• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.118-121
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• 2003

In the conventional boundary estimation in EIT (Electrical Impedance Tomography), the interface between anomalies and background is expressed in usual as Fourier series and the boundary is reconstructed by obtaining the Fourier coefficients. This paper proposes a method for the boundary estimation, where the boundary of anomaly is approximated as the interpolation of front points located discretely along the boundary and is imaged by tracking the points in the region of interest. In the solution to the inverse problem to estimate the front points, the multi-layer neural network is introduced. For the verification of the proposed method, numerical experiments are conducted and the results indicate a good performance.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.04c
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• pp.54-57
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• 2008

A new modular Lateral Information Propagation Networks can be implemented in a IC chip with the circuit VLSI technology for detection of bad materials. The proposed modular architecture is propagated the neural network through inter module connections. For such inter module connections, the host(computer or logic) mediates the exchange of information among modules. Also border nodes in each module have capacitors for temporarily retaining the information from outer modules. For detecting of Faulty Insulator, $4\;{\times}\;4$ neural network modules has been designed and simulation of interpolation with the designed networks has been done.

• Journal of Integrative Natural Science
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• v.12 no.4
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• pp.105-115
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• 2019

In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.8
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• pp.367-375
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• 2001

In Underwater Flight Vehicle depth control system, the followings must be required. First, it needs robust performance which can get over modeling error, parameter variation and disturbance. Second, it needs accurate performance which have small overshoot phenomenon and steady state error to avoid colliding with ground surface or obstacles. Third, it needs continuous control input to reduce the acoustic noise and propulsion energy consumption. Finally, it needs interpolation method which can sole the speed dependency problem of controller parameters. To solve these problems, we propose a depth control method using Fuzzy Sliding Mode Controller with feedforward control-plane bias term and Neural Network Interpolator. Simulation results show the proposed method has robust and accurate control performance by the continuous control input and has no speed dependency problem.

• Computational Structural Engineering
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• v.10 no.2
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• pp.265-272
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• 1997

The purpose of this paper is to develop Neural Network models for Approximate Structural Analysis (NNASA). As an initial stage, the paper classifies the characteristics and the active role of neural networks in the numerical analysis by comparing neural networks with conventional numerical analysis algorithms. The paper proposed two methods of finding solutions of linear algebraic equations by a modified neural network algorithm, and presents that multilayer feedforward networks are a class of universal approximators by comparing the neural network with regression and interpolation techniques.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10a
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• pp.124-140
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• 1995

The merit of fuzzy rule based systems stems from their capability of encoding qualitative knowledge of experts into quantitative rules. Recent advancement in automatic tuning or self-organization of fuzzy rules from experimental data further enhances their power, allowing the integration of the top-down encoding of knowledge with the bottom-up learning of rules. In this paper, methods of self-organizing fuzzy rules and of performing defuzzification and inference is presented based on a multi-resolution radial basis function network. The network learns an arbitrary input-output mapping from sample distribution as the union of hyper-ellipsoidal clusters of various locations, sizes and shapes. The hyper-ellipsoidal clusters, representing fuzzy rules, are self-organized based of global competition in such a way as to ensute uniform mapping errors. The cooperative interpolation among the multiple clusters associated with a mapping allows the network to perform a bidirectional many-to-many mapping, representing a particular from of defuzzification. Finally, an inference engine is constructed for the network to search for an optimal chain of rules or situation transitions under the constraint of transition feasibilities imposed by the learned mapping. Applications of the proposed network to skill acquisition are shown.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.12
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• pp.1690-1696
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• 2014

Network RTK generates spatial corrections by using differenced measurements from reference stations in the network, and the corrections are then provided to a rover. The rover, generally, uses linear interpolation, which assumes that the corrections at each reference station are spatially correlated, to obtain a precise correction of its location. However, an irregularity of the tropospheric delay is a real-world factor that violates this assumption. Tropospheric delay is a result of weather conditions, such as humidity, temperature and pressure, and it can cause spatial decorrelation when there are changes in the local climate. In this paper, we have defined the non-linear characteristics of the tropospheric delay between reference stations or user within a region as the "irregularity of tropospheric delay". Such an irregularity can negatively impact the network RTK performance. Therefore, we analyze the influence of the irregularity of tropospheric delay in network RTK based on meteorological data.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.251-254
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• 2006

A precise real-time method of using the IGS ultra rapid products (IGU) and the GPS broadcast ephemeris to calculate the VRS orbit corrections was presented here which was suited for GPS/VRS reference station network based positioning. Test data acquired from both the SGRSN (Sichuan GPS Reference Station Network) and SCIGN (Southern California integrated GPS network) were used to evaluate the performance of the modeling techniques. The new method was proven to be more precise and reliable compared with the existing conventional network-based orbit error interpolation method. It was shown that 0.004ppm relative accuracy was reached, namely the influence from the orbit bias for the RTK positioning within 100km area can be of sub-millimeter level.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.96-102
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• 2018

For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.