• Water for future
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• v.28 no.5
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• pp.219-233
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• 1995

A non-stationary multivariate model is selected in which the mean and variance of rainfall are not temporally or spatially constant. And the rainfall prediction system is constructed which uses the recursive estimation algorithm, Kalman filter, to estimate system states and parameters of rainfall model simulataneously. The on-line, real-time, multivariate short-term, rainfall prediction for multi-stations and lead-times is carried out through the estimation of non-stationary mean and variance by the storm counter method, the normalized residual covariance and rainfall speed. The results of rainfall prediction system model agree with those generated by non-stationary multivariate model. The longer the lead time is, the larger the root mean square error becomes and the further the model efficiency decreases form 1. Thus, the accuracy of the rainfall prediction decreases as the lead time gets longer. Also it shows that the mean obtained by storm counter method constitutes the most significant part of the rainfall structure.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.179-183
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• 2009

본 연구에서는 GCM 기후변화 전망 시나리오를 이용하여 유역단위의 기후변화를 추정하였다. 원시 GCM 시나리오를 지역화 시키기 위해서 인공신경망 모형을 사용하였다. GCM에서 모의되는 강수플럭스, 해면기압, 지표면 근처에서의 일 평균온도, 지표면으로부터 발생하는 잠열플럭스 등과 같은 22개의 변수는 인공신경망의 잠재적 예측인자로 사용되었으며, AWS에서 관측된 강수량과 온도는 예측변수로 사용되었다. 원시 GCM 데이터는 CCCma(Canadian Centre for Climate Modeling and Analysis)에서 제공되는 CGCM3.1/T63 20C3M 시나리오를 사용하였으며, 인공신경망 학습과정에서 사용된 기준시나리오(reference scenario)자료의 기간은 1997년부터 2000년까지의 데이터를 사용하였다. 인공신경망을 학습을 통하여 결정된 각 층사이의 가중치를 이용하여 이산화탄소 배출농도를 가정하여 생성된 CGCM3.1/T63 SRES B1 기후변화시나리오(project scenario)를 인공신경망의 입력값으로 하여 미래의 기온과 강수변화를 전망하였다. 신경망의 학습효과를 높이기 위하여 기온과 강수에 대한 평균 및 누적기간을 각각 일단위와 월단위로 설정하였다. 본 연구에서 사용된 인공신경망은 3층 퍼셉트론(다층 퍼셉트론)을 사용하였으며, 학습방법으로는 역전파알고리즘(back-propagation algorithm)을 이용하였다. 민감도분석을 통하여 선택된 예측인자는 소양강댐유역(1011, 1012소유역)에서의 인공신경망 예측인자로 활용되었으며, 2001년부터 2100년까지의 일 평균온도와 일 강수량의 변화경향을 추정하였다. 1011유역, 1012유역에서는 여름철의 온도변화경향이 겨울철에 비하여 높게 나타났다. 일 평균온도의 통계분석 결과 평균예측오차가 가장 적게 나타나는 지역은 1001유역으로 -0.08로 평균예측오차가 가장 적게 나타났으며, 인공신경망기법을 이용하여 스케일 상세화된 일 평균온도와 관측된 일 평균온도가 얼마나 잘 일치하는지를 확인할 수 있는 1012유역에서 CORR이 0.74로 가장 높게 나타났다.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1242-1242
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• 2001

The use of fibre optic probes for NIR quality control in the industry is becoming very important, as it provides a powerful tool to reduce sample analysis time and it facilitates the implementation of on-line analyses. However, most of the applications of fibre optics and probes have been done on suspensions, clear liquids and films, chemical and pharmaceutical products and also on fruits and animal products. Traditional applications of near infrared spectroscopy in agriculture have been developed in reflectance mode and calibration transfer could be an interesting way to reduce efforts. Classical methods for calibration transfer between different instruments involve the use of sealed reference cups, but, as fibre optic analysis does not use cups, it is necessary to develop new methods for calibration transfer without standards (Blank et al., 1996). In this paper, we have studied how the most used mathematical pretreatments (three methods of Multiplicative Scatter Correction, Standard Normal Variate, Detrending and derivatives) and their combinations applied to calibration development can contribute to reduce spectral differences between instruments. Calibration equations were obtained for three sets of cereals (barley, wheat and maize) scanned in reflectance mode and then they were validated with samples analysed in reflectance and interactance-reflectance mode (fibre optic). Preliminary results show how some combination of pretreatments reduce the differences in the predicted values, measured as standard error of differences, facilitating the use of calibrations obtained in reflectance for samples analysed by interactance-reflectance. However, the application of pretreatments is not enough to satisfy the control limits for calibration transfer suggested by Shenk et al. (1992), and it should be necessary to combine them with a specific algorithm for instruments standardization.

