• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.21-40
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• 2018

In this paper, we propose the distributed robust beamforming design scheme in cognitive two-way amplify-and-forward (AF) relay networks with imperfect channel state information (CSI). Assuming the CSI errors follow a complex Gaussian distribution, the objective of this paper is to design the robust beamformer which minimizes the total transmit power of the collaborative relays. This design will guarantee the outage probability of signal-to-interference-plus-noise ratio (SINR) beyond a target level at each secondary user (SU), and satisfies the outage probability of interference generated on the primary user (PU) above the predetermined maximum tolerable interference power. Due to the multiple CSI uncertainties in the two-way transmission, the probabilistic constrained optimization problem is intractable and difficult to obtain a closed-form solution. To deal with this, we reformulate the problem to the standard form through a series of matrix transformations. We then accomplish the problem by using the probabilistic approach based on two sorts of Bernstein-type inequalities and the worst-case approach based on S-Procedure. The simulation results indicate that the robust beamforming designs based on the probabilistic method and the worst-case method are both robust to the CSI errors. Meanwhile, the probabilistic method can provide higher feasibility rate and consumes less power.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권5호
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• pp.2588-2609
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• 2019

Speech homomorphic encryption has become one of the key components in secure speech storing in the public cloud computing. The major problem of speech homomorphic encryption is the huge data expansion of speech cipher-text. To address the issue, this paper presents a speech homomorphic encryption scheme with less data expansion, which is a probabilistic statistics and addition homomorphic cryptosystem. In the proposed scheme, the original digital speech with some random numbers selected is firstly grouped to form a series of speech matrix. Then, a proposed matrix encryption method is employed to encrypt that speech matrix. After that, mutual information in sample speech cipher-texts is reduced to limit the data expansion. Performance analysis and experimental results show that the proposed scheme is addition homomorphic, and it not only resists statistical analysis attacks but also eliminates some signal characteristics of original speech. In addition, comparing with Paillier homomorphic cryptosystem, the proposed scheme has less data expansion and lower computational complexity. Furthermore, the time consumption of the proposed scheme is almost the same on the smartphone and the PC. Thus, the proposed scheme is extremely suitable for secure speech storing in public cloud computing.

• 비파괴검사학회지
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• 제15권2호
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• pp.395-406
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• 1995

초음파탐상시험을 통해 용접 결함의 종류를 정확히 구분하는 것은 정량적 비파괴시험을 위한 기본적인 단계로서 매우 중요한 문제인데, 이 문제는 최근 활발한 연구가 진행중인 초음파 형상 인식 기법의 적용에 의해 해결할 수 있다. 여기에서는 특징 추출, 특징 선택 그리고 결함 분류 등 초음파 형상 인식 기법의 세부 기술과 함께, 특히 최근 효율적인 분류기로 관심을 모으고 있는 확률 신경 회로망의 적용에 대해 논의하였다. 그리고 강 용접부 내부에 존재하는 결함을 균열, 기공, 슬래그 혼입의 3 종류로 분류하는 문제에 확률 신경 회로망을 적용한 예를 통하여, 초음파 형상 인식 기법의 효용성을 검증하였다. 또한 민감한 특징을 효율적으로 선별하는데 널리 사용되는 전방 특징 선택법과 그 적용에 대해서도 논의하였다.

• International Journal of Control, Automation, and Systems
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• 제5권6호
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• pp.674-680
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• 2007

The probabilistic roadmap (PRM) method, which is a popular path planning scheme, for a manipulator, can find a collision-free path by connecting the start and goal poses through a roadmap constructed by drawing random nodes in the free configuration space. PRM exhibits robust performance for static environments, but its performance is poor for dynamic environments. On the other hand, reinforcement learning, a behavior-based control technique, can deal with uncertainties in the environment. The reinforcement learning agent can establish a policy that maximizes the sum of rewards by selecting the optimal actions in any state through iterative interactions with the environment. In this paper, we propose efficient real-time path planning by combining PRM and reinforcement learning to deal with uncertain dynamic environments and similar environments. A series of experiments demonstrate that the proposed hybrid path planner can generate a collision-free path even for dynamic environments in which objects block the pre-planned global path. It is also shown that the hybrid path planner can adapt to the similar, previously learned environments without significant additional learning.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.52-56
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• 2009

In this study a dynamic modeling scheme is presented to derive the probabilistic structure of soil water and plant water stress when subject to stochastic precipitation conditions. The newly developed model has the form of the Fokker-Planck equation, and its applicability as a model for the probabilistic evolution of the soil water and plant water stress is investigated under climate change scenarios. This model is based on the cumulant expansion theory, and has the advantage of providing the probabilistic solution in the form of probability distribution function (PDF), from which one can obtain the ensemble average behavior of the dynamics. The simulation result of soil water confirms that the proposed soil water model can properly reproduce the results obtained from observations, and it also proves that the soil water behaves with consistent cycle based on the precipitation pattern. The plant water stress simulation, also, shows two different PDF patterns according to the precipitation. Moreover, with all the simulation results with climate change scenarios, it can be concluded that the future soil water and plant water stress dynamics will differently behave with different climate change scenarios.

