• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2013.04a
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• pp.41-41
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• 2013

화학산업이 발달함에 따라 화학 산업 현장에서 사용되고 있는 가연성물질들의 여러 가지 화재 및 폭발 위험이 증가되고 있으며, 화재 및 폭발의 예방 안전을 위한 화학공정설계 및 대처에 있어, 물질의 연소특성치 데이터를 필요로 한다. 인화점은 가연성 액체를 다루는 공정에서 안전한 취급과 사고방지를 위해 중요한 자료가 되며, 화재의 위험을 나타내는 지표로서 가연성액체의 액면 가까이서 인화할 때 필요한 증기를 발산하는 액체의 최저온도, 그리고 가연성증기의 포화증기압이 공기와 혼합기체의 폭발한계 하한농도와 같게 되는 온도로 정의한다. 본 연구에서는 2성분계 혼합물에 대해 인화점을 측정하였고, 측정값을 Raoult의 법칙과 다중회귀분석(Multiple Regression)을 도입하여 이론값과 비교 하였다. 따라서 본 연구에서 제시된 방법론에 의해 아직까지 밝혀지지 않은 순수가연성액체와 가연성혼합물의 인화점을 예측하는 방법을 전개하고자 하며, 실험에서 찾고자하는 자료에 도움을 주고자 한다. 본 연구를 바탕으로 혼합물의 인화점 예측 방법과 실험에서 측정한 자료를 화재 및 폭발을 방지하는 기초 자료로 제공하고자하며, 산업현장에서 취급되고 있고 위험성 평가가 되지 않은 보다 많은 물질에 대한 이론 및 실험 연구에 활용 되도록 하는데 그 목적이 있다.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.7-8
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• 1994

최근 들어 원자재, 재공품 또는 완제품을 신속하고 정확하게 공급/배분하기 위해 저장과 인출을 담당하는 Material Handling System을 이용하여 작업자의 개입요소를 줄이며, 제고관리 Computer를 이용하여 입고/출고 명령을 유효적절하게 처리하는 ASRS(Atomated Storage and Retreival System : 자동창고 시스템)가 널리 공급되고 있다. 중앙은행의 현금창고, 병원의 약품창고, 식품/화장품 회사의 배송창고, 군수물자의 군납창고에 이르기까지 물품의 저장 또는 공급의 필용성을 갖는 곳에서는 어디든지 찾아볼 수 있는 ASRS는 가깝게는 관공소나 대형빌딩의 주차장에도 이의 개념이 도입되어 사용됨을 볼 수 있다. 최근의 인금인상, 구인난등의 이유로 ASRS설치는 계속 증가할 추세에 있으나 자동 창고 시스템을 설치하기 위해서는 막대한 초기 투자가 필요하며 시스템의 설계 및 설치후 운영에 대한 연구가 반드시 필요하다. ASRS의 운영 Rule 검증, 수행능력 분석등의 목적을 갖는 연구에는 여러 접근방법이 있을 수 있으나 구성 설비와 운영 Rule의 복잡한 관계로 컴퓨터 시뮬레이션의 거의 유일한 문제해결 방법이다. ASRS의 Modeling에 관한 기존의 연구로는 수리모델 수립. 이산사건 시스템의 관점에서 event-graphy, petri-net을 이용한 modeling이 있으며 ASRS에 대한 전용 Simulator 개발등이 진행되었다. 본 연구의 대상 시스템은 2개의 Rack과 하나의 Stacker Crane 으로 구성된 Aisle과 입출고의 물류를 처리하는 순환 RGVS(Rail Guided Vehicle System), 입/출고장을 구성하는 Conveyor Net등으로 이루어진 제조-물류시스템의 일반적인 ASRS이다. 또 이 ASRS의 입/출고 방식은 전수 입/출고만을 포함하며 Blocking 방지를 위한 Capaicty 예약, 다중설비 선택등의 문제등을 고려하고 있다. 본 연구의 접근방법으로는 ASRS의 개념적인 Reference Model을 수립하고 이 Reference Model에 대한 Formal Model로 DEVS(Discrete Event System Specification)을 이용하여 시스템을 Modeling하였다. 이의 Computer Simulation을 위하여 DEVS형식론 환경에서의 Simulation Language인 DEVSim ++ⓒ를 이용하여 시스템을 구현하였다.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.11
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• pp.819-830
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• 2009

When simulating large crowds, it is inevitable that the models and motions of many characters will be cloned. McDonnell et al. analyzed user's perception to find cloned characters. They established that clones of appearance are far easier to detect than motion clones. In this paper, we expand McDonnell's research[1], with the focus on multiple clones and the appearance variety in real-time game environment. Introducing the perceptual organization, we show the appearance variety of crowd clones by using game items and texture modulation. Other factors that influence the ability to detect clones were examined, such as the moving direction and distance between character clones. Our results provide novel insights and useful thresholds that will assist in creating more realistic crowds of game environments.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.369-377
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• 2023