• Journal of Biosystems Engineering
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• v.31 no.6 s.119
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• pp.514-523
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• 2006

Production of green pepper has been increased due to customer's preference and a projected ten-year boom in the industry in Korea. This study was carried out to develop an automatic grading and sorting system for green pepper using machine vision. The system consisted of a feeding mechanism, segregation section, an image inspection chamber, image processing section, system control section, grading section, and discharging section. Green peppers were separated and transported using a bowl feeder with a vibrator and a belt conveyor, respectively. Images were taken using color CCD cameras and a color frame grabber. An on-line grading algorithm was developed using Visual C/C++. The green peppers could be graded into four classes by activating air nozzles located at the discharging section. Length and curvature of each green pepper were measured while removing a stem of it. The first derivative of thickness profile was used to remove a stem area of segmented image of the pepper. While pepper is moving at 0.45 m/s, the accuracy of grading sorting for large, medium and small pepper are 86.0%, 81.3% and 90.6% respectively. Sorting performance was 121 kg/hour, and about five times better than manual sorting. The developed system was also economically feasible to grade and sort green peppers showing the cost about 40% lower than that of manual operations.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.529-536
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• 2016

In this paper we proposed an unsupervised algorithm to estimate the reverberation time (RT) directly from the reverberant speech signal. For estimation process we use maximum likelihood estimation (MLE) which is a very well-known and state of the art method for estimation in the field of signal processing. All existing RT estimation methods are based on the decay rate distribution. The decay rate can be obtained either from the energy envelop decay curve analysis of noise source when it is switch off or from decay curve of impulse response of an enclosure. The analysis of a pre-existing method of reverberation time estimation is the foundation of the proposed method. In one of the state of the art method, the reverberation decay is modeled as a Laplacian distribution. In this paper, the proposed method models the reverberation decay as a Gamma distribution along with the unification of an effective technique for spotting free decay in reverberant speech. Maximum likelihood estimation technique is then used to estimate the RT from the free decays. The method was motivated by our observation that the RT of a reverberant signal when falls in specific range, then the decay rate of the signal follows Gamma distribution. Experiments are carried out on different reverberant speech signal to measure the accuracy of the suggested method. The experimental results reveal that the proposed method performs better and the accuracy is high in comparison to the state of the art method.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.11
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• pp.1434-1443
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• 1999

The turbulent fluctuations of temperature and two components of velocity have been measured with hot- and cold-wires in the Thermally Stratified Wind Tunnel(TSWT). Using the fin-tube heat exchanger type heaters and the neural network control algorithm, both stable ($dT/dz=109.4^{\circ}C$) and unstable ($dT/dz=-49.1^{\circ}C$) stratifications were realized. An ambient air jet was issued normally into the cross flow($U_{\infty}=1.0 m/s$) from a round nozzle(d = 6 mm) flushed at the bottom waII of the wind tunnel with the velocity ratio of $5.8(U_{jet}/U_{\infty})$. The characteristics of turbulent dispersion in the cross flow jet are found to change drastically depending on the thermal stratification. Especially, in the unstable condition, the vertical velocity fluctuation increases very rapidly at downstream of jet. The fluctuation velocity spectra and velocity-temperature cospectra along the jet centerline were obtained and compared. In the case of stable stratification, the heat flux cospectra changes Its sign from a certain point at the far field because of the restratification phenomenon. It is inferred that the main reason in the difference between the vertical heat fluxes is caused by the different length scales of the large eddy motions. The turbulent kinetic energy and scalar dissipation rates were estimated using partially non-isotropic and isotropic turbulent approximation. In the unstable case, the turbulent energy dissipation decreases more rapidly with the downstream distance than in the stable case.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.1
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• pp.1-9
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• 1996

Many signal-processing techniques have been found to be useful in ultrasonic and nondestructive evaluation. Among the most popular techniques are signal averaging, spatial compounding, matched filters and homomorphic processing. One of the significant new process is split-spectrum processing(SSP), which can be equally useful in signal-to-noise ratio(SNR) improvement and grain characterization in several specimens. The purpose of this paper is to explore the utility of SSP in ultrasonic NDE. A wide variety of engineering problems are reviewed, and suggestions for implementation of the technique are provided. SSP uses the frequency-dependent response of the interfering coherent noise produced by unresolvable scatters in the resolution range cell of a transducer. It is implemented by splitting the frequency spectrum of the received signal by using gaussian bandpass filter. The theoretical basis for the potential of SSP for grain characterization in SUS 304 material is discussed, and some experimental evidence for the feasibility of the approach is presented. Results of SNR enhancement in signals obtained from real four samples of SUS 304. The influence of various processing parameters on the performance of the processing technique is also discussed. The minimization algorithm, which provides an excellent SNR enhancement when used either in conjunction with other SSP algorithms like polarity-check or by itself, is also presented.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.10
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• pp.1347-1352
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• 2000

