• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.765-774
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• 2013

In this paper, we present a cooperative compressed spectrum sensing scheme for correlated signals in decentralized wideband cognitive radio networks. Compressed sensing is a signal processing technique that can recover signals which are sampled below the Nyquist rate with high probability, and can solve the necessity of high-speed analog-to-digital converter problem for wideband spectrum sensing. In compressed sensing, one of the main issues is to design recovery algorithms which accurately recover original signals from compressed signals. In this paper, in order to achieve high recovery performance, we consider the multiple measurement vector model which has a sequence of compressed signals, and propose a cooperative sparse Bayesian recovery algorithm which models the temporal correlation of the input signals.

• Journal of Korea Multimedia Society
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• v.11 no.3
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• pp.301-314
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• 2008

The existing event recognition is accomplished with the limited systematic foundation, and thus much longer learning time is needed for emergency scenario interpretation due to large scale of probability data. In this paper, we propose a method for nile-based event recognition of an independent object(human) which extract a feature vectors from the object and analyze the behavior pattern of each object and interpretation of emergency scenarios using a probability and object's events. The event rule of an independent object is composed of the Primary-event, Move-event, Interaction-event, and 'FALL DOWN' event and is defined through feature vectors of the object and the segmented motion orientated vector (SMOV) in which the dynamic Bayesian network is applied. The emergency scenario is analyzed using current state of an event and its post probability. In this paper, we define diversified events compared to that of pre-existing method and thus make it easy to expand by increasing independence of each events. Accordingly, semantics information, which is impossible to be gained through an.

• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.13-23
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• 2022

In this study, from January 2015 to April 2020, we propose a stochastic volatility model to capture the leverage effect on daily freight yields in the dry cargo market and analyze the freight yields. Estimation involving the Bayesian Markov Chain Monte Carlo method for the leverage effect based on the negative correlation that exists between returns and volatility in stochastic volatility analysis yields similar estimates, and the statistcs indicates significant. That is, the results of the empirical analysis show that the degree of correlation between returns and volatility, and the magnitude and sign of fluctuations differ, which suggests that taking into account the leverage effect in the SV model improves the goodness of fit of the estimates. In addition to the statistical significance of the estimated model's leverage effect, the analysis by log predictive power score presents the estimated results with improved predictive power of the model considering the leveraged effect. These astatistically significant empirical results show that the stochastic volatility model considering the leverage effect is important for freight rate risk modeling in the marine industry.

• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.275-276
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• 2009

This paper provides a new approach for predicting the corrosion resistivity of reinforced concrete structures exposed to carbonation. In this method, the prediction can be updated successively by a Bayesian theory when additional data are available. The stochastic properties of model parameters are explicitly taken into account into the model. To simplify the procedure of the model, the probability of the durability limit is determined from the samples obtained from the Latin hypercube sampling technique. The new method may be very useful in designing important concrete structures and help to predict the remaining service life of existing concrete structures which have been monitored.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.539-544
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• 2004

We investigate a visual object tracking algorithm based upon particle filters, namely CONDENSATION, in order to combine multiple observation models such as active contours of digitally subtracted image and the particle measurement of object color. The former is applied to matching the contour of the moving target and the latter is used to independently enhance the likelihood of tracking a particular color of the object. Particle filters are more efficient than any other tracking algorithms because the tracking mechanism follows Bayesian inference rule of conditional probability propagation. In the experimental results, it is demonstrated that the suggested contour tracking particle filters prove to be robust in the cluttered environment of robot vision.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.466-466
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• 2015

