• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.21-28
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• 2015

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 into spheres within which the data exist from the same class. Prototypes are the center of spheres and their radii are computed by the mid-point of the two distances to the farthest same class point and the nearest another class point. And we transform the prototype selection problem into a set covering problem in order to determine the smallest set of prototypes that include all the training data. The proposed prototype selection method is based on a greedy algorithm that is applicable to the training data per class. The complexity of the proposed method is not complicated and the possibility of its parallel implementation is high. The prototype-based classification learning takes up the set of prototypes and predicts the class of test data by the nearest neighbor rule. In experiments, the generalization performance of our prototype classifier is superior to those of the nearest neighbor, Bayes classifier, and another prototype classifier.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.221-226
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• 2004

Ant Colony System(ACS) Algorithm is new meta-heuristic for hard combinational optimization problem. It is a population-based approach that uses exploitation of positive feedback as well as greedy search. Recently, various methods and solutions are proposed to solve optimal solution of graph coloring problem that assign to color for adjacency node($v_i, v_j$) that they has not same color. In this paper introducing ANTCOL Algorithm that is method to solve solution by Ant Colony System algorithm that is not method that it is known well as solution of existent graph coloring problem. After introducing ACS algorithm and Assignment Type Problem, show the wav how to apply ACS to solve ATP And compare graph coloring result and execution time when use existent generating functions(ANT_Random, ANT_LF, ANT_SL, ANT_DSATUR, ANT_RLF method) with ANT_XRLF method that use XRLF that apply Randomize to RLF to solve ANTCOL. Also compare graph coloring result and execution time when use method to add re-search to ANT_XRLF(ANT_XRLF_R) with existent generating functions.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.1
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• pp.79-85
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• 2017

Vehicular ad-hoc networks (VANETs) cause link disconnection problems due to the rapid speed and the frequent moving direction change of vehicles. Link disconnection in vehicle-to-vehicle communication is an important issue that must be solved because it decreases the reliability of packet forwarding. From the characteristics of VANETs, greedy forwarding protocols using the position information based on the inter-vehicle distance have gained attention. However, greedy forwarding protocols do not perform well in the urban environment where the direction of the vehicle changes greatly. It is because greedy forwarding protocols select the neighbor vehicle that is closest to the destination vehicle as the next transmission vehicle. In this paper, we propose a greedy anycast forwarding (GAF) protocol to improve the reliability of the inter-vehicle communication. The proposed GAF protocol combines the greedy forwarding scheme and the anycast forwarding method. Simulation results show that the GAF protocol can provide a better packet delivery rate than existing greedy forwarding protocols.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.1
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• pp.33-44
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• 2012

A class of recurrent neural networks is proposed and proven to be capable of identifying any discrete-time dynamical system. The applications of the proposed network are addressed in the encoding, identification, and extraction of finite state automata. Simulation results show that the identification of finite state automata using the proposed network, trained by the hybrid greedy simulated annealing with a modified error function in the learning stage, exhibits generally better performance than other conventional identification schemes.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.11a
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• pp.11-14
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• 2009

기존의 반이중방식 단방향 중계 네트워크는 하나의 정보를 두 개의 시간 슬롯 동안에 보내므로 주파수 효율에서 감소가 생기게 된다. 이러한 주파수 효율의 감소를 막기 위해 제안된 양방향 중계 네트워크는 중계기에 중첩 부호화 또는 네트워크 부호화를 적용함으로써 단방향 중계 네트워크에 비해 향상된 주파수 효율을 제공한다. 한편, OFDMA 네트워크는 사용자에게 부반송파, 전력 등의 자원을 적응적으로 할당하여 네트워크의 성능 향상을 얻을 수 있다. 본 논문에서는 다중사용자 다중중계기 양방향 OFDMA 중계 네트워크를 위한 새로운 적응적 부반송파 할당 알고리듬을 제안한다. 먼저 모든 사용자 쌍에 대한 달성 합 전송속도(achievable sum-rate over all user pairs)를 최대화하기 위한 최적화 문제를 정형화한다. 시스템의 수명을 늘이고 각 사용자의 최소 전송속도를 보장하기 위해 공정성 제한을 고려한다. 그리고 이로부터 새로운 적응적 부반송파 할당 알고리듬을 제안한다. 모의실험을 통해 제안된 알고리듬이 정적 알고리듬과 그리디 알고리듬, 두 알고리듬 모두 보다 훨씬 낮은 불능확률을 얻음을 확인한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.92-95
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• 2010

