• 정보처리학회논문지A
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• 제9A권3호
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• pp.363-370
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• 2002

OPKFDD(Ordered Pseudo-Kronecker Functional Decision Diagram)는 각 노드에서 다양한 확장방법(decomposition)을 취할 수 있는 Ordered-DD(Decision Diagram)의 한 종류로서 각 노드마다 Shannon, positive Davio, 그리고 negative Davio 확장중의 하나를 사용하도록 하며 다른 종류의 DD와 비교해서 작은 수의 노드로 함수를 표현할 수 있다. 그러나 각 노드마다 각기 다른 확장 방법을 선택할 수 있는 특징 때문에 입력 노드에 대한 확장 방법의 결정에 의해서 OPKFDD의 크기가 좌우되며 최소의 노드 수를 갖는 OPKFDD의 구성은 매우 어려운 문제로 알려져 있다. 본 논문에서는 DD 크기의 기준을 노드 수로 하여 기존의 OBDD(Ordered Binary Decision Diagram) 자료구조에서 각 노드의 확장방법을 결정하는 직관적(heuristic)인 방법을 제시하고, 주어진 입력변수 순서에 대해서 각 노드의 확장 방법을 결정하는 알고리즘을 제안하고 실험 결과를 제시한다.

• Journal of Information Processing Systems
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• 제17권4호
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• pp.721-736
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• 2021

For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.843-859
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• 2024

The university course scheduling problem (UCSP) aims at optimally arranging courses to corresponding rooms, faculties, students, and timeslots with constraints. Previously, the university staff solved this thorny problem by hand, which is very time-consuming and makes it easy to fall into chaos. Even some meta-heuristic algorithms are proposed to solve UCSP automatically, while most only utilize one single algorithm, so the scheduling results still need improvement. Besides, they lack an in-depth analysis of the inner algorithms. Therefore, this paper presents a novel and practical approach based on Tabu search and simulated annealing algorithms for solving USCP. Firstly, the initial solution of the UCSP instance is generated by one construction heuristic algorithm, the first fit algorithm. Secondly, we defined one union move selector to control the moves and provide diverse solutions from initial solutions, consisting of two changing move selectors. Thirdly, Tabu search and simulated annealing (SA) are combined to filter out unacceptable moves in a parallel mode. Then, the acceptable moves are selected by one adaptive decision algorithm, which is used as the next step to construct the final solving path. Benefits from the excellent design of the union move selector, parallel tabu search and SA, and adaptive decision algorithm, the proposed method could effectively solve UCSP since it fully uses Tabu and SA. We designed and tested the proposed algorithm in one real-world (PKNU-UCSP) and ten random UCSP instances. The experimental results confirmed its effectiveness. Besides, the in-depth analysis confirmed each component's effectiveness for solving UCSP.

• Transactions on Control, Automation and Systems Engineering
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• 제4권3호
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• pp.239-243
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• 2002

Multi-valued decision making for transitional stochastic events was newly derived based on conditional probability of knowledge database which included experts'knowledge and experience. The proposed multi-valued decision making was successfully adopted to the determination of the five levels of the vigilance of a subject during the EEG (electroencephalogram) recording; awake stage (stage W), and sleep stages (stage REM (rapid eye movement), stage 1, stage 2, stage $\sfrac{3}{4}$). Innovative feature of the proposed method is that the algorithm of decision making can be constructed only by use of the knowledge database, inspected by experts. The proposed multi-valued decision making with a mathematical background of the probability can also be applicable widely, in industries and in other medical fields for purposes of the multi-valued decision making.

• 한국ITS학회 논문지
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• 제9권1호
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• pp.19-26
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• 2010

In this paper, we propose an efficient decision rule in order to get better chance to detect the unused spectrum assigned to a licensed user and improve reliability of spectrum sensing performance. Each secondary user receives the signals from the licensed user. And the resulting signals input to an energy detector. Then, each sensing result is combined and used to make a decision whether the primary user is present at the licensed spectrum band or not. In order to make the reliable decision, we apply an efficient decision rule that is called as a majority rule in this paper. The simulation results show that spectrum sensing performance with the proposed decision rule is more reasonable and efficient than that with conventional decision rules.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.56-62
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• 2021

The surge in generic attacks execution against cipher text on the computer network has led to the continuous advancement of the mechanisms to protect information integrity and confidentiality. The implementation of explicit decision tree machine learning algorithm is reported to accurately classifier generic attacks better than some multi-classification algorithms as the multi-classification method suffers from detection oversight. However, there is a need to improve the accuracy and reduce the false alarm rate. Therefore, this study aims to improve generic attack classification by implementing two hybridized decision tree algorithms namely Naïve Bayes Decision tree (NBTree) and Logistic Model tree (LMT). The proposed hybridized methods were developed using the 10-fold cross-validation technique to avoid overfitting. The generic attack detector produced a 99.8% accuracy, an FPR score of 0.002 and an MCC score of 0.995. The performances of the proposed methods were better than the existing decision tree method. Similarly, the proposed method outperformed multi-classification methods for detecting generic attacks. Hence, it is recommended to implement hybridized decision tree method for detecting generic attacks on a computer network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2942-2960
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• 2022

