• 통합자연과학논문집
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• 제4권3호
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• pp.238-242
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• 2011

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. The system that uses the artificial neural network(ANN) falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the SVM/ANN hybrid algorithm is proposed in this paper. The proposed algorithm is the method that ANN learns selectively after clustering the SNR data by SVM, then more improved performance estimation can be obtained than using ANN only and The proposed algorithm can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure. Experimental results indicate that the proposed SVM/ANN hybrid algorithm generally outperforms ANN algorithm.

• Journal of information and communication convergence engineering
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• 제13권2호
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• pp.123-131
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• 2015

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.882-885
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• 2003

In this paper, nonlinear echo cancellation using a correlation LMS (CLMS) algorithm is proposed to cancel the undesired nonlinear echo signals generated in the hybrid system of the telephone network. In the telephone network, the echo signals may result the degradation of the network performance. Furthermore, digital to analog converter (DAC) and analog to digital converter (ADC) may be the source of the nonlinear distortion in the hybrid system. The adaptive filtering technique based on the nonlinear Volterra filter has been the general technique to cancel such a nonlinear echo signals in the telephone network. But in the presence of the double-talk situation, the error signal for tap adaptations will be greatly larger, and the near-end signal can cause any fluctuation of tap coefficients, and they may diverge greatly. To solve a such problem, the correlation LMS (CLMS) algorithm can be applied as the nonlinear adaptive echo cancellation algorithm. The CLMS algorithm utilizes the fact that the far-end signal is not correlated with a near-end signal. Accordingly, the residual error for the tap adaptation is relatively small, when compared to that of the conventional normalized LMS algorithm. To demonstrate the performance of the proposed algorithm, the DAC of hybrid system of the telephone network is considered. The simulation results show that the proposed algorithm can cancel the nonlinear echo signals effectively and show robustness under the double-talk situations.

• 전력전자학회논문지
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• 제9권6호
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• pp.544-552
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• 2004

본 논문은 Single Tuned Filter를 사용하여 11차와 13차 고조파를 동시에 제거하는 Hybrid 능동필터를 제안하였다. 제안된 Hybrid 능동필터는 Hybrid 능동필터의 Detuning 보상능력을 이용한 것으로, 하나의 필터를 이용하여 2개 이상의 고조파를 제거할 수 Topology를 보여주고 있다. 본 논문에서 다루는 Hybrid 능동필터는 Detuning 보상능력을 이용한 것이기 때문에 인버터 출력전압을 최적으로 설계하는 것이 필수적이다. 본 논문은 수동필터 부분의 커패시터 용량과 HVDC 시스템에서 발생되는 고조파 전류의 크기 그리고 수동필터의 공진주파수와 제어모드에 따라 인버터 출력전압이 변화함을 확인하였고, 이에 따른 최적의 인버터 전압을 구하였다.

• 한국정보과학회논문지:시스템및이론
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• 제36권6호
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• pp.502-510
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• 2009

Multiprecision Squaring은 공개키 알고리즘을 구성하는 연산 중에서 가장 중요한 연산 중 하나이다. 본 논문에서는 기존의 Multiprecision Squaring 알고리즘을 개선하여 연산 양을 줄임으로 성능을 항상시키는 Squaring 기법들을 제시하고 구현하였다. Scott이[1]에서 제안한 Carry-Catcher Hybrid 곱셈 알고리즘은 Gura가 제안한 Hybrid 곱셈 알고리즘[2]을 계승 발전시킨 것으로 MRACL 라이브러리에 구현되어 있으며, Carry-Catcher Hybrid 방법 사용한 Multiprecision Squaring 알고리즘도 MIRACL에 함께 구현되어 있다. 본 논문에서 이 Carry-Catcher Hybrid Squaring 알고리즘을 발전시켜 보다 효율적인 Squaring 알고리즘인 Lazy Doubling Squaring 알고리즘을 제안하고 구현하였으며, atmega128상에서 성능테스터를 수행하여 Carry-Catcher Hybrid Squaring 알고리즘과 비교하여 더 효율적인 알고리즘임을 보였다. 표준 Squaring 알고리즘이 $S_{ij}\;=\;x_i\;{\ast}\;x_j\;=\;S_{ij}$인 사실을 기반으로 곱셈의 횟수를 절반 가까이 줄인 알고리즘이라면 본 논문에서 제시한 Lazy Doubling Squaring 알고리즘은 $a_0\;{\ast}\;2\;+\;a_1\;{\ast}\;2\;+\;...\;+\;a_{n-1}\;{\ast}\;2\;+\;a_n\;{\ast}\;2\;=\;(a_0\;+\;a_1\;+\;...\;+\;a_{n-1}\;+\;a_n)\;{\ast}\;2$ 라는 사실을 기반으로 하여 doubling 연산 횟수를 획기적으로 줄인 알고리즘으로, MIRACL에 구현되어 있는 Multiprecision Squaring 알고리즘 보다 atmega128상에서 약 25% 정도의 빠른 결과를 얻을 수 있었으며, 저자가 아는 바로는 현재까지 나온 어떤 방법보다 빠르다.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• 한국산업정보학회논문지
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• 제22권2호
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• pp.79-89
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• 2017

