• Ocean and Polar Research
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• 제30권2호
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• pp.129-140
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• 2008

As a preliminary effort to establish a data assimilative ocean forecasting system, we reviewed the theory of the Ensemble Kamlan Filter (EnKF) and developed practical techniques to apply the EnKF algorithm in a real ocean circulation modeling system. To verify the performance of the developed EnKF algorithm, a wind-driven double gyre was established in a rectangular ocean using the Regional Ocean Modeling System (ROMS) and the EnKF algorithm was implemented. In the ideal ocean, sea surface temperature and sea surface height were assimilated. The results showed that the multivariate background error covariance is useful in the EnKF system. We also tested the sensitivity of the EnKF algorithm to the localization and inflation of the background error covariance and the number of ensemble members. In the sensitivity tests, the ensemble spread as well as the root-mean square (RMS) error of the ensemble mean was assessed. The EnKF produces the optimal solution as the ensemble spread approaches the RMS error of the ensemble mean because the ensembles are well distributed so that they may include the true state. The localization and inflation of the background error covariance increased the ensemble spread while building up well-distributed ensembles. Without the localization of the background error covariance, the ensemble spread tended to decrease continuously over time. In addition, the ensemble spread is proportional to the number of ensemble members. However, it is difficult to increase the ensemble members because of the computational cost.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2435-2437
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• 2004

In this paper, an obstacle avoidance algorithm is proposed for a network-based robot considering network delay by distribution. The proposed algorithm is based on the VFH(Vector Field Histogram) algorithm, and for the network-based robot system, in which it is assumed robot localization information is transmitted through network communication. In this paper, target vector for the VFH algorithm is estimated through the robot localization information and the measurement of its delay by distribution. The delay measurement is performed by time-stamp method. To synchronize all local clocks of the nodes distributed on the network, a global clock synchronization method is adopted. With the delay measurement, the robot localization estimation is performed by calculating the kinematics of the robot. The validation of the proposed algorithm is performed through the performance comparison of the obstacle avoidance between the proposed algorithm and the existing VFH algorithm on the network-based autonomous mobile robot.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 추계학술대회
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• pp.944-946
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• 2014

최근 기계학습 방법을 도입하여 센서 노드에 대한 위치를 파악하는 방법이 관심을 받고 있다. 많은 기계학습 알고리즘 중, 지지벡터머신은 프로그래밍 언어로 구현하기 간편하고, 병렬로 수행이 가능하다. 본 논문에서는 파이썬 프로그래밍 언어로 지지벡터머신을 구현하고, 5대의 라즈베리파이를 사용하여 실험적인 하둡 센서 네트워크와 5개의 노드를 가진 맵리듀스 하둡 소프트웨어 프레임워크를 구성하였다. 기존 지지벡터머신 알고리즘을 분산 처리가 가능하도록 변형하여 위치 측정을 수행하였고, 다양한 파라미터를 변경해가면서 센서 네트워크를 구성하여 효율성, 자원분배, 처리속도를 비교하였다.

• Journal of information and communication convergence engineering
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• 제13권3호
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• pp.152-158
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• 2015

We consider the problem of designing independently operating local quantizers at nodes in distributed estimation systems, where many spatially distributed sensor nodes measure a parameter of interest, quantize these measurements, and send the quantized data to a fusion node, which conducts the parameter estimation. Motivated by the discussion that the estimation accuracy can be improved by using the quantized data with a high probability of occurrence, we propose an iterative algorithm with a simple design rule that produces quantizers by searching boundary values with an increased likelihood. We prove that this design rule generates a considerably reduced interval for finding the next boundary values, yielding a low design complexity. We demonstrate through extensive simulations that the proposed algorithm achieves a significant performance gain with respect to traditional quantizer designs. A comparison with the recently published novel algorithms further illustrates the benefit of the proposed technique in terms of performance and design complexity.

• Journal of information and communication convergence engineering
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• 제12권4호
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• pp.215-220
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• 2014

We consider quantization optimized for distributed estimation, where a set of sensors at different sites collect measurements on the parameter of interest, quantize them, and transmit the quantized data to a fusion node, which then estimates the parameter. Here, we propose an iterative quantizer design algorithm with a weighted distance rule that allows us to reduce a system-wide metric such as the estimation error by constructing quantization partitions with their optimal weights. We show that the search for the weights, the most expensive computational step in the algorithm, can be conducted in a sequential manner without deviating from convergence, leading to a significant reduction in design complexity. Our experments demonstrate that the proposed algorithm achieves improved performance over traditional quantizer designs. The benefit of the proposed technique is further illustrated by the experiments providing similar estimation performance with much lower complexity as compared to the recently published novel algorithms.

