• 아시안잔디학회지
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• 제18권1호
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• pp.29-36
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• 2004

The efficient refinement of seed is required to reduce the cost and labor input in artificial propagation of wild plant. This study was carried out to develop methods for collecting and refining tiny seeds from wild plants. For obtaining Phragmites communis seeds, the inflorescence was cut into small fragments using a Straw Cutter and subsequently detached pappus hairs from seed coat by Hammer Mill. The primary refined seeds were passed 1.0 mm sieve. The screened seeds were subjected to Seed Blower with wind speed of 0.25 mㆍsec-1 to collected intact and well-ripen seeds. The seeds of Imperata cylindrica were refined as follows. Inflorescences were cut using a Straw Cutter first. The pappus was removed from cut fragments using a Hammer Mill and subsequently subjected to Seed Scarifier at 500rpm for 60 sec. for further separation. The separated seeds were passed 1.0 mm screen and collected after blowing with Seed Blower of wind speed of 0.15 mㆍsec-1. When the amount of seed was too little to refine with Seed Scarifier and Blower, the procedure was slightly modified from the procedure described above. The crude seed mixture obtained from Hammer Mill step was hand-refined roughly and then immersed into cone. (95%) sulfuric acid for 2 min. and collected floating portion after dilution of sulfuric acid solution 100 times with tap water. The collected seeds were dried and passed 0.149 mm sieve. During seed refining process using mechanical or sulfuric acid treatments, a small portion of damaged seed were evolved, however, the amount was not noticeable as compared to the total amount of collected seeds. Because the germination percentages between hand-refined seeds and seeds refined by above methods were not statistically different, the developed procedures for refining tiny seed of wild plants are helpful to reduce the cost and labor input in artificial propagation of two species.

• 한국자원식물학회지
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• 제22권5호
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• pp.389-393
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• 2009

수요증가가 예상되는 겨우살이의 인공번식을 위한 기초자료로 활용하고자 겨우살이의 분포지역의 기주수종과 서식지 환경을 조사하여 분석하였다. 겨우살이의 자생지는 전국에 분포하고 있었으며, 기주식물로는 졸참나무와 같은 참나무류가 대부분이였고 밤나무, 벚나무, 오리나무, 돌배나무 등에서 볼 수 있었다. 군락지의 같이 자생하는 침엽수인 소나무, 잣나무와 활엽수인 느티나무, 감나무, 고로쇠나무, 단풍나무, 뽕나무, 은행나무에서는 관찰되지 않았다. 자생지의 고도는 해발 $0{\sim}1200\;m$까지 잘 생육되고 방향과 지형을 가리지 않고 기주식물이 잘 생육 할 수 있는 곳이면 전국에서 재배가 가능할 것으로 판단되었으며, 종자 전파는 조류에 의함이 확인할 수 있었다. 겨우살이의 기생은 흡기 발생부터 기주식물의 표피 또는 조직성분에 따라 영향이 있을 것으로 생각되며 인공재배 연구의 좋은 성과를 위해 겨우살이가 잘 기생하는 기주식물의 기주 특이성에 대한 연구가 이루어져야 할 것으로 사료된다.

• 전자공학회논문지B
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• 제33B권9호
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• pp.49-61
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• 1996

Back-propagation(BP) algorithm needs a lot of time to train the artificial neural network (ANN) to get high accuracy level in classification tasks. So there have been extensive researches to process back-propagation algorithm on parallel processors. This paper prsents a linear systolic array which calculates forward-backward propagation of BP algorithm at the same time using effective space-time transformation and PE structure. First, we analyze data flow of forwared and backward propagations and then, represent the BP algorithm into data dapendency graph (DG) which shows parallelism inherent in the BP algorithm. Next, apply space-time transformation on the DG of ANN is turn with orthogonal direction projection. By doing so, we can get a snakelike systolic array. Also we calculate the interval of input for parallel processing, calculate the indices to make the right datas be used at the right PE when forward and bvackward propagations are processed in the same PE. And then verify the correctness of output when forward and backward propagations are executed at the same time. By doing so, the proposed system maximizes parallelism of BP algorithm, minimizes th enumber of PEs. And it reduces the execution time by 2 times through making idle PEs participate in forward-backward propagation at the same time.

