• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.158-163
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• 2008

In inverse synthetic aperture radar (ISAR) imaging, An ISAR autofocusing algorithm is essential to obtain well-focused ISAR images. Traditional methods have relied on the approximation that the phase error due to target motion is a function of the cross-range dimension only. However, in the stepped-frequency radar system, it tends to become a two-dimensional function of both down-range and cross-range, especially when target's movement is very fast and the pulse repetition frequency (PRF) is low. In order to remove the phase error along down-range, this paper proposes a method called SAEM (subarray averaging and entropy minimization) [1] that uses a subarray averaging concept in conjunction with the entropy cost function in order to find target motion parameters, and a novel 2-D optimization technique with the inherent properties of the proposed entropy-based cost function. A well-focused ISAR image can be obtained from the combination of the proposed method and a traditional autofocus algorithm that removes the phase error along the cross-range dimension. The effectiveness of this method is illustrated and analyzed with simulated targets comprised of point scatters.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.89-97
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• 2004

An object to reflect or emit light is captured by imaging system as distorted image due to various distortion. It is called image restoration that estimates original object by removing distortion. There are two categories in image restoration method. One is a deterministic method and the other is a stochastic method. In this paper, image restoration using Minimum Fisher Information(MFI), derived from B. Roy Frieden is proposed. In MFI restoration, experimental results to be made according to noise control parameter were investigated. And cross entropy(Kullback-Leibler entropy) was used as a standard measure of restoration accuracy, It is confirmed that restoration results using MFI have various roughness according to noise control parameter.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.201-212
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• 2017

This study presented the new evaluation index which can evaluate the discrimination of DEA models. To evaluate the discrimination of DEA models, data were analyzed using importance index as suggested in previous study and the coefficient of variation as suggested in this study for the discrimination evaluation. This study selected the CCR-DEA, BCC-DEA, entropy, bootstrap, super efficiency, and cross efficiency DEA model for the discrimination evaluation and accomplished empirical analysis. In order to grasp the rank correlation of the models, this study implemented the rank correlation analysis between the efficiency of CCR model and BCC model and entropy, bootstrap, super efficiency, and efficiency of the cross efficiency model. The obtained results of this study are as follows. First, the discrimination rank of models using the importance index and the coefficient of variation was shown to be identical. Therefore, the coefficient of variation can be used the discrimination evaluation index of DEA model. Second, the discrimination of the super efficiency model was found to be the highest rank among 4 models according to the analysis of this present study. Third, the highest rank correlation with CCR model was the super efficiency model. In addition, the super efficiency model was found to be the highest rank correlation with BCC model.

• ETRI Journal
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• v.17 no.1
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• pp.11-22
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• 1995

This paper proposes a modified error function to improve the error back-propagation (EBP) algorithm for multi-Layer perceptrons (MLPs) which suffers from slow learning speed. It can also suppress over-specialization for training patterns that occurs in an algorithm based on a cross-entropy cost function which markedly reduces learning time. In the similar way as the cross-entropy function, our new function accelerates the learning speed of the EBP algorithm by allowing the output node of the MLP to generate a strong error signal when the output node is far from the desired value. Moreover, it prevents the overspecialization of learning for training patterns by letting the output node, whose value is close to the desired value, generate a weak error signal. In a simulation study to classify handwritten digits in the CEDAR [1] database, the proposed method attained 100% correct classification for the training patterns after only 50 sweeps of learning, while the original EBP attained only 98.8% after 500 sweeps. Also, our method shows mean-squared error of 0.627 for the test patterns, which is superior to the error 0.667 in the cross-entropy method. These results demonstrate that our new method excels others in learning speed as well as in generalization.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.115-125
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• 2021

Simulation-based hull form optimization is a typical HEB (high-dimensional, expensive computationally, black-box) problem. Conventional optimization algorithms easily fall into the "curse of dimensionality" when dealing with HEB problems. A recently proposed Cross-Entropy (CE) optimization algorithm is an advanced stochastic optimization algorithm based on a probability model, which has the potential to deal with high-dimensional optimization problems. Currently, the CE algorithm is still in the theoretical research stage and rarely applied to actual engineering optimization. One reason is that the Monte Carlo (MC) method is used to estimate the high-dimensional integrals in parameter update, leading to a large sample size. This paper proposes an improved CE algorithm based on quasi-Monte Carlo (QMC) estimation using high-dimensional truncated Sobol subsequence, referred to as the QMC-CE algorithm. The optimization performance of the proposed algorithm is better than that of the original CE algorithm. With a set of identical control parameters, the tests on six standard test functions and a hull form optimization problem show that the proposed algorithm not only has faster convergence but can also apply to complex simulation optimization problems.