• Nuclear Engineering and Technology
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• 제37권3호
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• pp.245-258
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• 2005

This paper addresses two types of uncertainty: stochastic uncertainty and subjective uncertainty in probabilistic accident consequence assessments. The off-site consequence assessment code OSCAAR has been applied to uncertainty and sensitivity analyses on the individual risks of early fatality and latent cancer fatality in the population outside the plant boundary due to a severe accident. A new stratified meteorological sampling scheme was successfully implemented into the trajectory model for atmospheric dispersion and the statistical variability of the probability distributions of the consequence was examined. A total of 65 uncertain input parameters was considered and 128 runs of OSCAAR with 144 meteorological sequences were performed in the parameter uncertainty analysis. The study provided the range of uncertainty for the expected values of individual risks of early and latent cancer fatality close to the site. In the sensitivity analyses, the correlation/regression measures were useful for identifying those input parameters whose uncertainty makes an important contribution to the overall uncertainty for the consequence. This could provide valuable insights into areas for further research aiming at reducing the uncertainties.

• 한국정보과학회논문지:정보통신
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• 제29권4호
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• pp.346-351
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• 2002

본 논문은 Okamoto와 Uchiyama가 이산대수 함수만으로 일방향함수를 정의하여 제안한 새로운 확률 공개키 암호 방식을 개선하였다. 평문은 이들 두 이산대수 함수의 모듈라 곱으로부터 구할 수 있는데 두 함수 값 중에서 하나는 주어진 공개키에 종속된 고정된 값을 가진다. 고정된 함수 값이 단위원 1을 갖는 정선된 공개키에 의해 개선이 이루어진다. 왜냐하면 복호화 할 때 계산량을 줄일 수 있기 때문이다. 또한 이러한 성질을 충족하는 공개키를 얻을 수 있는 구체적 방법도 제시한다.

• 한국음향학회지
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• 제27권2호
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• pp.75-79
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• 2008

본 논문에서는 support vector machine (SVM) 기반의 global soft decison (GSD)을 이용한 새로운 음성 향상 기법을 제시한다. 일반적으로 soft decision (SD) 이득 수정 및 잡음 전력 추정에 근거한 음성 향상 기법이 hard decision을 이용한 음성향상 기법 보다 우수한 성능을 보이는 것으로 알려져 있다. 특히, 각 프레임에서의 음성 부재에 대한 효과적인 척도인 전역음성 부재확률 (global speech absence probability, GSAP)을 SD 기반의 음성 향상 기법에 적용한 여러 연구가 진행되었다. 본 논문에서는 sigmoid 함수를 이용하여 얻어진 SVM의 확률 출력에 의해 추정된 새로운 GSAP를 음성 향상 기법에 적용한다. 제안된 알고리즘의 성능은 다양한 잡음 환경에 적용하여 PESQ 및 MOS 평가 방법을 바탕으로 기존의 GSD 기반의 스펙트럼 향상 기법과 비교하여 향상된 결과를 나타내었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(4)
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• pp.41-44
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• 2001

Variable length coding (VLC) has been used in many well known standard video coding algorithms such as MPEG and H.26x. However, VLC can not be processed parallelly because of its sequentiality. This sequentiality is a big barrier for implementing a real-time software video codec since parallel schemes can not be applied. In this paper, we propose a new fast VLD (Variable Length Decoding) method based on the probabilistic distribution of symbols in VLC tables used in MPEG as well as H.263 standard codecs. Even though MPEG suggests the table partitioning method, they do not show theoretically why the number of partitioned tables is two or three. We suggest the method for deciding the number of partitioned tables. Applying our scheme to several well-known MPEG-2 test sequences, we can reduce the computational time up to about 10% without any sacrificing video quality

• Journal of Electrical Engineering and Technology
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• 제10권2호
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• pp.511-517
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• 2015

Determination of the required reserve capacity has an important function in operation of power system and it is calculated based on the largest loss of supply. However, conventional method cannot be applied in future power system, because potential grid-connected distributed generator and abnormal temperature cause the large load imbalance. Therefore this paper address new framework for determining the optimal required reserve capacity taking into account the real time load imbalance. At first, we introduce the way of operating reserve resources which are the secondary, tertiary, Direct Load Control (DLC) and Load shedding reserves to make up the load imbalance. Then, the formulated problem can be solved by the Probabilistic Dynamic Programming (PDP) method. In case study, we divide two cases for comparing the cost function between the conventional method and the proposed method.