In the industrial manufacturing sector, quality control is pivotal for minimizing defect rates; inadequate management can result in additional costs and production delays. This study underscores the significance of detecting texture defects in manufactured goods and proposes a more precise defect detection technique. While the DFR(Deep Feature Reconstruction) model adopted an approach based on feature map amalgamation and reconstruction, it had inherent limitations. Consequently, we incorporated a new loss function using statistical methodologies, integrated a skip connection structure, and conducted parameter tuning to overcome constraints. When this enhanced model was applied to the texture category of the MVTec-AD dataset, it recorded a 2.3% higher Defect Segmentation AUC compared to previous methods, and the overall defect detection performance was improved. These findings attest to the significant contribution of the proposed method in defect detection through the reconstruction of feature map combinations.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.75-87
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• 2011

This paper presents a method for region-based building extraction from high resolution satellite images(HRSI) using integrated information of spectral and color invariant features without user intervention such as selecting training data sets. The purpose of this study is also to evaluate the effectiveness of the proposed method by applying to IKONOS and QuickBird images. Firstly, the image is segmented by the MSRG method. The vegetation and shadow regions are automatically detected and masked to facilitate the building extraction. Secondly, the region merging is performed for the masked image, which the integrated information of the spectral and color invariant features is used. Finally, the building regions are extracted using the shape feature for the merged regions. The boundaries of the extracted buildings are simplified using the generalization techniques to improve the completeness of the building extraction. The experimental results showed more than 80% accuracy for two study areas and the visually satisfactory results obtained. In conclusion, the proposed method has shown great potential for the building extraction from HRSI.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.11 no.2
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• pp.133-156
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• 2013

Natural analogue studies play an important role in the safety case which requires multiple lines of evidence including the safety assessment for the geological disposal of radioactive wastes. In this study, foreign status of natural analogue studies was investigated by summarizing natural analogue results according to the research topics related with repository materials and radionuclide migration and retardation. Main results, issues, and applicability of the foreign natural analogue studies were also analyzed. The results of domestic natural analogue studies were classified into studies using uranium ore bodies, rocks, groundwaters, and archeological artifacts, respectively, and their main results were summarized. There are massive materials for natural analogue studies which have been carried out during last several decades but they have not been actively applied to the safety assessment and safety case development for the radioactive waster disposal. Thus, in this study, applicable methods of natural analogues were summarized and a methodology for improving their applicability was examined. Natural analogue study is apparently necessary to improve and illustrate the reliability of safety assessment for a radioactive waste repository. Therefore, it is necessary to develop a methodology and construct a natural analogue information database for the application of the results from natural analogue studies to safety case development.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.7
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• pp.165-174
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• 2018

The purpose of this study is to verify the effects of R&D process maturity on product development performance and to verify whether R&D project performance mediating effect. The questionnaire was collected and analyzed to 131 persons related to the R&D work in 55 companies and public institutions performing R&D work. The results of this study are as follows, the effect of R&D process maturity on product development performance and mediating effect of R&D project performance were analyzed by simple and multiple regression analysis. Regression analysis was analyzed by SPSS statistical program. The higher the maturity level of the R&D process, the higher the product development performance and the R&D project performance, and the R&D project performance is partially mediated. It is concluded that it is important to establish and operate a systematic R&D process and to develop a project management methodology suitable for the characteristics of a company in order to improve product development performance. It is expected that if this study are utilized as practical R&D management methodology in corporate management, it will contribute to improvement of company product performance.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.489-499
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• 2012

In this paper, a classifier ensemble framework for remote sensing data classification is presented that combines classification results generated from both different training sets and different classifiers. A core part of the presented framework is to increase a diversity between classification results by using both different training sets and classifiers to improve classification accuracy. First, different training sets that have different sampling densities are generated and used as inputs for supervised classification using different classifiers that show different discrimination capabilities. Then several preliminary classification results are combined via a majority voting scheme to generate a final classification result. A case study of land-cover classification using multi-temporal ENVISAT ASAR data sets is carried out to illustrate the potential of the presented classification framework. In the case study, nine classification results were combined that were generated by using three different training sets and three different classifiers including maximum likelihood classifier, multi-layer perceptron classifier, and support vector machine. The case study results showed that complementary information on the discrimination of land-cover classes of interest would be extracted within the proposed framework and the best classification accuracy was obtained. When comparing different combinations, to combine any classification results where the diversity of the classifiers is not great didn't show an improvement of classification accuracy. Thus, it is recommended to ensure the greater diversity between classifiers in the design of multiple classifier systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.220-227
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• 2021

PCT (Power Cable Tunnel) and UT (Utility Tunnel), which are non-transport underground infrastructures, are mostly RC (Reinforced Concrete) structures, and their durability decreases due to the deterioration caused by carbonation over time. In particular, since the rate of carbonation varies by use and region, a predictive model based on actual carbonation data is required for individual maintenance. In this study, a carbonation prediction model was developed for non-transport underground infrastructures, such as PCT and UT. A carbonation prediction model was developed using multiple regression analysis and deep neural network techniques based on the actual data obtained from a safety inspection. The structures, region, measurement location, construction method, measurement member, and concrete strength were selected as independent variables to determine the dependent variable carbonation rate coefficient in multiple regression analysis. The adjusted coefficient of determination (Ra2) of the multiple regression model was found to be 0.67. The coefficient of determination (R2) of the model for predicting the carbonation of non-transport underground infrastructures using a deep neural network was 0.82, which was superior to the comparative prediction model. These results are expected to help determine the optimal timing for repair on carbonation and preventive maintenance methodology for PCT and UT.