• Title/Summary/Keyword: Quantitative classification

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Characterization of Eco-Design Checklists (에코디자인 체크리스트 특성 분석)

  • Masoudi, Ali;You, Hee-Cheon;Suh, Suk-Hwan
    • Journal of the Korean Society for Precision Engineering
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    • v.29 no.9
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    • pp.964-970
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    • 2012
  • Various eco-design tools have been developed which can be classified into quantitative, semi-quantitative, and qualitative tools. Practitioners are reluctant to utilize quantitative tools in light of their time-demanding nature. Among the qualitative tools, checklists are simple tools that allow a quick and effective evaluation and consideration of environmental impacts over the entire life cycle of a product. A profound and better understanding of eco-design checklists is needed so that practitioners can apply them appropriately to their product development context. Various types of eco-design checklists are analyzed in the present study based on their attributes and classified in a structured way for their efficient utilization in product development contexts.

Quantitative Golf Swing Analysis based on Kinematic Mining Approach (데이터마이닝을 활용한 골프 스윙 최적화 분석)

  • Lee, Kyu Jong;Ryou, Okhyun;Kang, Jihoon
    • Korean Journal of Applied Biomechanics
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    • v.31 no.2
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    • pp.87-94
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    • 2021
  • Objective: Identification of meaningful patterns and trends in large volumes of unstructured data is an important task in various research areas. In the present study, we gathered golf swing image data and did quantitative analysis of swing image. Method: We collected golf swing images of 30 novice players and 30 professional players in this study. Results: We selected important features of swing posture and employed data mining algorithm to classify whether a player is an expert or a novice. Moreover, our proposed method could offer quantitative advices for golf beginners for correcting their swing. Conclusion: Finally, we found a possibility that our proposed method can be expanded to golf swing correction system

Pulmonary Emphysema: Visual Interpretation and Quantitative Analysis (폐기종의 시각적 분류 및 정량적 평가)

  • Jihang Kim
    • Journal of the Korean Society of Radiology
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    • v.82 no.4
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    • pp.808-816
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    • 2021
  • Pulmonary emphysema is a cause of chronic obstructive pulmonary disease. Emphysema can be accurately diagnosed via CT. The severity of emphysema can be assessed using visual interpretation or quantitative analysis. Various studies on emphysema using deep learning have also been conducted. Although the classification of emphysema has proven clinically useful, there is a need to improve the reliability of the measurement.

A study on the Classification Schemes of Internet Resources for Industry (산업 분야 인터넷 자원의 분류체계에 관한 연구)

  • 한상길
    • Journal of the Korean Society for information Management
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    • v.18 no.3
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    • pp.285-309
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    • 2001
  • The industry information grows faster than any other information resources in the Internet age. Unfortunately, however, there is no consensus on the standard of the classification among the information providers of the industry fields. This may a problematic issue not only in building a continuous and systematic development of the industry information, but also in the use of the information among the users. This study aims to propose a well-structured and/or an efficient classification scheme for the industry information to help the users with easy to retrieve the Internet resources. To do this, we analyzed the subject classification scheme of the domestic industry information on the web sites, which is largely adopted the \"Korean Standard for the Industry Classification\". In addition, we suggested the principle of the subject classification and their hierarchial structure derived from the analysis of the knowledge and document classification scheme. As a result, it was suggested an optimized industry classification scheme based on the analysis of the validity test of classification item measured by the quantitative analysis of the industry information, which it currently accessible through the Internet. Internet.

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A Study on Inspection-ability and Classification-ability Evaluation for Mechanical Parts (기계부품의 검사 및 분류성 평가에 관한 연구)

  • Chang-Su Jeon
    • Journal of the Korean Society of Industry Convergence
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    • v.26 no.6_2
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    • pp.1055-1062
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    • 2023
  • Globally, the need for remanufacturing or reusing ships and various mechanical parts continues to increase due to environmental problems including global warming. Research on remanufacturing is being carried out in many areas. However, research on inspection and classification to identify the performance or degree of wear of mechanical parts is insufficient. In particular, studies on the inspection-ability and classification-ability of mechanical parts equipped with various materials and complex forms are highly required. Remanufacturing must be considered from the stage of design to extend the life cycle of mechanical parts. Particularly, it is very important to perform research for evaluating the degree of ease to inspect and classify various sorts of wear or deterioration of parts caused by long-term use easily. In this study, the degree of ease in inspecting or classifying mechanical parts for remanufacturing is defined as inspection-ability and classification-ability. In fact, to remanufacture old parts, inspection-ability and classification-ability should be reflected from the stage of design. The purpose of this study is to evaluate the inspection-ability and classification-ability of ships and various mechanical parts. This researcher has presented the quantitative evaluation procedure of inspection-ability and classification-ability, derived the factors and ranges that influence each of the details of easiness, assigned scores according to the ranges of the factors, and calculated weights. Lastly, this study presents the procedure of scoring to evaluate the overall weights of inspection-ability and classification-ability and also inspection-ability and classification-ability quantitatively.

