• Journal of the Ergonomics Society of Korea
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• v.36 no.3
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• pp.197-211
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• 2017

Objective: We propose to apply color information to facilitate finding books in the library. Background: Currently, books are classified in the basis of a decimal classification system and a call number in the library. Users find a book using the call number. However, this classification system causes various difficulties. Method: In a process analysis and survey study, we identify what the real problem is and where the problem is occurred. To solve the real problems, we derived a new search method using color information. We conducted a comparative experiment with 48 participants to see whether the new method can show higher performance. Results: The new method using color information showed faster time and higher subjective rating scores than current call number method. Also, the new method showed faster time regardless of the skill level while the call number method showed time differences in terms of the skill level. Conclusion: The effectiveness of the proposed method was verified by experiments. Users will be able to find the desired book without difficulty. This method can improve the quality of service and satisfaction of library use. Application: Our book search method can be applied as a book search tool in a real public library. We hope that the method can provide higher satisfaction to users.

• Safety and Health at Work
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• v.6 no.4
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• pp.279-288
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• 2015

Background: In this study, the measurement of job stress of electric overhead traveling crane operators and quantification of the effects of operator and workplace characteristics on job stress were assessed. Methods: Job stress was measured on five subscales: employee empowerment, role overload, role ambiguity, rule violation, and job hazard. The characteristics of the operators that were studied were age, experience, body weight, and body height. The workplace characteristics considered were hours of exposure, cabin type, cabin feature, and crane height. The proposed methodology included administration of a questionnaire survey to 76 electric overhead traveling crane operators followed by analysis using analysis of variance and a classification and regression tree. Results: The key findings were: (1) the five subscales can be used to measure job stress; (2) employee empowerment was the most significant factor followed by the role overload; (3) workplace characteristics contributed more towards job stress than operator's characteristics; and (4) of the workplace characteristics, crane height was the major contributor. Conclusion: The issues related to crane height and cabin feature can be fixed by providing engineering or foolproof solutions than relying on interventions related to the demographic factors.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.21-31
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• 2021

As the COVID-19 pandemic rapidly changes healthcare around the globe, the need for smart healthcare that allows for remote diagnosis is increasing. The current classification of respiratory diseases cost high and requires a face-to-face visit with a skilled medical professional, thus the pandemic significantly hinders monitoring and early diagnosis. Therefore, the ability to accurately classify and diagnose respiratory sound using deep learning-based AI models is essential to modern medicine as a remote alternative to the current stethoscope. In this study, we propose a deep learning-based respiratory sound classification model using data collected from medical experts. The sound data were preprocessed with BandPassFilter, and the relevant respiratory audio features were extracted with Log-Mel Spectrogram and Mel Frequency Cepstral Coefficient (MFCC). Subsequently, a Parallel CNN network model was trained on these two inputs using stacking ensemble techniques combined with various machine learning classifiers to efficiently classify and detect abnormal respiratory sounds with high accuracy. The model proposed in this paper classified abnormal respiratory sounds with an accuracy of 96.9%, which is approximately 6.1% higher than the classification accuracy of baseline model.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.207-218
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• 2014

The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.2
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• pp.294-302
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• 2009

In this paper, the trend of technology and the business strategy on 'Hearing Aids' are investigated for the industry promotion of medical devices based on IT engineering from the 316 patents of 'Hearing Aids' registered in Korean Intellectual Property Office(KIPO). The classification of technology on 'Hearing Aids' is performed according to the IPC(International Patent Classification) code to and the core technology of 'Hearing Aids' As the results of classification of IPC code, the number of patents with IPC code 'H04R', 'H04B', 'H01M', and 'A61F' are 160, 46, 40, and 19 respectively. We found that the Digital technology and the Medical Transplants technology are come to the front of 'Hearing Aids' and the foreign 'Hearing Aids' companies are filed an application with the Korean Intellectual Property Office(KIPO) before their business.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.45-52
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• 2017

In this paper, we investigated the following items for the development of gas safety work mobile app. In this study, which is a follow-up study after the completion of the scenario design and the first, second image extraction of the mobile app based on the initial research that has been studied, 1) Suggested classification of gas works by type classification and risk classification 2) The research and proposal of interaction method for effective interworking of mobile app and worker in many industrial fields of two-hand work have been made. In particular, the development of a mobile app that interacts with the main system that manages not only the gas work but also the field of each industrial field is the first attempt in Korea and has helped the worker to work freely and safely through various interaction methods.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.1
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• pp.77-85
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• 2006

In this paper, we propose a hierarchical classifier of object images using neural networks for content-based image classification. The images for classification are object images that can be divided into foreground and background. In the preprocessing step, we extract the object region and shape-based texture features extracted from wavelet transformed images. We group the image classes into clusters which have similar texture features using Principal Component Analysis(PCA) and K-means. The hierarchical classifier has five layes which combine the clusters. The hierarchical classifier consists of 59 neural network classifiers learned with the back propagation algorithm. Among the various texture features, the diagonal moment was the most effective. A test with 1000 training data and 1000 test data composed of 10 images from each of 100 classes shows classification rates of 81.5% and 75.1% correct, respectively.

• Journal of Engineering Education Research
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• v.13 no.4
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• pp.93-108
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• 2010

The goal of this study is an analysis on linkage system between in machinery sector. The development of skill framework and national technical qualification items. This paper researched skills and created the skill level through reviewing domestic & foreign documents, interview with experts and in-depth discussions with expert group focusing on terminologies commonly used in the industrial settings. As a result of skill classification, authors were able to classify skills into three categories in medium-scale classification and 11 categories in small-scale classification, and also into total 42 categories through the re-classification of the small-scale classification. The skill level in the area of machine were classified the skill level in the area of machine into 7 level by reflecting the level system of the korean qualification frameworks, qualification and education course, and skill level in the industrial setting. Based on the skill frameworks, we provided definition of skill and skill group, definition of each different skill, and performance standards by skill and level. also, This paper suggests improving measure of national technical qualification items through analysizing linkage situation between skill frameworks & qualification items.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.978-983
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• 2010

Acoustic target classification in wireless sensor network is important research at environmental surveillance, invasion surveillance, multiple target separation. General sensor node signal processing methods concentrated on received signal energy based target detection and received raw signal compression. The former is not suited to target classification because of almost every target information are lost except target energy. The latter bring down life-time of sensor node owing to high computational complexity and transmission energy. In this paper, we introduce an feature extraction algorithm for acoustic target classification in wireless sensor network which has time and frequency information. The proposed method extracts time information and de-noised target classification information using wavelet decomposition step. This method reduces communication energy by 28% of original signal and computational complexity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5150-5155
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• 2011

Alzheimer's disease (AD) and vascular dementia (VD) are the most common dementia. In this paper, we proposed a region selection for classification of AD, VD and normal (NOR) based on micro-Raman spectra from platelet. The preprocessing step is a smoothing followed by background elimination to the original spectra. Then we applied the minmax method for normalization. After the inspection of the preprocessed spectra, we found that 725-777, 1504-1592 and 1632-1700 $cm^{-1}$ regions are the most discriminative features in AD, VD and NOR spectra. We applied the feature transformation using PCA (principal component analysis) and NMF (nonnegative matrix factorization). The classification result of MAP(maximum a posteriori probability) involving 327 spectra transformed features using proposed local region showed about 92.8 % true classification average rate.