• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.687-699
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• 2020

The government-led top-down development approach for weapons system faces the problem of technological obsolescence now that technology has rapidly grown. As a result, the government has gradually expanded the corporate-led bottom-up project implementation method to the defense industry. The key success factor of the bottom-up project implementation is the ability of defense companies to plan their technologies. This paper presented a method of analyzing patent data through data mining technique so that domestic defense companies can utilize it for technology planning activities. The main content is to propose corporate selection techniques corresponding to the defense communication-electronics sectors and conduct principal component analysis and cluster analysis for the International Patent Classification. Through this, the technology was classified into four groups based on the patents of nine companies and the representative enterprises of each group were derived.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.155-167
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• 2024

Distribution and logistics industries contribute some of the biggest GDP(gross domestic product) in South Korea and the number of related companies are quarter of the total number of industries in the country. The number of retail tech companies are quickly increased due to the acceleration of the online and untact shopping trend. Furthermore, major distribution and logistics companies try to achieve integrated data management with the fulfillment process. In contrast, small and medium distribution companies still lack of the capacity and ability to develop digital innovation and smartization. Therefore, in this paper, a deep learning-based demand forecasting & recommendation model is proposed to improve business competitiveness. The proposed model is developed based on real sales transaction data to predict future demand for each product. The proposed model consists of six deep learning models, which are MLP(multi-layers perception), CNN(convolution neural network), RNN(recurrent neural network), LSTM(long short term memory), Conv1D-BiLSTM(convolution-long short term memory) for demand forecasting and collaborative filtering for the recommendation. Each model provides the best prediction result for each product and recommendation model can recommend best sales product among companies own sales list as well as competitor's item list. The proposed demand forecasting model is expected to improve the competitiveness of the small and medium-sized distribution and logistics industry.

• Journal of radiological science and technology
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• v.45 no.1
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• pp.41-47
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• 2022

The purpose of this study is to apply a deep learning model that can distinguish lung perfusion and lung ventilation images in nuclear medicine, and to evaluate the image classification ability. Image data pre-processing was performed in the following order: image matrix size adjustment, min-max normalization, image center position adjustment, train/validation/test data set classification, and data augmentation. The convolutional neural network(CNN) structures of VGG-16, ResNet-18, Inception-ResNet-v2, and SE-ResNeXt-101 were used. For classification model evaluation, performance evaluation index of classification model, class activation map(CAM), and statistical image evaluation method were applied. As for the performance evaluation index of the classification model, SE-ResNeXt-101 and Inception-ResNet-v2 showed the highest performance with the same results. As a result of CAM, cardiac and right lung regions were highly activated in lung perfusion, and upper lung and neck regions were highly activated in lung ventilation. Statistical image evaluation showed a meaningful difference between SE-ResNeXt-101 and Inception-ResNet-v2. As a result of the study, the applicability of the CNN model for lung scintigraphy classification was confirmed. In the future, it is expected that it will be used as basic data for research on new artificial intelligence models and will help stable image management in clinical practice.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.208-208
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• 2022

Recently, the frequency of abnormal weather due to complex factors such as global warming is increasing frequently. From the past rainfall patterns, it is evident that climate change is causing irregular rainfall patterns. This phenomenon causes difficulty in predicting rainfall and makes it difficult to prevent and cope with natural disasters, casuing human and property damages. Therefore, accurate rainfall estimation and rainfall occurrence time prediction could be one of the ways to prevent and mitigate damage caused by flood and drought disasters. However, rainfall prediction has a lot of uncertainty, so it is necessary to understand and reduce this uncertainty. In addition, when accurate rainfall prediction is applied to the rainfall-runoff model, the accuracy of the runoff prediction can be improved. In this regard, this study aims to increase the reliability of rainfall prediction by analyzing the uncertainty of the Korean rainfall ensemble prediction data and the outflow analysis model using the Limited Area ENsemble (LENS) and the Grid based Rainfall-runoff Model (GRM) models. First, the possibility of improving rainfall prediction ability is reviewed using the QM (Quantile Mapping) technique among the bias correction techniques. Then, the GRM parameter calibration was performed twice, and the likelihood-parameter applicability evaluation and uncertainty analysis were performed using R², NSE, PBIAS, and Log-normal. The rainfall prediction data were applied to the rainfall-runoff model and evaluated before and after calibration. It is expected that more reliable flood prediction will be possible by reducing uncertainty in rainfall ensemble data when applying to the runoff model in selecting behavioral models for user uncertainty analysis. Also, it can be used as a basis of flood prediction research by integrating other parameters such as geological characteristics and rainfall events.

• Fire Science and Engineering
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• v.34 no.2
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• pp.141-146
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• 2020

This study examines the development of content scenarios to facilitate the training of on-site commanders in firefighting activities. To establish the training content scenario system, the three core competencies of the on-site commanders were set as situation judgment, communication, and decision-making. A system of scenarios was established to actively reflect these three core competencies when designing the scenarios. All the contents of these scenarios are based on Standard Operating Procedures (SOP). The scenarios comprise 14 stages that are divided into four steps with the exception of stages 1 and 14, which mark the beginning and end of the training. It consists of the situation setting stage and the first, second, and third decision-making stages. Specifically, situation judgment and communication are important factors in each stage.

