• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1141-1141
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• 2001

The aim of the study was to evaluate the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural network (ANN) on the prediction of chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with information relative to moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected with 10 different Foss NIR Systems instruments, which were either standardized or not standardized to one master instrument. The spectra were trimmed to a wavelength range between 1100 and 2498 nm. Two data sets, one standardized (IVAL) and the other not standardized (SVAL) were used as independent validation sets, but 10% of both sets were omitted and kept for later expansion of the calibration database. The remaining samples were combined into one database (n=21,696), which was split into 75% calibration (CALBASE) and 25% validation (VALBASE). The chemical components in the 3 validation data sets were predicted with each model derived from CALBASE using the calibration database before and after it was expanded with 10% of the samples from IVAL and SVAL data sets. Calibration performance was evaluated using standard error of prediction corrected for bias (SEP(C)), bias, slope and R2. None of the models appeared to be consistently better across all validation sets. VALBASE was predicted well by all models, with smaller SEP(C) and bias values than for IVAL and SVAL. This was not surprising as VALBASE was selected from the calibration database and it had a sample population similar to CALBASE, whereas IVAL and SVAL were completely independent validation sets. In most cases, Local and ANN models, but not modified PLS, showed considerable improvement in the prediction of IVAL and SVAL after the calibration database had been expanded with the 10% samples of IVAL and SVAL reserved for calibration expansion. The effects of sample processing, instrument standardization and differences in reference procedure were partially confounded in the validation sets, so it was not possible to determine which factors were most important. Further work on the development of large databases must address the problems of standardization of instruments, harmonization and standardization of laboratory procedures and even more importantly, the definition of the database population.

• Agricultural and Biosystems Engineering
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• 제3권1호
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• pp.18-28
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• 2002

Neural network based models were developed and evaluated for predicting corn yield from aerial images based on 1998 and 1994 image data. The model used images in multi-spectral bands such as R, G, B, and IR (Red, Green, Blue and Infrared). The inputs to the neural network consisted of mean and standard deviation of multispectral bands of the aerial images. Performances of several neural network architectures using back-propagation with momentum were compared. The maximum yield prediction accuracy obtained was 97.81%. The BPNN model prediction accuracy could be enhanced by using more number of observations to the model, other data transformation techniques, or by performing optical calibration of the aerial image.

• 상하수도학회지
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• 제21권3호
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• pp.305-314
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• 2007

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Dalchun station in Han River. Input data is consist of monthly data of concentration of DO, BOD, COD, SS and river flow. And this study selected optimal neural network model through changing the number of hidden layer based on input layer(n) from n to 6n. After neural network theory is applied, the models go through training, calibration and verification. The result shows that the proposed model forecast water quality of high efficiency and developed web-based water quality forecasting system after extend model

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2020-2023
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• 2003

In this paper, our proposed system uses the regional Gabor wavelet and Neural Network to implement face recognition similar to human face recognition system, because the Gator wavelet expresses visual recognition system of human mathematically and the regional Neural Network is robust to white noise and partial illumination. This system consists of two stages of building database and recognizing face. One is composed by using the supervised learning of Neural Network. At this time, the Neural Network is applied to the upper and the lower part of face images respectively. The Backpropagation algorithm is used to learn Neural Network. Another consists of calibration of slope of face image, measurement of illumination variant using deviation with average face image and similarity comparison using Euclidean distance measure.

• 한국공작기계학회논문집
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• 제17권3호
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• pp.1-7
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• 2008

This paper describes the kinematics which is a new type of parallel manipulator, and the neural network is applied to solving the forward kinematics problem. The parallel manipulator called it as a Stewart platform has an easy and unique solution about the inverse kinematics. However, the forward kinematics is difficult to get a solution because of the lack of an efficient algorithm caused by its highly nonlinearity. This paper proposes the neural network scheme of an Newton-Raphson method alternatively. It is found that the neural network can be improved its accuracy by adjusting the offset of the obtained result.

• 한국대기환경학회:학술대회논문집
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• 한국대기환경학회 2006년도 춘계학술대회 논문집
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• pp.527-528
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• 2006

• 한국철도학회:학술대회논문집
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• 한국철도학회 2006년도 춘계학술대회 논문집
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• pp.129-133
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• 2006

• 대한교통학회지
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• 제27권5호
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• pp.7-16
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• 2009

본 연구는 도로사업의 경제적 타당성 분석시 적용되는 편익에 대한 정확성을 높이기 위한 방안을 제시하는 연구이다. 기존 경제적 평가방법의 문제점은 모형의 정산시 교통량만을 정산하기 때문에 실제 교통량과 비슷하게 모형이 정산되지만 모형에서 나오는 통행속도는 실제 속도와 차이를 나타낼 수 있다는 점이다. 실제통행속도와 다른 모형속도는 차량운행비, 통행시간, 대기오염 절감 편익들이 잘못 산정될 수 있게 한다. 따라서, 본 연구에서는 2개의 서로 다른 VDF를 가지고 도로망에서 비슷한 교통량 정산에 의해 서로 다른 속도로 산출된 후 서로 다른 모형속도 결과에 따른 경제적 편익의 차이가 크게 발생되는 문제점을 확인하였다. 이러한 문제점에 대한 개선방향으로 교통량에 따른 정확한 속도를 산출하는 VDF 함수 개발을 포함한 3가지 개선방안을 제시하여 향후 정확한 편익산정과 이로 인한 합리적인 경제성분석이 수행될 수 있는 토대를 마련하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2278-2291
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• 2023

Imported ornamental fish should be quarantined because they can have dangerous diseases depending on their habitat. The quarantine requires a lot of time because quarantine officers collect various information on the imported ornamental fish. Inefficient quarantine processes reduce its work efficiency and accuracy. Also, long-time quarantine causes the death of environmentally sensitive ornamental fish and huge financial losses. To improve existing quarantine systems, information on ornamental fish was collected and structured, and a server was established to develop quarantine performance support software equipped with a text search engine. However, the long names of ornamental fish in general can cause many typos and time bottlenecks when we type search words for the target fish information. Therefore, we need a technique that can correct typos. Typical typo character calibration compares input text with all characters in a calibrated candidate text dictionary. However, this approach requires computational power proportional to the number of typos, resulting in slow processing time and low calibration accuracy performance. Therefore, to improve the calibration accuracy of characters, we propose a fusion system of simple Artificial Neural Network (ANN) models and character preprocessing methods that accelerate the process by minimizing the computation of the models. We also propose a typo character generation method used for training the ANN models. Simulation results show that the proposed typo character correction system is about 6 times faster than the conventional method and has 10% higher accuracy.

• 한국전자파학회논문지
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• 제23권8호
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• pp.967-975
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• 2012

본 논문에서는 샘플링 오실로스코프를 이용한 시간 영역의 해석을 통해 초고주파 소자 및 안테나의 반사 계수를 평가하는 방법을 소개한다. 20 GHz 급의 펄스 입력 신호에 대한 반사 신호를 측정한 후, 이를 푸리에 변환하여 반사 계수를 추출하였다. 초고주파 반사파 회로망의 보정에 필요한 3가지 오차 계수들은 3.5 mm calibration kit을 이용하여 유도하였다. 또한, 반사파 측정을 위하여 결합기를 사용한 경우의 오차 계수를 유도하고, 이를 적용하여 얻은 반사 계수와 벡터 회로망 분석기로 측정한 결과를 비교하였다.