• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.187-198
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• 2019

Purpose: In this study, a reliability prediction based reliability growth management is suggested especially for the early development phase of a system and the case study of surveillance system is given. Methods: The proposed reliability prediction based reliability growth management procedures consists of 7 Steps. In Step 1, the stages for reliability growth management are classified according to the major design changes. From Step 2 to Step 5, system reliability is predicted based on reliability structures and the predicted reliabilities of subsystems (Level 2) and modules (Level 3). At each stage, by comparing the predicted system reliability with that of the previous stage, the reliability growth of the system is checked in Step 6. In Step 7, when the predicted value of sustem reliability does not satisfy the reliability goal, some design alternatives are considered and suggested to improve the system reliability. Results: The proposed reliability prediction based reliability growth management can be an efficient alternative for managing reliability growth of a system in its early development phase. The case study shows that it is applicable to weapon system such as a surveillance system. Conclusion: In this study, the procedures for a reliability prediction based reliability growth management are proposed to satisfy the reliability goal of the system efficiently. And it is expected that the use of the proposed procedures would reduce, in the test and evaluation phase, the number of corrective actions and its cost as well.

• Ocean and Polar Research
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• v.43 no.1
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• pp.45-51
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• 2021

Studies on marine zooplankton diversity and ecology are important for understanding marine ecosystem, as well as environmental conservation and fisheries management. DNA metabarcoding is known as a useful tool to reveal and understand diversity among animals, but a comparative evaluation with classical microscopy is still required in order to properly use it for marine zooplankton research. This study compared crustacean mesozooplankton taxa revealed by morphological analysis and metabarcoding of the cytochrome oxidase I (COI). A total of 17 crustacean species were identified by morphological analysis, and 18 species by metabarcoding. Copepods made up the highest proportion of taxa, accounting for more than 50% of the total number of species delineated by both methods. Cladocerans were not found by morphological analysis, whereas amphipods and mysids were not detected by metabarcoding. Unlike morphological analysis, metabarcoding was able to identify decapods down to the species level. There were some discrepancies in copepod species, which could be due to a lack of genetic database, or biases during DNA extraction, amplification, pooling and bioinformatics. Morphological analysis will be useful for ecological studies as it can classify and quantify the life history stages of marine zooplankton that metabarcoding cannot detect. Metabarcoding can be a powerful tool for determining marine zooplankton diversity, if its methods or database are further supplemented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2008.04a
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• pp.235-240
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• 2008

The environment has played a key role to improve the living condition and develop the industry. In building industries, we should consider the environment and mitigate the environmental affect. For mitigating the its affect, various areas of building technology have been developed and applied into filed work. In addition, the process in applying into field requires to conduct the assessment of the environmental affect and improve its applied technology. A lot of assessment methods are proposed in evaluate the building condition such as post-occupancy evaluation, life cycle management and life cycle assessment. Among these assessment methods, life cycle assessment is effectively utilized the environmental affect in building life cycle. Therefore, this paper aimed at analyzing the energy consumption and $CO_2$ emission in building life cycle, using the life cycle assessment and application of the example in apartment housing. This study shows that the maintenance and the production of building materials stage shares most of the amount of energy consumption and $CO_2$ emission and therefore plays an important role to planning the building in terms of the life cycle. Second, the other stages brings about a very small amount. It is important to decide the building shape and contents to mitigate the environmental affect in terms of material, volume, the pattern of the energy use and others.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.707-722
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• 2021

The need for Maker Education has received attention as an educational environment for cultivating the active and creative ability that can solve new problems in this era, and it is applied in various educational fields. Many of them use Design Thinking as a stage of maker activities. However, the educational value of each concept has not been magnified, since maker programs are designed by simply borrowing steps without considering the similar but different features of them. Therefore, this study developed a model of Maker Education utilizing Design Thinking based on complementary relationships. To this end, formative research methodology was conducted by the following procedures, developing a draft, conducting a formative evaluation, and completing the final model. As a result, the stages of Maker Education were visualized and detailed activities and instructing strategies in each step by reflecting the features of Maker Education, the autonomy of the learner and producing visible outputs using various tools and materials, and Design Thinking, the specific process of solving problems and enabling social participation.

• Spatial Information Research
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• v.5 no.2
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• pp.185-194
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• 1997

Spatial decision support systems(SDffi), a new class of decision support system(DSS), result from the melding together of GIS and DSS, Planning support systems(PS5) add more advanced spatial analysis functions than GIS and intertemporal functions to the functions of SDSS. This paper reports the development of a planning support system providing a framework that facilitates urban planners and civil engineers in conducting coherent deliberations about the generation of satisficing sewers. 1he planning support system for the generation of satisficing sewers(PS5/GSS) was designed from the understanding that land use and development drive the demand for storm and sanitary sewers. Through four stages of supply, demand, alternative generation, and evaluation, PSS/GSS integrates basic planning, preliminary design, and engineering design of sewer. GIS and graphic user interface are excellent toolboxes for designing sewer networks, estimating the quantity of wastewater, and showing generated alternative sewers. A sewer model using simulation tedmique can generate an initial sewer. Users can define alternative sewers by the direct manipulation of sewer networks or by the manipulation of parameters in the sewer model. The sewer model evaluates the performance of the user defined alternatives.

