eight participants took part in the present study; their EEG data were recorded while they performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequently between trials in the same class than those in different classes, indicating that these spatiospectral pattern maps could be used to classify different mental states. The classification accuracy was evaluated for each participant from both the proposed approach and a conventional mental state classification method based on the inter-hemispheric spectral power asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (${\pm}9.64%$) was attained for the proposed method; whereas an average accuracy of 57% (${\pm}5.68%$) was attained for the conventional method (significance was assessed by the one-tail paired $t$-test, $p$ < 0.01), showing that the proposed simple classification approach might be one of the promising methods in discriminating various mental states.