Segmentation of Immunohistochemical Breast Carcinoma Images Using ML Classification (ML분류를 사용한 유방암 항체 조직 영상분할)

  • 최흥국
    • Journal of Korea Multimedia Society
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    • v.4 no.2
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    • pp.108-115
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    • 2001
  • In this paper we are attempted to quantitative classification of the three object color regions on a RGB image using of an improved ML(Maximum Likelihood) classification method. A RGB color image consists of three bands i.e., red, green and blue. Therefore it has a 3 dimensional structure in view of the spectral and spatial elements. The 3D structural yokels were projected in RGB cube wherefrom the ML method applied. Between the conventionally and easily usable Box classification and the statistical ML classification based on Bayesian decision theory, we compared and reviewed. Using the ML method we obtained a good segmentation result to classify positive cell nucleus, negative cell Nucleus and background un a immuno-histological breast carcinoma image. Hopefully it is available to diagnosis and prognosis for cancer patients.

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A Geostatistical Study Using Qualitative Information for Multiple Rock Classification -1. Theory (다분적 암반분류를 위한 정성적 자료의 지구통계학적 연구 1.이론)

  • 유광호
    • Geotechnical Engineering
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    • v.11 no.2
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    • pp.71-78
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    • 1995
  • In this paper, a study was performed on classifying a rock mass into multiple classes as in rock mass classification systems, such as RMR system and Q system etc. In a situation with only limited quantitative data available, it was sought to employ a way of incorporating qualitative data in a systematical and reasonable manner. It is based on the realm of Geostatistics. In particular, indicator kriging technique, which is one of non-parametric approaches, was used. As a selection criterion for an optimal classification, the cost of errors was adopted. As a result, the binary rock classification method developed before was extended and generalized for multiple rock classification with its total number of classes unlimited.

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Supervised classification for greenhouse detection by using sharpened SWIR bands of Sentinel-2A satellite imagery

  • Lim, Heechang;Park, Honglyun
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.38 no.5
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    • pp.435-441
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    • 2020
  • Sentinel-2A satellite imagery provides VNIR (Visible Near InfraRed) and SWIR (ShortWave InfraRed) wavelength bands, and it is known to be effective for land cover classification, cloud detection, and environmental monitoring. Greenhouse is one of the middle classification classes for land cover map provided by the Ministry of Environment of the Republic of Korea. Since greenhouse is a class that has a lot of changes due to natural disasters such as storm and flood damage, there is a limit to updating the greenhouse at a rapid cycle in the land cover map. In the present study, we utilized Sentinel-2A satellite images that provide both VNIR and SWIR bands for the detection of greenhouse. To utilize Sentinel-2A satellite images for the detection of greenhouse, we produced high-resolution SWIR bands applying to the fusion technique performed in two stages and carried out the detection of greenhouse using SVM (Support Vector Machine) supervised classification technique. In order to analyze the applicability of SWIR bands to greenhouse detection, comparative evaluation was performed using the detection results applying only VNIR bands. As a results of quantitative and qualitative evaluation, the result of detection by additionally applying SWIR bands was found to be superior to the result of applying only VNIR bands.

Data Augmentation Effect of StyleGAN-Generated Images in Deep Neural Network Training for Medical Image Classification (의료영상 분류를 위한 심층신경망 훈련에서 StyleGAN 합성 영상의 데이터 증강 효과 분석)

  • Hansang Lee;Arha Woo;Helen Hong
    • Journal of the Korea Computer Graphics Society
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    • v.30 no.4
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    • pp.19-29
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    • 2024
  • In this paper, we examine the effectiveness of StyleGAN-generated images for data augmentation in training deep neural networks for medical image classification. We apply StyleGAN data augmentation to train VGG-16 networks for pneumonia diagnosis from chest X-ray images and focal liver lesion classification from abdominal CT images. Through quantitative and qualitative analyses, our experiments reveal that StyleGAN data augmentation expands the outer class boundaries in the feature space. Thanks to this expansion characteristics, the StyleGAN data augmentation can enhance classification performance when properly combined with real training images.