• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.103-108
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• 2023

We study the problem of selecting a subset of sensor nodes in sensor networks in order to maximize the performance of parameter estimation. To achieve a low-complexity sensor selection algorithm, we propose a greedy iterative algorithm that allows us to select one sensor node at a time so as to maximize the log-determinant of the inverse of the estimation error covariance matrix without resort to direct minimization of the estimation error. We apply QR factorization to the observation matrix in the log-determinant to derive an analytic selection rule which enables a fast selection of the next node at each iteration. We conduct the extensive experiments to show that the proposed algorithm offers a competitive performance in terms of estimation performance and complexity as compared with previous sensor selection techniques and provides a practical solution to the selection problem for various network applications.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1165-1170
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• 2022

In WSNs, a clustering algorithm groups sensor nodes on a unit called cluster and periodically selects a cluster head (CH) that acts as a communication relay on behalf of nodes in each cluster for the purpose of energy conservation and relay efficiency. Meanwhile, attack techniques also have emerged to intervene in the CH election process through compromised nodes (inside attackers) and have a fatal impact on network operation. However, existing countermeasures such as encryption key-based methods against outside attackers have a limitation to defend against such inside attackers. Therefore, we propose a statistical detection method that detects abnormal CH election behaviors occurs in a WSN cluster. We design two attack methods (Selfish and Greedy attacks) and our proposed defense method in WSNs with two clustering algorithms and conduct experiments to validate our proposed defense method works well against those attacks.

• 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 Industrial Convergence
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• v.21 no.1
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• pp.87-93
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• 2023

Determining the timing of buying and selling in stock investment is one of the most important factors to increase the return on stock investment. Buying low and selling high makes a profit, but buying high and selling low makes a loss. The price is determined by the quantity of buying and selling, which determines the price of a stock, and buying and selling is also related to corporate performance and economic indicators. The fear and greed index provided by CNN uses seven factors, and by assigning weights to each element, the weighted average defined as greed and fear is calculated on a scale between 0 and 100 and published every day. When the index is close to 0, the stock market sentiment is fearful, and when the index is close to 100, it is greedy. Therefore, we analyze the trading criteria that generate the maximum return when buying and selling the US S&P 500 index according to CNN fear and greed index, suggesting the optimal buying and selling timing to suggest a way to increase the return on stock investment.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.4
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• pp.7-15
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• 2010

This study proposes a rad o resource management scheme to maximize the performance of the LTE(Long Term Evolution) uplink, using SC-FDMA(Single Carrier-Frequency Division Multiple Access). Rather than the single-cell SC-FDMA system the existing studies are mainly concerning, this study focuses on multi-cell system which needs considering the interaction among cells. Radio resource management is divided into two phases, planning and operation phases. The former is for the master eNB(e-NodeB) to allocate RBs(radio bearer) to eNB, the latter for eNB to assign RBs to the mobiles in the cell. For each phase, an optimization model and greedy algorithm are proposed. Optimization models aim to maximize the system performance while satisfying the constraints for both QoS and RB continuity. The greedy algorithms, like generic ones, move from a solution to a neighboring one having the best objective value among neighboring ones. From the numerous numerical experiments, the performance and characteristics of the algorithms are analyzed. This study is expected to play a volunteering role in radio resource management for the multi-cell SC-FDMA system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.165-170
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• 2023

This paper proposes an algorithm that can obtain a solution with linear time for a set cover problem(SCP) in which there is no polynomial time algorithm as an NP-complete problem so far. Until now, only heuristic greed algorithms are known to select sets that can be covered to the maximum. On the other hand, the proposed algorithm is a competitive algorithm that applies an inclusion-exclusion principle rule to N nodes up to 2nd or 3rd in the maximum number of elements to obtain a set covering all k nodes, and selects the minimum cover set among them. The proposed algorithm compensated for the disadvantage that the greedy algorithm does not obtain the optimal solution. As a result of applying the proposed algorithm to various application cases, an optimal solution was obtained with a polynomial time of O(kn²).

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.113-120
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• 2024

With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.1
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• pp.47-50
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• 2006

An active contour model has been used to detect the edges in a still image. In order to apply the active contour model to edge detection, the energy function which consists of internal, external and image energies should be defined. After defining the energy function, the edge of an object is detected through minimization of the value of the energy function. In this paper, the modified internal energy function is proposed to improve the convergence of the energy function when the active contour model is applied to the tracking of deformable objects using the greedy algorithm. In order to show the performance of the proposed energy function, experiments were carried out for the still and animated images.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.778-780
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• 2005

최근 디지털 융합(Digital Convergence)이 활발히 진행되면서 이동형 장치(Mobile Device)는 더욱 대용량, 고성능화 되고 비휘발성 메모리 요구가 커지고 있다. 이에 휴대가 용이하여, 접근시간이 빠르고, 전력소비가 적은 플래시 메모리가 많이 사용되고 있으나 상대적으로 느린 지움 시간과 지움 횟수의 한계 등 극복해야할 문제점이 있다. 본 논문에서는 이러한 문제점을 해결하기 위한 플레인 지움 정책을 제안하고 성능평가를 실시한다. 제안하는 플레인 지움 정책은 기존의 지움 정책과 같이 플래시의 블록단위의 균등한 사용을 고려할 뿐만 아니라 임베디드 시스템의 제한된 성능을 고려하여 연산을 최소화한다. 제안된 방법은 Greedy, Cost-benefit 방법에 비해 Wear-leveling에서 성능을 향상시켰고, RCP(Ranked Cleaning Policy)에 비해 연산횟수를 감소시켰다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.160-162
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• 1999

대용량의 정보를 다루는 정보검색 시스템은 정보 처리 과정에서 디스크 접근 시간이 큰 오버헤드로 작용한다. 본 논문에서는 단일 기계에서 작동하는 정보검색 시스템이 가지는 이러한 문제점을 해결하기 위해 PC 클러스터 기반 정보검색 시스템을 구현하였다. 색인어 간의 동시 등장 빈도 정보를 이용한 Greedy De-clustering 알고리즘으로 클러스터에 색인어 역파일을 병렬 분산하여 저장하고, SCI 기반의 효율적인 통신 시스템을 구축하여 클러스터 노드간의 통신이 원활하게 하였다. 따라서 사용자 질의어를 처리할 때 질의어별로 가져오는 색인어 역파일의 디스크 접근 시간이 감소하는 효과를 얻을 수 있었으며, 기존의 단일 기계에서 수행되는 정보 검색 시스템보다 수행속도가 2.3배 빠른 시스템을 구현하였음을 실험을 통해 확인하였다.