• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.161-167
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• 2019

Recently, demand forecasting techniques have been actively studied due to interest in stable power supply with surging power demand, and increase in spread of smart meters that enable real-time power measurement. In this study, we proceeded the deep learning prediction model experiments which learns actual measured power usage data of home and outputs the forecasting result. And we proceeded pre-processing with moving average method. The predicted value made by the model is evaluated with the actual measured data. Through this forecasting, it is possible to lower the power supply reserve ratio and reduce the waste of the unused power. In this paper, we conducted experiments on three types of networks: Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) and we evaluate the results of each scheme. Evaluation is conducted with following method: MSE(Mean Squared Error) method and MAE(Mean Absolute Error).

• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.231-241
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• 1996

The reliability in software has expand in quality and quantity, also its importance and role are increased. But, a study of software reliability is lack of development. this paper software reliability growth models(SRGM) described by NonHome-geneous Poisson(NHPP)processes. Using actual software error data observed by software testing the SRGM's are composition of error-class, and error-class by three class. this paper made the reliability-model of software using three error- class. The purpose of this study to increase software productivity and to improve software quality. So to achive these goals we focused a study of software reliability model using the error-class.

• Proceedings of the ESK Conference
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• 1996.04a
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• pp.113-127
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• 1996

In Human Reliability Analysis(HRA), the uncertainties involved in many factors that affect human reliability have to be represented as the quantitative forms. Conventional probability- based human reliability theory is used to evaluate the effect of those uncertainties but it is pointed out that the actual human reliability should be different from that of conventional one. Conventional HRA makes use of error rates, however, it is difficult to collect data enough to estimate these error rates, and the estimates of error rates are dependent only on engineering judgement. In this paper, the error possibility that is proposed by Onisawa is used to represent human reliability, and the error possibility is obtained by use of fuzzy reasoning that plays an important role to clarify the relation between human reliability and human error. Also, assuming these factors are connected to the top event through Fault Tree structure, the influence and correlation of these factors are measured by fuzzy operation. When a fuzzy operation is applied to Fault Tree Analysis, it is possible to simplify the operation applying the logic disjuction and logic conjuction to structure function, and the structure of human reliability can be represented as membership function of the top event. Also, on the basis of the the membership function, the characteristics of human reliability can be evaluated by use of the concept of pattern recognition.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1448-1456
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• 2022

This paper studies a model to diagnose efficiency reduction of inverter using Multilayer Perceptron(MLP). In this study, two inverter data which started operation at different day was used. A Multilayer Perceptron model was made to predict photovoltaic power data of the latest inverter. As a result of the model's performance test, the Mean Absolute Percentage Error(MAPE) was 4.1034. The verified model was applied to one-year-old and two-year-old data after old inverter starting operation. The predictive power of one-year-old inverter was larger than the observed power by 724.9243 on average. And two-year-old inverter's predictive value was larger than the observed power by 836.4616 on average. The prediction error of two-year-old inverter rose 111.5572 on a year. This error is 0.4% of the total capacity. It was proved that the error is meaningful difference by t-test. The error is predicted value minus actual value. Which means that PV system actually generated less than prediction. Therefore, increasing error is decreasing conversion efficiency of inverter. Finally, conversion efficiency of the inverter decreased by 0.4% over a year using this model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.6 no.2
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• pp.10-18
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• 1988

In the three-dimensional analysis and deformation analysis of large structures, efficient is the use of the multiple method of close range photogrammetry which approaches the object distance. This study analyzes the influence of errors according to the overlap, the control points, and the object distance, to solve the problems which are raised in the multiple method. A wall-board, 7 meters by 3 meters, was used as a test field on which a total of 225 unknown points were equally disposed. The photographs with changing the overlap and object distance were taken by P-31 camera system. a total of 143 negatives are used in this study for computing 3-dimensional coordinates and its standard errors, and bundle adjustment of strips and blocks developed with on-line system is applied. In case of decreasing the number of control points, simulation error increases but actual error decreases and increases again. Due to the changed of object distances Z error represents largely compared to X, Y error, but good results in Z can be obtained by increasing the redundancy. And simulation error or actual error shows best results at the endlap of about 70%. To sum up this study, approprate arrangement of control points and overlap is meaningful, and multiple method by short object distance will be widely used to precision and deformation analysis of critical structures.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.163-170
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• 2002

In the form of B2C transaction, making an on-line order by using the website increases rapidly. An off-line order at the whole sale outlets is geometrically increasing. However, it is so true that both types of market are showing a tendency to become computerization. As to apply this type of computerized B2B transaction to this kind of large scale wholesale outlets, the most important fact to be considered is that there must have no data error. In addition to this, an accurate counting of actual stock is a precondition to decide a suitable amount of production and a timely delivery of goods. In this study, reducing elements of the risk that may create a difference In between actual stock and that in the computer, the integrated B2B ordering system is designed by taking actual cases an example in order to manage the information for a sale, which is conducted by various system in the large scale wholesale outlets such as E-mart, Carefour or the same kinds.

• Proceedings of the KIEE Conference
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• 2003.10b
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• pp.203-205
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• 2003

This paper is proposed a newly developed approach to identify the mechanical speed of an induction motor based on artificial neural networks technique. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.690-696
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• 2004

This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.545-550
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• 2004

This paper is proposed a fuzzy neural network controller based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed estimation and control of speed of induction motor using ANN Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.12
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• pp.67-73
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• 2011

A method obtaining position data(x, y) of object accurately is proposed by using a pair of ultrasonic sensors composed of one transmitter and two receivers. And the driving system which controls the light of flat LED lamp locally using array of ultrasonic sensors (3 transmitters and 6 receivers) is developed. As a result, measured values of y are relatively reliable due to its small average of absolute errors of 1.03[cm]. The measured values of x have average error of 8.52[cm], and it is a large value. However, the average error is decreased by 0.65[cm] after applying algorithm for error correction. The experiments to control the light of flat LED lamp locally with algorithm for error correction are carried out. From the result, measured values of x with average error of 0.97[cm] are obtained and they are very good approximations of actual values.