• Computers and Concrete
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• 제25권2호
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• pp.133-144
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• 2020

This paper aims to study vibration characteristics of chiral and zigzag double-walled carbon nanotubes entrenched on Donnell shell model. The Eringen's nonlocal elastic equations are being combined with Donnell shell theory to observe small scale response. Wave propagation is proposed technique to establish field equations of model subjected to four distinct end supports. A nonlocal model has been formulated to explore the frequency spectrum of both chiral and zigzag double-walled CNTs along with diversity of indices and nonlocal parameter. The significance of scale effect in relevance of length-to-diameter and thickness- to- radius ratios are discussed and displayed in detail. The numerical solution based on this nonlocal Donnell shell model can be further used to predict other frequency phenomena of double-walled and multi-walled CNTs.

• 화약ㆍ발파
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• 제23권3호
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• pp.43-56
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• 2005

폭속은 화약에서 폭굉의 전파속도이다. 화약의 폭굉 속도는 발파 환경이나 시험 조건 하에서 제품의 성능과 관련된 주요한 변수로 사용되고 있다. 또한 발파현장이나 시험장에서 가장 용이하게 측정할 수 있는 화약의 특성치이며 또한 정확한 수치로 측정 할 수 있어 품질 평가 도구로 사용되고 있다. 이 논문에서는 발파에 있어서 폭속과의 관련성을 논의 및 분석한다. 발파 기술자에게 유용하게 사용 될 수 있도록 폭속과 발파 공정 관의 관련성을 정밀히 검토해보았다. 그러나 폭속과 폭약의 품질, 효율성과는 직접 관련이 없는 것으로 나타났다.

• 환경영향평가
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• 제18권6호
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• pp.331-346
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• 2009

The serious problem facing two-dimensional finite element hydraulic model is the treatment of wet and dry areas. This situation is encountered in most practical river and coastal engineering problems, such as flood propagation, dam break analysis and so on. Especially, dry areas result in mathematical complications and require special treatment. The objective of this study is to investigate the wet and dry parameters that have direct relevance to model performance in situations where inundation of initially dry areas occurs. Several numerical simulations were carried out, which examined the performance of the marsh porosity method of RMA-2 model to investigate for application of parameters. Experimental channel with partly dry side slopes, straight channel with irregular geometry and Han river were performed for tests. As a result of this study, effectively applied marsh porosity method provide a reliable results for flow distribution of wet and dry area, it could be further developed to basis for extending to water quality and sediment transport analysis.

• 한국정보시스템학회지:정보시스템연구
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• 제30권4호
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• pp.203-226
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• 2021

Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.

• 천문학회보
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• 제46권2호
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• pp.74.2-75
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• 2021

We demonstrate that a deep learning classifier that only uses to gravitational wave (GW) detectors auxiliary channel data can distinguish various types of non-Gaussian noise transients (glitches) with significant accuracy, i.e., ≳ 80%. The classifier is implemented using the multi-scale neural networks (MSNN) with PyTorch. The glitches appearing in the GW strain data have been one of the main obstacles that degrade the sensitivity of the gravitational detectors, consequently hindering the detection and parameterization of the GW signals. Numerous efforts have been devoted to tracking down their origins and to mitigating them. However, there remain many glitches of which origins are not unveiled. We apply the MSNN classifier to the auxiliary channel data corresponding to publicly available GravitySpy glitch samples of LIGO O1 run without using GW strain data. Investigation of the auxiliary channel data of the segments that coincide to the glitches in the GW strain channel is particularly useful for finding the noise sources, because they record physical and environmental conditions and the status of each part of the detector. By only using the auxiliary channel data, this classifier can provide us with the independent view on the data quality and potentially gives us hints to the origins of the glitches, when using the explainable AI technique such as Layer-wise Relevance Propagation or GradCAM.

