• 한국통신학회논문지
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• 제40권1호
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• pp.193-197
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• 2015

선박 내 안전과 정보전달 시스템 구축에 대한 수요가 증대함에 따라 선내 신호전달 특성에 대한 측정이 새로운 사안으로 등장하고 있다. 선박 내에 기 구축된 유선망을 활용함과 동시에 부가적인 선내 안전서비스에 필요한 망의 구성을 위해서 구조물의 일부를 불가피하게 변경하는 등의 비용증가를 최소화하여 무선 감지기 망을 구축함으로써 비용과 구축시간을 단축하기 위한 향상된 방안들이 필요하다. 이 논문에서는 선박 내 무선신호전달 특성을 측정하고 분석하는데 적합한 서명 수열-세방향 패킷 방식 기반 선내 전파전달측정 시스템을 설계하고 기존의 양방향 시스템에 대한 특성을 살펴본다. 그리고, 모의 실험을 통해 동작특성을 검증한 후, 구성된 시스템을 기반으로 선박 내 환경과 유사한 상황에서 측정한 데이터를 분석한다. 이와 같이 새로운 측정 시스템과 알고리즘을 적용함으로써 신뢰성 있고 비용효율적인 선박 내 무선 망을 구성하고 안정성 있는 망의 구성과 유지를 할 수 있을 것으로 기대한다.

• 한국통신학회논문지
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• 제40권1호
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• pp.183-186
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• 2015

최근 들어 선박 내 안전과 정보전달 시스템 구축에 대한 수요가 증대하고 있다. 선박 내에 기 구축된 유선망을 활용하는 방안이 있으나 이는 새로운 배선을 해야 하고 구조물의 일부를 불가피하게 변형해야 하고 이에 따른 구축시간의 증가와 비용증가가 크므로 이에 따른 무선 감지기 망을 구축함으로써 비용과 구축시간을 단축할 수 있다. 이 논문에서는 선박 내 무선신호전달 특성을 측정하고 분석하는데 적합한 서명 수열-양방향 패킷 방식 기반 선내 전파전달측정 시스템을 설계하고 모의 실험을 통해 검증한 뒤, 구성된 시스템을 기반으로 측정한 데이터를 분석한다. 이와 같은 선내 채널 측정시스템을 활용함으로써 신뢰성 있고 비용효율적인 선박 내 무선 망을 구성할 수 있을 것으로 기대한다.

• Nuclear Engineering and Technology
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• 제48권3호
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• pp.702-710
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• 2016

Global concern and interest in the safety of nuclear power plants have increased considerably since the Fukushima accident. In the event of a severe accident, the reactor vessel water level cannot be measured. The reactor vessel water level has a direct impact on confirming the safety of reactor core cooling. However, in the event of a severe accident, it may be possible to estimate the reactor vessel water level by employing other information. The cascaded fuzzy neural network (CFNN) model can be used to estimate the reactor vessel water level through the process of repeatedly adding fuzzy neural networks. The developed CFNN model was found to be sufficiently accurate for estimating the reactor vessel water level when the sensor performance had deteriorated. Therefore, the developed CFNN model can help provide effective information to operators in the event of a severe accident.

• 대한안전경영과학회지
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• 제7권5호
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• pp.155-174
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• 2005

Parametric life-cycle cost(LCC) models have been integrated with traditional design tools, and used in prior work to demonstrate the rapid solution of holistic, analytical tradeoffs between detailed design variations. During early designs stages there may be competing concepts with dramatic differences. Additionally, detailed information is scarce, and decisions must be models. for a diverse range of concepts, and the lack of detailed information make the integration make the integration of traditional LCC models impractical. This paper explores an approximate method for providing preliminary life-cycle cost. Learning algorithms trained using the known characteristics of existing products be approximated quickly during conceptual design without the overhead of defining new models. Artificial neural networks are trained to generalize on product attributes and life cycle cost date from pre-existing LCC studies. The Product attribute data to quickly obtain and LCC for a new and then an application is provided. In additions, the statistical method, called regression analysis, is suggested to predict the LCC. Tests have shown it is possible to predict the life cycle cost, and the comparison results between a learning LCC model and a regression analysis is also shown

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2723-2725
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• 2001

Nuclear power generations have been developed gradually since 1950. Nowadays, 440 nuclear power generations are taking charge of 16% of electric power production in the world. The most important factor to operate the nuclear power generations is safety. It is not easy way to control nuclear power generations with safety because nuclear power generations are very complicated systems. In the main control room of the nuclear power generations, about 4000 numbers of alarms and monitoring devices are equipped to handle the signals corresponding to operating equipments. Thus, operators have to deal with massive information and to grasp the situation immediately. If they could not achieve these task, then they should make big problem in the power generations Owing to too many variables, operators could be also in the uncontrolled situation. So in this paper, automatic systems to diagnose the fault are constructed using 2 steps neural networks. This diagnosis method is based on the pattern of the principal variables which could represent the type and severity of faults.

