• Earthquakes and Structures
• /
• v.12 no.6
• /
• pp.653-663
• /
• 2017

A seismic margin assessment evaluates how much margin exists for the system under beyond design basis earthquake events. Specifically, the seismic margin for the entire system is evaluated by utilizing a systems analysis based on the sub-system and component seismic fragility data. Each seismic fragility curve is obtained by using empirical, experimental, and/or numerical simulation data. The systems analysis is generally performed by employing a fault tree analysis. However, the current practice has clear limitations in that it cannot deal with the uncertainties of basic components and accommodate the newly observed data. Therefore, in this paper, we present a Bayesian-based seismic margin assessment that is conducted using seismic fragility data and fault tree analysis including Bayesian inference. This proposed approach is first applied to the pooltype nuclear research reactor system for the quantitative evaluation of the seismic margin. The results show that the applied approach can allow updating by considering the newly available data/information at any level of the fault tree, and can identify critical scenarios modified due to new information. Also, given the seismic hazard information, this approach is further extended to the real-time risk evaluation. Thus, the proposed approach can finally be expected to solve the fundamental restrictions of the current method.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.14 no.6
• /
• pp.667-681
• /
• 2012

In general, risk management consists of a series of processes or steps including risk identification, risk analysis, risk evaluation, risk mitigation measures, and risk re-evaluation. In this paper, potential risk factors that occur in shield TBM tunnels were investigated based on many previous case studies and questionaries to tunnel experts. The risk factors were classified as geological, design or construction management features. Fault Tree was set up by dividing all feasible risks into four groups that associated with: cutter; machine confinement; mucking (driving) and segments. From the Fault Tree Analysis (FTA), 12 risk items were identified and the probability of failure of each chosen risk item was obtained.

• Journal of Advanced Navigation Technology
• /
• v.21 no.5
• /
• pp.472-478
• /
• 2017

In this paper, we deals with optimizing traffic signal timing in grid networks by using a network topology design method. Optimizing traffic signal timing includes minimizing delay time delay between departure and destination by interlocking straight traffic signal in the minimum spanning tree(MST). On the assumption that users of network abide by the paths provided in this paper, this paper shows optimizing traffic signal timing in grid networks. the paths provided in this paper is gathered by using Dijkstra algorithm used in computer networks. The results indicate minimizing delay time of passing through the grid network and interlocking traffic signal in the grid network.

• Journal of Distribution Science
• /
• v.13 no.6
• /
• pp.11-15
• /
• 2015

Purpose - Robot path planning, a constrained optimization problem, has been an active research area with many methods developed to tackle it. This study proposes the use of a Rapidly-exploring Random Tree and Particle Swarm Optimizer algorithm for path planning. Research design, data, and methodology - The grid method is built to describe the working space of the mobile robot, then the Rapidly-exploring Random Tree algorithm is applied to obtain the global navigation path and the Particle Swarm Optimizer algorithm is adopted to obtain the best path. Results - Computer experiment results demonstrate that this novel algorithm can rapidly plan an optimal path in a cluttered environment. Successful obstacle avoidance is achieved, the model is robust, and performs reliably. The effectiveness and efficiency of the proposed algorithm is demonstrated through simulation studies. Conclusions - The findings could provide insights to the validity and practicability of the method. This method makes it is easy to build a model and meet real-time demand for mobile robot navigation with a simple algorithm, which results in a certain practical value for distribution environments.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.6 no.5
• /
• pp.28-39
• /
• 2003

This study was carried out to elucidate effective restoration measures for natural forest with recycling native tree un site from November 2001 to October 2002 to obtain a basic information for revegetation measure, eight experimental treatment was done and the length of stump, root-ball size of stump, antisepsis treatment of trunk cut, Planting season and contents of organic matter in soil were effective on regrowth of root-stock. Thirteen tree species including Quercus acutissima among twenty tree species showed outstanding sprout and survival rate(over 90 percent), Planting in November and combinated planting with 5 trees and 9 shrubs of root-stock per 100$m^2$ plot showed a good growth. And 10 percent of organic matter plot showed a good crown coverage.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.4
• /
• pp.1188-1202
• /
• 2012

