• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.88-97
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• 2013

Most of the traditional spectrum sensing methods consider only the current detected data sets of Primary User (PU). However previous state of PU is a kind of conditional probability that strengthens the reliability of the detector. Therefore, in the cross entropy spectrum sensing method, relationship of the previous and current spectrum sensing is considered to detect PU signal more effectively. But these cross entropy spectrum sensing methods only consider the ideal system. In other words, PU always occupy the channel during the same period. However, PU can occupy the channel either for a longer or a shorter period than the ideal case in the real system. For this reason, the spectrum sensing performance can be varied. In this paper, we propose the method that can maintain the performance of spectrum sensing in the real system and we confirm the results with the help of simulation.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.300-300
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• 2022

가뭄 취약성은 다양한 평가 요소가 반영되는 다기준 구성으로 개념화될 수 있으며 관련하여 수반되는 영향을 집계하여 측정해야 하므로 여러 변수가 제공하는 정보를 통합해야 한다. 따라서 가뭄 취약성 평가의 일반적인 절차에는 (1) 고려할 변수 선택, (2) 가중치 체계 정의 및 (3) 변수 집계가 포함된다. 여기서 가중치 산정은 평가결과에 막대한 영향을 미칠 수 있는 중요한 과정이다. 각 평가 요소는 내재된 의미가 다르기 때문에 모두 동일한 가중치를 가지고 있다고 가정 할 수 없다. 따라서 각 평가 요소별로 영향력을 가늠하는 가중치를 찾는 것이 다기준 평가에서 주요한 연구 분야이다. 본 연구에서는 밭 가뭄 취약성 평가를 위한 평가 요소의 자료로부터 각 요소를 통계적 기법으로 분석하여 평가 결과에 반영함으로써 주관적인 가중치를 적용하는 평가기법에 따른 편향 가능성을 해소하고자 한다. 객관적 가중치 산정기법인 Entropy, PCA 기법을 적용하였다. 평가 결과는 가중치 산정기법에 따라 차이가 발생하였으며 특히 Entropy 가중치의 경우, 다른 방법에 비하여 차이가 많이 나타났으며 이 같은 차이는 Entropy 가중치 산정기법상 정보의 변화량이 많은 평가인자에 과도한 가중치가 반영된 결과로 판단된다. 본 연구에서 제시한 밭 가뭄과 연관되는 지표를 적용하여 가뭄취약성을 평가하는 방안은 각 지역에 내재된 밭 가뭄취약정도를 파악하여 사전에 대응하기 위한 정책 수립 등에 기여할 수 있다.

• Broadcasting and Media Magazine
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• v.7 no.3
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• pp.54-64
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• 2002

• Proceedings of the Korea Water Resources Association Conference
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• 1999.05a
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• pp.599-606
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• 1999

• Proceedings of the Korea Water Resources Association Conference
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• 1999.05a
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• pp.607-612
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• 1999

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.643-652
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• 2006

Decision trees are mainly used for the classification and prediction in data mining. The distribution of spatial data and relationships with their neighborhoods are very important when conducting classification for spatial data mining in the real world. Spatial decision trees in previous works have been designed for reflecting spatial data characteristic by rating Euclidean distance. But it only explains the distance of objects in spatial dimension so that it is hard to represent the distribution of spatial data and their relationships. This paper proposes a decision tree based on spatial entropy that represents the distribution of spatial data with the dispersion and dissimilarity. The dispersion presents the distribution of spatial objects within the belonged class. And dissimilarity indicates the distribution and its relationship with other classes. The rate of dispersion by dissimilarity presents that how related spatial distribution and classified data with non-spatial attributes we. Our experiment evaluates accuracy and building time of a decision tree as compared to previous methods. We achieve an improvement in performance by about 18%, 11%, respectively.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.527-540
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• 2010

Rule-based classification analysis is widely used for massive datamining because it is easy to understand and its algorithm is uncomplicated. In this classification analysis, majority vote of rules or weighted combination of rules using their supports are frequently used in order to combine rules. We propose a method to combine rules by using the multinomial distribution in this paper. Iterative proportional fitting algorithm is used to estimate the multinomial distribution which maximizes entropy constrained on rules' support. Simulation experiments show that this method can compete with other well known classification models in the case of two similar populations.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.277-286
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• 2009

Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. In this paper, we introduce a robust brain registration technique for correcting the difference between two temporal images by the different coordinate systems in MR and CT image obtained from the same patient. Two images are registered where this measure is minimized using a modified conditional entropy(MCE: Modified Conditional Entropy) computed from the joint histograms for the intensities of two given images, we conduct the rendering for visualization of 3D volume image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5A
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• pp.545-553
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• 2004

Expansion of network environment and multimedia demand universality of application service as HDTV, etc. During these processes, it is essential to process multimedia in real time in the wireless communication system based on mobile phone network and in the wire communication system due to fiber cable and xDSL. So, in this Paper the optimal memory allocation algorithm combines the merit of huffman encoding which is superior in simultaneous decoding ability and lempel-ziv that is distinguished in execution of compress is proposed to improve the channel transmission rate and processing speed in the compressing procedure and is verified in the entropy encoder of HDTV. Because the entropy encoder system using proposed optimal memory allocation algorithm has memory saturation prevention we confirms that the compressing ratio for moving pictures is superior than Huffman encoding and LZW.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.10
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• pp.947-954
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• 2009

Starting from the Eu's GH(Generalized Hydrodynamic) theory, the multi-species GH numerical solver is developed in this research and its computatyional behaviors are examined for the hypersonic rarefied flow over an axisymmetric body. To improve the accuracy of the developed multi-species GH solver, various slip-wall boundary conditions are tested and the computed results are compared. Additionally, in order to validate the entropy characteristics of the GH equation, the entropy production and entropy generation rates of the GH equation are investigated in the 1-dimensional normal shock structure test at a high Knudsen number.