• 한국정보통신학회논문지
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• 제18권10호
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• pp.2331-2338
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• 2014

북한의 핵폭탄과 미사일 기술개발이 진전됨에 따라 고 고도 핵전자기파(HEMP)에 대한 위협이 새롭고 절박하게 인지되고 있는데, 일례로 이미 북한이 수개의 핵폭탄을 개발 보유하고 있으며 북한이 남한에 대한 핵탄두 운반 능력을 가지고 남한을 위협하고 있다. ITU K78, K81 그리고 IEC에서는 EMP/HEMP로부터 프로세서 내장 기기의 오동작을 줄이기 위해 항해 통신장비를 포함한 산업용 설비에 대한 대책을 권장하고 있으나, 이에 대한 의사시험은 1960-1990년대 미국공군무기연구소(USA/AFWL)의 논문들을 토대로 수행할 수 밖에 없다. 이 모의 시험결과는 모든 HEMP 관련 제품이 강력하게 수출을 통제하고 있기 때문에 북한의 위협에 직면한 남한으로서는 매우 중요한 연구 활동의 결과이다. 저자 등이 새롭게 개발한 HEMP cord는 HEMP의 발생과 전파현상 분석, 방호실 설계 툴, 흙과 암반으로 구성된 다충 구조에서 전자파 에너지의 감쇠량 그리고 HEMP 필터 설계 툴을 포함하고 있다. 특히 다층구조에서 전자파 감쇠량 연산 툴은 흙과 암반이 매우 다양한 특성을 가지고 있기 때문에 많은 실측 데이터를 바탕으로 최소자승법에 의하여 해석하였다.

• Safety and Health at Work
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• 제3권2호
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• pp.92-100
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• 2012

Objectives: The French National Occupational Diseases Surveillance and Prevention Network (RNV3P) is a French network of occupational disease specialists, which collects, in standardised coded reports, all cases where a physician of any specialty, referred a patient to a university occupational disease centre, to establish the relation between the disease observed and occupational exposures, independently of statutory considerations related to compensation. The objective is to compare the relevance of disproportionality measures, widely used in pharmacovigilance, for the detection of potentially new disease ${\times}$ exposure associations in RNV3P database (by analogy with the detection of potentially new health event ${\times}$ drug associations in the spontaneous reporting databases from pharmacovigilance). Methods: 2001-2009 data from RNV3P are used (81,132 observations leading to 11,627 disease ${\times}$ exposure associations). The structure of RNV3P database is compared with the ones of pharmacovigilance databases. Seven disproportionality metrics are tested and their results, notably in terms of ranking the disease ${\times}$ exposure associations, are compared. Results: RNV3P and pharmacovigilance databases showed similar structure. Frequentist methods (proportional reporting ratio [PRR], reporting odds ratio [ROR]) and a Bayesian one (known as BCPNN for "Bayesian Confidence Propagation Neural Network") show a rather similar behaviour on our data, conversely to other methods (as Poisson). Finally the PRR method was chosen, because more complex methods did not show a greater value with the RNV3P data. Accordingly, a procedure for detecting signals with PRR method, automatic triage for exclusion of associations already known, and then investigating these signals is suggested. Conclusion: This procedure may be seen as a first step of hypothesis generation before launching epidemiological and/or experimental studies.

• 한국콘텐츠학회논문지
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• 제19권5호
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• pp.128-137
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• 2019

본 연구는 지방자치단체 혁신정책을 브랜드과제를 통해 유형화하여 분석하고 정책적 시사점을 제시하였다. 이를 위해 전국 자치단체 브랜드과제 844개를 대상으로 지방자치단체 8개 유형별로 브랜드과제의 정책분야를 분류하는 방법을 활용하였다. 주요 분석결과는 다음과 같이 요약된다. 첫째, 전체 자치단체를 대상으로 보았을 때 사회적경제/지역경제 분야의 비율이 가장 높은 것으로 나타났다. 둘째, 자치단체 유형별로 중점을 두고 있는 정책분야가 상이하다. 셋째, 혁신정책의 선정에는 지역의 특수성이 반영되고 있다. 넷째, 브랜드과제의 유형별로 중점을 두는 자치단체가 차별적으로 나타났다. 따라서 이러한 결과는 이론적 배경에서 제시한 지방자치단체 유형론의 이론적 적실성을 확인시켜주는 결과라 할 수 있다. 정책적 시사점으로는 지역경제 활성화를 위한 혁신정책의 필요, 지역특성에 맞는 혁신정책의 수립, 우수 혁신정책의 전파를 제시하였다.

• Computers and Concrete
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• 제19권5호
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.