• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.1071-1072
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• 2012

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.1946-1958
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• 2011

Wireless Sensor Network (WSN) is made up of a number of sensor nodes and base stations. Traffic flow in WSN appears self-similar due to its data delivery process, and this impacts queue length greatly and makes queuing delay worse. Active queue management can be designed to improve QoS performance for WSN. In this paper, we propose self-similar traffic rate estimating algorithm named Power-Law Moving Averaging (PLMA) to regulate packet marking probability. This algorithm improves the availability of the rate estimation algorithm under the self-similar traffic condition. Then, we propose an adaptive Proportional Integral algorithm (SSPI) based on the estimation of the Self-Similar traffic rate by PLMA. Simulation results show that SSPI can achieve lower queue length jitter and smaller setting time than PI.

• Management Science and Financial Engineering
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• v.17 no.2
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• pp.39-62
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• 2011

In wireless sensor network, since each sensor is equipped with a limited power, efficient use of the energy is important. One possible network management scheme is to cluster the sensors into several sets, so that the sensors in each of the sets can completely perform the monitoring task. Then the sensors in one set become active to perform the monitoring task and the rest of the sensors switch to a sleep state to save energy. Therefore, we rotate the roles of the active set among the sensors to maximize the network lifetime. In this paper, we suggest an optimal algorithm for the maximum lifetime coverage problem which maximizes the network lifetime. For comparison, we implemented both the heuristic proposed earlier and our algorithm, and executed computational experiments. Our algorithm outperformed the heuristic concerning the obtained network lifetimes, and it found the solutions in a reasonable amount of time.

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.721-724
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• 2010

In a network router, packet loss may occur when it overflows due to sudden burst traffic. This paper studies how much large buffers are required to eliminate the packet losses. There are buffers on which packet arrive and one output port to which a packet is transmitted at a time. The buffer management algorithm should determine one of the buffers whose packet is transmitted to the output port at each time. The front packet belonging to the buffer determined by the algorithm is transmitted. The goal is to minimize the sum of the lengths of buffers to transmit all the packets. We provide an optimal off-line algorithm and also we show the lower bounds of on-line algorithms. The on-line algorithm has no prior information of the packets having arrived in the future. Its performance is compared to that of the optimal off-line algorithm.

• Management Science and Financial Engineering
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• v.11 no.1
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• pp.49-61
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• 2005

We consider the graph disconnection problem, which is to find a set of edges such that the total cost of destroying the edges is no more than a given budget and the weight of nodes disconnected from a designated source by destroying the edges is maximized. The problem is known to be NP-hard. We present an integer programming formulation for the problem and develop an algorithm that includes a preprocessing procedure for reducing the problem size, a heuristic for providing a lower bound, and a cutting plane algorithm for obtaining an upper bound. Computational results for evaluating the performance of the proposed algorithm are also presented.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.2
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• pp.183-203
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• 1993

One of the most difficult and time-consuming stages in the development of the knowledge-based system is a knowledge acquisition. A splitting algorithm is developed to infer a rule-tree which can be converted to a rule-typed knowledge. A market segmentation may be performed in order to establish market strategy suitable to each market segment. As the sales data of a product market is probabilistic and noisy, it becomes necessary to prune the rule-tree-at an acceptable level while generating a rule-tree. A splitting algorithm is developed using the pruning measure based on a total amount of information gain and the measure of existing algorithms. A user can easily adjust the size of the resulting rule-tree according to his(her) preferences and problem domains. The algorithm is applied to a market segmentation problem of a medium-large computer market. The algorithm is illustrated step by step with a sales data of a computer market and is analyzed.

• Management Science and Financial Engineering
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• v.16 no.1
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• pp.81-117
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• 2010

Semi-supervised learning incorporates unlabeled examples, whose labels are unknown, as well as labeled examples into learning process. Although transductive support vector machine (TSVM), one of semi-supervised learning models, was proposed about a decade ago, its application to large-scaled data has still been limited due to its high computational complexity. Our previous research addressed this limitation by introducing a branch-and-bound algorithm for finding an optimal solution to TSVM. In this paper, we propose three new techniques to enhance the performance of the branch-and-bound algorithm. The first one tightens min-cut bound, one of two bounding strategies. Another technique exploits a graph-based approximation to a support vector machine problem to avoid the most time-consuming step. The last one tries to fix the labels of unlabeled examples whose labels can be obviously predicted based on labeled examples. Experimental results are presented which demonstrate that the proposed techniques can reduce drastically the number of subproblems and eventually computational time.

• Journal of the Korea Society for Simulation
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• v.12 no.2
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• pp.13-24
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• 2003

In this study, we propose a scheduling analysis method of the Flexible management system using the transitive matrix. The Scheduling problem is a combination-optimization problem basically, and a complexity is increased exponentially for a size of the problem. To reduce an increase of a complexity, we define that the basic unit of concurrency (short BUC) is a set of control flows based on behavioral properties in the net. And we propose an algorithm to divide original system into some BUC. To sum up, we divide a petri net model of the Flexible management system Into the basic unit of concurrency through the division algorithm using the transitive matrix. Then we apply it to the division-scheduling algorithm to find an efficient scheduling. Finally, we verify its efficiency with an example.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.129-137
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• 2011

To date, facility layout problems has been solved and applied for job shop situations. Since flow shop has more restrictions, the solution space is much smaller than job shop. An efficient heuristic algorithm for facility layout problems for flow shop layouts is needed to be developed. In this thesis, a heuristic algorithm for rectangular bay layouts in a flow shop situation is presented. The proposed algorithm is developed by using slicing tree representation and applied to various flow shop layout problems. The effectiveness of the proposed algorithm in terms of exploration rate and objective function value are shown by comparing our results to simulated annealing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3771-3788
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• 2015

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.