• Korean Journal of Adult Nursing
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• v.12 no.4
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• pp.507-516
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• 2000

This study examines that North American Nursing Diagnosis Association(NANDA) and Home Health Care Classification(HHCC) is appropriate to classify home health care client's nursing problems and suggests a modified nursing diagnosis classification system. Two hundred and forty-nine clients' records at a general hospital were reviewed and nursing problems were diagnosed according to each classification system. Results of this study are as follows. The major client's medical diagnosis are pregnancy, childbirth and puerperium, malignant neoplasm, and benign neoplasm. Of four hundred and sixty-three nursing problems, all nursing problems made a diagnos according to HHCC, while three hundred and eighty-five made a diagnosis according to NANDA. The HHCC diagnosis included 78 more nursing problems than NANDA. The discrepancy in the results may indicate a significant advantage to HHCC diagnosis because HHCC nomenclature was created empirically from hard data. However, this may be due to limitations in the data collection method so determination of which classification system is more useful is difficult to judge. However, nursing components of the HHCC are more concrete and clearer than human response patterns of the NANDA. Also the HHCC facilitates the documentation of patient care by computer, while using a conceptual framework consisting of 20 Care Components based on the nursing process: assessment, diagnosis, outcome identification, planning, implementation and evaluation. Accordingly, the practical application of HHCC is more useful than NANDA. Limitations of this study include a retrospective data collecting method and universality of samples. Further research for various samples that use prospective data collection method is recommended.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.235-245
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• 2012

Logistic discrimination is an useful statistical technique for quantitative analysis of financial service industry. Especially it is not only easy to be implemented, but also has good classification rate. Generalized additive model is useful for credit scoring since it has the same advantages of logistic discrimination as well as accounting ability for the nonlinear effects of the explanatory variables. It may, however, need too many additive terms in the model when the number of explanatory variables is very large and there may exist dependencies among the variables. Mixtures of factor analyzers can be used for dimension reduction of high-dimensional feature. This study proposes to use the low-dimensional factor scores of mixtures of factor analyzers as the new features in the generalized additive model. Its application is demonstrated in the classification of some real credit scoring data. The comparison of correct classification rates of competing techniques shows the superiority of the generalized additive model using factor scores.

• Journal of Environmental Science International
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• v.28 no.4
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• pp.455-464
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• 2019

BAT-AEL(Best Available Techniques Associate Emission Level) is the basis for establishing permissible emission standards for the workplace. Therefore, it is necessary to formulate a regulated BAT-AEL setting methodology that is generally applicable to all relevant industries. For the BAT-AEL settings, various factors should be considered such as the pollutants item, whether the workplace is subject to integrated pollution prevention and control, whether BAT is applicable, the basic data type, the emission classification system, and the suitability of the collected data. Among these factors, it is the most important factor to establish the classification system for the emitting facilities such that the emission characteristics of an industrial facility and its pollutants can be effectively reflected. Furthermore the target of the survey workplace should adhere to the BAT guidelines, even if it is a workplace that is subject to an the integrated environmental system. Certified data (SEMS, TMS, cleanSYS, WEMS, etc.) can be used to prioritize the classification system for the emission facility and the emission levels of pollutants. However, the self-measured data, daily logs, and questionnaire data from the workplace can also be used upon agreement of the relevant TWG. The collected data should only be used only when the facility is operating normally. Data that have been determined to be outliers or inappropriate validation methods should also be excluded. The BAT-AEL can be establish by adhering to the following procedure: 1) investigate all relevant workplaces with in the industry, 2)select workplaces for integrated management, 3)Identify BAT application, 4)identify whether BAT is generally applicable, 5)establish a classification system for emitting facilities, 6)collection available data, 7)verify conformity, 8)remove of outliers, 9)prepare the BAT-AEL draft, 10)deliberate, and 11) perform the confirmation procedure.

• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.39-46
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• 2021

AI is deeply applied to various algorithms that assists us, not only daily technologies like translator and Face ID, but also contributing to innumerable fields in industry, due to its dominance. In this research, we provide convenience through AI categorization, extracting the only data that users need, with objective classification, rather than verifying all data to find from the internet, where exists an immense number of contents. In this research, we propose a model using LSTM(Long-Short Term Memory Network), which stands out from text classification, and compare its performance with models of RNN(Recurrent Neural Network) and BiLSTM(Bidirectional LSTM), which is suitable structure for natural language processing. The performance of the three models is compared using measurements of accuracy, precision, and recall. As a result, the LSTM model appears to have the best performance. Therefore, in this research, text classification using LSTM is recommended.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.668-674
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• 2022

Machine learning techniques using visual data have high usability in fields of industry and service such as scene recognition, fault detection, security and user analysis. Among these, user analysis through the videos from CCTV is one of the practical way of using vision data. Also, many studies about lightweight artificial neural network have been published to increase high usability for mobile and embedded environment so far. In this study, we propose the network combining the object detection and classification for mobile graphic processing unit. This network detects pedestrian and face, classifies age and gender from detected face. Proposed network is constructed based on MobileNet, YOLOv2 and skip connection. Both detection and classification models are trained individually and combined as 2-stage structure. Also, attention mechanism is used to improve detection and classification ability. Nvidia Jetson Nano is used to run and evaluate the proposed system.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.563-570
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• 2018

