• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.32 no.5D
• /
• pp.453-461
• /
• 2012

Life analysis is conducted for economic analysis of equipment or facilities. The purpose of life analysis is to predict future indicators for scrapping construction equipment, and establish and utilize a wide variety of business strategies according to data predictions. First, this study shows the methods to figure out average life, life expectancy and life prediction of construction equipment and the analysis of life making methods, using survival curves. Second, the study proposes and examines the life expectancy making model depending on revenues and expenses. The result of the study reveals that the economic life of the same equipment varies with expenses, revenues and the initial cost. The life expectancy making model for construction equipment reflects respective management status for equipment and will help efficient management for companies.

• Convergence Security Journal
• /
• v.11 no.6
• /
• pp.67-72
• /
• 2011

A system that provides a ubiquitous service is a networked system that has to overcome their circumstances that the service survivability is weak. the survivability of a networked system is defined as an ability of the system that can offer their services without interruption, regardless of whether components comprising the system are under failures, crashes, or physical attacks. This paper presents an approach that end users can obtain a quantitative evaluation of U-service survivability to reflect intended cyber attacks causing the networked system to fall into byzantine failures in addition to the definition of the survivability. In this paper, a Jini system based on wireless local area networks is used as an example for quantitative evaluation of U-service survivability. This paper also presents an continuous time markov chain (CTMC) Model for evaluation of survivability of U-service that a Jini system provides, and an approach to evaluate the survivability of the U-service as a blocking probability that end users can not access U-services.

• The Korean Publising Journal, Monthly
• /
• s.277
• /
• pp.12-13
• /
• 2000

• Journal of the Korean Society of Radiology
• /
• v.9 no.6
• /
• pp.417-420
• /
• 2015

We obtained Surviving Fraction (SF) after irradiation carbon beam to compare the applicability of the Linear-Quadratic model, Incomplete Repair model, Marchese model. Mathematica software(ver 9.0) used to calcurate parameters and compared result. LQ model could not explain the entire response of fractionated carbon beam irradiation. It becomes necessary to construct models that extend the LQ model of conventional radiotherapy for the carbon beam therapy. By combining both Potentially Lethal Damage Repair (PLDR) and Sublethal Damage Repair (SLDR) a new LQ model can develop that aptly modeled the cellular response to fractionated irradiation.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.12
• /
• pp.2581-2588
• /
• 2012

Survivability of combat system is changed by various facts in dynamic battle field. Existing survivability analysis programs for a combat system analyze statically survivability for combat system in spite of dynamic battle environment. To overcome this limitation, we propose an agent-based modeling and simulation method for dynamic survivability analysis of the combat system. To do this, we have adopted DEVS formalism, SES/MB framework and agent technology for modeling components of the combat system and crews. The proposed method has advantages of being able to analyze not only a static survivability of the combat system but also a dynamic survivability of combat system by applying responses of crews in battle field.

• Proceedings of the Korea Database Society Conference
• /
• 2001.11a
• /
• pp.555-566
• /
• 2001

급변하는 주식시장 정보를 인터넷을 통하여 제공하는 증권정보 제공[IP] 사이트들이 치열한 경쟁을 치루면서도 빈약한 수익모델로 향후 기업의 생존여부 조차 불확실할 정도로 어려움에 처해있다. (중략)

• Proceedings of the Korean Information Science Society Conference
• /
• 2007.06d
• /
• pp.520-525
• /
• 2007

센서 네트워크에서 에너지의 제한은 이전의 다른 많은 네트워크 시스템과 구별되는 특징을 가지게 한다. 따라서 센서 네트워크에서의 가장 중요한 연구 주제는 에너지를 절약하도록 하는 것에 초점이 맞춰진다. 기존의 센서 네트워크에서는 주로 Network, Mac, Phy 계층에서 연구가 진행되었으며, 또한 사용자가 센서 네트워크의 Lifetime을 미리 정할 수 없었다. 하지만, 어떤 센서 네트워크에서는 어떤 특정한 시간까지 생존할 필요가 있을 것이다. 이 논문에서는 미리 정해진 Lifetime을 보장하는 노드의 응용 수준에서의 에너지 소모 모델을 제공한다. 이와 같은 일을 하기위해서, 첫 번째로, 센서 네트워크에서의 응용을 6가지로 구분하고, 응용에서 필요로 되는 5가지 연산을 정의했다. 두 번째로 이러한 5가지의 연산을 포함하는 에너지 소모 모델을 만들었으며, 이것을 가지고 각각의 연산에서 소모되는 에너지를 통계적인 기법을 통해서 계산한다. 마지막으로 노드가 미리 정해진 시간까지 생존하도록 각각의 노드에서 응용의 에너지 소모 계획을 다시 작성한다. 예를들면, 응답주기와 센싱주기의 변경을 통하여 에너지의 소모를 줄일 수 있다. 결과적으로 이러한 일들은 노드가 정해진 시간까지 생존하도록 보장해 준다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.5
• /
• pp.1269-1276
• /
• 2015

The mission capability of tank depends on its survivability. The survivability is ability for protection and tolerance by damage from threats. To improve the survivability of tank, we need an effectiveness analysis for loss of components, and accomplish performance enhancement using the result of analysis. In this paper, we develop a survivability analysis system for tank based on the importance. The importance numerically represents weight of each component which consisting of whole tank, also the importance is basic method of quantitative survivability analysis. To do this, we assign weight values to each component of tank, compose a weight tree, apply the importance calculation equation, and analyze the survivability of tank. Also we develop the system that consists of component structuralization and weight value setting program and survivability analysis and visualization program, and evaluate the system using implemented 3D CAD models of components of tank. The developed system apply to arrangement components.

• Progress in Medical Physics
• /
• v.11 no.2
• /
• pp.157-165
• /
• 2000

Purpose: For estimation of yields of l)NA damages induced by radiation and enhanced by oxygen, a mathematical model was used and tested. Materials and Methods: Reactions of the products of water radiolysis were modeled as an ordinary time dependant equations. These reactions include formation of radicals, DNA damage, damage repair, restitution, and damage fixation by oxygen and H-radical. Several rate constants were obtained from literature while others were calculated by fitting an experimental data. Sensitivity studies were performed changing the chemical rate constant at a constant oxygen number density and varying the oxygen concentration. The effects of oxygen concentration as well as the damage fixation mechanism by oxygen were investigated. Oxygen enhancement ratio(OER) was calculated to compare the simulated data with experimental data. Results: Sensitivity studies with oxygen showed that DNA survival was a function of both oxygen concentration and the magnitude of chemical rate constants. There were no change in survival fraction as a function of dose while the oxygen concentration change from 0 to 1.0 x 10$^{7}$ . When the oxygen concentration change from 1.0 $\times$ 107 to 1.0 $\times$ 101o, there was significant decrease in cell survival. The OER values obtained from the simulation study were 2.32 at 10% cell survival level and 1.9 at 45% cell survival level. Conclusion: Sensitivity studies with oxygen demonstrated that the experimental data were reproduced with the effects being enhanced for the cases where the oxygen rate constants are largest and the oxygen concentration is increased. OER values obtained from the simulation study showed good agreement for a low level of cell survival. This indicated that the use of the semi-empirical model could predict the effect of oxygen in cell killing.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.48 no.2
• /
• pp.47-55
• /
• 2011

Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.