• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.367-374
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• 2022

A sampling set selection algorithm is proposed to reconstruct original graph signals from the sampled signals generated on the nodes in the sampling set. Instead of directly minimizing the reconstruction error, we focus on minimizing the upper bound on the reconstruction error to reduce the algorithm complexity. The metric is manipulated by using QR factorization to produce the upper triangular matrix and the analytic result is presented to enable a greedy selection of the next nodes at iterations by using the diagonal entries of the upper triangular matrix, leading to an efficient sampling process with reduced complexity. We run experiments for various graphs to demonstrate a competitive reconstruction performance of the proposed algorithm while offering the execution time about 3.5 times faster than one of the previous selection methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.600-619
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• 2015

We propose a cost-efficient hybrid algorithm for online location updates that efficiently combines feature point detection with the online trajectory-based sampling algorithm. Our algorithm is designed to minimize the average trajectory error with the minimal number of sample points. The algorithm is composed of 3 steps. First, we choose corner points from the map as sample points because they will most likely cause fewer trajectory errors. By employing the online trajectory sampling algorithm as the second step, our algorithm detects several missing and important sample points to prevent unwanted trajectory errors. The final step improves cost efficiency by eliminating redundant sample points on straight paths. We evaluate the proposed algorithm with real GPS trajectory data for various bus routes and compare our algorithm with the existing one. Simulation results show that our algorithm decreases the average trajectory error 28% compared to the existing one. In terms of cost efficiency, simulation results show that our algorithm is 29% more cost efficient than the existing one with real GPS trajectory data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1960-1963
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• 2021

In this paper, we propose a multiplay Thompson sampling algorithm in multipath communication system. Multipath communication system has advantages on communication capacity, robustness, survivability, and so on. It is important to select appropriate network path according to the status of individual path. However, it is hard to obtain the information of path quality simultaneously. To solve this issue, we propose Thompson sampling which is popular in machine learning area. We find some issues when the algorithm is applied directly in the proposal system and suggested some modifications. Through simulation, we verified the proposed algorithm can utilize the entire network paths. In summary, our proposed algorithm can be applied as a path allocation in multipath-based communications system.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.255-266
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• 2007

A Bayesian estimation of the four-parameter gamma distribution is considered under the noninformative prior. The Bayesian estimators are obtained by the Gibbs sampling. The generation of the shape/power parameter and the power parameter in the Gibbs sampler is implemented using the adaptive rejection sampling algorithm of Gilks and Wild (1992). Also, the location parameter is generated using the adaptive rejection Metropolis sampling algorithm of Gilks, Best and Tan (1995). Finally, the simulation result is presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4940-4957
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• 2019

Compared with the localization methods in the static sensor networks, node localization in dynamic sensor networks is more complicated due to the mobility of the nodes. Dynamic Sampling Localization Algorithm Based on Virtual Anchor (DSLA) is proposed in this paper to localize the unknown nodes in dynamic sensor networks. Firstly, DSLA algorithm predicts the speed and movement direction of nodes to determine a sector sampling area. Secondly, a method of calculating the sampling quantity with the size of the sampling area dynamically changing is proposed in this paper. Lastly, the virtual anchor node, i.e., the unknown node that got the preliminary possible area (PLA), assists the other unknown nodes to reduce their PLAs. The last PLA is regarded as a filtering condition to filter out the conflicting sample points quickly. In this way, the filtered sample is close to its real coordinates. The simulation results show that the DSLA algorithm can greatly improve the positioning performance when ensuring the execution time is shorter and the localization coverage rate is higher. The localization error of the DSLA algorithm can be dropped to about 20%.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.1
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• pp.18-28
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• 1993

Processes in the real-time distributed control systems share a network medium to exchange their data. Performance of feedback control loops in the real-time distributed control systems is subject to the network-induced delays from sensor to controller, and from controller to actuator. The network-induced delays are directly dependent upon the data sampling times of control components which share a network medium. In this study, an algorithm of determining data sampling times is developed using the "window concept". where the sampling datafrom the control components dynamically share a limited number of windows. The scheduling algorithm is validated through the aimulation experiments.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.135-140
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• 2000

베이지안 신경망 모형(Bayesian Neural Networks Models)에서 주어진 입력값(input)은 블랙 박스(Black-Box)와 같은 신경망 구조의 각 층(layer)을 거쳐서 출력값(output)으로 계산된다. 새로운 입력 데이터에 대한 예측값은 사후분포(posterior distribution)의 기대값(mean)에 의해 계산된다. 주어진 사전분포(prior distribution)와 학습데이터에 의한 가능도함수(likelihood functions)를 통해 계산되어진 사후분포는 매우 복잡한 구조를 갖게 됨으로서 기대값의 적분계산에 대한 어려움이 발생한다. 이때 확률적 추정에 의한 근사 방법인 몬테칼로 적분을 이용한다. 이러한 방법으로서 Hybrid Monte Carlo 알고리즘은 우수한 결과를 제공하여준다(Neal 1996). 본 논문에서는 Hybrid Monte Carlo 알고리즘과 기존에 많이 사용되고 있는 Gibbs sampling, Metropolis algorithm, 그리고 Slice Sampling등의 몬테칼로 방법들을 비교한다.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.130-134
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• 2013

This paper describes the basic principles of 3D broadcast system and proposes new 3D broadcast technology that reduce the amount of data by applying CS(Compressed Sensing). Differences between Sampling theory and the CS technology concept was described. Recently proposed CS algorithm AMP(Approximate Message Passing) and CoSaMP(Compressive Sampling Matched Pursuit) was described. Image data that compressed and restored by these algorithm was compared. Calculation time of the algorithm having a low complexity is determined.

• Journal of KIISE:Software and Applications
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• v.36 no.3
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• pp.208-216
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• 2009

A number of estimation of distribution algorithms have been proposed that do not use explicitly crossover and mutation of traditional genetic algorithms, but estimate the distribution of population for more efficient search. But because it is not easy to discover higher-order correlations of variables, lower-order correlations are estimated most cases under various constraints. In this paper, we propose a new estimation of distribution algorithm that represents higher-order correlations of the data and finds global optimum more efficiently. The proposed algorithm represents the higher-order correlations among variables by building random hypergraph model composed of hyperedges consisting of variables which are expected to be correlated, and generates the next population by Bayesian sampling algorithm Experimental results show that the proposed algorithm can find global optimum and outperforms the simple genetic algorithm and BOA(Bayesian Optimization Algorithm) on decomposable functions with deceptive building blocks.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1044-1049
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• 2000

In detection of a scene change of the moving pictures which has massive information capacity, the temporal sampling method has faster searching speed than the sequential searching method for the whole moving pictures, yet employed searching algorithm and detection interval greatly affect searching time and searching precision. In this study, the whole moving pictures were primarily retrieved by the temporal sampling method. When there exist a scene change within the sampling interval, we suggested a fast searching algorithm using binary searching and derived an equation formula to determine optimal primary retrieval which can minimize computation, and showed the result of the experiment on MPEG moving pictures. The result of the experiment shows that the searching speed of the suggested algorithm is maximum 13 times faster than the one of he sequential searching method.