• Journal of Korean Institute of Industrial Engineers
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• v.24 no.3
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• pp.447-455
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• 1998

It is important to identify and evaluate poor working postures properly to prevent work-related musculoskeletal disorders. The purpose of this study is to develope a new upper body micropostural classification scheme for analyzing postural stress in industry. Most of the existing postural classification schemes were based either on the literature, or on simple biomechanical principles, or on a subjective ranking system. The scheme suggested in this study was based on perceived joint discomfort measured through experiment, in which nineteen subjects participated and the magnitude estimation method was employed to obtain subjects' joint discomfort. Also, the criteria for evaluating postural stress of working postures were presented for practitioners of health and safety to be able to redesign working methods and workplaces, which was based on maximum holding time by Miedema and other people. It is expected that the scheme developed in this study could be used as a valuable tool when evaluating working postures.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.121-125
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• 2020

With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• ETRI Journal
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• v.35 no.6
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• pp.1105-1114
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• 2013

Owing to the large number of video programs available, a method for accessing preferred videos efficiently through personalized video summaries and clips is needed. The automatic recognition of user states when viewing a video is essential for extracting meaningful video segments. Although there have been many studies on emotion recognition using various user responses, electroencephalogram (EEG)-based research on preference recognition of videos is at its very early stages. This paper proposes classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores for four preference classes. As a result, the quadratic-discriminant-analysis-based model using BP features achieves a classification accuracy of 97.39% (${\pm}0.73%$), and the models based on the other nonlinear classifiers using the BP features achieve an accuracy of over 96%, which is superior to that of previous work only for binary preference classification. The result proves that the proposed approach is sufficient for employment in personalized video segmentation with high accuracy and classification power.

• Journal of the Korean Society for information Management
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• v.2 no.2
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• pp.115-149
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• 1985

The classification systems generally used in library are for the external information such as books and periodicals, so it is difficult to apply them to the internal information of specific organization such as documents and records. Therefore, it is necessary for the information centers of any enterprise or specific organization to found the Record Management System (RMS), and to develop and use the specific classification system which is called the coding system for internal information. Documents and components of nuclear power plant are controlled by coding (numbering) system, but they are different each other greatly. So the coordination of work and the cross reference between components and documents are difficult. In this paper, the unified Record Management Coding System is developed for Nuclear Power Plant unit 7 & 8 in Korea on the basis of the existing document and component coding system. The expected effects are the easy cross reference between components and documents, the effective coordination of work and the consistence of central file.

• MALSORI
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• no.65
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• pp.125-141
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• 2008

In this work, we propose an automatic word spacing system for Korean using conditional random fields (CRFs) with Korean features. We map a word spacing problem into a classification problem in our work. We build a basic system which uses CRFs and Eumjeol bigram. After then, we analyze the result of inner-test. We extend a basic system added by some Korean features which are Josa, Eomi and two head Eumjeols of word extracting from lexicon. From the results of experiment, we can see that the proposed method is better than previous methods. Additionally the proposed method will be able to use mobile and speech applications because of very small size of model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.3
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• pp.331-345
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• 2009

This study aims to standardize the work classification system in spatial data. Up to now, a systematic standard for constructing process and quality management has not yet been established in Korea, thus, it is possible for the national budget to be wasted. The regulations related to constructing spatial data are also obscure, and absurd for feasible application to reality, which results in a lack of reliability of the quality of spatial data. This study was conducted by investigating and analyzing regulations related to spatial data quality and various literature, including studies on spatial data quality conducted by the NGII. And also, the study was conducted by investigating and analyzing the constructing processes and working methods of major firms that have experience in constructing a GIS for a local governing body. Based on the analyzed data, we standardized work classification and management methodology for control point surveying using GPS, leveling, aerial photographing, digital mapping, topographic mapping, digital elevation modeling, aerial photographic DB construction, digital orthophotomap.

• Journal of the military operations research society of Korea
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• v.23 no.2
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• pp.45-71
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• 1997

There has been considerable research recently on uncertainty handling in the fields of artificial intelligence and knowledge-based system. Various numerical and non-numerical methods have been proposed for representing and propagating uncertainty in knowledge-based system. The Bayesian method, the Dempster-Shafer's Evidence Theory, the Certainty Factor model and the Fuzzy Set Theory are most frequently appeared in the knowledge-based system. Each of these four methods views uncertainty from a different perspective and propagates it differently. There is no single method which can handle uncertainty properly in all kinds of knowledge-based systems' domain. Therefore a knowledge-based system will work more effectively when the uncertainty handling method in the system fits to the system's environment. This paper proposed a framework for selecting proper uncertainty handling methods in knowledge-based system with respect to characteristics of problem domain and cognitive styles of experts. A schema with strategic/operational and unstructured/structured classification is employed to differenciate domain. And a schema with systematic/intuitive and preceptive/receptive classification is employed to differenciate experts' cognitive style. The characteristics of uncertainty handling methods are compared with characteristics of problem domains and cognitive styles respectively. Then a proper uncertainty handling method is proposed for each category.

• Journal of the Korea Safety Management & Science
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• v.9 no.2
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• pp.59-69
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• 2007

The main objective of this study is to suggest the new sizing system proper to the boys in the high school students by classifying their somatotype for the development of educational environment and uniform. The sample for this work was chosen from data which were collected and measured by Size Korea during two years $(2003\sim2004)$. In order to analyze feature of the somatotype of boys in the high school students, analysis was performed about 479 subjects on 37 body parts such as height (9 parts), width (5 parts), thickness (6 parts), circumference (7 parts), length (8 parts), body weight and $R\ddot{o}hrer$ Index. The result of this study can be utilized in various fields such as design of classroom, student uniforms, facilities and equipments for education at high school and university, etc.

• Journal of Digital Contents Society
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• v.8 no.4
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• pp.587-592
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• 2007

This paper explains the design and implementation of the EC_NH. EC_NH is a system that is suitable for detecting, sharing and recovering software error based on multimedia CSCW(Computer Supportes Cooperated Work). With error sharing system, a group cooperating users can share error applications. From the perspective of multimedia collaborative environment, an error application becomes another interactive presentation error is shared with participants engaged in a cooperative work. Our Digital Carousel enables user to share media objects through media synchronization mechanism. We implemented the Digital Carousel so that the users participated in collaborative work may refer shared media or error objects as the same view to others.