• Tunnel and Underground Space
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• v.14 no.5
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• pp.353-362
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• 2004

Prediction of fragmentation in bench blasting is one of the most important factors to establish the production plan. It is widely accepted that fragmentation could be accurately predicted using the Kuz-Ram model in bench blasting. Nevertheless, the model has an ambiguous or subjective aspect in evaluating the model parameters such as joint condition, rock strength, density, burden, explosive strength and spacing. This study proposes a new method to evaluate the parameters of Kuz-Ram model, and the predicted mean fragment sizes using the proposed method are examined by comparing the measured sizes in the field. The results show that the predictions using Kuz-Ram model with the proposed method coincide with field measurements, but Kuz-Ram model does not reflect the in-situ rock condition and hence needs to be improved.

• Proceedings of the Korean Reliability Society Conference
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• 2001.06a
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• pp.513-520
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• 2001

신규개발장비에 대하여 사용자가 제시한 RAM-D 요구조건을 만족하기 위한 하부 체계의 RAM-D 설계목표 설정 절차 및 방법에 대하여 기동무기체계의 대표적 장비인 전차를 대상으로 연구하여 RAM-D 요소별 할당 모델을 개발하였다.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.10a
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• pp.102-114
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• 1998

항공기 개발단계에서의 RAM(Reliability, Availability, Maintainability) 예측은 진행중인 설계개념이 RAM 개발 목표값을 달성할 수 있는지를 판단하여 이를 설계에 반영하기 위한 것이다. 본 연구에서 신뢰도 예측 모델은 항공기의 임무에 초점을 둔 임무신뢰도와 시스템신뢰도를 산출하고, 정비도 예측 모델은 군수지원분석자료(LSAR)와의 호환성을 유지할 수 있도록 하였으며, 가용도 예측 모델은 신뢰도와 정비도 자료를 활용한 운용가용도를 예측하는 데에 기준을 두었다. 본 연구는 기존의 RAM 예측이 각각 독립적으로 수행된 점을 보완하여 서로간의 상호관계를 반영한 통합 예측 모델을 개발하는 데에 초점을 두었으며, 실제적인 운용개념을 반영한 모델링으로서 항공기 개발단계에서 뿐만 아니라 실제 운용단계에서의 RAM 분석에 효과적이라 판단된다.

• Tunnel and Underground Space
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• v.26 no.2
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• pp.120-130
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• 2016

Considering the applicability of Kuz-Ram model, which has been used extensively for predicting rock fragmentation size distribution by blasting, to domestic open-pit limestone mine, a total of 21 blasting tests have been executed at an open-pit limestone mine in eastern Gangwon of South Korea. A comparative analysis of field measured value and Kuz-Ram predicted value showed that there are a considerable amount of error in the predicted values regardless of application of various correction parameters for rock factor and uniformity factor; up to 56.45% in mean fragmentation size and 37.52% in uniformity index. Also the problem of applying different correction parameters has been derived even though a similar blasting pattern has been adopted for a same blasting bench. The authors therefore suggest that Kuz-Ram model needs to be modified for a proper application to domestic open-pit limestone mine.

• The Journal of the Acoustical Society of Korea
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• v.21 no.8
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• pp.679-685
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• 2002

Low-frequency mono-static reverberation model for shallow-water environment is presented. It is necessary to develop the transmission loss model to calculate the sub-bottom interaction because the ray-based transmission loss model is difficult to compute the pressure accurately which penetrates the bottom medium. In this paper reverberation level is calculated using the RAM (Range dependent Acoustic Model) to augment the multi-path expansion model because it does not estimate transmission loss accurately in shallow water. The signals generated by the L-HYREV and the GSM are compared with the observed signals and it is showed that the L-HYREV model provides a closer fit to the observed signals than those obtained using the GSM.

• Proceedings of the Korean Reliability Society Conference
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• 2001.06a
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• pp.503-511
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• 2001

신규 개발되는 기동장비의 RAM-D 요구조건 설정방법에 관하여 기동 무기체계의 대표적 장비인 전차를 대상으로 연구하였으며, 사용자의 장비운용에 대한 요구 가용 능력 및 운용유지 조건을 고려한 RAM-D 요구조건 설정절차 및 분석 모델을 개발하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.201-207
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• 2012

A RAM-based neural network is a weightless neural network based on binary neural network(BNN) which is efficient neural network with a one-shot learning. RAM-based neural network has multiful information bits and store counts of training in BNN. Supervised learning based on the RAM-based neural network has the excellent performance in pattern recognition but in pattern categorization with unsupervised learning as unsuitable. In this paper, we propose a unsupervised learning algorithm in the RAM-based neural network to perform pattern categorization. By the proposed unsupervised learning algorithm, RAM-based neural network create categories depending on the input pattern by itself. Therefore, RAM-based neural network for supervised learning and unsupervised learning should proof of all possible complex models. The training data for experiments provided by the MNIST offline handwritten digits which is consist of 0 to 9 multi-pattern.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.1
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• pp.67-76
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• 2010

RAM(Reliability, Availability, Maintainability) characteristics of weapon system is a part of Required Operational Capability, must be reasonable and achievable. In this study, we studied the criteria, important factors to establish RAM requirement and reviewed the current process. Then we propose the new process and method to establish the reasonable and achievable RAM requirement by BCS(Baseline Comparison System) model.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.1
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• pp.41-49
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• 2010

In this paper, we propose a hierarchical RAM simulation model framework which are used to analyze the RAM specifications on the concept refinement phase. The hierarchical RAM simulation model framework consists of RAM simulation models, class library and each model's input and output data lists. The hierarchical RAM simulation models are co-operated with 3 kinds of model - type I, II, III. Type I, II models are used to analyze the target operational availability and Type III is used to establish the initial RAM specifications. Each model's input and output data lists are defined by considering each model's purpose of RAM analysis. The class library is arranged with each model's classes for implementing the hierarchical simulation models. The proposed framework may be applied for executing the RAM activities effectively.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.44-47
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• 2000

제조설비의 운영은 적절한 생산제품의 품질(신뢰도 생산단가 등)을 유지하는 조건으로 유지되어야 한다. 본 연구에서는 제조설비의 적정운영조건을 구하기 위하여 제조설비의 RAM 및 순기비용(LCC)이 제조설비의 성능에의 영향을 분석하고 최적대안을 구하였다. 이를 위하여 우선 설비의 RAM 및 LCC산정모델을 개발하고 이를 이용하여 제조설비의 성능에 미치는 영향을 분석하기 위한 수리모델을 제시하였다. 이를 위한 전산프로그램을 개발하고 이를 이용하여 제조시스템의 성능 분석 사례를 들어 보였다. 또한 다양한 환경에서 제조시스템의 성능을 예측하기 위하여 시스템의 복잡성이 큰 문제를 분석하는데 적절한 GMDH방법을 사용하여 추정하였다. 이를 이용한 성능예측의 실 예를 들어 본 연구의 과정을 보였다.