• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1342-1348
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• 2015

The objective of this study is to propose a method to calculate prediction intervals for one-day-ahead hourly forecasts of photovoltaic power generation and to evaluate its performance. One year of data of two systems, representing contrasting examples of forecast’ accuracy, were used. The method is based on the maximum likelihood estimation, the similarity between the input data of future and past forecasts of photovoltaic power, and on an assumption about the distribution of the error of the forecasts. Two assumptions for the forecast error distribution were evaluated, a Laplacian and a Gaussian distribution assumption. The results show that the proposed method models well the photovoltaic power forecast error when the Laplacian distribution is used. For both systems and intervals calculated with 4 confidence levels, the intervals contained the true photovoltaic power generation in the amount near to the expected one.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.395-400
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• 1999

At present, some evolutional system has been used to promote the efficiency of the DNC(Direct Numerical Control) Controller. However, these are many inconvenience to this operator because it lacks harmony in interaction between the computer and the NC(Numerical Control). Also, there are some controversial poults when data error occurs at the Data Input/output. According1y, this thesis explores a new Data Remote Control System. In this study, the NC Controller of the DNC network has to Bet full data by removing data error in this system. In this system, the main merits are easy manufacturing and the convenience of Data Input/output. That is, remote control of the NC machine tool is possible without mutual interaction between the computer and itself.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.12
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• pp.30-40
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• 1997

This paper presents a design method of fuzzy control systems by clustering the data in the subspace of the input-output produyct space. In the case of servo control, most input-outputdata are concentrated in thye steady-state region, and the the clustering will result in only steady-state fuzzy rules. To overcome this problem, we divide the input-output product space into some subspaces according to the state of input variables. The fuzzy control system designed by the subspace clustering showed good transient response and smaller steady-state error, which is comparable with the reference fuzzy system.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.1
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• pp.64-72
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• 2016

The coverage area of a GNSS regional ionospheric delay model is mainly determined by the distribution of GNSS ground monitoring stations. Extrapolation of the ionospheric model data can extend the coverage area. An extrapolation algorithm, which combines observed ionospheric delay with the environmental parameters, is proposed. Neural network and least square regression algorithms are developed to utilize the combined input data. The bi-harmonic spline method is also tested for comparison. The IGS ionosphere map data is used to simulate the delays and to compute the extrapolation error statistics. The neural network method outperforms the other methods and demonstrates a high extrapolation accuracy. In order to determine the directional characteristics, the estimation error is classified into four direction components. The South extrapolation area yields the largest estimation error followed by North area, which yields the second-largest error.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11a
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• pp.409-412
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• 2001

Identification of systems operating in closed loop has long been of prime interest in industrial applications. The fundamental problem with closed-loop data is the correlation between the unmeasurable noise and the input. This is the reason why several methods that work in open loop fail when applied to closed-loop data. The prediction error based approaches to the closed-loop system are divided to direct method and indirect method. Both of direct and indirect methods are known to be applied to the closed-loop data without critical modification. But the direct method induces the bias error in the experimental frequency response function and this bias error may deteriorates the parameter estimation performance

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.30-31
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• 2021

This study aims to build a deep learning model that can predict the value of concrete mixing properties according to a given concrete strength value. A model was created for a total of 1,291 concrete data, including 8 characteristics related to concrete mixing elements and environment, and the compressive strength of concrete. As the deep learning model, DNN-3L-256N, which showed the best performance on the prior study, was used. The average value for each characteristic of the data set was used as the initial input value. In results, in the case of 'curing temperature', which had a narrow range of values in the existing data set, showed the lowest error rate with less than 1% error based on MAE. The highest error rate with an error of 12 to 14% for fly and bfs.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.717-720
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• 2000

The sensorless scheme for Switched Reluctance Motor(SRM) dives must have the robustness and reliability because the noise and error are sensitive. These elements make electrically noisy environments due to the proximity of high current power circuits with small signal electronic circuits when SRM drives. Also the leakage inductances and finite coupling capacitances these can cause the noise on any low voltage current and voltage measurement. the error can occur because the current and voltage including the noise are used as the input of sensorless algorithm In this paper the high robustness and resistance of input noise are described and the fuzzy logic based rotor estimation algorithm is used to reduce the tolerance of input data.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.8
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• pp.68-74
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• 1998

The neural network predictiv econtroller (NNPC) is proposed for the attempt to mimic the function of brain that forecasts the future. It consists of two loops, one is for the prediction of output (NNP:neural network predictor) and the other one is for control the plant(NNC: neural network controller). The output of NNC makes the control input of plant, which is followed by the variation of both plant error and predictin error. The NNP forecasts the future output based upon the current control input and the estimated control output. The input and the output data of a system and a new method using evolution strategy are used to train the NNP. A two-step NNPC is applied to control the temeprature in boiler systems. It was compared with PI controller and auto-tuning PID controller. The computer simulaton and experimental results show that the proposed method has better performances than the other method.

• JSTS:Journal of Semiconductor Technology and Science
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• v.6 no.4
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• pp.252-256
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• 2006

A low-power ECC check bit generator is presented with competent DRAM implementation with minimal speed loss, area overhead and power consumption. The ECC used in the proposed scheme is a variant form of the minimum weight column code. The spatial and temporal correlations of input data are analyzed and the input paths of the check bit generator are ordered for the on-line adaptable power savings up to 24.4% in the benchmarked cases. The chip size overhead is estimated to be under 0.3% for a 80nm 1Gb DRAM implementation.