• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.105-113
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• 1996

In this paper, a new korean digita speech recognition technique was proposed using muktolayer perceptron (MLP). In spite of its weakness in dynamic signal recognition, MLP was adapted for this model, cecause korean syllable could give static features. It is so simle in its structure and fast in its computing that MLP was used to the suggested system. MLP's input vectors was transformed using karhunen-loeve transformation (KLT), which compress signal successfully without losin gits separateness, but its physical properties is changed. Because the suggested technique could extract static features while it is not affected from the changes of syllable lengths, it is effectively useful for korean numeric recognition system. Without decreasing classification rates, we can save the time and memory size for computation using KLT. The proposed feature extraction technique extracts same size of features form the tow same parts, front and end of a syllable. This technique makes frames, where features are extracted, using unique size of windows. It could be applied for continuous speech recognition that was not easy for the normal neural network recognition system.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.1
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• pp.79-85
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• 2011

Bridge Weigh-in-Motion(BWIM) system calculates a travelling vehicle's weight without interruption of traffic flow by analyzing the signals that are acquired from various sensors installed in the bridge. BWIM system or data accumulated from the BWIM system can be utilized to development of updated live load model for highway bridge design, fatigue load model for estimation of remaining life of bridges, etc. Field test with moving trucks including various load cases should be performed to guarantee successful development of precise BWIM system. In this paper, a numerical simulation technique is adopted as an alternative or supplement to the vehicle traveling test that is indispensible but expensive in time and budget. The constructed numerical model is validated by comparison experimentally measured signal with numerically generated signal. Also vehicles with various dynamic characteristics and travelling conditions are considered in numerical simulation to investigate the variation of bridge responses. Considered parameters in the numerical study are vehicle velocity, natural frequency of the vehicle, height of entry bump, and lateral position of the vehicle. By analyzing the results, it is revealed that the lateral position and natural frequency of the vehicle should be considered to increase precision of developing BWIM system. Since generation of vehicle travelling signal by the numerical simulation technique costs much less than field test, a large number of test parameters can effectively be considered to validate the developed BWIM algorithm. Also, when artificial neural network technique is applied, voluminous data set required for training and testing of the neural network can be prepared by numerical generation. Consequently, proposed numerical simulation technique may contribute to improve precision and performance of BWIM systems.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1714-1728
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• 2016

This paper presents improved harmonics extraction based on Adaptive Linear Neuron (ADALINE) algorithm for single phase photovoltaic (PV) shunt active power filter (SAPF). The proposed algorithm, named later as Improved ADALINE, contributes to better performance by removing cosine factor and sum of element that are considered as unnecessary features inside the existing algorithm, known as Modified Widrow-Hoff (W-H) ADALINE. A new updating technique, named as Fundamental Active Current, is introduced to replace the role of the weight factor inside the previous updating technique. For evaluation and comparison purposes, both proposed and existing algorithms have been developed. The PV SAPF with both algorithms was simulated in MATLAB-Simulink respectively, with and without operation or connection of PV. For hardware implementation, laboratory prototype has been developed and the proposed algorithm was programmed in TMS320F28335 DSP board. Steady state operation and three critical dynamic operations, which involve change of nonlinear loads, off-on operation between PV and SAPF, and change of irradiances, were carried out for performance evaluation. From the results and analysis, the Improved ADALINE algorithm shows the best performances with low total harmonic distortion, fast response time and high source power reduction. It performs well in both steady state and dynamic operations as compared to the Modified W-H ADALINE algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.436-444
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• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• Journal of Korea Water Resources Association
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• v.45 no.12
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• pp.1321-1330
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• 2012

