• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.2
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• pp.393-406
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• 1993

In resistance spot welding process, size of molten nuggest have been utilized to assess the integrity of the weld quality. However real-time monitoring of the nugget size is an extremely difficult problem. This paper describes the design of an artificial neural networks(ANN) estimator to predict the nugget size for on-line use of weld quality monitoring. The main task of the ANN estimator is to realize the mapping characteristics from the sampled dynamic resistance signal to the actual negget size through training. The structure of the ANN estimator including the number of hidden layers and nodes in a layer is determined by an estimation error analysis. A series of welding experiments are performed to assess the performance of the ANN estimator. The results are quite promissing in that real-time estimation of the invisible nugget size can be achieved by analyzing the dynamic resistance signal without any conventional destructive testing of welds.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.9
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• pp.1247-1253
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• 2003

Selection for increased milk production in dairy cows has often resulted in a higher incidence of disease and thus incurred a greater health costs. Considerable interests have been shown in breeding dairy cattle for disease resistance in recent years. This paper discusses the limitations of breeding dairy cattle for genetic resistance in six parts: 1) complexity of disease resistance, 2) difficulty in estimating genetic parameters for planning breeding programs against disease, 3) undesirable relationship between production traits and disease, 4) disease as affected by recessive genes, 5) new mutation of the pathogens, and 6) variable environmental factors. The hidden problems of estimating genetic and phenotypic parameters involving disease incidence were examined in terms of categorical nature, non-independence, heterogeneity of error variance, non-randomness, and automatic relationship between disease and production traits. In light of these limitations, the prospect for increasing genetic resistance by conventional breeding methods would not be so bright as we like. Since the phenomenon of disease is the result of a joint interaction among host genotype, pathogen genotype and environment, it becomes essential to adopt an integrated approach of increasing genetic resistance of the host animals, manipulating the pathogen genotypes, developing effective vaccines and drugs, and improving the environmental conditions. The advances in DNA-based technology show considerable promise in directly manipulating host and pathogen genomes for genetic resistance and producing vaccines and drugs for prevention and medication to promote the wellbeing of the animals.

• Journal of Biosystems Engineering
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• v.34 no.1
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• pp.15-20
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• 2009

This study was conducted to develop a moisture sensor for rapeseed, a bio-diesel material. A typical rapeseed, SUNMANG, was used as a raw material. The rapeseed moisture content sensor consists of three components, such as upper and bottom electrodes, a test material dish, and a fixing housing. To evaluate the performance, a data acquisition system was equipped with the rapeseed moisture sensor, computer, printer, and main board. The findings of this study were: 1) the rapeseed moisture content was inversely proportional to electric resistance, and 2) values of electric resistance were recorded in a range of $10{\sim}100\;M{\Omega}$, depending upon a change of the moisture content. The determination of coefficient ($R^2$) and standard error between rapeseed moisture content and electric resistance were 0.9921 and ${\pm}0.289$, which indicated a highly correlative relationship. The response of rapeseed moisture sensor to temperature change was also observed for further performance test. Satisfying results were obtained, such as the determination of coefficient ($R^2$) of 0.9918, predicted standard error of ${\pm}0.373%$, deviation of 0.103%, measurement error of $0.14{\sim}0.48%$, standard deviation of $0.01{\sim}0.22%$, and measurement time of 28.3 s per point, respectively.

• Journal of Sensor Science and Technology
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• v.9 no.4
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• pp.288-296
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• 2000

Thin-film chromel-alumel multijunction thermal converters with a low output resistance of $64{\sim}85\;{\Omega}$ showed approximately the square law-dependent input-output relation. The voltage responsivities were very low with $0.34{\sim}0.67\;V/W$ in air and $1.15{\sim}1.48\;V/W$ in vacuum, respectively, and the ac-dc voltage transfer error was very large with about +340 ppm in the frequency range of $40\;Hz{\sim}10\;kHz$ in the case of 1 V-input sinewave rms voltage. It can be concluded that the large transfer error of the thermal converter was mainly caused by the low voltage responsivity and the large heat loss due to low output resistance, which implies that the optimization for small ac-dc transfer error is required.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.550-558
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• 2014

Direct Torque controlled induction motor (DTC-IM) drives use stator resistance of the motor for stator flux estimation. So, stator resistance estimation properly is very important for a stable and effective operation of the induction motor. Stator resistance variations because of changing in temperature make DTC operation difficult mainly at low speed. A method based on artificial neural network (ANN) to estimate the stator resistance online of IM for DTC drive is modeled and verified in this paper. To train the neural network a back propagation algorithm is used. Weight adjustment of neural network is done by back propagating the error signal between measured and estimated stator current. An extensive simulation has been carried out in MATLAB/SIMULINK to prove the efficacy of the proposed stator resistance estimator. The simulation & experimental result reveals that proposed method is able to obtain precise torque and flux control at low speed.

• Proceedings of the KWS Conference
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• 1993.05a
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• pp.115-119
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• 1993

Many qualitive analyses of sampled process variables have been attempted to predict nugget size in resistance spot welding process. In this paper, dynamic resistance and electrode movement signal which is a good indicative of the nugget size was examined by introducing an artificial neural network estimator. An artificial neural feedforward network with back-propagation of error was applied for the estimation of the nugget size. To assess the advantage of this method. results have been compared with conventional regression method.

• Proceedings of the Korea Concrete Institute Conference
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• 1990.04a
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• pp.90-95
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• 1990

It is widely recognized that the strengths of reinforced concrete members have random characteristics due to the variability of the mechanical properties of concrete and steel, the dimensional error as well as incorrect placement of reinforcing bars. Statistical models of the variabilities of strengths of reinforced concrete members, therefore, need to be developed to evaluate the safety level implied in current practices. Based on the probabilistic models of basic factors affecting the R.C. member strengths, in this study, the probabilistic characteristics of member resistance have been studied through Monte Carlo simulation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.3
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• pp.96-99
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• 2002

We developed 1:1 resistance ratio measurement system for precision measurement of a series-resistor type dc voltage divider. By using active guard technique and exchange technique, any leakage effects, which are one of the most critical error sources, were successfully removed. The best measurement uncertainty with the developed ratio measurement system was estimated to be approximately $10^{-8}\;for\;10\;k{\Omega}\;and\;100\;k{\Omega}$.

• 전기의세계
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• v.22 no.4
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• pp.11-16
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• 1973

This paper presents an optimum control of the field resistance for the self-excited DC shunt generator to keep a constant terminal voltage in case of the load change or the torque variation in the system. The non-linearity of the system is linearized by applying the small signal technique and the linearized equation is solved by the maximum principle with the digital computer. The optimal control value of the field resistance for the step error of the generator output voltage is obtained and the transient voltage characteristics in the system are investigated.

• Transactions of the Korean Society of Automotive Engineers
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• v.3 no.2
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• pp.51-61
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• 1995

A coast down test mothod has been used to determine the resistance forces on running vehicle due to the aerodynamic drag, rolling resistance and driveline resistance. Most of the tests, however, are based on the Velocity-Time measurements, which require a sophisticated velocity measuring device and contain much error by nature. In the present study a coast down test method based on Distance-Time measurements is introduced, which contains the original idea of Russian scientist Prof. Petrushov along with the suggestions for improvement of the accuracy.