• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.255-266
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• 2009

In this study, a robust aerospace system design process for the aerospace system is developed by considering the uncertainties of user requirements, manufacturing errors, and operational environment variation. User requirements are analyzed and quantified by decision making models and system engineering methods to select alternative concepts which satisfies the various requirements. Robust design and optimization method is applied to derive the robust solution of the selected system. First, a variance of objective function is calculated, and a mean value and a variance of target value are determined by the deterministic design optimization results of the system. A robust optimum design formulation is then needed to derive the robust solution that minimizes the variance of the response and moves the mean values to the target value. It is applied to Very Light Jet (VLJ) aircraft to which much attention is paid recently in civil aerospace market.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.322-322
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• 2016

국내 1, 2종 댐 시설물은 "시설물의 안전관리에 관한 특별법(이하 시특법)" 제7조에 의해 주기적으로 정밀안전진단을 실시하여 상태평가 및 안전성평가에 의한 종합평가 등급을 부여하고 있다. 정밀안전진단 중 안전성 평가의 수문학적 안전성 평가는 댐의 구조형식과 현장진단 결과에 의한 댐의 상태평가에 따라 1차적으로 수행된다. 여유고 부족 또는 월류 발생 등의 1차적인 조건을 만족시키지 못할 경우 그에 따른 댐체의 구조적 안전을 검토(콘크리트댐)하고, 최종적으로 댐 붕괴 발생 시 하류에 미치는 인적, 경제적 위험요인 등을 기준으로 평가하는 3단계 평가가 수행된다. 본 논문에서는 위 단계적 평가가 수행된 기존댐의 수문학적 안전성 평가 및 상태평가 결과를 사용하여, 1종 기존댐에 대한 수문학적 안전성 취약 순위를 선정하고자 한다. 취약 순위를 선정하기 위하여 다기준의사결정기법(Multi-Criteria Decision Making: 이하 MCDM)을 적용하였고, MC DM의 Payoff Matrix 설정을 위하여 수문학적 안전성 평가 지표를 기준으로 설정하였으며, 이 외의 기준으로는 대상 댐의 연최대 강수량 값을 사용하였다. 연최대강수량값을 산정하기 위하여 기상관측소별 연최대 강수량 30년치의 평균값을 kriging 기법에 적용하여 대상댐에 대한 연최대강수량 값을 도출하여 수문학적 안전성 평가 지표 및 기존댐의 기상 관측값에 따른 댐별 수문학적 안전성 취약 순위 선정에 관한 연구를 수행하였다. 위 결과를 활용하여 수문학적 안전성 평가등급에 대한 검토 및 기후변화 적응 댐 수문학적 안전성 평가 프레임워크 개발을 위한 기초 자료로 활용될 것으로 기대된다.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Journal of Environmental Science International
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• v.29 no.12
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• pp.1185-1198
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• 2020

This study aimed to suggest a framework for biotope classification and evaluation based on habitat values to rationally establish management areas of national parks. The factors and indicators related to the biotope classification and evaluation were established based on integration from those of previous studies. The decision tree evaluation process was applied to the classification and evaluation of the biotope type level. The evaluation of the biotope group level was carried out to determine the weight and the AUEM (Adding Up Estimation Matrix) was applied for the final grades. As a result, the biotope type of Seolaksan National Park was classified into 43 types and Odaesan National Park was classified into 41 types. Bukhansan National Park, which is located in a metropolitan city, was classified into 49 types. In terms of biotope evaluation, grade III had a ratio of 50.6%, the highest in Seolaksan national park. The ratios of grade I and grade II, which have great ecological-value, were respectively 12.0% and 36.5%. Grade II was 48.2%, the highest ratio in Odaesan National Park. The ratios of grades I and II were 10.8% and 37.9%, respectively. Grade III was 54.8%, the highest ratio in Bukhansan national park, and the ratios of grade I and grade II, which have great ecological-value were, respectively, 11.4% and 25.7%. The biotope values of major national parks were evaluated according to the type focusing on the actual vegetation. This framework can be considered for application to the spatial management of other types of protected areas.

• Journal of Industrial Convergence
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• v.20 no.4
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• pp.31-37
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• 2022

Research on what factors affect the success of the movie market is very important for reducing risks in related industries and developing the movie industry. In this study, in order to find out the degree of correlation of independent variables that affect movie performance, a survey was conducted on film experts using the AHP method and the importance of each measurement factor was evaluated. In addition, we hypothesized that factors derived from big data related to search portals and SNS will affect the success of movies due to the increase in the spread and use of smart phones. And a prediction model that reflects both the expert survey information and big data mentioned above was proposed. In order to check the accuracy of the prediction of the proposed model, it was confirmed that it was improved (10.5%) compared to the existing model as a result of verification with real data.Therefore, it is judged that the proposed model will be helpful in decision-making of film production companies and distributors.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.234-243
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• 2022

