• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.4
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• pp.694-704
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• 1988

A recursive parameter estimation scheme utilizing the variance perturbation method is applied to the workpiece deflection model during CNC turning process, in order to improve the cylindricity of slender workpiece. It features that it is based on exponentially weighted recursive least squares method with post-process measurement of finish surfaces at two locations and it does not require a priori knowledge on the time varying deflection model parameter. The measurements of finish surfaces by using two proximity sensors mounted face to face enable one to identify the straightness, guide-way, run-out eccentricity errors. Preliminary cutting tests show that the straightness error of the finish surface due to workpiece deflection during cutting is most dominant. Identifying the errors and recursive updating the parameter, the off-line control is carried out to compensate the workpiece deflection error, through single pass cutting. Experimental results show that the proposed method is superior to the conventional multi-pass cutting and the direct compensation control in cutting accuracy and efficiency.

• Structural Engineering and Mechanics
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• v.34 no.6
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• pp.739-753
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• 2010

Pointing to the design requirement of prestressed space grid structure being the target cable force, the pretension scheme decision analysis method is studied when there's great difference between structural actual state and the analytical model. Based on recursive formulation of cable forces, the simulative recursive system for pretension process is established from the systematic viewpoint, including four kinds of parameters, i.e., system initial value (structural initial state), system input value (tensioning control force scheme), system state parameters (influence matrix of cable forces), system output value (pretension accomplishment). The system controllability depends on the system state parameters. Based on cable force observation values, the influence matrix for system state parameters can be calculated, making the system controllable. Next, the pretension scheme decision method based on cable force observation values can be formed on the basis of iterative calculation for recursive system. In this way, the tensioning control force scheme that can meet the design requirement when next cyclic supplemental tension finished is obtained. Engineering example analysis results show that the proposed method in this paper can reduce a lot of cyclic tensioning work and meanwhile the design requirement can be met.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.48-53
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• 1999

This paper presents an analytic derivation of the erroneous effect when the sliding-DFT is implemented in a recursive way with the finite-bit approximation of the twiddle factors. The analysis result is obtained in a closed form equation of the noise-to-signal power ratio(NSR) employing the zero-mean white Gaussian signal as the target input of the DFT. The parameters of the wordlength used in representing the twiddle factors and the blocklength of the DFT appear in the NSR explicitly as its function variables. The derivation is based on the error dynamic equation which is derived from the recursive SDFT, and on the analytic exploration of the statistical characteristics of the approximation coefficients treating them as random variables of having spatial distributions. The analytically derived results are verified through the comparison with the data actually measured from the computer simulation experiment.

• The Journal of the Acoustical Society of Korea
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• v.16 no.6
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• pp.85-93
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• 1997

Fixed systems are limited in their performances to achieve the more complicated and higher level operations. Accordingly adaptive system, which adjusts itself in accorance with the time-varying environments, has been introduced to camouflarge the defficiency of fixed systems in varying environment, and adaptive filter is the outstanding fields in adaptive system. Adaptive recursive filter is far more efficient in that it can perform the signal processing with relatively lower filter order, but there remains severe problem in stability(convergency). On the basis of hyperstability introduced by V.M. Popov, a hyperstable new adaptive recursive filter is introduced which is theoretically guaranteed in stability. In this paper a more stable algorithm for adaptive recursive filter is devised.

• The Journal of Engineering Research
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• v.3 no.1
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• pp.85-95
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• 1998

In this study, we consider a recursive procedure to obtain the stationary probability distribution for analyzing Coxian queueing networks with finite queues. This network deals with multiple class customers. Due to the state space representing multiple class customers, the sub-matrices corresponding to states can not be square matrices and can not be inverted. Therefore, we introduce more complex recursive method to avoid the singular problem. The open queueing network that we study consists of 3 parallel first-level sources linked to a single second level queue. We consider two types of schemes for entering a queue. The first scheme is assumed to be the first-blocked-first-enter (FBFE) and the second scheme is the higher-priority-first-enter (HPFE). Arrival and service times are assume to have a Coxian distribution with two phases. Comparison between the resulting using Gauss-Seidel method and recursive procedure will be shown.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3543-3557
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• 2017