• Journal of Korean Ophthalmic Optics Society
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• v.13 no.4
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• pp.1-8
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• 2008

Purpose: We investigated opticians' and the public (including optometry students) opinions about university/college affiliated optical shops. Methods: Opinions about the university/college affiliated optical shops, their positive or negative effects, services range, how to obtain their operation cost, and so on were asked to 50 opticians and 51 public people (including optometry students) by a questionnaire survey, being statistically analysed. Results: Most respondents answered positive opinions about the university/college affiliated optical shops, anticipating better eye test ability of Koean optometry graduates through improved clinical education. Conclusions: The university/college affiliated optical shops are expected to contribute to clinical education and research in Korea. Legal revisions and efforts of each university/college are required.

• Journal of the Korea Safety Management & Science
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• v.18 no.4
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• pp.19-30
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• 2016

This paper addresses the problem of how to effectively use virtual reality(VR) for improving the quality of safety training systems. As the working environment and the working system in the industry are more and more complex and large-scaled, the concern with system safety is accordingly growing. Safety training systems are regarded as an effective way for increasing workers' interest in system safety and enhancing their ability of preventing and handling accidents/incidents. Recently, it has been reported that VR would be effectively used for improving the quality of safety training systems, with its technically specialized features. However, little attention has been given to the problem of how to effectively use VR for safety training systems. In order to make the best use of new technology such as VR, it is important to examine its advantages and disadvantages and the contexts to which its use can be beneficial. This paper firstly reviews the current status of safety training systems and the use of VR for safety training systems in the inside and outside of the country. Next, we summarize the interview with safety managers in four manufacturing companies, which was conducted to understand the requirements of stake-holders of the issue. Based on the review and the interview, we suggested the ways of using VR in safety training systems in an effective manner. They are described from the four perspectives: development and maintenance cost, lack of specialized workers, design of accident scenarios used with VR, and empirical demonstration of the effectiveness of VR in safety training.

• Journal of Biosystems Engineering
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• v.33 no.6
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• pp.430-437
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• 2008

Optical diffuse reflectance sensing in visible and near-infrared wavelength ranges is one approach to rapidly quantify soil properties for site-specific management. The objectives of this study were to investigate effects of preprocessing of reflectance data and determine the accuracy of the reflectance approach for estimating physical and chemical properties of selected Missouri and Illinois, USA surface soils encompassing a wide range of soil types and textures. Diffuse reflectance spectra of air-dried, sieved samples were obtained in the laboratory. Calibrations relating spectra to soil properties determined by standard methods were developed using partial least squares (PLS) regression. The best data preprocessing, consisting of absorbance transformation and mean centering, reduced estimation errors by up to 20% compared to raw reflectance data. Good estimates ($R^2=0.83$ to 0.92) were obtained using spectral data for soil texture fractions, organic matter, and CEC. Estimates of pH, P, and K were not good ($R^2$ < 0.7), and other approaches to estimating these soil chemical properties should be investigated. Overall, the ability of diffuse reflectance spectroscopy to accurately estimate multiple soil properties across a wide range of soils makes it a good candidate technology for providing at least a portion of the data needed in site-specific management of agriculture.

• Journal of Information Technology Services
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• v.9 no.1
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• pp.27-42
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• 2010

Recently, many organizations are actively adopting Enterprise Architecture (EA) as a methodology to manage IT assets and build IT-based business system. However, most existing studies on EA have focused on the adoption stage of EA. Now the research concerning effective management and utilization of EA after adopting EA is keenly required. This study intended to empirically examine how the role of EA operating unit and the EA utilization capability of organizational members impact on EA performance at the post-adoption stage of EA. Based on Resource Based View (RBV), this study proposed the model and the hypotheses describing that the impact of the role of EA operating unit on EA performance is mediated by the EA utilization capability of organizational members. In order to test the hypotheses, the field survey whose respondents were seventy four Korean public agencies which have adopted EA was conducted by means of questionnaire. Data analysis was done with partial least square (PLS), which is a structural equation modeling (SEM) technique that uses a component-based approach to estimation. The results of the empirical analyses showed that the organizational operation ability of EA operating unit significantly influenced EA performance via the EA utilization capability of organizational members, but that EA education and training performed by EA operating unit did not. The results of this study provided a lot of theoretical and practical implications regarding EA management activities at the post-adoption stage of EA to enhance EA performance.

• Journal of Korean Dental Hygiene Science
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• v.5 no.1
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• pp.39-45
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• 2022

Background: Dental hygienists undergo 3 or 4 years of college education, and dental hygienist education must receive continuous feedback through evaluation. The purpose of this study was to compare the educational performance of students from the Department of Dental Hygiene in 2018 with those from other departments in the healthcare field. Methods: We used data from the National Assessment of Student Engagement in Learning, conducted by the Korean Educational Development Institute in 2018. The survey data of 55 dental hygiene students and 60 healthcare students at K University were provided after excluding all identifying information. An independent t-test was used for comparisons between the Department of Dental Hygiene and other healthcare departments. Results: Regarding class-related activities, dental hygiene students were passive in presentations, discussions, and projects and had significantly lower grades in cooperative learning and challenging learning. Regarding extra-class activities, dental hygiene students had significantly lower global learning and external experiences, domestic experiences, club activities, and interactions with professors. Regarding learning outcomes, students had significantly lower grades in writing, speaking, critical and analytical thinking, data evaluation, understanding of data meaning, problem-solving ability, goal setting and execution, core content extraction, human and material resource utilization, creative convergence thinking, statistical understanding and analysis, information technology use, collaboration, sense of community, stress management, time management, and foreign language proficiency. Conclusions: Dental hygiene education requires innovation in educational methods and efforts of instructors to improve poor learning activities and outcomes.