• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1845-1865
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• 2019

Current Internet is designed by lots of service providers with different objects and policies which make the direct deployment of radically new architecture and protocols on Internet nearly impossible without reaching a consensus among almost all of them. Network virtualization is proposed to fend off this ossification of Internet architecture and add diversity to the future Internet. As an important part of network virtualization, virtual network embedding (VNE) problem has received more and more attention. In order to solve the problems of large embedding cost, low acceptance ratio (AR) and environmental adaptability in VNE algorithms, cognitive method is introduced to improve the adaptability to the changing environment and a cognitive virtual network embedding algorithm based on weighted relative entropy (WRE-CVNE) is proposed in this paper. At first, the weighted relative entropy (WRE) method is proposed to select the suitable substrate nodes and paths in VNE. In WRE method, the ranking indicators and their weighting coefficients are selected to calculate the node importance and path importance. It is the basic of the WRE-CVNE. In virtual node embedding stage, the WRE method and breadth first search (BFS) algorithm are both used, and the node proximity is introduced into substrate node ranking to achieve the joint topology awareness. Finally, in virtual link embedding stage, the CPU resource balance degree, bandwidth resource balance degree and path hop counts are taken into account. The path importance is calculated based on the WRE method and the suitable substrate path is selected to reduce the resource fragmentation. Simulation results show that the proposed algorithm can significantly improve AR and the long-term average revenue to cost ratio (LTAR/CR) by adjusting the weighting coefficients in VNE stage according to the network environment. We also analyze the impact of weighting coefficient on the performance of the WRE-CVNE. In addition, the adaptability of the WRE-CVNE is researched in three different scenarios and the effectiveness and efficiency of the WRE-CVNE are demonstrated.

• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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• pp.149-152
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• 2004

A new joint source channel coding reduces both input-width and average current consumption to transmit image data to LCD source drivers. As a source coding, it is based on entropy coding of differential pulse code modulation scheme, especially using median edge detector of image predictor. As a channel coding, it is not a simple pulse amplitude modulation, but linked by source entropy to reduce average amplitude. Simulation results show 1/4 width is achievable by 16-valued transmission with keeping conventional current consumption (0.36 to 1.3).

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.9
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• pp.1299-1307
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• 1989

A Korean syllable is regarded as a random variable according to its probabilistic property in occurrence. A Korean syllable is divided into a 'choseong', a 'jungseong', and a 'jongseong' which are regarded as random variables. From the cumulative freaquency of a Korean syllable all possible joint probabilities and conditional probabilities are computed for the three ramdom variables. From the joint probabilities and the conditional probabilities all possible joint entropies and conditional entropies are computed for the three random varibles. Also all possible average mutual informations are calculated for the three random variables. Average mutual informatin between two random variables hss its biggest value between choseong and jungseong. Average mutual information between a random variable and other two random variables has its biggest value between jungseong and choseong-jongseong.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1101-1104
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• 2019

키넥트는 RGBD 카메라로 인체의 뼈대와 관절을 3D 공간에서 스켈레톤 데이터수집을 가능하게 해주었다. 스켈레톤 데이터를 활용한 행동 분류는 RNN, CNN 등 다양한 인공 신경망으로 접근하고 있다. 본 연구는 키넥트를 이용해서 Skeleton Joints를 수집하고, DNN 기반 스켈레톤 모델링 학습으로 행동을 분류한다. Skeleton Joints Processing 과정은 키넥트의 Depth Map 기반의 Skeleton Tracker로 25가지 Skeleton Joints 좌표를 얻고, 학습을 위한 전처리 과정으로 각 좌표를 상대좌표로 변경하고 데이터 수를 제한하며, Joint가 트래킹 되지 않은 부분에 대한 예외 처리를 수행한다. 스켈레톤 모델링 학습 과정에선 3계층의 DNN 신경망을 구축하고, softmax_cross_entropy 함수로 Skeleton Joints를 집는 모션, 내려놓는 모션, 팔짱 낀 모션, 얼굴을 가까이 가져가는 모션 해서 4가지 행동으로 분류한다.

