• Journal of KIISE:Computer Systems and Theory
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• v.36 no.5
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• pp.422-428
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• 2009

NAND flash memory is widely used in embedded systems because of many attractive features, such as small size, light weight, low power consumption and fast access speed. However, it requires garbage collection, which includes erase operations. Erase operation is slower than other operations. Further, a block has a limited erase lifetime (typically 100,000) after which a block becomes unusable. The proposed garbage collection policy focuses on minimizing the total number of erase operations, the deviation value of each block and the garbage collection time. NAND flash memory consists of pages of three types, such as valid pages, invalid pages and free pages. In order to achieve above goals, we use a page ratio to decide when to do garbage collection and to select the target victimblock. Additionally, we implement allocating method and group management method. Simulation results show that the proposed policy performs better than Greedy or CAT with the maximum rate 85% of reduction in the deviation value of the erase operations and 6% reduction in garbage collection time.

• Journal of Broadcast Engineering
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• v.15 no.2
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• pp.173-181
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• 2010

In this paper, we propose a new resource allocation scheme for an OFDMA relay network with multicells. In the proposed scheme, by sharing the channel state information (CSI) between base stations, resources are allocated to users and relays to maximize the overall sum of the achievable rate under fairness constraints. In order to reduce the computational complexity, a resource allocation scheme is proposed by separating subcarrier allocation and power allocation into two parts. First of all, by considering inter-cell interference (ICI), a subcarrier is allocated to a user-relay pair, and power is allocated relays. Simulation results show that the proposed scheme achieves higher spectral efficiency per subcarrier than the static scheme and reduces the outage probability compared to the static and greedy schemes.

• 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 the Korea Society of Computer and Information
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• v.13 no.4
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• pp.133-140
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• 2008

The cable TV network has delivered downward broadcasting signals from distribution centers to subscribers. Since the traditional coaxial cable has been upgraded by the Hybrid Fiber Coaxial(HFC) cable, the upward channels has expanded broadband services such as Internet. This upward channel is vulnerable to ingress noises. When the noises from the children nodes accumulated in an amplifier exceeds a certain level, that node has to be cut off to prevent the noise propagation. The node selection problem(NSP) is defined to select nodes so that the noise in each node does not exceed the given threshold value and the sum of Profits of selected nodes can be maximized. The NSP has shown to be NP-hard. In this paper, we have proposed heuristics to find the near-optimal solution for NSP. The experimental results show that interval partitioning is better than greedy approach. Our heuristics can be used by the HFC network management system to provide privileged services to the premium subscribers on HFC networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8B
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• pp.839-845
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• 2009

The Vehicular Ad-hoc Network (VANET) has two main problems to be overcome due to high mobility and frequently changing density: one is short link duration time and the other is high packet loss ratio. To solve the problems, there have been many studies to predict vehicular mobility. Most of the studies try to enhance link expire time and link reliability, however the distance between two relay nodes becomes too short to have high network throughput. In this paper, we propose a new routing algorithm that considers both link expire time and network throughput in the VANET. The proposed algorithm aims to find path with long link expire time and high throughput. Our simulation results show that the proposed algorithm outperforms the legacy greedy algorithm and its variants.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.7
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• pp.580-591
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• 2013

The Orthogonal Frequency Division Multiple Access (OFDMA) is considered as a novel modulation and multiple access technique for 4th generation wireless systems. In this paper, we formulate a base station's power allocation algorithm for each user to maximize the user's sum rate, subject to constraints on total power, bit error rate, and rate proportionality among the users for a better proportional rate adaptive (RA) resource allocation method for OFDMA based system. We propose a novel power allocation method based on the proportion of subcarrier allocation and the user's normalized proportionality constant. We adapt a greedy algorithm and waterfilling technique for allocating the subcarriers among the users. In an end-to-end simulation, we validate that the proposed technique has higher system capacity and lower CPU execution times, while maintaining the acceptable rate proportionality among users.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1C
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• pp.79-85
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• 2008

Dirty Paper Coding(DPC) can achieve the sum capacity of a multiuser multiple-input multiple-output(MU MIMO) broadcast channels. However, due to the high computational complexity of the successive encoding and decoding, deploying DPC in real systems is impractical. As one of practical alternatives to DPC, Block Diagonalization(BD) was researched. BD is an extension of the zero-forcing preceding technique that eliminates interuser interference(IUI) in downlink MIMO systems. Though BD has lower complexity than DPC, BD shows poor sum capacity performance. We show that sum capacity performance of BD is degraded due to no IUI constraint. Then, we modify BD to improve its sum capacity performance with relaxing the constraint and sub optimal channel set searching. With simulation results, it can be verified that our modification in BD induces some improvement in sum capacity performance.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.2
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• pp.189-197
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• 2016

Mobile ad-hoc networks (MANETs) experience frequent link disconnections due to non-uniform node distribution and mobility. Thus, end-to-end path establishment-based routing protocols cause frequent transmission failures in MANETs, resulting in heavy control messages for path reestablishment. While location-based MANET routing protocols, such as Greedy Perimeter Stateless Routing (GPSR), use location information to forward messages in a hop-by-hop routing fashion without an end-to-end path establishment procedure, such protocols encounter communication void problems when message forwarding to the next hop fails due to the absence of a relay node. Therefore, to solve this problem, this paper proposes a Delay Tolerant-GPSR (DT-GPSR) protocol, which combines Delay Tolerant Networking (DTN) technology with the GPSR protocol. The performance of DT-GPSR is compared with the performances of the original GPSR and PRoPHET routing protocols through simulation using NS-2. The simulation results confirm that DT-GPSR outperforms GPSR and PRoPHET in terms of the message delivery ratio and message delivery delay.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.483-488
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• 2016

In this paper, we propose a prototype-based classification learning by using the nearest-neighbor rule. The nearest-neighbor is applied to segment the class area of all the training data with hyperspheres, and a hypersphere must cover the data from the same class. The radius of a hypersphere is computed by the mid point of the two distances to the farthest same class point and the nearest other class point. And we transform the prototype selection problem into a set covering problem in order to determine the smallest set of prototypes that cover all the training data. The proposed prototype selection method is designed by a greedy algorithm and applicable to process a large-scale training set in parallel. The prediction rule is the nearest-neighbor rule and the new training data is the set of prototypes. In experiments, the generalization performance of the proposed method is superior to existing methods.