• 한국전기전자재료학회:학술대회논문집
• /
• 한국전기전자재료학회 1996년도 추계학술대회 논문집
• /
• pp.225-229
• /
• 1996

In this study, in order to develop outdoor insulating materials, SIN(simultaneous interpenetrating polymer network) was introduced to Epoxy resin and the environment resistance was investigated. The single network structure specimen(E series) formed of Epoxy resin alone and simultaneous interpenetrating polymer network specimen (EM series) in which epoxy resin was taken as the first network and methyl methacrylate resin as the second network were manufactured. Ten kinds of specimens were manufacture by filler (SiO$_2$) content. SEM were utilized in order to confirm their network structure changes, and AC voltage dielectric strength was measured. Also, UV-test and tracking test were carried out investigate the environment resistance characteristic. Therefore the variations of network structure were happened as a result of SEM test, and it was confirmed that simultaneous interpenetrating polymer network specimens were more excellent than single network structure specimens.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2006년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
• /
• pp.50-52
• /
• 2006

This paper Proposes a new method of on-line estimation for rotor resistance of the induction motor in the indirect vector controlled drive, using artificial neural network (ANN). The back propagation algorithm is used for training of the neural networks. The error between the desired state variable of an induction motor and actual state variable of a neural network model is back propagated to adjust the weight of a neural network model, so that the actual state variable tracks the desired value. The performance of rotor resistance estimator and torque and flux responses of drive, together with these estimators, are investigated variations rotor resistance from their nominal values. The rotor resistance are estimated analytically, using the proposed ANN in a vector controlled induction motor drive.

• Journal of Welding and Joining
• /
• 제18권3호
• /
• pp.114-121
• /
• 2000

Since resistance spot welding has become one of the most popular sheet metal fabrication processes, a strong emphasis is being put on the quality of the welds. Throughout the years many quality estimation systems have been developed by many researchers to ensure weld quality. In this study, the process variables, which were monitored in the primary circuit of the welding machine, are used to estimate the weld quality with Hopfield neural network. The primary dynamic resistance is vectorized and stored as five patterns in the network. As the welding is done, the dynamic resistance patterns are recognized and the quality is estimated with the proposed method. Due to the primary process variables, it is possible to utilize this algorithms as an in-process real time quality monitoring system.

• The Plant Pathology Journal
• /
• 제27권2호
• /
• pp.170-182
• /
• 2011

Plants possess multiple resistance mechanisms that protect themselves against pathogen attack. To identify unknown components of the defense machinery in Arabidopsis, gene-expression changes were monitored in Arabidopsis thaliana under 18 different biotic or abiotic conditions using a DNA microarray representing approximately 25% of all Arabidopsis thaliana genes (www.genevestigator.com). Seventeen genes which are early responsive to salicylic acid (SA) treatment as well as pathogen infection were selected and their T-DNA insertion mutants were obtained from SALK institute. To elucidate the role of each gene in defense response, bacterial pathogen Pseudomonas syringae pv. tomato (Pst) DC3000 was inoculated onto individual T-DNA insertion mutants. Four mutants exhibited decreased resistance and five mutants displayed significantly enhanced resistance against Pst DC3000-infection as measured by change in symptom development as compared to wild-type plants. Among them, member of uridin diphosphate (UDP)-glycosyltransferase (UGT) was of particular interest, since a UGT mutant (At1g05680) showed enhanced resistance to Pst-infection in Arabidopsis. In systemic acquired resistance (SAR) assay, this mutant showed enhanced activation of SAR. Also, the enhanced SAR correlated with increased expression of defense-related gene, AtPR1. These results emphasize that the glycosylation of UGT74E2 is a part of the SA-mediated disease-resistance mechanism.