• Journal of Environmental Science International
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• v.26 no.9
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• pp.1073-1080
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• 2017

Manufacturing and technology industries produce large amounts of air pollutants. Ulsan Metropolitan City, South Korea, is well-known for its large industrial complexes; in particular, the concentration of $SO_2$ here is the highest in the country. We assessed $SO_2$ monitoring sites based on conditional and joint entropy, because this is a common method for determining an optimal air monitoring network. Monthly $SO_2$ concentrations from 12 air monitoring sites were collected, and the distribution of spatial locations was determined by kriging. Mean absolute error, Root Mean Squared Error (RMSE), bias and correlation coefficients were employed to evaluate the considered algorithms. An optimal air monitoring network for Ulsan was suggested based on the improvement of RMSE.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.796-798
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• 2005

본 연구에서는 엔트로피 이론을 사용하여 ICA(Independent Component Analysis) 점수함수를 생성하는 새로운 밀도추정자(Density Estimator)를 제안한다. 원 신호에 대한 밀도함수의 추정은 적당한 점수함수를 생성하기 위해 필요하고, 미분 가능한 밀도함수인 커널을 이용한 밀도추정법(Kernel Density Estimation)을 이용하여 점수함수를 생성하였다. 보다 빠른 점수함수의 생성을 위해서 식의 형태를 convolution 형태로 표현하였으며, ICA 학습을 위해서 결합엔트로피를 최대화(Joint Entropy Maximization)하는 방향으로 커널의 폭을 학습하였다. 이를 위해서 기울기 강하법(Gradient descent method)를 사용하였으며, 이러한 제약 사항은 새로운 밀도 추정자를 설계하기 위한 기본적인 개념을 나타낸다. 실험결과, 커널의 폭을 담당하는 smoothing parameters들이 일정한 값으로 학습함을 알 수 있었다.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.297-307
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• 2006

Most methods for describing the relationship among random variables require specific probability distributions and some assumptions of random variables. The mutual information based on the entropy to measure the dependency among random variables does not need any specific assumptions. And the redundancy which is a analogous version of the mutual information was also proposed. In this paper, the redundancy and mutual information are explored to multi-dimensional categorical data. It is found that the redundancy for categorical data could be expressed as the function of the generalized likelihood ratio statistic under several kinds of independent log-linear models, so that the redundancy could also be used to analyze contingency tables. Whereas the generalized likelihood ratio statistic to test the goodness-of-fit of the log-linear models is sensitive to the sample size, the redundancy for categorical data does not depend on sample size but its cell probabilities itself.

• Journal of Korea Multimedia Society
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• v.5 no.2
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• pp.147-157
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• 2002

Noise in image sequences is visually offensive and may mask important image detail. In addition to degradation of visual quality, the noise pattern increases the entropy of the image, and thus hinders effective compression. This paper proposes a spatial and a temporal joint filters to reduce the noise by jointly connecting two adaptive noise reducers with different characteristics, and we also propose an IIR-type 3D noise reduction litter scheme connecting the spatial and the temporal joint filters. The proposed 3D IIR filter not only strongly removes noise in uniform image regions while preserving edges and details but also effectively suppresses temporal flicker caused by noise. Experimental results show that the proposed scheme improves subjective quality as well as objective quality as compared with the various noise filtering techniques.

• Convergence Security Journal
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• v.24 no.2
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• pp.123-132
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• 2024

In the era of the Internet of Things, 7 billion diverse devices have been interconnected worldwide. Ensuring information security across these varied devices is crucial in this hyper-connected age. To achieve essential security functions such as confidentiality, integrity, and authentication, it is imperative to implement true random number generators (TRNGs). Therefore, this study proposes a method to rapidly characterize the randomness of TRNGs. While there are international standards for formally characterizing the randomness of TRNGs, adhering to these standards often requires significant time and resources. This study aims to help TRNG developers enhance efficiency in both time and cost by characterizing rough randomness and unpredictability. Firstly, we propose applying auto-correlation and cross-correlation metrics for analog signals. Secondly, we suggest adopting joint entropy and mutual information metrics for digital signals.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.104-112
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• 2008

In modern war information system development makes battlefield materialize, and combat factors can maximize combat power exhibition as that apply synchronization. Information system is the core of combat power operation under NCW(Network centric Warfare). This paper proposed a new MOE(Measure of Effectiveness) that can assess the combat power synergistic effect of information system at the theater joint fire operation in NCW environment. This methodology applied the rule of Newton's second law $F=(m{\Delta}{\upsilon})/t{\Rightarrow}(M{\upsilon}I)/T$) Details factor in combat power evaluation is as following. (1) M : Network power; (2) v : Movement velocity; (3) I : Information superiority; (4) T : C2(command and control) time. We applied this methodology to the "JFOS-K(Joint Fire Operating System-Korea) in Joint Chief of Staff" in the real military affair section.