This study aims to characterize the irradiated RBMK-1500 nuclear graphite in terms of both structural and radiological properties. The experimental results of morphological and structural analysis of the irradiated graphite samples by using SEM, Raman spectroscopy as well as the theoretical evaluation of primary displacement damage are presented. Moreover, the experimental and theoretical evaluation of the neutron flux is provided and the presence of several γ emitters in the analyzed graphite samples is assessed. Furthermore, the improved version of rapid analysis method for ¹⁴C activity determination is applied and the experimentally obtained results are compared with calculated ones. Results indicate that structural changes are uniform enough in all the analyzed samples. However, the distribution of radionuclides is non-homogeneous in the irradiated RBMK-1500 reactor graphite matrix. The comprehensive understanding of both structural and radiological characteristics of nuclear graphite is very important when dealing with decision about irradiated graphite waste management strategy or treatment options prior to its final disposal.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1462-1468
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• 2022

Among the data used for the diagnosis of calf disease, feces play an important role in disease diagnosis. In the image of calf feces, the health status can be known by the shape, color, and texture. For the fecal image that can identify the health status, data of 207 normal calves and 158 calves with diarrhea were pre-processed according to fecal status and used. In this paper, images of fecal variables are detected among the collected calf data and images are trained by applying GLCM-CNN, which combines the properties of CNN and GLCM, on a dataset containing disease symptoms using convolutional network technology. There was a significant difference between CNN's 89.9% accuracy and GLCM-CNN, which showed 91.7% accuracy, and GLCM-CNN showed a high accuracy of 1.8%.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4809-4818
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• 2022

The decommissioning project of a nuclear facility is a large-scale process that is expected to take about 15 years or longer. The range of risks to be considered is large and complex, then, it is expected that various risks will arise in decision-making by area during the project. Therefore, in this study, the risk family derived from the Decommissioning Risk Management (DRiMa) project was reconstructed into a decommissioning project risk profile suitable for the Kori Unit 1. Two criteria of uncertainty and importance are considered in order to prioritize the selected 26 risks of decommissioning project. The uncertainty is scored according to the relevant laws and decommissioning plan preparation guidelines, and the project importance is scored according to the degree to which it primarily affects the triple constraints of the project. The results of risks are divided into high, medium, and low. Among them, 10 risks are identified as medium level and 16 risks are identified as low level. 10 risks, which are medium levels, are classified in five categories: End state of decommissioning project, Management of waste and materials, Decommissioning strategy and technology, Legal and regulatory framework, and Safety. This study is a preliminary assessment of the risk of the decommissioning project that could be considered in the preparation stage. Therefore, we expect that the project risks considered in this study can be used as an initial data for reevaluation by reflecting the detail project progress in future studies.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.205-220
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• 2023

This study aimed to analyze the needs and interests of the public on endangered wildlife using complaint big data. We collected 1,203 complaints and their corresponding text data on endangered wildlife, pre-processed them, and constructed a document-term matrix for 1,739 text data. We performed LDA (Latent Dirichlet Allocation) topic modeling and network analysis. The results revealed that the complaints on endangered wildlife peaked in June-August, and the interest shifted from insects to various endangered wildlife in the living area, such as mammals, birds, and amphibians. In addition, the complaints on endangered wildlife could be categorized into 8 topics and 5 clusters, such as discovery report, habitat protection and response request, information inquiry, investigation and action request, and consultation request. The co-occurrence network analysis for each topic showed that the keywords reflecting the call center reporting procedure, such as photo, send, and take, had high centrality in common, and other keywords such as dung beetle, know, absence and think played an important role in the network. Through this analysis, we identified the main keywords and their relationships within each topic and derived the main issues for each topic. This study confirmed the increasing and diversifying public interest and complaints on endangered wildlife and highlighted the need for professional response. We also suggested developing and extending participatory conservation plans that align with the public's preferences and demands. This study demonstrated the feasibility of using complaint big data on endangered wildlife and its implications for policy decision-making and public promotion on endangered wildlife.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.3
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• pp.202-216
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• 2024

In today's rapidly changing business environment, rapid decision making and effective project management are essential for business growth. This study examines how project manager competencies and organizational structures affect business performance. Successful project execution depends on the strategic use of project managers' skills and organizational resources to maximize performance. An empirical study was conducted with 475 participants from the construction and engineering sectors. The applied analyses included multiple regression analysis and two-way ANOVA to assess how project manager competencies and organizational types affect business performance. The results of the study show that project manager competencies significantly improve business performance, especially when combined with appropriate organizational types. Effective use of organizational frameworks leads to better financial results, increased market competitiveness, and greater innovation. The results of the study are as follows: First, project manager competencies were found to have a significant positive effect on business performance. Second, the use of functional, project, and matrix organizations had a significant positive effect on business performance. This suggests that aligning organizational structures with business objectives is important for achieving optimal performance. Overall, this study provides valuable insights into the academic literature and practical applications of project management and organizational research. In addition, if we can select organizational members based on the learning effects of previous projects when operating new projects in the future, it will help reduce risks. Ultimately, it will improve the project manager's competency level, promote the individual abilities and knowledge sharing of team members, and provide opportunities for the company to build efficient new systems. This will be evaluated as a valuable study in terms of academic and practical productivity.