기존의 반이중방식 단방향 중계 네트워크는 하나의 정보를 두 개의 시간 슬롯 동안에 보내므로 추가적인 자원을 필요로 하며 이로 인해 주파수 효율에서 감소가 생기게 된다. 이러한 주파수 효율의 감소를 막기 위해 제안된 양방향 중계 네트워크는 중계기에 중첩 부호화 또는 네트워크 부호화를 적용함으로써 기존의 단방향 중계 네트워크에 비해 향상된 주파수 효율을 제공한다. 한편, OFDMA 네트워크는 사용자에게 부반송파, 전력 등의 자원을 적응적으로 할당하여 네트워크의 성능 향상을 얻을 수 있다. 본 논문에서는 복호 후 전송 중계 기법을 사용하는 양방향 OFDMA 중계 네트워크를 위한 적응적 부반송파 할당 알고리즘을 제안한다. 제안된 알고리즘은 각 사용자 쌍의 최소 전송속도를 보장하며 모든 사용자 쌍에 대한 달성 합 전송속도를 최대화 하기 위해 부반송파를 사용자 쌍과 중계기에 적응적으로 할당한다. 모의실험을 통해 제안된 알고리즘이 정적 알고리즘과 그리디 알고리즘, 두 알고리즘 모두 보다 훨씬 낮은 불능확률을 얻음을 확인한다.

• Journal of Broadcast Engineering
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• v.15 no.6
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• pp.783-790
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• 2010

A two-way relay network provide improved spectral efficiency compared with a conventional one-way relay network by using either superposition coding or network coding. OFDMA network provides imptoved performance by adaptive resource allocation. In this paper, we propose a adaptive subcarrier allocation for a multiuser two-way OFDMA relay network. In the proposed algorithm, subcarriers are allocated to the user-pairs and relays to maximize the achievable sum-rate over all user-pairs while satisfying the minimum rate requirement for each user-pair. Simulation results show that the proposed algorithm provides improved performance compared with the static and greedy algorithms.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.193-199
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• 2005

One of the important fields for heuristics algorithm is how to balance between Intensificationand Diversification. Ant Colony System(ACS) is a new meta heuristics algorithm to solve 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 propose Multi Colony Interaction Ant Model that achieves positive negative interaction through elite strategy divided by intensification strategy and diversification strategy to improve the performance of original ACS. And, we apply multi colony interaction ant model by this proposed elite strategy to TSP and compares with original ACS method for the performance.

• The Journal of the Acoustical Society of Korea
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• v.30 no.6
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• pp.330-336
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• 2011

In this paper, we propose a new method to improve the performance of supervector linear kernel SVM (Support Vector Machine) for speaker identification. This method is based on splitting one training datum into several pieces of utterances. We use four different databases for evaluating performance and use PCA (Principal Component Analysis), GKPCA (Greedy Kernel PCA) and KMDA (Kernel Multimodal Discriminant Analysis) for feature enhancement. As a result, the proposed method shows improved performance for speaker identification using supervector linear kernel SVM.

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.1-9
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• 2010

This paper propose a novel scheme called selective evaluation to improve convergence of ACO (ant colony optimization) for feature selection. The scheme cutdown the computational load by excluding the evaluation of unnecessary or less promising candidate solutions. The scheme is realizable in ACO due to the valuable information, pheromone trail which helps identify those solutions. With the aim of checking applicability of algorithms according to problem size, we analyze the timing requirements of three popular feature selection algorithms, greedy algorithm, genetic algorithm, and ant colony optimization. For a rigorous timing analysis, we adopt the concept of atomic operation. Experimental results showed that the ACO with selective evaluation was promising both in timing requirement and recognition performance.