The core of cognitive radio is the problem concerning intelligent decision-making for communication parameters, the objective of which is to find the most appropriate parameter configuration to optimize transmission performance. The current algorithms have the disadvantages of high dependence on prior knowledge, large amount of calculation, and high complexity. We propose a new decision-making model by making full use of the interactivity of reinforcement learning (RL) and applying the Q-learning algorithm. By simplifying the decision-making process, we avoid large-scale RL, reduce complexity and improve timeliness. The proposed model is able to find the optimal waveform parameter configuration for the communication system in complex channels without prior knowledge. Moreover, this model is more flexible than previous decision-making models. The simulation results demonstrate the effectiveness of our model. The model not only exhibits better decision-making performance in the AWGN channels than the traditional method, but also make reasonable decisions in the fading channels.

• 대한전자공학회논문지SP
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• 제44권3호
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• pp.88-96
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• 2007

지난 수년간 많은 국제 비디오 부호화 표준들이 연구되고 제안되었다. 그 중에서도 H.264는 가장 최근에 제안된 부호화 방식으로 가장 높은 부호화 효율을 제공한다. 이는 기존의 부호화 방식들보다 향상된 부호화 기법들을 사용했으며, 특히 다양한 매크로블록 모드와 라그랑지안(Lagrangian) 최적화 기법을 통한 최적 모드 결정법은 부호화 효율 향상에 결정적인 역할을 했다. 비록 H.264는 부호화 효율 측면에서 기존의 방식보다 월등한 성능 향상을 보이지만, 최적 모드를 결정하는 과정에서 모든 부호화 매개 변수를 고려하므로 실시간 부호화가 어려울 정도로 복잡도가 크게 증가한다. 본 논문에서는 이러한 복잡도를 최소화하기 위해 매크로블록 모드를 복잡도 측면에서 분류하고 복잡도가 낮은 최적 모드를 조기에 결정하는 고속 모드결정 방식을 제안한다. 실험 결과, 제안한 방식은 여러 종류의 실험 영상에 대해 현저한 PSNR 감소나 비트량 증가 없이 부호화 시간을 평균 34.95%까지 감소시켰다. 또한, 본 논문에서 제공한 실험 결과의 타당성을 보이기 위해 부호화 효율과 복잡도에 대한 하위 경계조건(low boundary condition)을 설정하고, 제안한 방식이 하위 경계조건을 만족함을 보였다.

• 한국통신학회논문지
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• 제41권8호
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• pp.868-876
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• 2016

MUSIC(multiple signal classification) 알고리즘은 고유값(eigenvalue)과 고유벡터(eigenvector)를 이용하여 표적의 도래각을 추정하는 대표적인 알고리즘이다. 일반적으로 고유값과 고유벡터는 고유치 해석(eigen-analysis)을 이용하여 구할 수 있으나, 계산 복잡도가 높고 수렴 시간의 긴 문제점이 있다. 그러므로 저가형 실시간 시스템 구현에 한계가 있다. 이런 문제를 개선한 고유치 해석 방법으로 QR 반복법이 제안되었으나, 기존의 QR 반복법 수렴 판단 방법으로는 MUSIC 알고리즘 적용에 부적합하다는 한계가 있다. 본 논문에서는 QR 반복법의 고유치 기반의 기존 수렴 판단 방법의 문제점을 분석하고, 고유벡터를 활용한 개선된 수렴 판단 방법을 제안한다.

• International Journal of Advanced Culture Technology
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• 제4권2호
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• pp.38-42
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• 2016

In the growing data saturated world, the question of "whether data can be used" has shifted to "can it be utilized effectively?" More data is being generated and utilized than ever before. As the collection of data increases, data mining techniques also must become more and more accurate. Thus, to ensure this data is effectively utilized, the analysis of the data must be efficient. Interpretation of results from the analysis of the data set presented, have their own on the basis it is possible to obtain the desired data. In the data mining method a decision tree, clustering, there is such a relationship has not yet been fully developed algorithm actually still impact of various factors. In this experiment, the classification method of data mining techniques is used with easy decision tree. Also, it is used special technology of one R and J48 classification technique in the decision tree. After selecting a rule that a small error in the "one rule" in one R classification, to create one of the rules of the prediction data, it is simple and accurate classification algorithm. To create a rule for the prediction, we make up a frequency table of each prediction of the goal. This is then displayed by creating rules with one R, state-of-the-art, classification algorithm while creating a simple rule to be interpreted by the researcher. While the following can be correctly classified the pattern specified in the classification J48, using the concept of a simple decision tree information theory for configuring information theory. To compare the one R algorithm, it can be analyzed error rate and accuracy. One R and J48 are generally frequently used two classifications${\ldots}$