• KDI Journal of Economic Policy
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• v.45 no.1
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• pp.69-86
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• 2023

Regulatory sentiment refers to the market's subjective evaluation of regulatory reform and is one of the most widely adopted indicators to those charged with implementing and diagnosing regulatory policies. The use of regulatory sentiment in advanced analysis has become universal, albeit it is often limited due to difficulties in articulating consistent and objective quantitative indicators that can meticulously reflect market sentiment overall. Thus, despite ample effort by scholars to read the economic impact of regulatory sentiment in the real economy, causal links are difficult to spot. To fill this gap in the literature, this study analyzes a regulatory sentiment index and economic performance indicators through a text analysis approach and by inspecting diverse tones in media articles. Using different stages of tests, the paper identifies a causal relationship between regulatory sentiment and actual economic activities as measured by private consumption, facility investment, construction investment, gross domestic investment, and employment. Additionally, as a result of analyzing one-unit impulse of regulatory perception, the initial impact on economic growth and private investment was found to be negligible; this was followed by a positive (+) response, after which it converged to zero. Construction investment showed a positive (+) response initially, which then rapidly changed to a negative (-) response and then converged to zero. Gross domestic investment as the initial effect was negligible after showing a positive (+) reaction. Unfortunately, the facility investment outcome was found to be insignificant in the impulse response test. Nevertheless, it can be concluded that it is necessary and important to increase the sensitivity to regulations to promote the economic effectiveness of regulatory reforms. Thus, instead of dealing with policies with the vague goal of merely improving regulatory sentiment, using regulatory sentiment as an indicator of major policies could be an effective approach.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.5
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• pp.919-926
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• 2000

The experiment in this study was conducted as a part of an effort to evaluate filter performance with pilot-filters consisting of one mono-media and two dual-media columns. Particle distribution analysis using a particle counter is more sensitive and better than turbidity analysis in observing particle detachments and a breakthrough. In sand media filters having 1.5 m of available head, caution is needed in the head loss of the late stages of filtration, and for dual-media filters, appropriate media configuration and effluent Quality monitoring should be used for preventing the final breakthrough. Also the time of particle breakthrough in the dual media filter can be deferred by increasing bed depth, and it is necessary to use a filtration aid prior to filtration to prevent breakthrough of these intermediate sized particles in high filtration rate.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.489-495
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• 2015

Image availability evaluated by the degree of agreement and sensitive using the process improve visualization according to the Algorithm modification in Image Post-Processing. Reliability measured by the Breast Imaging Reporting and Data System. 172 patients visit same period divided by BI-RADS, category five stages, and contents of breast parenchyma into Calcification, Nodule and Mass. Evaluated the TE/PV image reliability, visualization sensitive, agreement of diagnosis. Convergence analysis was an in various fields. According to the result of this research, PV has higher sensitive and accuracy about lesions than TE visual and there is a difference insensitive by contents of breast parenchyma. Therefore, practical use of Algorithm Modification(Tissue Equalization: TE, Premium View: PV) is expected to improve more accurate, useful diagnosis, which has not been easy until now.

• Journal of Korean Elementary Science Education
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• v.39 no.1
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• pp.117-132
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• 2020

The purpose of this study is to investigate elementary school teachers' awareness of Artificial Intelligence (AI) and find out how to apply it in elementary science education. The survey was conducted online and involved 95 teachers working in the metropolitan area. The results of this study are as follows. First, teachers need to learn about the general characteristics of AI and how to apply it to education. Second, science classes had the highest preference for AI among elementary school subjects. Third, the preference for AI application by elementary science field was 68.4% for earth and space, 54.7% for exercise and energy, 32.6% for matter, 27.4% for life. Fourth, AI-based Science Education (AISE) teaching- learning strategies were developed based on AI characteristics and the changing perspective of elementary science education, AISE's teaching-learning strategies are five: 'automation', 'individualization', 'diversification', 'cooperation' and 'creativity' and teachers can use them in teaching design, class practice and evaluation stages. Finally, the creative problem-solving Doing Thinking Making Sharing (DTMS) model was devised to implement the creativity strategy in AISE. This model consists of four-steps teaching courses: Doing, Thinking, Making and Sharing based on the empirical learning theory. In the future, follow-up research is needed to verify the effectiveness of this model by applying it to elementary science education.