• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.3
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• pp.50-59
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• 2011

In this paper, we present a spectrum sensing method by exploiting the relationship of previous and current detected data sets in frequency domain. Most of the traditional spectrum sensing methods only consider the current detected data sets of Primary User (PU). Previous state of PU is a kind of conditional probability that strengthens the reliability of the detector. By considering the relationship of the previous and current spectrum sensing, cross entropy-based spectrum sensing is proposed to detect PU signal more effectively, which has a strengthened performance and is robust. When previous detected signal is noise, the discriminating ability of cross entropy-based spectrum sensing is no better than conventional entropy-based spectrum sensing. To address this problem, we propose an improved cross entropy-based frequency-domain spectrum sensing. Regarding the spectrum sensing scheme, we have derived that the proposed method is superior to the cross entropy-based spectrum sensing. We proceed a comparison of the proposed method with the up-to-date entropy-based spectrum sensing in frequency-domain. The simulation results demonstrate the performance improvement of the proposed spectrum sensing method.

• Korean Chemical Engineering Research
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• v.52 no.3
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• pp.294-301
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• 2014

This study focuses on analysis and comparison of entropy generation in various cross-sectional ducts along with fully developed laminar flow and constant uniform wall heat flux. The obtained results were compared in ducts with circular, semicircular, and rectangular with semicircular ends, equilateral triangular, and square and symmetrical hexagonal cross-sectional areas. These results were separately studied for aspect ratio of different rectangular shapes. Characteristics of fluid were considered at average temperature between outlet and inlet ducts. Results showed that factors such as Reynolds number, cross section, hydraulic diameter, heat flux and aspect ratio were effective on entropy generation, and these effects are more evident than heat flux and occur more in high heat fluxes. Considering the performed comparisons, it seems that semicircular and circular cross section generates less entropy than other cross sections.

• International Journal of Contents
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• v.11 no.2
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• pp.57-62
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• 2015

Relative entropy is a divergence measure between two probability density functions of a random variable. Assuming that the random variable has only two alphabets, the relative entropy becomes a cross-entropy error function that can accelerate training convergence of multi-layer perceptron neural networks. Also, the n-th order extension of cross-entropy (nCE) error function exhibits an improved performance in viewpoints of learning convergence and generalization capability. In this paper, we derive a new divergence measure between two probability density functions from the nCE error function. And the new divergence measure is compared with the relative entropy through the use of three-dimensional plots.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.1-9
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• 2017

Recently, the Q-Learning algorithm, which is one kind of reinforcement learning, is mainly used to implement artificial intelligence system in combination with deep learning. Many research is going on to improve the performance of Q-Learning. Therefore, purpose of theory try to improve the performance of Q-Learning algorithm. This Theory apply Cross Entropy Error to the loss function of Q-Learning algorithm. Since the mean squared error used in Q-Learning is difficult to measure the exact error rate, the Cross Entropy Error, known to be highly accurate, is applied to the loss function. Experimental results show that the success rate of the Mean Squared Error used in the existing reinforcement learning was about 12% and the Cross Entropy Error used in the deep learning was about 36%. The success rate was shown.

• International Journal of Safety
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• v.4 no.2
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• pp.1-5
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• 2005

Design of manufacturing process, in general, facilitates the creation of new process that may potentially harm the workers. Design of safety-guaranteed manufacturing process is, therefore, very important since it determines the ultimate outcomes of manufacturing activities involving safety of workers. This study discusses application of discrimination information (cross entropy) to safety assessment of manufacturing processes. The idea is based on the general principles of design and their applications. An example of Cartesian robotic movement is given.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.298-299
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• 2018

In this study, the discriminant power of the DEA models which do not require the prior information of decision makers was evaluated. Entropy model, Bootstrap model, Benevolent Cross Efficiency model, Aggressive Cross Efficiency model and Game Cross Efficiency model were selected as the DEA model for discriminant power evaluation. The discriminant power of five DEA models was evaluated using coefficient of variation and degree of importance. According to the evaluation results, the rank of discrimination power was evaluated in the order of Entropy model, Aggressive CE model, Benevolent CE model, Game CE model, and Bootstrap model in both evaluation indexes.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.37-45
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• 2022

In this paper, a multi agent-multi task assignment problem, which is a representative problem of combinatorial optimization, is presented. The objective of the problem is to determine the coordinated agent-task assignment that maximizes the sum of the achievement rates of each task. The achievement rate is represented as a concave down increasing function according to the number of agents assigned to the task. The problem is expressed as an NP-hard problem with a non-linear objective function. In this paper, to solve the assignment problem, we propose a hybrid cross-entropy algorithm as an effective and efficient solution methodology. In fact, the general cross-entropy algorithm might have drawbacks (e.g., slow update of parameters and premature convergence) according to problem situations. Compared to the general cross-entropy algorithm, the proposed method is designed to be less likely to have the two drawbacks. We show that the performances of the proposed methods are better than those of the general cross-entropy algorithm through numerical experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3889-3903
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• 2022

To address the problem of low detection accuracy due to training noise caused by mislabeling when Tri-training for network intrusion detection (NID), we propose a Tri-training algorithm based on cross entropy and K-nearest neighbors (TCK) for network intrusion detection. The proposed algorithm uses cross-entropy to replace the classification error rate to better identify the difference between the practical and predicted distributions of the model and reduce the prediction bias of mislabeled data to unlabeled data; K-nearest neighbors are used to remove the mislabeled data and reduce the number of mislabeled data. In order to verify the effectiveness of the algorithm proposed in this paper, experiments were conducted on 12 UCI datasets and NSL-KDD network intrusion datasets, and four indexes including accuracy, recall, F-measure and precision were used for comparison. The experimental results revealed that the TCK has superior performance than the conventional Tri-training algorithms and the Tri-training algorithms using only cross-entropy or K-nearest neighbor strategy.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.56-59
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• 2017

CNN(Convolutional Neural Network)을 이용하여 발화 주제 다중 분류 task를 multi-labeling 방법과, cluster 방법을 이용하여 수행하고, 각 방법론에 MSE(Mean Square Error), softmax cross-entropy, sigmoid cross-entropy를 적용하여 성능을 평가하였다. Network는 음절 단위로 tokenize하고, 품사정보를 각 token의 추가한 sequence와, Naver DB를 통하여 얻은 named entity 정보를 입력으로 사용한다. 실험결과 cluster 방법으로 문제를 변형하고, sigmoid를 output layer의 activation function으로 사용하고 cross entropy cost function을 이용하여 network를 학습시켰을 때 F1 0.9873으로 가장 좋은 성능을 보였다.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.125-137
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• 2005

Many discriminant analysis models for binary data have been used in real applications, but none of the classification models dominates in all varying circumstances(Asparoukhov & Krzanowski(2001)). Lee and Hwang (2003) proposed a new classification model by using multinomial distribution with the maximum entropy estimation method. The model showed some promising results in case of small number of variables, but its performance was not satisfactory for large number of variables. This paper explores to use the iterative cross entropy minimization estimation method in replace of the maximum entropy estimation. Simulation experiments show that this method can compete with other well known existing classification models.