• Journal of Information Technology Applications and Management
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• 제19권1호
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• 한국지반공학회논문집
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• 제16권1호
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• pp.83-97
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• 2000

본 연구에서는, 흙막이 벽체의 변위 예측시스템 개발을 위하여 다층퍼셉트론을 이용해 임의의 인공신경망 모델을 구축하고 그 성능을 평가하여 최적의 모델을 선정하였다. 인공신경망모델의 학습과 검증을 위해 국내 도심지에 실제 시공이 완료된 다양한 현장의 계측자료를 수집하였고, 수집된 계측자료의 분석을 통해 흙막이벽체의 거동에 영향을 미치는 인자를 조사하였다. 아울러 실행비를 기준으로 선별한 신뢰성 있는 계측자료를 조사된 영향인자를 토대로 데이터 베이스화하여 인공신경망 모델의 학습과 검증에 사용하였으며, 학습은 최급강하법을 기초로 하는 역전파 알고리즘을 이용하여 수행하였다. 학습에 포함되지 않은 현장들에 대하여 흙막이벽체의 최대수평변위와 그 발생위치를 예측하고 이를 계측치와 비교하여, 제시한 변위 예측시스템의 적용성을 부분적으로 확인하였다.

• 한국정밀공학회지
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• 제21권8호
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• pp.83-88
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• 2004

The Classification of defected oil-seals using a vision system with the artificial neural network is presented. The artificial neural network fur classification consists of 27 input nodes, 10 hidden nodes, and one output node. The selection of the number of the input nodes is based on an observation that the difference among the defected, non-defected, and smeared oil-seals is greatly pronounced in the 26 step gray-scale level thresholding. The number of the hidden nodes is chosen as a result of a trade-off between accuracy and computing time. The back-propagation algorithm is used for teaching the network. The proposed network is capable of successfully classifying the defected from the smeared oil-seals which tend to be classified as the defected ones using the binary thresholding. It is envisaged that the proposed method improves the reliability and productivity of the automotive vision inspection system.

• 한국생활과학회지
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• 제12권1호
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• pp.63-73
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• 2003

The purpose of this study was to quantify the hands of fabrics for the Korean folk clothes using both a KES-FB and an artificial neural network. In order to select the proper input parameters, we calculated the correlation using step-wise regression between mechanical properties and the hand value of fabrics. For the classification, the primary hand values and total hand value, five neural networks with three-layered structure were constructed using the error back propagation algorithm and, in order to reduce errors and to speed up learning, the momentum method was selected. From the analysis of the primary and total hands using a self-constructed artificial intelligence system, the error rates of sleekness, stiffness, silkiness, and roughness compared with the judgement of expert panels were found to be 3.3%, 3.3%, 1.6%, and 4.9%, respectively, while that of the total hand was 9.83%.

• 한국세라믹학회지
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• 제41권5호
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• pp.353-358
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• 2004

Spray drying is a unique drying process for powder production. Spray dried product must be free-flowing in order to fill the pressing dies rapidly, especially in the ceramic production. The important powder characteristics are; the particle size distribu-tion and moisture content of the finished product that can be estimated and adjusted by the spray dryer operation, within limits, through regulation of atomizer and drying conditions. In order to estimate the moisture content of the resultant dried product, we modeled the control system of the drying process using two different Artificial Neural Network (ANN) approaches, namely the Back-Propagation Multiplayer Perceptron (BPMLP) algorithm and the Radial Basis Function (RBF) network. It was found out that the performance of both of the artificial neural network models were quite significant and the total testing error for the 100 data was 0.8 and 0.7 for the BPMLP algorithm and the RBF network respectively.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권4호
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• pp.220-227
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• 2005

This paper presents hybrid artificial intelligent(HAI) controller based on the vector controlled IPMSM drive system. And it is based on artificial technologies that adaptive neural network fuzzy(A-NNF) is to speed control and artificial neural network(ANN) is to speed estimation. The salient feature of this technique is the HAI controller The hybrid action tolerates any inaccuracies in the fuzzy logic assignment rules or in the neural network stationary weights. Speed estimators using feedforward multilayer and artificial neural network(ANN) are compared. The back-propagation algorithm is easy to derived the estimated speed tracks precisely the actual motor speed. This paper presents the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid intelligent control.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.500-504
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• 1995

A neural network based upon the back propagation algorithm was designed and applied to acoustic power spectra of electrohydraulic total artificial hearts in order to diagnose mechanical failure of devices. The trained network distinguished spectra of the mechanically damaged device from those of the undamaged device with overall success rate of 63%. Moreover, the network correctly classified more than 70% of spectra in the frequency bands of 0-100 Hz and 700-950 Hz. Consequently, the neural network analysis was useful for the diagnosis of mechanical failure of a total artificial heart.