In this paper, we propose a new multi-scale connected coherence tree algorithm (MCCTA) by improving the connected coherence tree algorithm (CCTA). In contrast to many multi-scale image processing algorithms, MCCTA works on multiple scales space of an image and can adaptively change the parameters to capture the coarse and fine level details. Furthermore, we design a Multi-scale Connected Coherence Tree algorithm plus Spectral graph partitioning (MCCTSGP) by combining MCCTA and Spectral graph partitioning in to a new framework. Specifically, the graph nodes are the regions produced by CCTA and the image pixels, and the weights are the affinities between nodes. Then we run a spectral graph partitioning algorithm to partition on the graph which can consider the information both from pixels and regions to improve the quality of segments for providing image segmentation. The experimental results on Berkeley image database demonstrate the accuracy of our algorithm as compared to existing popular methods.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.14 no.1
• /
• pp.27-34
• /
• 2018

Various messages generated in vehicles are transmitted based on the wireless telecommunication which is a core technology of vehicle to everything (V2X). However, the hackers attack them upon penetration to the system and network to cause the generation of users' inconveniences for vehicular communication. Moreover, huge damage could be occurred in terms of physical and materialistic areas if the users in the vehicles were attacked in the communication environment. Therefore, this study was to design the safe communication protocol using hash tree technique in the V2X environments. Using hash tree technique, processes of issuing certificate and registration and communication protocol were designed, and safety analysis was performed on the attacking technique which is occurred in the existing vehicles. Approximately 62% of decrease in the capacity analysis was found upon comparative analysis of telecommunication processes with the system to issue the certificate which is used in the existing vehicles.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1997.10a
• /
• pp.24-31
• /
• 1997

Recently, in Korea, demand for establishment of systematic risk assessment techniques for construction projects has increased, especially after the large construction failures occurred during construction such as New Haengju Bridge construction projects, subway construction projects, gas explosion accidents etc. Most of existing risk analysis modeling techniques such as Event Tree Analysis and Fault Tree Analysis may not be available for realistic risk assessment of construction projects because it is very complex and difficult to estimate occurrence frequency and failure probability precisely due to a lack of data related to the various risks inherent in construction projects like natural disasters, financial and economic risks, political risks, environmental risks as well as design and construction-related risks. Therefor the main objective of this paper is to suggest systematic probabilistic risk assessment model and demonstrate an approach for probabilistic risk assessment using advanced Event Tree Analysis introducing Fuzzy set theory concepts. It may be stated that the Fuzzy Event Tree AnaIysis may be very usefu1 for the systematic and rational risk assessment for real constructions problems because the approach is able to effectively deal with all the related construction risks in terms of the linguistic variables that incorporate systematically expert's experiences and subjective judgement.

• Journal of Korean Society for Geospatial Information Science
• /
• v.3 no.2 s.6
• /
• pp.137-149
• /
• 1995

A spatial index is necessary to support an efficient search in a geographical information system (GIS) that is important in these days. In this paper, we design and implement a geographical data manager, called GOOD (Geo-object Oriented Data Manager) 2.0, by extending GOOD 1.0 with a spatial index processing module. That is, R-tree and R*-tree are used as a spatial index in this paper to make an efficient search possible. In addition, this paper conducts a performance evaluation to measure the improvement in search efficiency and analyzes the results of the performance evaluation. When the performance evaluation is carried out, we consider various environment factors to allow an GIS administrator to use the results as a basic data in selecting an appropriate spatial index.

• Proceedings of the KIEE Conference
• /
• 1998.07g
• /
• pp.2330-2332
• /
• 1998

This paper suggests a method to recognize the various defect patterns of cold mill strip using binary decision tree automatically constructed by genetic algorithm. The genetic algorithm and K-means algorithm were used to select a subset of the suitable features at each node in binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes by a linear decision boundary. This process was repeated at each node until all the patterns are classified into individual classes. The final recognizer is accomplished by neural network learning of a set of standard patterns at each node. Binary decision tree classifier was applied to the recognition of the defect patterns of cold mill strip and the experimental results were given to demonstrate the usefulness of the proposed scheme.