• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1979-1990
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• 2021

Evacuation Time Estimate (ETE) can provide decision-makers with a likelihood to implement evacuation of a population with radiation exposure risk by a nuclear power plant. Thus, the ETE is essential for developing an emergency response preparedness. However, studies on ETE have not been conducted adequately in Korea to date. In this study, different cohorts were selected based on assumptions. Existing local data were collected to construct a multi-model network by TSIS-CORSIM code. Furthermore, several links were aggregated to make simple calculations, and post-processing was conducted for dealing with the stochastic property of TSIS-CORSIM. The average speed of each cohort was calculated by the link aggregation and post-processing, and the evacuation time was estimated. As a result, the average cohort-based evacuation time was estimated as 2.4-6.8 h, and the average clearance time from ten simulations in 26 km was calculated as 27.3 h. Through this study, uncertainty factors to ETE results, such as classifying cohorts, degree of model complexity, traffic volume outside of the network, were identified. Various studies related to these factors will be needed to improve ETE's methodology and obtain the reliability of ETE results.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• Journal of Korean Society of Transportation
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• v.25 no.2 s.95
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• pp.73-81
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• 2007

The purpose of this paper is to perform a basic study on the interaction between lanes, which can be achieved through analyzing traffic breakdown at the microscopic level. Using aerial photographic data for the microscopic analysis, this study analyzed the characteristics of traffic flow at a merging area. This research produced aggregated traffic data such as flows, speeds, and densities in 30 second intervals by lane for the macroscopic analysis and individual headway data by lane for the microscopic analysis. The paper contains an analysis of lane characteristics through flows, speeds, densities, and headway variations and also investigates the influence of ramp flows on mainline flows with space-time diagrams. Firstly, the merging area in this study is divided into three sections: before-merging, during-merging, and after-merging. The transition process was analyzed at each lane. Secondly, the breakdown was observed in detail with data divided in 50-foot units. The breakdown was checked through the relationships between ramp and freeway mainline flows, various techniques were proposed to analyze the breakdown, and the formation of breakdown was introduced as three stages in this study. In the near future, the findings of this study could contribute to determining the dynamic capacity on freeways by easily understanding changeable traffic breakdown patterns over time and space.

• Journal of KIISE:Information Networking
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• v.37 no.5
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• pp.374-385
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• 2010

Wireless mesh networks can be deployed for various networks from home networking to last-mile broadband Internet access. Wireless mesh networks are composed of mesh routers and mesh clients. In these networks, static nodes form a multi-hop backbone of a large wireless access network that provides connectivity to end-users' mobile terminals. The network nodes cooperate with each other to relay data traffic to its destinations. In order to increase connectivity and better performance, researchers are getting interested in multi-channel and multi-interface wireless mesh networks. In these networks, non-overlapping multiple frequency channels are used simultaneously to increase the aggregate bandwidth available to end-users. Recently, researches have focused on finding suitable channel assignments for wireless network interfaces, equiped in a mesh node, together with efficient routing to improve overall system throughput in wireless mesh networks. This goal can be achieved by minimize channel interference. Less interference among using channels in a network guarantees more aggregated channel capacity and better connectivity of the networks. In this thesis, we propose interference aware channel assignment and routing algorithms for multi-channel multi-hop wireless mesh networks. We propose Channel Assignment and Routing algorithms using Traffic Profiles(CARTP) and Routing algorithms allowing detour routing(CARTP+2). Finally, we evaluate the performance of proposed algorithms in comparison to results from previous methods using ns-2 simulations. The simulation results show that our proposed algorithms can enhance the overall network performance in wireless mesh networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.198-204
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• 2012

Generally, the clustering of sensor nodes in WSN is a useful mechanism that helps to cope with scalability problem and, if combined with network data aggregation, may increase the energy efficiency of the network. The Hierarchical clustering routing algorithm is a typical algorithm for enhancing overall energy efficiency of network, which selects cluster-head in order to send the aggregated data arriving from the node in cluster to a base station. In this paper, we propose the improved-LEACH that uses comparably simple and light-weighted policy to select cluster-head nodes, which results in reduction of the clustering overhead and overall power consumption of network. By using fine-grained power model, the simulation results show that i-LEACH can reduce clustering overhead compared with the well-known previous works such as LEACH. As result, i-LEACH algorithm and LEACH algorithm was compared, network power-consumption of i-LEACH algorithm was improved than LEACH algorithm with 25%, and network-traffic was improved 16%.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7B
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• pp.697-710
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• 2004

Differentiated services (DiffServ) model has been prevailed as a scalable approach to provide quality of service in the Internet. However, there are difficulties in providing the guaranteed service in terms of end-to-end systems since differentiated services network considers quality of service of aggregated traffic due to the scalability and many researches have been mainly focused on per hop behavior or a single domain behavior. Furthermore quality of service may be time varying according to the network conditions. In this paper, we study dynamic class mapping mechanism to guarantee the end-to-end quality of service for multimedia traffics with the minimum network cost over differentiated services network. The proposed algorithm consists of an effective implementation of relative differentiated service model, quality of service advertising mechanism and dynamic class mapping mechanism. Finally, the experimental results are provided to show the performance of the proposed algorithm.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.113-123
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• 2011

The application of micro-simulation model has been extended farther with improvement of computer performance and development of complicated model. To make a micro-simulation model accurately replicate field traffic conditions, model calibration is very crucial. Studies on calibration of micro-simulation model have not been enough while lots of studies on calibration of macro-simulation model have been continued in our country. This paper presents an auto-calibration of parameter values in micro-simulation model(VISSIM) using genetic algorithm. RMSE(Root Mean Square Error) of collected volume on the urban expressway versus simulated volume is set as MOP(measure of performance) and objective function of optimization is set as to minimize the RMSE. Applying to urban expressway(Nae-bu circular) as a case study, it shows that RMSE of optimized parameter values decrease 60.4%($19.3{\longrightarrow}7.6$) compared to default parameter values and the proposed auto-calibration system is very effective.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4B
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• pp.192-201
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• 2005

DiffServ supports aggregated traffic classes to provide various QoS to different classes of traffics. However, current DiffServ specifications do not have a complete QoS management framework. It is possible to lead to serious QoS violations without a QoS management support. From this reasoning, a QoS management system that can manage differentiated QoS provisioning is required. This paper proposes and implements a policy-based QoS management platform for differentiated services networks, which specifies QoS policies to guarantee dynamic QoS requirements. The implementation of the proposed platform is built on W framework and uses XML to represent and validate high-level QoS policies. High-level QoS policies are represented as valid XML documents and are mapped into Em beans of the EJB-based policy server of the platform. The policy distribution and the QoS monitoring are processed using SNMP.

• Journal of Korea Multimedia Society
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• v.7 no.5
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• pp.698-712
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• 2004

DiffServ network architecture does not define a call admission control procedure explicitly. In this paper, a new DiffServ QoS control mechanism is suggested which, after the call admission control, can execute packet process according to the class while, at the same time, executing on the flow based call admission control in the DiffServ network. Routers on the path from the source to the destination estimate the aggregated class traffic of the existing flows and the new incoming flow and then, perform a call admission control in accordance with the type of classes efficiently based on the required bandwidth per each class that can meet the user's QoS requirements. In order to facilitate the packet process according to the class after the flow based call admission control, a mechanism is suggested that can adjust the network resources to classes dynamically. The performance analysis on this mechanism is carried out through a simulation.