• 대한안전경영과학회지
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• 제17권3호
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• pp.239-246
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• 2015

Enforced environmental regulations call for extending the domain of manufacturers' responsibility to the entire product life cycle. To comply with the environmental regulations, manufacturers have constructed reverse logistics networks to re-collect their leftover waste for recycling consumed resources. However, the operational activities associated with storage, loading and transportation processes within the networks inevitably impose environmental burdens. Particularly, the transportation process largely influences environmental performance due to perpetual uses of transportation vehicles. Therefore, there is a need to develop an environmentally-conscious transportation model that can efficiently manage the uses of transportation vehicles. Additionally, it is vital to analyze its significances of environmental performance to compare quantitatively it with existing models. This paper proposes a transportation model for improving environmental performance in a reverse logistics network. This paper also presents a case study to perform its comparative analysis using Life Cycle Assessment that evaluates potential environmental impacts of a product system.

• Nuclear Engineering and Technology
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• 제53권12호
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• pp.4014-4021
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• 2021

If safety injection systems (SISs) do not work in the event of a loss-of-coolant accident (LOCA), the accident can progress to a severe accident in which the reactor core is exposed and the reactor vessel fails. Therefore, it is considered that a technology that provides recoverable maximum time for SIS actuation is necessary to prevent this progression. In this study, the corresponding time was defined as the golden time. To achieve the objective of accurately predicting the golden time, the prediction was performed using the deep fuzzy neural network (DFNN) with rule-dropout. The DFNN with rule-dropout has an architecture in which many of the fuzzy neural networks (FNNs) are connected and is a method in which the fuzzy rule numbers, which are directly related to the number of nodes in the FNN that affect inference performance, are properly adjusted by a genetic algorithm. The golden time prediction performance of the DFNN model with rule-dropout was better than that of the support vector regression model. By using the prediction result through the proposed DFNN with rule-dropout, it is expected to prevent the aggravation of the accidents by providing the maximum remaining time for SIS recovery, which failed in the LOCA situation.

• 한국공간구조학회논문집
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• 제23권3호
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• 한국항행학회논문지
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• 제12권6호
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• pp.611-618
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• 2008

방재통신시스템은 방재행정무선시스템을 주체로 정비되고 최근에는 위성통신을 확대하고, 유선통신과의 상호보완 멀티미디어화 등을 들 수 있다. 유비쿼터스 센서 네트워크를 기반으로 방재정보통신 분야로는 풍수해, 시설안전, 교통안전, 산업안전, 에너지안전, 소방안전, 정보통신 안전, 특수안전 분야에는 방사능 누출사고, 환경오염, 해양오염, 산림재해 등이 있다. 본 논문에서는 USN(Ubiquitous Sensor Network) 기술을 이용한 방재정보통신시스템을 통신 기반시설로서 방재와 관련한 정보를 일원적으로 관리 운용하는 시스템 및 용용에 대하여 연구한다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 춘계학술대회 논문집
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• pp.343-352
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• 2008

Safety-critical systems related to the railway communications are currently undergoing changes. Mechanical and electro-mechanical devices are being replaced by programmable electronics that are often controlled remotely via communication networks. Therefore designers and operators now not only have to contend with component failures and user errors, but also with the possibility that malicious entities are seeking to disrupt the services provided by theirs systems. Recognizing the safety-critical nature of the types of communications required in rail control operations, the communications infrastructure will be required to meet a number of safety requirements such as system faults, user errors and the robustness in the presence of malicious attackers who are willing to take determined action to interfere in the correct operation of a system. This paper discusses the safety strategies employed in the railway communications and proposes a security mechanism for Korean railway communication system. We present the developed communication safety evaluation tool based on the proposed security mechanism and also evaluate its protecting capability against the threats of masquerading, eavesdropping, and unauthorized message manipulation.