• Aerospace Engineering and Technology
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• v.7 no.1
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• pp.223-228
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• 2008

In order to acquire the images requested by users, it is very important to calculate mission schedule parameters such as imaging execution time and attitude tilt angle accurately. These parameters are based on orbit prediction. This paper describes the accuracy of orbit propagation for image planning. The orbit prediction data from PSS and MAPS has a certain discrepancy due to different orbit propagator. It is necessary for mission planner to confirm this value during mission planning phase. The pointing error which means the difference between target center and real image received is calculated and analyzed using KOMPSAT-2 image data.

• Journal of The Geomorphological Association of Korea
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• v.20 no.4
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• pp.85-99
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• 2013

The objective of this paper is to analyze the spatial distribution of errors in the DEM generated using waterlines from multi-temporal remote sensing data and to assess flood vulnerability. Unlike conventional research in which only global statistics of errors have been generated, this paper tries to quantitatively analyze the spatial distribution of errors from a probabilistic viewpoint using geostatistical simulation. The initial DEM in Baramarae tidal flats was generated by corrected tidal level values and waterlines extracted from multi-temporal Landsat data in 2010s. When compared with the ground measurement height data, overall the waterline-based DEM underestimated the actual heights and local variations of the errors were observed. By applying sequential Gaussian simulation based on spatial autocorrelation of DEM errors, multiple alternative error distributions were generated. After correcting errors in the initial DEM with simulated error distributions, probabilities for flood vulnerability were estimated under the sea level rise scenarios of IPCC SERS. The error analysis methodology based on geostatistical simulation could model both uncertainties of the error assessment and error propagation problems in a probabilistic framework. Therefore, it is expected that the error analysis methodology applied in this paper will be effectively used for the probabilistic assessment of errors included in various thematic maps as well as the error assessment of waterline-based DEMs in tidal flats.

• Advances in Computational Design
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• v.2 no.3
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• pp.225-240
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• 2017

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.10
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• pp.15-23
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• 2016

To support the telematics and infotainment services, vehicle-to-everything (V2X) communication requires a robust and reliable network. To do this, the 3rd Generation Partnership Project (3GPP) has recently developed V2X communication. For reliable communication, accurate channel estimation should be done. However, because vehicle speed is very fast, radio channel is rapidly changed with time. Therefore, it is difficult to accurately estimate the channel. In this paper, we propose the new linear minimum mean square error (LMMSE) channel interpolation scheme based on the Long Term Evolution (LTE) sidelink system in vehicle-to-vehicle (V2V) environments. In our proposed reduced decision error (RDE) channel estimation scheme, LMMSE channel estimation is applied in the pilot symbol, and then in the data symbol, smoothing and LMMSE channel interpolation scheme is applied. After that, time and frequency domain averaging are applied to obtain the whole channel frequency response. In addition, the LMMSE equalizer of the receiver side can reduce the error propagation due to the decision error. Therefore, it is possible to detect the reliable data. Analysis and simulation results demonstrate that the proposed scheme outperforms currently conventional schemes in normalized mean square error (NMSE) and bit error rate (BER).

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05c
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• pp.194-199
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• 2003

Depth error correction effect for maladjusted stereo cameras with calibrated pixel distance parameter is presented. The proposed neural network technique is the real time computation method based theory of inter-node diffusion for searching the safety distances from the sudden appearance-objects during the work driving. The main steps of the distance computation using the theory of stereo vision like the eyes of man is following steps. One is the processing for finding the corresponding points of stereo images and the other is the interpolation processing of full image data from nonlinear image data of objects. All of them request much memory space and time. Therefore the most reliable neural-network algorithm is derived for real-time matching of objects, which is composed of a dynamic programming algorithm based on sequence matching techniques.

• Journal of Korean Society on Water Environment
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• v.22 no.3
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• pp.427-430
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• 2006

Uncertainty is one of the key issues of the water quality management. Uncertainty occurs in the course of all water quality management stages including monitoring, modeling, and regulation enforcement. To reduce uncertainties of water quality monitoring, manualized monitoring methodology should be developed and implemented. In addition, long-term monitoring is essential for acquiring reliable water quality data which enables best water quality management. For the water quality management in the watershed scale, fate of pollutant including its generation, transport and impact should be considered while regarding each stage of water quality management as an unit process. Uncertainties of each stage of water quality management should be treated properly to prevent error propagation transferred to the next stage of management for successful achievement of water quality conservation.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.797-799
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• 1999

In this paper, we design the Multi-FNN(Fuzzy-Neural Networks) using HCM Method. The proposed Multi-FNN uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. Also, We use HCM(Hard C-Means) method of clustering technique for improvement of output performance from pre-processing of input data. The parameters such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. We use the training and testing data set to obtain a balance between the approximation and the generalization of our model. Several numerical examples are used to evaluate the performance of the our model. From the results, we can obtain higher accuracy and feasibility than any other works presented previously.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3004-3006
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• 2000

In this paper, we propose an optimal design method of Fuzzy-Neural Networks model with parallel structure for complex and nonlinear systems. The proposed model is consists of a multiple number of FNN connected in parallel. The proposed FNNs with parallel structure is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. We use a HCM clustering and GAs to identify the structure and the parameters of the proposed model. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model. we use the time series data for gas furnace and the numerical data of nonlinear function.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.875-878
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• 2006

In the case of a vibration sensitive equipment, it require a vibration free environment to provide its proper function, therefore, it is very important to predict precisely vibration environment of microelectronics production facility due to adjacent blast work. However, it is not easy to evaluate a quantitative vibration response of structure due blast because it can be determined by the characteristics of vibration sources, propagation through rock and soil and dynamic properties of building. In this paper, vibration influence evaluation of micro-electronic Production building induced from adjacent blast activity was performed by real measurement data obtained on ground and structure at same time. And blast vibration allowable limit on ground was supposed by measurement data analysis in order to avoid operation error of precision equipments

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.177-178
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• 2007

WiBro system provides reliable broadband communication services for mobile and portable subcribers. It allows interference-free reception under the conditions of multipath propagation and transmission errors. Thus, powerful channel-error correction ability Is required. CC/CTC Decoder which Is mandatory for WiBro system needs lots of computations for real-time operation. So, it is desired to design a CC/CTC Decoder having highly optimized hardware scheme for low latency operation under high data rates. This paper proposes an efficient CC/CTC Decoder structure for high data rate WiBro system. Particularly, the proposed CTC Decoder architecture reduces decoding delay by applying pipelining and multiple decoding blocks. Simulation results show that reduction of about 80% of processing time is enabled with the proposed CC/CTC Decoder despite of increase in are.