• Journal of KIISE:Information Networking
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• v.31 no.2
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• pp.123-130
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• 2004

Routing and wavelength assignment(RWA) problem is an important issue in optical transport networks based on wavelength division multiplexing(WDM) technique. It is typically solved using a combination of linear programming and graph coloring, or path selection based graph algorithms. Such methods are either complex or make extensive use of heuristics. In this paper we propose a novel and efficient approach which basically obtains the maximum edge disjoint paths (EDPs) for each source-destination demand pair. And those EDPs obtained are stored in Lookup Table and used for the update of weight matrix. Routes are determined in order by the weight matrix for the demand set. The comprehensive computer simulation shows that the Proposed algorithm uses similar or fewer wavelengths with significantly less execution time than bounded greedy approach (BGA) for EDP which is currently known to be effective in practice.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8A
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• pp.691-701
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• 2011

In rotatable directional sensor networks, maximizing network lifetime while covering all the targets and forwarding the sensed data to the sink is a challenge problem. In this paper, we address the Maximum Directional Cover Tree (MDCT) problem of organizing the directional sensors into a group of non-disjoint subsets to extend the network lifetime. Each subset in which the directional sensors cover all the targets and forward the sensed data to the sink is activated at one time. For the MDCT problem, we first present an energy consumption model which mainly takes into account the energy consumption for rotation work. We also develop the Directional Coverage and Connectivity (DCC)-greedy algorithm to solve the MDCT problem. To evaluate the algorithm, we conduct simulations and show that it can extend the network lifetime.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.12
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• pp.856-864
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• 2019

The weapon-target assignment (WTA) problem is a matter of effectively allocating weapons to a number of threats. The WTA in a rapidly changing dynamic environment of engagement must take into account both of properties of the threat and the weapon and the effect of the previous decision. We propose a method of applying the Greedy Randomized Adaptive Search Procedure (GRASP) algorithm, a kind of meta-heuristic method, to derive optimal solution for a dynamic WTA problem. Firstly, we define a dynamic WTA problem and formulate a mathematical model for applying the algorithm. For the purpose of the assignment strategy, the objective function is defined and time-varying constraints are considered. The dynamic WTA problem is then solved by applying the GRASP algorithm. The optimal solution characteristics of the formalized dynamic WTA problem are analyzed through the simulation, and the algorithm performance is verified via the Monte-Carlo simulation.

• Journal of Korea Game Society
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• v.7 no.4
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• pp.93-104
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• 2007

In the complex real-time multi-agent system such as game environment, dynamic task allocations are repeatedly performed to achieve a goal in terms of system efficiency. In this research, we present a task allocation scheme suitable for the real-time multi-agent environment. The scheme is to optimize the task allocation by complementing existing coordination agent with $A^*$ algorithm. The coordination agent creates a status graph that consists of nodes which represent the combinations of tasks and agents, and refines the graph to remove nodes of non-execution tasks and agents. The coordination agent performs the selective utilization of the $A^*$ algorithm method and the greedy method for real-time re-allocation. Then it finds some paths of the minimum cost as optimized results by using $A^*$ algorithm. Our experiments show that the coordination agent with $A^*$ algorithm improves a task allocation efficiency about 25% highly than the coordination agent only with greedy algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2375-2381
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• 2014

In case the coefficient set of an FIR filter is represented in the canonic signed digit (CSD) format with a few nonzero digits, it is possible to implement high data rate digital filters with low hardware cost. Designing an FIR filter with CSD format coefficients, whose number of nonzero signed digits is minimal, is equivalent to finding sparse nonzero signed digits in the coefficient set of the filter which satisfies the target frequency response with minimal maximum error. In this paper, a compressive sensing based CSD coefficient FIR filter design algorithm is proposed for multiplierless and high speed implementation. Design examples show that multiplierless FIR filters can be designed using less than two additions per tap on average with approximate frequency response to the target, which are suitable for high speed filtering applications.

