• The KIPS Transactions:PartA
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• v.16A no.1
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• pp.35-42
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• 2009

This paper proposes a new coverage algorithm for intelligent robot. Many algorithms for improving the performance of coverage have been focused on minimizing the total coverage completion time. However, if one does not have enough time to finish the whole coverage, the optimal path could be different. To tackle this problem, we propose a new coverage algorithm, which we call MaxCoverage algorithm, for covering maximal area within the deadline. The MaxCoverage algorithm decides the navigation flow by greedy algorithm for Set Covering Problem. The experimental results show that the MaxCoverage algorithm performs better than other algorithms for random deadlines.

• Journal of IKEEE
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• v.16 no.4
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• pp.316-321
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• 2012

In this paper, we study the energy-efficient deployment strategy for micro base station (BS) in wireless cellular systems. Firstly, we formulate a general problem pertaining to total energy consumption minimization with the requirement of area spectral efficiency (ASE). We start from an observation about the correlation between the area covered by an additional micro BS and the increment of ASE. Under such an observation, we propose an efficient greedy micro BS deployment algorithm. Simulations show that the proposed deployment algorithm can deploy micro BSs with a slight performance reduction comparing with the optimal solution.

• Industrial Engineering and Management Systems
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• v.15 no.3
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• pp.268-277
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• 2016

Load balancing is a significant technique to prolong a network's lifetime in sensor network. This paper introduces a hybrid approach named as Load Distributing Hybrid Routing Protocol (LDHRP) composed with a border node routing protocol (BDRP) and greedy forwarding (GF) strategy which will make the routing effective, especially in mobility scenarios. In an existing solution, because of the high network complexity, the data delivery latency increases. To overcome this limitation, a new approach is proposed in which the source node transmits the data to its respective destination via border nodes or greedily until the complete data is transmitted. In this way, the whole load of a network is evenly distributed among the participating nodes. However, border node is mainly responsible in aggregating data from the source and further forwards it to mobile sink; so there will be fewer chances of energy expenditure in the network. In addition to this, number of hop counts while transmitting the data will be reduced as compared to the existing solutions HRLBP and ZRP. From the simulation results, we conclude that proposed approach outperforms well than existing solutions in terms including end-to-end delay, packet loss rate and so on and thus guarantees enhancement in lifetime.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2087-2093
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• 2014

In this paper, an orthogonal matching pursuit (OMP) method combined with genetic algorithm (GA), named GAOMP, is proposed for sparse signal recovery. Some recent greedy algorithms such as SP, CoSaMP, and gOMP improved the reconstruction performance by deleting unsuitable atoms at each iteration. However they still often fail to converge to the solution because the support set could not avoid the local minimum during the iterations. Mutating the candidate support set chosen by the OMP algorithm, GAOMP is able to escape from the local minimum and hence recovers the sparse signal. Experimental results show that GAOMP outperforms several OMP based algorithms and the $l_1$ optimization method in terms of exact reconstruction probability.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.237-242
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• 2003

Ant Colony System (ACS) Algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP). In this paper, we introduce ACS of new method that adds reinforcement value for each edge that visit to Local/Global updating rule. and the performance results under various conditions are conducted, and the comparision between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with tile original ACS in terms of solution quality and computation speed to these problem.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.2
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• pp.99-105
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• 2012

The focus of this paper is a proposal for a computationally efficient dynamic subcarrier allocation (DSA) algorithm for orthogonal frequency-division multiple access (OFDMA) systems. The proposed DSA algorithm considerably reduces the computational complexity and the amount of channel quality information (CQI) compared to amplitude craving greedy (ACG) algorithms, which use full CQI. At the same time, the performance of the proposed algorithm closely appear to ACG algorithms. Moreover, the authors present a new bandwidth-assignment algorithm produced by modifying bandwidth assignment based on the signal-to-noise ratio (BABS). This modified BABS algorithm enables the proposed DSA algorithm to produce a strong outage performance gain over the conventional scheme.

• Journal of Korea Game Society
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• v.19 no.3
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• pp.5-14
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• 2019

The gaming server is based on a distributed server. In order to distribute workloads of gaming servers, distributed gaming servers apply some algorithms which divide each of gaming server's workload into balanced workload among the gaming servers and as a result, efficiently manage response time and fusibility of server requested by the clients. In this paper, we propose a load balancing agent using PPO(Proximal Policy Optimization) which is one of the methods from a greedy algorithm and Policy Gradient which is from Reinforcement Learning. The proposed load balancing agent is compared with the previous researches based on the simulation.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.45-46
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• 2019

본 논문에서는 Upper Confidence Bound (UCB)를 이용한 효율적인 패킷 스케줄링 기법을 제안한다. 기존 e-greedy 등 강화학습의 보상을 극대화 할 수 있는 행동을 선택하는 것과 다르게, 제안된 UCB를 이용한 강화학습 패킷 스케줄링 기법은 각 상태에서 행동을 선택한 횟수를 추가적으로 고려한다. 이는 보다 효율적인 강화학습의 탐구(Exploration)를 가능케 한다. 본 논문에서는 컴퓨터 시뮬레이션을 통하여 제안하는 UCB를 이용한 강화학습 패킷 스케줄링 기법이 기존의 e-greedy 및 softmax를 기반으로 한 패킷 스케줄링 기법에 비해 정확도 측면에서 향상된 정확도를 보인다.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.153-160
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• 2022

A greedy algorithm is proposed to select a subset of nodes of a graph for bandlimited graph signals in which each signal value is generated with its weight. Since graph signals are weighted, we seek to minimize the weighted reconstruction error which is formulated by using the QR factorization and derive an analytic result to find iteratively the node minimizing the weighted reconstruction error, leading to a simplified iterative selection process. Experiments show that the proposed method achieves a significant performance gain for graph signals with weights on various graphs as compared with the previous novel selection techniques.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.367-374
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• 2022

A sampling set selection algorithm is proposed to reconstruct original graph signals from the sampled signals generated on the nodes in the sampling set. Instead of directly minimizing the reconstruction error, we focus on minimizing the upper bound on the reconstruction error to reduce the algorithm complexity. The metric is manipulated by using QR factorization to produce the upper triangular matrix and the analytic result is presented to enable a greedy selection of the next nodes at iterations by using the diagonal entries of the upper triangular matrix, leading to an efficient sampling process with reduced complexity. We run experiments for various graphs to demonstrate a competitive reconstruction performance of the proposed algorithm while offering the execution time about 3.5 times faster than one of the previous selection methods.