• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.5
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• pp.79-85
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• 2009

A sink node and its neighbor nodes spend more energy than other nodes since a stationary sink node collects data from wireless sensor networks(WSNs). For larger WSNs, the unbalanced energy of nodes causes the operation of WSNs to stop rapidly. This paper proposes a data gathering method by adapting the mobile sink to prolong the life time of large WSNs. After partitioning a network into several clusters, a mobile sink visits each cluster and collects data from it. An efficient algorithm is proposed to improve the energy efficiency by delivering the message from the mobile sink to the cluster head as well as to reduce the data gathering delay, which is the disadvantage of the mobile sink. Also, The algorithm is analyzed for the energy consumption and the data gathering delay. The validity of the ananlysis result is confirmed by the simulation.

• Journal of Information Technology and Architecture
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• v.9 no.4
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• pp.455-464
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• 2012

Due to the recent data explosion, methods which can meet the requirement of large data analysis has been studying. This paper proposes MRIterativeBUC algorithm which enables efficient computation of large data cube by distributed parallel processing with MapReduce framework. MRIterativeBUC algorithm is developed for efficient iterative operation of the BUC method with MapReduce, and overcomes the limitations about the storage size and processing ability caused by large data cube computation. It employs the idea from the iceberg cube which computes only the interesting aspect of analysts and the distributed parallel process of cube computation by partitioning and sorting. Thus, it reduces data emission so that it can reduce network overload, processing amount on each node, and eventually the cube computation cost. The bottom-up cube computation and iterative algorithm using MapReduce, proposed in this paper, can be expanded in various way, and will make full use of many applications.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.608-616
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• 2022

Power peaking factors (PPF) is an important parameter for safe and efficient reactor operation. There are several methods to calculate the PPF at TRIGA research reactors such as MCNP and TRIGLAV codes. However, these methods are time-consuming and required high specifications of a computer system. To overcome these limitations, artificial intelligence was introduced for parameter prediction. Previous studies applied the neural network method to predict the PPF, but the publications using the ANFIS method are not well developed yet. In this paper, the prediction of PPF using the ANFIS was conducted. Two input variables, control rod position, and neutron flux were collected while the PPF was calculated using TRIGLAV code as the data output. These input-output datasets were used for ANFIS model generation, training, and testing. In this study, four ANFIS model with two types of input space partitioning methods shows good predictive performances with R² values in the range of 96%-97%, reveals the strong relationship between the predicted and actual PPF values. The RMSE calculated also near zero. From this statistical analysis, it is proven that the ANFIS could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.

• Convergence Security Journal
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• v.22 no.1
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• pp.99-111
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• 2022

With current battlefield environment relying heavily on Network Centric Warfare(NCW), existing weaponary systems are evolving into a new concept that converges IT technology. Majority of the weaponary systems are implemented with numerous embedded softwares which makes such softwares a key factor influencing the performance of such systems. Furthermore, due to the advancements in both IoT technoogies and embedded softwares cyber threats are targeting various embedded systems as their scope of application expands in the real world. Weaponary systems have been developed in various forms from single systems to interlocking networks. hence, system level cyber security is more favorable compared to application level cyber security. In this paper, a secure real-time operating system has been designed, implemented and measured to protect embedded softwares used in weaponary systems from unknown cyber threats at the operating system level.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.7
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• pp.650-664
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• 2000

NOW(Network of Workstations) is considered as a platform for running parallel programs by many people. One of the fundamental problems that must be addressed to achieve good performance for parallel programs on NOW is the determination of efficient job scheduling policies. Currently most research on NOW assumes that all the workstations in the NOW have the same processing power. In this paper we consider a NOW in which workstations may have different computing power. We introduce 10 classes of space sharing-based scheduling policies that can be applied to the NOW with heterogeneous computing power. We compare the performance of these scheduling policies by using the simulator which accepts synthetically generated sequential and parallel workloads and generates the response time and waiting time of parallel jobs as performance indices of various scheduling strategies. Through the experiments the case when a parallel program is partitioned heterogeneously in proportion to the computing power of workstations is shown to have better performance than when a parallel program is partitioned into parallel processes of the same size. When the owner returns to the workstation which is executing a parallel process, the policy which just lowers the priority of the parallel process shows better performance than the one which migrates the parallel process to a new idle workstation. Among the policies which use heterogeneous partitioning and process priority lowering, the adaptive policy performed best across the wide range of inter-arrival time of parallel programs but when the load imbalance among parallel processes becomes very high, the modified adaptive policy performed better.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7B
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• pp.685-696
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• 2004

