• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1062-1071
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• 2003

A genre-based classification means classifying documents by the purpose for which they were written, not by the semantics or subject areas. Most genre classifying methods in the past were based on the existing documents categorization algorithms and ineffective for feature selections, resulting in low quality classification results. In this research, we propose a new method for automatic classification of digital documents by genre. The genre classifier we developed uses the deviation statistic between the genre-revealing term frequencies and between the subject-revealing term frequencies within a genre. We collected Web documents to evaluate the proposed genre classification method. The experimental results show that the proposed method outperforms a direct application of a kai-square feature selection and bayesian classifier often used for subject classification by proving an excellent accuracy of about 30 percent.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.5 s.110
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• pp.537-543
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• 2006

The impact sound of the golf club is one of the major factors to purchase it. Sound quality metrics has been mostly developed for harmonic sounds. Sound quality evaluation techniques for the impact sound have been contrived in this study. Jury test, one of the typical subjective evaluation scheme, is carried out for evaluating the sound quality of 13 different golf drivers, which classification results are assumed to be correct answers. Conventional objective evaluation methods such as Zwicker loudness sensory pleasantness are calculated. Wavelet analysis and instantaneous loudness are applied in order to evaluate the sound quality of transient sounds, which scheme shows better correlation with the results from jury test.

• Clinics in Shoulder and Elbow
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• v.26 no.1
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• pp.55-63
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• 2023

Background: Common questions about shoulder arthroplasty (SA) searched online by patients and the quality of this content are unknown. The purpose of this study is to uncover questions SA patients search online and determine types and quality of webpages encountered. Methods: The "People also ask" section of Google Search was queried to return 900 questions and associated webpages for general, anatomic, and reverse SA. Questions and webpages were categorized using the Rothwell classification of questions and assessed for quality using the Journal of the American Medical Association (JAMA) benchmark criteria. Results: According to Rothwell classification, the composition of questions was fact (54.0%), value (24.7%), and policy (21.3%). The most common webpage categories were medical practice (24.6%), academic (23.2%), and medical information sites (14.4%). Journal articles represented 8.9% of results. The average JAMA score for all webpages was 1.69. Journals had the highest average JAMA score (3.91), while medical practice sites had the lowest (0.89). The most common question was, "How long does it take to recover from shoulder replacement?" Conclusions: The most common questions SA patients ask online involve specific postoperative activities and the timeline of recovery. Most information is from low-quality, non-peer-reviewed websites, highlighting the need for improvement in online resources. By understanding the questions patients are asking online, surgeons can tailor preoperative education to common patient concerns and improve postoperative outcomes. Level of evidence: IV.

• Korean Journal of Digital Imaging in Medicine
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• v.9 no.1
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• pp.53-65
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• 2007

PACS has been run at the Kyung Hee University Medical Center(KHMC) since 2001, and the installation and operation of PACS have contributed to automation and quantification of KHMC's medical environment During these five years our greatest concern is how to make our own guiding principle of diagnostic monitor QA which is adapted to international standards. In accordance with the terms of 'KHMC QA Guideline', 'AAPM TG18', 'SMPTE RP133', 'DICOM Part14', 'DIN V 6868-57', 'JESRA X-0093', 'JIS Z4752-2-5' and 'KCARE', concern about quality assurance of medical images are on the increase. With the investigation of acceptance testing and quality control of international standards for medical display devices, and data collection and analysis for recommended guideline, it is reported that acceptance testing(quality control), including geometrical distortion, display reflection, luminance response, luminance uniformity, display resolution, display noise, veiling glare and color chromaticity being adequate and effective to domestic hospital environments for medical display devices and assessment methods according to each performance. Accordingly, KHMC classified the checkpoint items by period, at the time of monitor setting, monthly, quarterly, half-yearly and annually. Periodic classification of checkpoint items for monitor QA makes a good guideline for image QA/QC and useful guideline for persistent good quality of monitor.

• 축산식품과학과 산업
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• v.6 no.1
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• pp.34-45
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• 2017

• 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.

• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.3
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• pp.374-383
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• 1998

Apparel Quality was one of the most important elements to evaluate the reputations of companies and products which affect the consumer's purchasing behavior. From researches on apparel quality, there was no common concept of quality as well as no common dimensions. The purposes of this study were to identify apparel quality concept and to classify the multi-dimensional concept of apparel quality. The research was carried out in theoretical as well as empirical studies. The theoretical study was conducted to find out apparel quality concept and divide apparel quality concept into four dimensions groups. The empirical study followed the theoretical study to confirm the multi-dimensional concept of apparel quality. The empirical study was investigated that the questionnaire was administered to 634 housewives in Seoul, Kwangju, and Busan during the fall of 1996. The data were analysed by LISREL analysis. This study identified that apparel quality was characteristics of consumer's desires for apparel. The results of the theoretical study verified that apparel quality concept was organized into four different dimensions: physical attribute, physical function, instrumental performance, and expressive performance.

• Journal of Environmental Science International
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• v.25 no.6
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• pp.767-787
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• 2016

In this study, we analyzed on-site monitoring data for 15 tributaries in Geumho watersheds for 3 years (2011-2013) in order to sort out priorities on water quality characteristics and improvement. As a result of estimating contribution to contamination of the tributary rivers, Dalseocheon showed the highest load densities, despite the smallest watershed area, with 22.7% $BOD_5$, 30.7% $COD_{Mn}$, 31.3% TOC and 47.6% TP. After conducting PCA (principal component analysis) and FA (factor analysis) to analyze water quality characteristics of the tributary rivers, the first factor was classified as $COD_{Mn}$, TOC, EC, TP and $BOD_5$, the second factor as pH, Chl-a and DO, the third factor as water temperature and TN, and the fourth factor as SS and surface flow. In addition, arithmetical sum of each factor's scores based on grading criteria revealed that Dalseocheon and Namcheon were classified into Group A for their highest scores - 96 and 93, respectively -, and selected as rivers that require water environmental management measures the most. Also, water environmental contamination inspection showed that Palgeocheon had the most number of aquatic factors to be controlled: $BOD_5$, $COD_{Mn}$, SS, TOC, T-P, Chl-a, etc.

• The Korean Journal of Health Service Management
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• v.11 no.4
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• pp.189-202
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• 2017

Objectives : This study aims to present a new classification system using evaluation standards to ensure the safety of transport ventilators used in Korea. Due to the rapid advancement of technology, various types of ventilators have been developed and as the range of application increases, so does demand. With quick access to information and a growing economy, the technology used in Korean hospitals is equivalent to that of other countries; however, regulatory and safety consciousness are still lagging in Korea. Methods : Based on researching overseas cases, reference data and standards for improving the current system in Korea are proposed. Results : According to the review of transport ventilator use in Korea, it was found that concerns about safety is due to the absence of a standardized classification system for evaluating the safety and effectiveness of transport ventilators. Conclusions : In order to improve the safety of patients and the quality of medical care, it is essential to establish guidelines and assessment standards guaranteeing the safety of transport ventilators in Korea. Clear definitions and classifications for devices must precede the application of such standards. In addition, effective evaluation standards should be developed in order to resolve problems and improve upon the current system through continuous validation.

• Transactions of Materials Processing
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• v.31 no.4
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• pp.229-239
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• 2022

Convolution Neural Network(CNN) is a class of deep learning algorithms and can be used for image analysis. In particular, it has excellent performance in finding the pattern of images. Therefore, CNN is commonly applied for recognizing, learning and classifying images. In this study, the surface defect classification performance of Al 6061 extruded material using CNN-based algorithms were compared and evaluated. First, the data collection criteria were suggested and a total of 2,024 datasets were prepared. And they were randomly classified into 1,417 learning data and 607 evaluation data. After that, the size and quality of the training data set were improved using data augmentation techniques to increase the performance of deep learning. The CNN-based algorithms used in this study were VGGNet-16, VGGNet-19, ResNet-50 and DenseNet-121. The evaluation of the defect classification performance was made by comparing the accuracy, loss, and learning speed using verification data. The DenseNet-121 algorithm showed better performance than other algorithms with an accuracy of 99.13% and a loss value of 0.037. This was due to the structural characteristics of the DenseNet model, and the information loss was reduced by acquiring information from all previous layers for image identification in this algorithm. Based on the above results, the possibility of machine vision application of CNN-based model for the surface defect classification of Al extruded materials was also discussed.