• 품질경영학회지
• /
• 제42권3호
• /
• pp.387-406
• /
• 2014

Purpose: The widespread use of social network services (SNS) has caused users concern about the disclosure of their privacy or personal information. The purpose of this study is to analyze the factors of privacy concern and self presentation that affect the user resistance in the use of social network service. Methods: This study verifies the factors that affecting the user resistance in SNS. The research model suggested in this study is tested via a survey of 260 SNS users. SPSS and Smart PLS had been used to test the suggested hypotheses. Results: This study shows that privacy experience, privacy awareness, self esteem, and social desirability significantly influence perceived risk and that privacy awareness, self esteem, self efficacy, and perceived risk significantly influence perceived trust. It also verifies that perceived risk and perceived trust positively affect user resistance. Conclusion: This paper suggests that high awareness on privacy of SNS user encourages the SNS companies to consider the privacy protection mechanism for eliminating various factors that affecting the risk. This study also shows that the privacy calculus model applies to understanding the mechanism on resistance of SNS user.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1997년도 하계학술대회 논문집 E
• /
• pp.1813-1815
• /
• 1997

In this study, in order to develop outdoor insulating materials, SIN(simultaneous interpenetrating polymer network) was introduced to Epoxy resin and the environment resistance was investigated. Six kinds of specimen were manufacture by filler($SiO_2$) content. SEM was untilized in order to confirm their network structure changes. Also, tracking test, UV test and acid rain test were carried out investigate the environment resistance characteristic. Therefore it was confirmed that simultaneous interpenetrating polymer network specimens were more excellent than single network structure specimens. But, acid rain almost never changed resistance.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2006년도 제37회 하계학술대회 논문집 B
• /
• pp.1073-1074
• /
• 2006

In this paper, we perform a simulation to verify the earth resistance values in Common Earth Network. The simulation is performed on the assumption that certain shorts are occurred in common earth network. Furthermore, from the result, we confirmed that very small earth resistance values in common earth network are given, by carrying out practical measurements in railway sections where common earth network is composed. From the effect, we could discover that the construction of common earth network is in a disadvantageous position on the financial aspect, while it is the most desirable way of construction for the purpose of Earth.

• Journal of Electrical Engineering and Technology
• /
• 제9권2호
• /
• pp.550-558
• /
• 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.

• 조명전기설비학회논문지
• /
• 제20권10호
• /
• pp.27-34
• /
• 2006

본 논문은 드라이브의 간적벡터제어에서 ANN을 이용하여 유도전동기의 회전자 저항을 온라인 추정하기 위한 새로운 기법을 제시한다. 약전파 알고리즘은 신경회로망의 학습을 위해 사용된다. 신경회로망의 실제 상태값과 유도전동기의 요구값 사이의 오차는 신경회로망 모델의 하중값 조절을 위하여 역전파 하여 실제값이 요구값을 추정하도록 한다. 드라이브의 회전자 저항, 토크, 자속응답 성능등 이러한 추정기의 성능은 고유값으로부터 회전자 저항을 연구하게 된다. 회전자 저항은 유도전동기 드라이브의 벡터제어에서 제시된 ANN을 사용하여 추정한다.

• International Journal of Air-Conditioning and Refrigeration
• /
• 제6권
• /
• pp.1-13
• /
• 1998

The basic heat transfer process that occurs in a building can best be illustrated by an electrical circuit network. Present paper reports the dynamic simulation of annual energy consumption in an office building by the thermal resistance capacitance network method. Unsteady thermal behaviors and annual energy consumption in an office building were examined in detail by solving the simultaneous circuit equations of thermal network. The results are used to evaluate the accuracy of the modified BIN method for the energy consumption analysis of a large building. Present thermal resistance-capacitance method predicts annual energy consumption of an office building with the same accuracy as that of response factor method. However, the modified BIN method gives 15% lower annual heating load and 25% lower cooling load than those from the present method. Equipment annual energy consumptions for fan, boiler and chiller in the HVAC system are also calculated for various control systems as CAV, VAV, FCU+VAV and FCU+CAV. FCU+CAV system appears to consume minimum annual energy among them.