Real-time multimedia applications that require large amount of bandwidth need resource reservation before starting service for providing the QoS(i.e., Quality of Service). To reserve resources in advance, each reservation request has to notify its expectation on the required amount of resources and service duration. Using this information, a resource manager can schedule advance reservations. However, most existing resource management systems are adopting straightforward call admission control process (i.e., only immediate reservation) by checking currently available resources without considering the service duration. Hence, the resource management system that supports advance reservation has to manage confliction caused by indefinite service duration of immediate reservation. Even though the separation of resource pool according to type of reservation can prevent the confliction, it causes low resource utilization. In this paper, we propose an effective resource management scheme that supports both immediate and advance reservations by sharing resources dynamically. Using network cost function, the proposed scheme determines and adaptively adjusts resource boundary according to the confliction rate by varying weight parameters. And also, we define user utility function to quantify user satisfaction based on how well the reserved resource is guaranteed during service time. Simulation results using NS-2 network simulator show that the proposed scheme can achieve better resource utilization with preferable QoS than other schemes like static resource partitioning.

• Economic and Environmental Geology
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• v.36 no.3
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• pp.243-255
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• 2003

The mathematical models for GIS-based spatial data integration have been developed for geological applications such as mineral potential mapping or landslide susceptibility analysis. Among various models, the effectiveness of fuzzy logic based integration of multiple sets of geological data is investigated and discussed. Unlike a traditional target-driven fuzzy integration approach, we propose a data-driven approach that is derived from statistical relationships between the integration target and related spatial geological data. The proposed approach consists of four analytical steps; data representation, fuzzy combination, defuzzification and validation. For data representation, the fuzzy membership functions based on the likelihood ratio functions are proposed. To integrate them, the fuzzy inference network is designed that can combine a variety of different fuzzy operators. Defuzzification is carried out to effectively visualize the relative possibility levels from the integrated results. Finally, a validation approach based on the spatial partitioning of integration targets is proposed to quantitatively compare various fuzzy integration maps and obtain a meaningful interpretation with respect to future events. The effectiveness and some suggestions of the schemes proposed here are illustrated by describing a case study for landslide susceptibility analysis. The case study demonstrates that the proposed schemes can effectively identify areas that are susceptible to landslides and ${\gamma}$ operator shows the better prediction power than the results using max and min operators from the validation procedure.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.5
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• pp.177-188
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• 2018

The scale of graph data has been increased rapidly because of the growth of mobile Internet applications and the proliferation of social network services. This brings upon the imminent necessity of efficient distributed and parallel graph processing approach since the size of these large-scale graphs are easily over a capacity of a single machine. Currently, there are two popular parallel graph processing approaches, vertex-centric graph processing and block centric processing. While a vertex-centric graph processing approach can easily be applied to the parallel processing system, a block-centric graph processing approach is proposed to compensate the drawbacks of the vertex-centric approach. In these systems, the initial quality of graph partition affects to the overall performance significantly. However, it is a very difficult problem to divide the graph into optimal states at the initial phase. Thus, several dynamic load balancing techniques have been studied that suggest the progressive partitioning during the graph processing time. In this paper, we present a load balancing algorithms for the block-centric graph processing approach where most of dynamic load balancing techniques are focused on vertex-centric systems. Our proposed algorithm focus on an improvement of the graph partition quality by dynamically reassigning blocks in runtime, and suggests block split strategy for escaping local optimum solution.