• Journal of KIISE:Information Networking
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• v.33 no.6
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• pp.420-427
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• 2006

An RFID system is an important technology that could replace the traditional bar code system changing the paradigm of manufacturing, distribution, and service industry. An RFID reader can recognize several hundred tags in one second. Tag identification is done by tags' random transmission of their IDs in a frame which is assigned by the reader at each round. To minimize tag identification time, optimal frame size should be selected according to the number of tags. This paper presents new query optimization methods in RFID systems. Query optimization consists of tag number estimation problem and frame length determination problem. We propose a simple yet efficient tag estimation method and calculate optimal frame lengths that minimize overall query time. We conducted rigorous performance studies. Performance results show that the new tag number estimation technique is more accurate than previous methods. We also observe that a simple greedy method is as efficient as the optimal method in minimizing the query time.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.219-224
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• 2022

Q-Learning is a technique widely used as a basic algorithm for reinforcement learning. Q-Learning trains the agent in the direction of maximizing the reward through the greedy action that selects the largest value among the rewards of the actions that can be taken in the current state. In this paper, we studied a policy that can speed up agent training using Q-Learning in Frozen Lake 8×8 grid environment. In addition, the training results of the existing algorithm of Q-learning and the algorithm that gave the attribute 'direction' to agent movement were compared. As a result, it was analyzed that the Q-Learning policy proposed in this paper can significantly increase both the accuracy and training speed compared to the general algorithm.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.164-172
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• 2004

For a local search algorithm to find a bettor quality solution it is required to generate and evaluate a sufficiently large number of candidate solutions as neighbors at each iteration, demanding quite an amount of CPU time. This paper presents a method of selectively generating only good-looking candidate neighbors, so that the number of neighbors can be kept low to improve the efficiency of search. In our method, a newly generated candidate solution is probabilistically selected to become a neighbor based on the quality estimation determined heuristically by a very simple evaluation of the generated candidate. Experimental results on the problem of load balancing for production scheduling have shown that our candidate selection method outperforms other random or greedy selection methods in terms of solution quality given the same amount of CPU time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.5
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• pp.280-292
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• 2014

Recently, heterogeneous networks consisting of small-cells on top of traditional macro-cellular network has attracted much attention, because traditional macro-cellular network is not suitable to support more demanding mobile data traffic due to its limitation of spatial reuse. However, due to the transmit power difference between macro- and small-cells, most users are associated with macro-cells rather than small-cells. To solve this problem, enhanced inter-cell interference coordination (eICIC) has been introduced. Particularly, in eICIC, the small-cell coverage is forcibly expanded to associate more users with small-cells. Then, to avoid cross-tier interference from macro-cells, these users are allowed to receive the data during almost blank subframe (ABS) in which macro-cells almost remain silent. However, this approach is not sufficient to balance the load between macro- and small-cells because it only expands the small-cell coverage. In this paper, we propose a load balance scheme improving proportional fairness for heterogeneous networks employing eICIC. In particular, the proposed scheme combines the greedy-based user association and the ABS rate determination in a recursive manner to perform the load balance.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.336-345
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• 2006

최근 컨테이너 선박의 대형화는 물동량을 크게 증가 시켰으며, 이로 인해 컨테이너 터미널에서는 규모의 경제를 달성하고, 선사의 요구를 만족시키기 위해 노력하고 있다. 이에 대한대안이 양현부두(Indented berth)의 등장이다. 양현부두에는 대형 컨테이너선의 물량을 신속히 처리하기 위해 선석에서 컨테이너를 취급하는 장비인 안벽크레인(Quay Crane : QC)의 작업 대수가 일반부두(Conventional berth)보다는 많이 할당될 수 있다. 본 연구에서는 일반부두와 양현부두의 작업 생산성을 비교하였다. 이를 위하여 탐색기법인 GRASP(Greedy Randomized Adaptive Search Procedure)를 적용한 안벽크레인 일정계획 알고리즘을 이용하였다. 또한 컨테이너 터미널의 실제 자료를 이용하여 두 가지 형태의 부두에서의 본선작업 완료시간을 비교 하였다.