• 전기전자학회논문지
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• 제22권3호
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• pp.558-564
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• 2018

In distributed estimation where each node can collect only partial information on the parameter of interest without communication between nodes and quantize it before transmission to a fusion node which conducts estimation of the parameter, we consider a novel quantization technique employed at local nodes. It should be noted that the performance can be greatly improved if each node can transmit its measurement to one designated node (namely, head node) which can quantize its estimate using the total rate available in the system. For this case, the best strategy at the head node would be simply to partition the parameter space using the generalized Lloyd algorithm, producing the global codewords, one of which is closest to the estimate is transmitted to a fusion node. In this paper, we propose an iterative design algorithm that seeks to efficiently assign the codewords into each of quantization partitions at nodes so as to achieve the performance close to that of the system with the head node. We show through extensive experiments that the proposed algorithm offers a performance improvement in rate-distortion perspective as compared with previous novel techniques.

• Journal of Communications and Networks
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• 제18권3호
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• pp.397-410
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• 2016

This paper proposes a novel cooperative localization method for distributed wireless networks in 3-dimensional (3D) global positioning system (GPS) denied environments. The proposed method, which is referred to as hybrid ellipsoidal variational algorithm (HEVA), combines the use of non-parametric belief propagation (NBP) and variational Bayes (VB) to benefit from both the use of the rich information in NBP and compact communication size of a parametric form. InHEVA, two novel filters are also employed. The first one mitigates non-line-of-sight (NLoS) time-of-arrival (ToA) messages, permitting it to work well in high noise environments with NLoS bias while the second one decreases the number of calculations. Simulation results illustrate that HEVA significantly outperforms traditional NBP methods in localization while requires only 50% of their complexity. The superiority of VB over other clustering techniques is also shown.

• Journal of information and communication convergence engineering
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• 제15권1호
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• pp.7-13
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• 2017

We consider a distributed estimation where many nodes remotely placed at known locations collect the measurements of the parameter of interest, quantize these measurements, and transmit the quantized data to a fusion node; this fusion node performs the parameter estimation. Noting that quantizers at nodes should operate in a non-regular framework where multiple codewords or quantization partitions can be mapped from a single measurement to improve the system performance, we propose a low-weight estimation algorithm that finds the most feasible combination of codewords. This combination is found by computing the weighted sum of the possible combinations whose weights are obtained by counting their occurrence in a learning process. Otherwise, tremendous complexity will be inevitable due to multiple codewords or partitions interpreted from non-regular quantized data. We conduct extensive experiments to demonstrate that the proposed algorithm provides a statistically significant performance gain with low complexity as compared to typical estimation techniques.

• 한국통신학회논문지
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• 제41권11호
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• pp.1490-1501
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• 2016

본 논문에서는 인메모리(In-memory) 병렬 분산 처리 시스템 Apache Spark(이하 Spark)를 활용하여 사용자에게 실시간 측위 정보를 제공할 수 있는 영상 기반 실내 위치인식 시스템을 제안한다. 제안하는 시스템에서는 사용자에게 실시간 측위 정보를 제공하기 위해서, Spark를 이용한 영상 특징점 추출 알고리즘의 병렬 분산화를 통해 알고리즘 연산 시간을 단축시킨다. 하지만 기존의 Spark 플랫폼에서는 영상 처리를 위한 인터페이스가 존재하지 않아, 영상 처리와 관련된 연산을 수행하는 것이 불가능하였다. 이에 본 논문에서는 Spark 영상 입출력 인터페이스를 구현하여 측위 연산을 위한 영상 처리를 Spark에서 수행 가능하게 하였다. 또한 무손실 압축(lossless compression)기법을 이용하여 특징점 기술자(descriptor)를 압축된 형태로 데이터베이스에 저장하여, 대용량의 실내 지도 데이터를 효율적으로 저장 및 관리하는 방법을 소개한다. 측위 실험은 실제 실내 환경에서 수행하였으며, 싱글 코어(Single-core) 시스템과의 성능 비교를 통해 제안하는 시스템이 최대 약 3.6배 단축된 시간으로 사용자에게 측위 정보를 제공 할 수 있다는 것을 입증하였다.

• 한국시뮬레이션학회논문지
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• 제1권1호
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• pp.64-76
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• 1992

The DEVS(discrete event system specificaition) formalism specifies a discrete event system in a hierarchical, modular form. DEVSIM++ is a C++based general purpose DEVS abstract simulator which can simulate systems modeled by the DEVS formalism in a sequential environment. This paper describes P-DEVSIM++which is a parallel version of DEVSIM++ . In P-DEVSIM++, the external and internal event of DEVS models can by processed in parallel. For such processing, we propose a parallel, distributed optimistic simulation algorithm based on the Time Warp approach. However, the proposed algorithm localizes the rollback of a model within itself, not possible in the standard Time Warp approach. An advantage of such localization is that the simulation time may be reduced. To evaluate its performance, we simulate a single bus multiprocessor architecture system with an external common memory. Simulation result shows that significant speedup is made possible with our algorithm in a parallel environment.