• Geomechanics and Engineering
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• v.19 no.3
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• pp.283-293
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• 2019

Evaluating the stability of the excavation face of the cross-river shield tunnel with good accuracy is considered as a nonlinear and multivariable complex issue. Understanding the stability evaluation method of the shield tunnel excavation face is vital to operate and control the shield machine during shield tunneling. Considering the instability mechanism of the excavation face of the cross-river shield and the characteristics of this engineering, seven evaluation indexes of the stability of the excavation face were selected, i.e., the over-span ratio, buried depth of the tunnel, groundwater condition, soil permeability, internal friction angle, soil cohesion and advancing speed. The weight of each evaluation index was obtained by using the analytic hierarchy process and the entropy weight method. The evaluation model of the cross-river shield construction excavation face stability is established based on the idea point method. The feasibility of the evaluation model was verified by the engineering application in a cross-river shield tunnel project in China. Results obtained via the evaluation model are in good agreement with the actual construction situation. The proposed evaluation method is demonstrated as a promising and innovative method for the stability evaluation and safety construction of the cross-river shield tunnel engineerings.

• The Korean Journal of Applied Statistics
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• v.3 no.2
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• pp.91-105
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• 1990

Several practical prior distributions are derived using the maximum entropy principle. Also, an interactive method for estimating a prior distribution which uses the minimum cross-entropy principle is proposed when there are many prior informations. The consistency of the prior distributions obtained by the entropy principles is discussed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.1
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• pp.154-159
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• 2008

This study proposes how to decide mean velocity which is one of the very important and efficient discharge measurement in water resources area. In order to achieve this goal, Chiu's velocity distribution equation recently developed from the probability and entropy concepts is used to establish, analyze and compare a linkage between the mean velocity obtained from the Manning's equation which is well known in the world. Besides, it becomes clear that a channel cross section also has a propensity to establish and maintain an equilibrium state that can be measured and classified by a function of entropy M, ratio of mean and maximum velocities irrespective of including sediment or varied channel slope. Therefore, The linkage to be established in this study can be used to compute the cross sectional velocity distribution with the maximum velocity.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.190-194
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• 2023

By looking the importance of communication, data delivery and access in various sectors including governmental, business and individual for any kind of data, it becomes mandatory to identify faults and flaws during cyber communication. To protect personal, governmental and business data from being misused from numerous advanced attacks, there is the need of cyber security. The information security provides massive protection to both the host machine as well as network. The learning methods are used for analyzing as well as preventing various attacks. Machine learning is one of the branch of Artificial Intelligence that plays a potential learning techniques to detect the cyber-attacks. In the proposed methodology, the Decision Tree (DT) which is also a kind of supervised learning model, is combined with the different cross-validation method to determine the accuracy and the execution time to identify the cyber-attacks from a very recent dataset of different network attack activities of network traffic in the UNSW-NB15 dataset. It is a hybrid method in which different types of attributes including Gini Index and Entropy of DT model has been implemented separately to identify the most accurate procedure to detect intrusion with respect to the execution time. The different DT methodologies including DT using Gini Index, DT using train-split method and DT using information entropy along with their respective subdivision such as using K-Fold validation, using Stratified K-Fold validation are implemented.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.39-40
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• 2014

신경회로망의 학습을 위하여 크로스 엔트로피 오차함수가 사용되며, 이의 성능향상을 위하여 확장된 크로스 엔트로피 함수도 제시되었다. 크로스 엔트로피 오차함수는 정보이론에서 제시된 상대 엔트로피(relative entropy)에서 유도된 함수이다. 상대 엔트로 피는 두 확률밀도함수의 발산(divergence) 함수이다. 이 논문에서는 상대 엔트로피와 크로스 엔트로피 관계를 파악한 후, 이를 기반으로 확장된 크로스 엔트로피에 상응하는 새로운 엔트로피 발산 함수를 n=2와 4인 경우에 대하여 유도한다.