Compression is a very important technique for remotely sensed hyperspectral images. The lossless compression based on the recursive least square (RLS), which eliminates hyperspectral images' redundancy using both spatial and spectral correlations, is an extremely powerful tool for this purpose, but the relatively high computational complexity limits its application to time-critical scenarios. In order to improve the computational efficiency of the algorithm, we optimize its serial version and develop a new parallel implementation on graphics processing units (GPUs). Namely, an optimized recursive least square based on optimal number of prediction bands is introduced firstly. Then we use this approach as a case study to illustrate the advantages and potential challenges of applying GPU parallel optimization principles to the considered problem. The proposed parallel method properly exploits the low-level architecture of GPUs and has been carried out using the compute unified device architecture (CUDA). The GPU parallel implementation is compared with the serial implementation on CPU. Experimental results indicate remarkable acceleration factors and real-time performance, while retaining exactly the same bit rate with regard to the serial version of the compressor.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.1070-1079
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• 2016

In this paper, the design of recursive radial basis function neural networks based on incremental fuzzy c-means is introduced for processing the big data. Radial basis function neural networks consist of condition, conclusion and inference phase. Gaussian function is generally used as the activation function of the condition phase, but in this study, incremental fuzzy clustering is considered for the activation function of radial basis function neural networks, which could effectively do big data processing. In the conclusion phase, the connection weights of networks are given as the linear function. And then the connection weights are calculated by recursive least square estimation. In the inference phase, a final output is obtained by fuzzy inference method. Machine Learning datasets are employed to demonstrate the superiority of the proposed classifier, and their results are described from the viewpoint of the algorithm complexity and performance index.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.334-337
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• 2006

현재 전 세계는 IPv4 주소 자원의 고갈에 대비한 IPv6의 도입을 위해 국가 차원의 전략적인 노력을 기울이고 있으며, 이에 따라 IPv4망에서 IPv6망으로의 안정적인 전이를 위한 상호 운영기술이 지속적으로 개발되고 있다. 이러한 기술을 적용하기 위한 다양한 응용 프로그램의 운영을 위해서 IPv6 기반의 DNS 기술은 그 핵심적인 요소라 할 수 있다. 그러나 현재 IPv4로 이루어져 있는 환경에서 바로 IPv6 환경으로의 변경이 어렵기 때문에 IPv6 네트워크 환경으로의 전이 과정에서 발생되는 IPv4와 IPv6의 네트워크의 공존 환경에서 트래픽을 안정적으로 전달하기 위한 기술이 필요하게 되었다. 이런 IPv4와 IPv6 상호 운용 기술을 이용하여 본 논문에서는 IPv4와 IPv6가 혼재한 망에서 안정적인 IPv6 Recursive DNS을 구성하기 위하여 상호 운용 기술 중 터널링 기술을 이용한 IPv6 Recursive DNS 구성 방안을 제시하여 혼재한 IPv4 와 IPv6 DNS를 운영하기 위한 구성모델을 제안한다.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.184-194
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• 2016

In existing identification methods for on-orbit spacecraft, such as eigensystem realization algorithm (ERA) and subspace method identification (SMI), singular value decomposition (SVD) is used frequently to estimate the modal parameters. However, these identification methods are often used to process the linear time-invariant system, and there is a lower computation efficiency using the SVD when the system order of spacecraft is high. In this study, to improve the computational efficiency in identifying time-varying modal parameters of large spacecraft, a faster recursive algorithm called fast approximated power iteration (FAPI) is employed. This approach avoids the SVD and can be provided as an alternative spacecraft identification method, and the latest modal parameters obtained can be applied for updating the controller parameters timely (e.g. the self-adaptive control problem). In numerical simulations, two large flexible spacecraft models, the Engineering Test Satellite-VIII (ETS-VIII) and Soil Moisture Active/Passive (SMAP) satellite, are established. The identification results show that this recursive algorithm can obtain the time-varying modal parameters, and the computation time is reduced significantly.