본 연구에서는 적응형혼합유전알고리즘(Adaptive Hybrid Genetic Algorithm: AHGA) 접근법을 이용한 폐쇄루프 공급망(Closed-Loop Supply Chain: CLSC) 모델 최적화를 다루고 있다. CLSC 모델 구축을 위해 공급업체(Part Supplier), 제품제조업체(Product Manufacturer)등으로 구성된 전방향물류(Forward Logistics)와 수집업체(Collection Center), 회복센터(Recovery Center)등으로 구성된 역물류(Reverse Logistics)를 함께 고려하고 있다. 제안된 CLSC 모델은 수리모형(Mathematical Model)으로 표현되며, AHGA접근법을 이용해 이행되어 그 최적해를 구하게 된다. 수치실험에서는 기존연구에서 제안된 몇몇 접근법과 AHGA 접근법을 함께 사용하여 그 수행도를 비교분석하였다.

• Journal of Biosystems Engineering
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• 제33권3호
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• pp.196-203
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• 2008

Hybrid robust image processing algorithm to extract visual features of melon during the cultivation was developed based on a wireless tele-operative interface. Features of a melon such as size and shape including position were crucial to successful task automation and future development of cultivation data base. An algorithm was developed based on the concept of hybrid decision-making which shares a task between the computer and the operator utilizing man-computer interactive interface. A hybrid decision-making system was composed of three modules such as wireless image transmission, task specification and identification, and man-computer interface modules. Computing burden and the instability of the image processing results caused by the variation of illumination and the complexity of the environment caused by the irregular stem and shapes of leaves and shades were overcome using the proposed algorithm. With utilizing operator's teaching via LCD touch screen of the display monitor, the complexity and instability of the melon identification process has been avoided. Hough transform was modified for the image obtained from the locally specified window to extract the geometric shape and position of the melon. It took less than 200 milliseconds processing time.

• 대한임베디드공학회논문지
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• 제12권3호
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• pp.169-176
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• 2017

To replace conventional DRAM, many researches have been done on nonvolatile memories. The DRAM&PCM hybrid memory is one of the effective structure because it can utilize an advantage of DRAM and PCM. However, in order to use this characteristics, pages can be replaced frequently between DRAM and PCM. Therefore, PCM still has major problem that has write-limits. Therefore, it needs an effective page management method for exploiting each memory characteristics dynamically and adaptively. So we aim reducing an average access time and write count of PCM by utilizing two locality for an effective page replacement. We proposed a page selection algorithm which is recently requested to write in DRAM and an algorithm witch uses two locality in PCM. According to our simulation, the proposed algorithm for the DRAM&PCM hybrid can reduce the PCM write count by around 22% and the average access time by 31% given the same PCM size, compared with CLOCK-DWF algorithm.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 춘계학술대회 논문집
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• pp.224-229
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• 2011

In this paper, in order to improve safety of hybrid electric vehicle a fault detection algorithm is introduced. The proposed algorithm uses SVDD techniques. Two methods for learning a lot of data are used in this technique. One method is to learn the data incrementally. Another method is to remove the data that does not affect the next learning. Using lines connecting support vectors selection of removing data is made. Using this method, lot of computation time and storage can be saved while learning many data. A battery data of commercial hybrid electrical vehicle is used in this study. In the study fault boundary via SVDD is described and relevant algorithm for virtual fault data is verified. It takes some time to generate fault boundary, nevertheless once the boundary is given, fault diagnosis can be conducted in real time basis.