지역빈도해석은 수문학에서 오랜 역사를 갖고 있으며, 수년에 걸쳐 수문학적 변량의 정량적 추정을 위해 다양한 접근방법들이 제안되어 왔다. 그러나 제안된 방법들의 가설설정 수준이 높기 때문에 실제 적용에 제약이 많고, 적용 시에도 예측에 대한 불확실성이 높은 문제점이 있다. 본 연구에서는 이러한 문제점을 개선하기 위한 방법으로 계층적 베이지안 모델을 이용한 지역빈도해석 모형을 제안하고자 한다. 본 모형은 2개의 계층적 구조로 구성된다. 첫번째 계층은 재현기간별 GEV 분포의 매개변수를 정규화하여 주변분포로 설정하고, Kriging 기법을 이용하여 지형학적, 기상학적 정보들과 극치강수량 효과를 적합시켜 공간적 이질성과 미계측 유역에 대한 효과적인 보간을 가능하게 한다. 두번째 계층은 지점의 특성을 나타내는 매개변수들간의 공분산을 Bayesian 모델에 연계하여 매개변수들의 공간적 변동성을 나타낸다. 2개 계층의 결합확률분포는 MCMC 기법을 이용하여 예측값에 대한 불확실성을 정량적으로 분석하게 된다. 본 모형을 통해 홍수량 추정 시 필요한 시간 단위 극치강수량의 공간적 분포를 효과적으로 추정할 수 있을 것으로 판단된다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.68-76
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• 2008

It is difficult to maintain mutes in the mobile ad hoc network (MANET) due to the high probability of disconnected routes break by frequent change of topology. The links can have the different reliability about data transmission due to these characteristics. Hence a measure which can evaluate this reliability and a algorithm which reflects this are required. In this paper, we propose routing algorithm based on reliability about transmission. First the bayesian inference which infers the hypothesis by past information is considered to obtain the link's transmission reliability. The other is that the link-based reliability estimation model which considers each link's reliability additionally is proposed while the standard uses only Dijkstra's shortest path algorithm. the simulation results using NS-2 show that the performance of proposed algorithm is superior to the standard OLSR in terms of throughput and stability.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.512-517
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• 2008

It is show that increasing of aged and handicapped people requires development of Ubiquitous computing technique to offer the specialized service for handicapped-people. For this, we need a development of Context Awareness and Service Reasoning Technique that the technique is supplied interaction between user and U-environment instead of the old unilateral relation. The old research of context awareness needed probabilistic presentation model like a Bayesian Network based on expert Systems for recognize given circumstance by a domain of uncertain real world. In this article, we define a domain of disorder activity assistant service application and context model based on ontology in diversified environment and minimized intervention of user and developer. By use this context model, we apply the structure learning of Bayesian Network and decide the service and activity to development of application service for handicapped people. Finally, we define the proper Conditional Probability Table of the structured Bayesian Network and if random situation is given to user, then present state variable of Activity and Service by given Causal relation of Bayesian Network based on Conditional Probability Table and it can be result of context awareness.

• Journal of Korea Water Resources Association
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• v.52 no.11
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• pp.895-904
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• 2019

It has been well recognized that extreme rainfall process often features a nonstationary behavior, which may not be effectively modeled within a stationary frequency modeling framework. Moreover, extreme rainfall events are often described by a two (or more)-component mixture distribution which can be attributed to the distinct rainfall patterns associated with summer monsoons and tropical cyclones. In this perspective, this study explores a Mixture Distribution based Nonstationary Frequency (MDNF) model in a changing rainfall patterns within a Bayesian framework. Subsequently, the MDNF model can effectively account for the time-varying moments (e.g. location parameter) of the Gumbel distribution in a two (or more)-component mixture distribution. The performance of the MDNF model was evaluated by various statistical measures, compared with frequency model based on both stationary and nonstationary mixture distributions. A comparison of the results highlighted that the MDNF model substantially improved the overall performance, confirming the assumption that the extreme rainfall patterns might have a distinct nonstationarity.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.131-140
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• 2009

This paper presents a new algorithm to the segmentation of the FISH images. First, for segmentation of the cell nuclei from background, a threshold is estimated by using the gaussian mixture model and maximizing the likelihood function of gray value of cell images. After nuclei segmentation, overlapped nuclei and isolated nuclei need to be classified for exact nuclei analysis. For nuclei classification, this paper extracted the morphological features of the nuclei such as compactness, smoothness and moments from training data. Three probability density functions are generated from these features and they are applied to the proposed Bayesian networks as evidences. After nuclei classification, segmenting of overlapped nuclei into isolated nuclei is necessary. This paper first performs intensity gradient transform and watershed algorithm to segment overlapped nuclei. Then proposed stepwise merging strategy is applied to merge several fragments in major nucleus. The experimental results using FISH images show that our system can indeed improve segmentation performance compared to previous researches, since we performed nuclei classification before separating overlapped nuclei.