In this study, the daily inflow at the basin of Chungju dam is predicted using wavelet-artificial neural network for nonlinear model. Time series generally consists of a linear combination of trend, periodicity and stochastic component. However, when framing time series model through these data, trend and periodicity component have to be removed. Wavelet transform which is denoising technique is applied to remove nonlinear dynamic noise such as trend and periodicity included in hydrometeorological data and simple noise that arises in the measurement process. The wavelet-artificial neural network (WANN) using data applied wavelet transform as input variable and the artificial neural network (ANN) using only raw data are compared. As a results, coefficient of determination and the slope through linear regression show that WANN is higher than ANN by 0.031 and 0.0115 respectively. And RMSE and RRMSE of WANN are smaller than those of ANN by 37.388 and 0.099 respectively. Therefore, WANN model applied in this study shows more accurate results than ANN and application of denoising technique through wavelet transforms is expected that more accurate predictions than the use of raw data with noise.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.1
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• pp.78-83
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• 2011

Traditional autopilot design requires an accurate aerodynamic model and relies on a gain schedule to account for system nonlinearities. This paper presents the control architecture applied to a dynamic model inversion at a single flight condition with an on-line neural network (NN) in order to regulate errors caused by approximate inversion. This eliminates the need for an extensive design process and accurate aerodynamic data. The simulation results using a developed full nonlinear 6 degree of freedom model are presented. This paper also presents the stability evaluation for control systems to which NNs were applied. Although feedback can accommodate uncertainty to meet system performance specifications, uncertainty can also affect the stability of the control system. The importance of robustness has long been recognized and stability margins were developed to quantify it. However, the traditional stability margin techniques based on linear control theory can not be applied to control systems upon which a representative non-linear control method, such as NNs, has been applied. This paper presents an alternative stability margin technique for NNs applied to control systems based on the system responses to an inserted gain multiplier or time delay element.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1091-1098
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• 2012

From the perspective of risk management, financial organization that have issued ELW require an efficient hedging methodology due to recently increased trade volume of ELW. This study presents an ELW hedging methodology using artificial neural network(ANN) to minimize hedging costs. The performance of the presented methodology in this study is examined by analysis utilizing the prices and volatilities of underlying assets, risk free interest rates, and maturities and computational experiments show that the proposed method is superior to existing dynamic delta hedging(DDH) technique in terms of hedging costs ranged from 25% to 250%.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1740-1751
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• 2018

This paper proposes a novel fault tolerant tracking control (FTTC) scheme for a class of nonlinear systems with actuator failures based on the policy iteration (PI) algorithm and the adaptive fault observer. The estimated actuator failure from an adaptive fault observer is utilized to construct an improved performance index function that reflects the failure, regulation and control simultaneously. With the help of the proper performance index function, the FTTC problem can be transformed into an optimal control problem. The fault tolerant tracking controller is composed of the desired controller and the approximated optimal feedback one. The desired controller is developed to maintain the desired tracking performance at the steady-state, and the approximated optimal feedback controller is designed to stabilize the tracking error dynamics in an optimal manner. By establishing a critic neural network, the PI algorithm is utilized to solve the Hamilton-Jacobi-Bellman equation, and then the approximated optimal feedback controller can be derived. Based on Lyapunov technique, the uniform ultimate boundedness of the closed-loop system is proven. The proposed FTTC scheme is applied to reconfigurable manipulators with two degree of freedoms in order to test the effectiveness via numerical simulation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.5
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• pp.671-683
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• 2018

The flood damage caused by heavy rains in urban watershed is increasing, and, as evidenced by many previous studies, urban flooding usually exceeds the water capacity of drainage networks. The flood on the area which considerably urbanized and densely populated cause serious social and economic damage. To solve this problem, deterministic and probabilistic studies have been conducted for the prediction flooding in urban areas. However, it is insufficient to obtain lead times and to derive the prediction results for the flood volume in a short period of time. In this study, IDNN, TDNN and NARX were compared for real-time flood prediction based on urban runoff analysis to present the optimal real-time urban flood prediction technique. As a result of the flood prediction with rainfall event of 2010 and 2011 in Gangnam area, the Nash efficiency coefficient of the input delay artificial neural network, the time delay neural network and nonlinear autoregressive network with exogenous inputs are 0.86, 0.92, 0.99 and 0.53, 0.41, 0.98 respectively. Comparing with the result of the error analysis on the predicted result, it is revealed that the use of nonlinear autoregressive network with exogenous inputs must be appropriate for the establishment of urban flood response system in the future.