This paper proposes an efficient dissolved gas analysis(DGA) and classification method of an oil-filled transformer using machine learning algorithms to solve problems inherent in IEC 60599. In IEC 60599, a certain diagnosis criteria do not exist, and duplication area is existed. Thus, it is difficult to make a decision without any experts since the IEC 60599 standard can not support analysis and classification of gas date of a power transformer in that criteria. To address these issue. we propose a dissolved gas analysis(DGA) and classification method using a machine learning algorithm. We evaluate the performance of the proposed method using support vector machines with dissolved gas dataset extracted from a power transformer in the real industry. To validate the performance of the proposed method, we compares the proposed method with the IEC 60599 standard. Experimental results show that the proposed method outperforms the IEC 60599 in the classification accuracy.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.26.1-26.15
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• 2023

Background: The objective of this study is to investigate the differences in incidence rates of targeted diseases by classification of occupations among construction workers in Korea. Methods: In a subject-based cohort of the Korean Construction Worker's Cohort, we surveyed a total of 1,027 construction workers. As occupational exposure, the classification of occupations was developed using two axes: construction business and job type. To analyze disease incidence, we linked survey data with National Health Insurance Service data. Eleven target disease categories with high prevalence or estimated work-relatedness among construction workers were evaluated in our study. The average incidence rates were calculated as cases per 1,000 person-years (PY). Results: Injury, poisoning, and certain other consequences of external causes had the highest incidence rate of 344.08 per 1,000 PY, followed by disease of the musculoskeletal system and connective tissue for 208.64 and diseases of the skin and subcutaneous tissue for 197.87 in our cohort. We especially found that chronic obstructive pulmonary disease was more common in construction painters, civil engineering welders, and civil engineering frame mold carpenters, asthma in construction painters, landscape, and construction water proofers, interstitial lung diseases in construction water proofers. Conclusions: This is the first study to systematically classify complex construction occupations in order to analyze occupational diseases in Korean construction workers. There were differences in disease incidences among construction workers based on the classification of occupations. It is necessary to develop customized occupational safety and health policies for high-risk occupations for each disease in the construction industry.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.185-207
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• 2022

Recently, with the development of deep learning technology, researches to apply a deep learning algorithm to analyze unstructured data such as text and images are being actively conducted. Text classification has been studied for a long time in academia and industry, and various attempts are being performed to utilize data characteristics to improve classification performance. In particular, a hierarchical relationship of labels has been utilized for hierarchical classification. However, the top-down approach mainly used for hierarchical classification has a limitation that misclassification at a higher level blocks the opportunity for correct classification at a lower level. Therefore, in this study, we propose a methodology for classifying hierarchical data using the autoencoder-based deeply supervised network that high-level classification does not block the low-level classification while considering the hierarchical relationship of labels. The proposed methodology adds a main classifier that predicts a low-level label to the autoencoder's latent variable and an auxiliary classifier that predicts a high-level label to the hidden layer of the autoencoder. As a result of experiments on 22,512 academic papers to evaluate the performance of the proposed methodology, it was confirmed that the proposed model showed superior classification accuracy and F1-score compared to the traditional supervised autoencoder and DNN model.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.527-532
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• 2007

Differentiation strategies have been suggested as the critical sources of competitive advantage in B2C industry where customers can switch internet shopping mall with one click with virtually no transaction cost. Indeed, competition on low pricing cannot be a viable strategy in B2C industry. Moreover, cultivating customer loyalty to attain profitability is still a challenging task for most internet shopping mall. In this study, we provide empirical analysis results on key managerial variables that indicate the difference between the product categories in terms of customer perception on relative value importance. We first identified comprehensive managerial variables and organized them in terms of customer decision stage. Next, with reference to extant literatures on product characteristics based e-commerce strategy, hypotheses are developed to formalize the customer value differences on the key managerial variables. Empirical testing results indicated that there are significant differences on customer perceived value of the key managerial variables between the product groups. The findings provide useful insight for further study on e-commerce differentiation strategy.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.1104-1111
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• 2009

Mechanical facilities in modern buildings and industrial plants become more important in the view points of energy and environment issues. However, mechanical construction fields are still considered as to be subjected to buildings, even though design and construction of mechanical fields in the construction production process is independent of other fields. Recently, 'Framework Act on the Construction Industry' has been revised since 2007. According to the revision, the barrier of general construction and specialized construction is collapsed and the construction company can register any type of construction classification if they are able to carry out the construction mission. The mechanical construction fields are exempt until 2011 because of the protection of mechanical construction industry. In the present study, the bidding system has been prospected due to the revision of 'Basic Law on Construction Industry' after 2011. The trends for development of mechanical construction fields has been also discussed.