• Water Engineering Research
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• v.4 no.2
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• pp.59-68
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• 2003

The applicabilities and validities of two methodologies fur the prediction of THM (trihalomethane) formation in a water pipeline system were proposed and discussed. One is the multiple regression technique and the other is an artificial neural network technique. There are many factors which influence water quality, especially THMs formations in water pipeline systems. In this study, the prediction models of THM formation in water pipeline systems are developed based on the independent variables proposed by American Water Works Association(AWWA). Multiple linear/nonlinear regression models are estimated and three layer feed-forward artificial neural networks have been used to predict the THM formation in a water pipeline system. Input parameters of the models consist of organic compounds measured in water pipeline systems such as TOC, DOC and UV254. Also, the reaction time to each measuring site along pipeline is used as input parameter calculated by a hydraulic analysis. Using these variables as model parameters, four models are developed. And the predicted results from the four developed models are compared statistically to the measured THMs data set. It is shown that the artificial neural network approaches are much superior to the conventional regression approaches and that the developed models by neural network can be used more efficiently and reproduce more accurately the THMs formation in water pipeline systems, than the conventional regression methods proposed by AWWA.

• Journal of Ocean Engineering and Technology
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• v.16 no.1
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• pp.8-15
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• 2002

This paper presents a neural net based nonlinear adaptive controller for an autonomous underwater vehicle (AUV). AUV's dynamics are highly nonlinear and their hydrodynamic coefficients vary with different operational conditions, so it is necessary for the high performance control system of an AUV to have the capacities of learning and adapting to the change of the AUV's dynamics. In this paper a linearly parameterized neural network is used to approximate the uncertainties of the AUV's dynamic, and the basis function vector of network is constructed according to th AUV's physical properties. A sliding mode control scheme is introduced to attenuate the effect of the neural network's reconstruction errors and the disturbances in AUV's dynamics. Using Lyapunov theory, the stability of the presented control system is guaranteed as well as the uniformly boundedness of tracking errors and neural network's weights estimation errors. Finally, numerical simulations for motion control of an AUV are performed to illustrate the effectiveness of the proposed techniques.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.3
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• pp.95-100
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• 2004

A control alogorithm using sliding mode and neural network for Buck type DC-DC converter is presented. Also, we conform a rightness the proposal method by comparing a theoretical values and experimental values obtained from experiment using DSP(digital signal processor). Performance comparisons made with the general hysteresis controller clearly bring out the superior performance of the proposal neural network controller. This paper will be applied to other power conversion system using the proposal neural network controller.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.97-106
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• 1999

This paper proposes the structure and the algorithm of the Fuzzy-Neural Controller(FNNC) which is able to adapt itself to unknown plant and the change of circumstances at the Fuzzy Logic Controller(FLC) with the Neural Network. This Learning Fuzzy Logic Controller is made up of Fuzzy Logic controller in charge of a main role and Neural Network of an adaptation in variable circumstances. This construct optimal fuzzy controller applied to the 2-area load frequency control of power system, and then it would examine fitness about parameter variation of plant or variation of circumstances. And it proposes the optimal Scale factor method wsint three preformance functions( E, , U) of system dynamics of load frequency control with error back-propagation learning algorithm. Applying the controller to the model of load frequency control, it is shown that the FNNC method has better rapidity for load disturbance, reduces load frequency maximum deviation and tie line power flow deviation and minimizes reaching and settling time compared to the Optimal Fuzzy Logic Controller(OFLC) and the Optimal Control for optimzation of performance index in past control techniques.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1186-1193
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• 1991

This paper proposed a trajectory controlmethod for a robot manipulator by using neural networks. The total torque for a manipulator is a sum of the linear feedback controller torque and the neural network feedfoward controller torque. The proposed neural network is a multilayer neural network with time delay elements, and learns the inverse dynamics of manipulator by means of PD(propotional denvative)controller error torque. The error backpropagation (BP) learning neural network controller does not directly require manipulator dynamics information. Instead, it learns the information by training and stores the information and connection weights. The control effects of the proposed system are verified by computer simulation.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2488-2491
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• 2000

A Neural network is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping. When a fault occur in system, a state of system is changed with transient state. Because of a previous state signal is considered as a information. DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights, so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.21-26
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• 2006

This paper focuses on modeling and predicting differential GPS corrections transmitted by marine radio-beacon systems using artificial neural networks. Various neural network structures with various training algorithms were examined, including Linear, Radial Biases, and Feedforward. Matlab Neural Network toolbox is used for this purpose. Data sets used in building the model are the transmitted pseudorange corrections and broadcast navigation message. Model design is passed through several stages, namely data collection, preprocessing, model building, and finally model validation. It is found that feedforward neural network with automated regularization is the most suitable for our data. In training the neural network, different approaches are used to take advantage of the pseudorange corrections history while taking into account the required time for prediction and storage limitations. Three data structures are considered in training the neural network, namely all round, compound, and average. Of the various data structures examined, it is found that the average data structure is the most suitable. It is shown that the developed model is capable of predicting the differential correction with an accuracy level comparable to that of beacon-transmitted real-time DGPS correction.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.11
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• pp.1199-1205
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• 1992

In this paper, a Korean word recognition method which usese Neural Network and Hidden Markov Models(HMM) is proposed to improve a recognition rate with a small amount of learning data. The method reduces the fluctuation due to personal differences which is a problem to a HMM recognition system. In this method, effective recognizer is designed by the complement of each recognition result of the Hidden Markov Models(HMM) and Neural Network. In order to evaluate this model, word recognition experiment is carried out for 28 cities which is DDD area names uttered by two male and a female in twenties. As a result of testing HMM with 8 state, codeword is 64, the recognition rate 91[%], as a result of testing Neural network(NN) with 64 codeword the recognition rate is 89[%]. Finally, as a result of testing NN-HMM with 64 codeword which the best condition in former tests, the recognition rate is 95[%].

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.139-144
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• 2009

Neural network is a very exciting and generic framework to develop almost all ranges of machine learning technologies and its potential is far beyond its current capabilities. Among other characteristics, neural network acts as associative memory obtained from the values structurally stored in synaptic inherent structure. Due to innate complexity of neural networks system, in its practical implementation and maintenance, multifaceted problems are known to be unavoidable. In this paper, we present design and implementation details of GUI software which can be valuable tool to maintain and develop neural networks. It has capability of displaying every state of synaptic weights with network nodal relation in each learning step.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.182-186
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• 2019

When a camera is employed for 3D sensing, accurate camera calibration is vital as it is a prerequisite for the subsequent steps of the sensing process. Camera calibration is usually performed by complex mathematical modeling and geometric analysis. On the other contrary, data learning using an artificial neural network can establish a transformation relation between the 3D space and the 2D camera image without explicit camera modeling. However, a neural network requires a large amount of accurate data for its learning. A significantly large amount of time and work using a precise system setup is needed to collect extensive data accurately in practice. In this study, we propose a two-step neural calibration method that is effective when only a small amount of learning data is available. In the first step, the camera projection transformation matrix is determined using the limited available data. In the second step, the transformation matrix is used for generating a large amount of synthetic data, and the neural network is trained using the generated data. Results of simulation study have shown that the proposed method as valid and effective.