• Journal of the Korean Society for Precision Engineering
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• v.21 no.5
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• pp.55-63
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• 2004

This paper presents neural network based controller using the feedback error loaming technique for a heavy load pointing system. Also the mathematical model was developed to analyze heavy load pointing system. The control scheme consists of a feedforward neural network controller and a fixed-gain feedback controller. This neural network controller is trained so as to make the output of the feedback controller zero. The proposed controller is compared with the conventional PI controller through simulations, and the results show that the pointing accuracy of the proposed control system are improved against the disturbance induced by vehicle running on the bump course.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.551-556
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• 2004

Coagulation process is one of the most important processes in water treatment procedures for stable and economical operation, and coagulant dosing of this process for most plants is generally determined by the jar test. However, this method does not only take a long time to analyze and get the result but also has difficulties in applying to automatic control. This paper shows the feasibility of applying neural network to control the coagulant dosing automatically in water treatment plant. To be specific, the predicted results of the neural network model is shown to be similar to that of jar test. The input variables for learning the neural network are turbidity, water temperature, pH, and alkalinity. Combining the neural network and SCD(Streaming Current Detector) for feedforward and feedback control of injecting coagulant, a rapid change of the raw water quality can be accommodated.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.243-248
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• 1996

In this paper, a general neural-network-based connectionist model, called Fuzzy Neural Network(FNN), is proposed for the realization of a fuzzy logic control system. The proposed FNN is a feedforward multi-layered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. Such FNN can be constructed from training examples by learning rule, and the connectionist structure can be trained to develop fuzzy logic rules and find optimal input/output membership functions. Computer simulation examples will be presented to illustrate the performance and applicability of the proposed FNN, and their associated learning algorithms.

• Geomechanics and Engineering
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• v.9 no.4
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• pp.465-481
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• 2015

Three-dimensional simulation of flow through dam foundation is performed using finite element (Seep3D model) and artificial neural network (ANN) models. The governing and discretized equation for seepage is obtained using the Galerkin method in heterogeneous and anisotropic porous media. The ANN is a feedforward four layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning, using the water level elevations of the upstream and downstream of the dam, as input variables and the piezometric heads as the target outputs. The obtained results are compared with the piezometric data of Shahid Abbaspour's Dam. Both calculated data show a good agreement with available measurements that demonstrate the effectiveness and accuracy of purposed methods.

• Journal of Ocean Engineering and Technology
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• v.35 no.5
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• pp.336-346
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• 2021

The prediction of wave conditions is crucial in the field of marine and ocean engineering. Hence, this study aims to predict the significant wave height through machine learning (ML), a soft computing method. The adopted metocean data, collected from 2012 to 2020, were obtained from the Korea Institute of Ocean Science and Technology. We adopted the feedforward neural network (FNN) and long-short term memory (LSTM) models to predict significant wave height. Input parameters for the input layer were selected by Pearson correlation coefficients. To obtain the optimized hyperparameter, we conducted a sensitivity study on the window size, node, layer, and activation function. Finally, the significant wave height was predicted using the FNN and LSTM models, by varying the three input parameters and three window sizes. Accordingly, FNN (W48) (i.e., FNN with window size 48) and LSTM (W48) (i.e., LSTM with window size 48) were superior outcomes. The most suitable model for predicting the significant wave height was FNN(W48) owing to its accuracy and calculation time. If the metocean data were further accumulated, the accuracy of the ML model would have improved, and it will be beneficial to predict added resistance by waves when conducting a sea trial test.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.441-448
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• 2019

Previous researches on automatic spacing of Korean sentences has been researched to correct spacing errors by using n-gram based statistical techniques or morpheme analyzer to insert blanks in the word boundary. In this paper, we propose an end-to-end automatic word spacing by using deep neural network. Automatic word spacing problem could be defined as a tag classification problem in unit of syllable other than word. For contextual representation between syllables, Bi-LSTM encodes the dependency relationship between syllables into a fixed-length vector of continuous vector space using forward and backward LSTM cell. In order to conduct automatic word spacing of Korean sentences, after a fixed-length contextual vector by Bi-LSTM is classified into auto-spacing tag(B or I), the blank is inserted in the front of B tag. For tag classification method, we compose three types of classification neural networks. One is feedforward neural network, another is neural network language model and the other is linear-chain CRF. To compare our models, we measure the performance of automatic word spacing depending on the three of classification networks. linear-chain CRF of them used as classification neural network shows better performance than other models. We used KCC150 corpus as a training and testing data.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.35-48
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• 1999

Many organizational contexts should be considered in designing EDI controls to make control systems effective and efficient. This paper gives a description of the neural network model for suggesting the extent of effective EDI controls for a company that has specific organizational environment. Feedforward backpropagation neural network models are designed to predict the state of 12 modes of EDI controls from the sate of environment. The predictive power of the system is compared with that of multivariate regression analysis to evaluate the effectiveness of using neural network model in predicting the level of EDI controls. The results show that the neural network model outperforms regression analysis in predictive accuracy. The controls that have high estimated value in the model are likely to be critical controls and EDI auditor or management can enhance investment of IS resources to enhance these controls.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.2
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• pp.53-59
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• 2006

Dynamic neural networks have been applied to diverse fields requiring temporal signal processing such as system identification and signal prediction. This paper proposes the gamma neural network(GAM), which uses gamma memory kernel in the hidden layer of feedforward multilayered network, to improve dynamics of networks and then describes nonlinear adaptive prediction using the proposed network as an adaptive filter. The proposed network is evaluated in nonlinear signal prediction and compared with feedforword(FNN) and recurrent neural networks(RNN) for the relative comparison of prediction performance. Simulation results show that the GAM network performs better with respect to the convergence speed and prediction accuracy, indicating that it can be a more effective prediction model than conventional multilayered networks in nonlinear prediction for nonstationary signals.

• Journal of Korean Society on Water Environment
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• v.21 no.1
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• pp.21-28
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• 2005

The training and prediction modeling using an artificial neural network was implemented to predict the turbidity of treated water as well as to estimate the optimized feed concentration of polyaluminium chloride (PACl) in a water treatment plant. The parameters used in the input layers were pH, temperature, turbidity and alkalinity, while those in output layers were PACl and turbidity of treated water. Levenberg-Marquadt method of feedforward back-propagation perceptron in the neural network toolbox of MATLAB program was used in this study. Correlation coefficients of the training data with the measured data were 0.9997 for PACl and 0.6850 for turbidity and those of the testing data with measured data were 0.9140 for PACl and 0.3828 for turbidity, when four parameters at input layer, 12-12 nodes each at both the first and the second hidden layers, and two parameters(PACl and turbidity) at output layer were used. Although the predictability of PACl was improved, compared to that of the previous studies to use the only coagulant dose as output layer, turbidity in treated water could not be predicted well. Acquisition of more data through several years obtained with the advanced on-line measuring system could make the artificial neural network useful and practical in actual water treatment plants.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.382-385
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• 2003

In this paper, in order to improve the control performance of piezoelectric actuator, an integrated control structure is proposed. The control structure consists of inverse hysteresis model , to compensate the hysteresis nonlinearty problem, and feedforward - feedback controller to give a good tracking performance. The inverse hysteresis model and neural network are used as feed-forward controller, and PID controller is used as a feedback controller. From diverse experiments it is concluded that the proposed control scheme gives good tracking performance than the classical control does.