The objectives of the study were to examine non-genetic factors that influence somatic cell counts in dairy cattle and to estimate the genetic parameters of somatic cell counts. A total of 34, 097-test day somatic cell count records were obtained from the Zimbabwe Dairy Services Association (ZDSA). The data were from 5, 615 Holstein daughters of 390 sires and 2, 541 dams tested between May 1994 and December 1998. First lactation cows contributed 22, 147 records to the data set, while 11, 950 records were from second and later parity cows. The model for analysis included fixed effects of month of calving, year of calving, stage of lactation, calving interval and test date. Milk yield and age on test day were fitted in the model as covariates. The additive genetic effects pertaining to cows, sires and dams and the residual error were the random effects. The Average Information Restricted Maximum Likelihood algorithm was used for analysis. The heritability of somatic cell scores was low at $0.027{\pm}0.013$ for parity one cows and $0.087{\pm}0.031$ for parity two and above. Repeatability estimates were $0.22{\pm}0.01$ and $0.30{\pm}0.01$ for the two lactation groups, respectively. Genetic and phenotypic correlations between the somatic cell scores and test day milk production were small and negative. It seems that there is no genetic link between somatic cell counts and milk yield in Holstein cattle in Zimbabwe. The results also seem to indicate that somatic cell count is a trait that is mainly governed by environmental factors.

• Asian-Australasian Journal of Animal Sciences
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• v.23 no.8
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• pp.1096-1104
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• 2010

This bio-acoustic study was aimed at classifying the different porcine wasting diseases through sound analysis with emphasis given to differences in the acoustic footprints of coughs in porcine circo virus type 2 (PCV2), porcine reproductive and respiratory syndrome (PRRS) virus and Mycoplasma hyopneumoniae (MH) - infected pigs from a normal cough. A total of 36 pigs (Yorkshire${\times}$Landrace${\times}$Duroc) with average weight ranging between 25-30 kg were studied, and blood samples of the suspected infected pigs were collected and subjected to serological analysis to determine PCV2, PRRS and MH. Sounds emitted by coughing pigs were recorded individually for 30 minutes depending on cough attacks by a digital camcorder placed within a meter distance from the animal. Recorded signals were digitalized in a PC using the Cool Edit Program, classified through labeling method, and analyzed by one-way analysis of variance and discriminant analysis. Input features after classification showed that normal cough had the highest pitch level compared to other infectious diseases (p<0.002) but not statistically different from PRRS and MH. PCV2 differed statistically (p<0.002) from the normal cough and PRRS but not from MH. MH had the highest intensity and all coughs differed statistically from each other (p<0.0001). PCV2 was statistically different from others (p<0.0001) in formants 1, 2, 3 and 4. There was no statistical difference in duration between different porcine diseases and the normal cough (p>0.6863). Mechanisms of cough sound creation in the airway could be used to explain these observed acoustic differences and these findings indicated that the existence of acoustically different cough patterns depend on causes or the animals' respiratory system conditions. Conclusively, differences in the status of lungs results in different cough sounds. Finally, this study could be useful in supporting an early detection method based on the on-line cough counter algorithm for the initial diagnosis of sick animals in breeding farms.

• Journal of Broadcast Engineering
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• v.14 no.3
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• pp.366-377
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• 2009

In this paper, we implement the real-time system which extracts 3-dimensional coordinates from right and left images captured by a stereo camera and provides users with reality through a virtual space operated by the 3-dimensional coordinates. In general, all pixels in correspondence region are compared for the disparity estimation. However, for a real time process, the central coordinates of the correspondence region are only used in the proposed algorithm. In the implemented system, 3D coordinates are obtained by using the depth information derived from the estimated disparity and we set user's hand as a region of interest(ROI). After user's hand is detected as the ROI, the system keeps tracking a hand's movement and generates a virtual space that is controled by the hand. Experimental results show that the implemented system could estimate the disparity in real -time and gave the mean-error less than 0.68cm within a range of distance, 1.5m. Also It had more than 90% accuracy in the hand recognition.