• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.835-837
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• 1996

In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network(Back-propagation) is proposed. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time. For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor mentioned above. The one day ahead forecast errors are about 1.4% for absolute percentage average error.

• Journal of the Korean Institute of Navigation
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• v.21 no.4
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• pp.21-28
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• 1997

Along with the rapid growth of shipping and transportation , the size of a ship larger and larger. Low speed maneuverabililty of a full ship has been received a great deal of attention concerting about the navigation safety, especially in the harbour area of waterway. And, the iperation of the full ship in harbour area is one fo tehmost difficult technique. Usually highly experienced experts can make a suitable decision considering various propeller ,rudder actions and environmental conditions. The Artificial Neural Network is applied to the automatic berthing control of a ship. The teaching data are made by the berthing simulation of a ship on the computer. And, the layer neural network is used and the 'Error Back-Propagation Algorithm' is used to teach the neural network. Finally, it is shown that the berthing control is successfully done by the established neural network.

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05a
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• pp.45-51
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• 2001

The daily inflow is apparently one of nonlinear and complicated phenomena. The nonlinear and complexity make it difficult to model the prediction of daily flow, but attractive to try the neural networks approach which contains inherently nonlinear schemes. The study focuses on developing the forecasting models of daily inflows to a large dam site using neural networks. In order to reduce the error caused by high or low outliers, the back propagation algorithm which is one of neural network structures is modified by combining a regression algorithm. The study indicates that continuous forecasting of a reservoir inflow in real time is possible through the use of modified neural network models. The positive effect of the modification using tole regression scheme in BP algorithm is showed in the low and high ends of inflows.

• Proceedings of the KWS Conference
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• 1992.10a
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• pp.91-95
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• 1992

The resistance spot welding process has been extensively used for joining of sheet metals, which are subject to variation of many process variables. Many qualitive analyses of sampled process variables have been attempted to predict nugget size. In this paper, dynamic resistance and electrode movement signal which is a good indicative of the nugget size was examined by introducing an artificial neural network estimator. An artificial neural feedforward network with back-propagation of error was applied for the estimation of the nugget size. The prediction by the neural network is in good agreement with the actual nugget size for resistance spot welding of galvanized steel. The results are quite promising in that the quantitative estimation of the invisible nugget size can be achieved without conventional destructive testing of welds.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2001.05a
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• pp.243-248
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• 2001

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Thus, in this paper, an Evolving Neural Network Controller(ENNC) without Error Back Propagation(EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC) are compared with the ones of conventional optimal controller and the conventional evolving neural network controller(CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1067-1072
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• 2001

In this study, the vibration diagnosis program for onboard machinery has been developed. The developed program includes signal monitoring module, system diagnosis module, and system modification module. The signal monitoring module is to monitor the vibration signal in time and frequency domains. And the system diagnosis module, which is developed by using Neural Network with error back propagation algorithm, can detect the abnormal symptom indicating the malfunction of the machinery onboard ships. The investigations of the developed system are presented through the experiment using Rotor Kit. Abnormal vibration signals are created by adding additional weight, manually misaligning the shaft, and loosening the bolts.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.4
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• pp.134-138
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• 2015

Heart sound is used for a basic clinical examination to check for abnormalities in the lungs and heart that can be heard with a stethoscope or phonocardiography. In this paper, we try to find an easier and non-invasive method to diagnose heart diseases using neural network classifier. The classifier has been developed for one normal heart sound and five murmurs by using Shannon entropy and conjugate scaled back propagation algorithm. The experimental results showed that the classification is possible with 1.63185e-6 of classification error.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.175-186
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• 2021

We have studied the speed regulation of the permanent magnet synchronous motor (PMSM) servo system in this paper. To optimize the PMSM servo system's speed-control performance with disturbances, a non-linear speed-control technique using a back-propagation neural network (BP-NN) algorithm forthe controller design of the PMSM speed loop is introduced. To solve the slow convergence speed and easy to fall into the local minimum problem of BP-NN, we develope an improved BP-NN control algorithm by limiting the range of neural network outputs of the proportional coefficient Kp, integral coefficient Ki of the controller, and add adaptive gain factor β, that is the internal gain correction ratio. Compared with the conventional PI control method, our improved BP-NN control algorithm makes the settling time faster without static error, overshoot or oscillation. Simulation comparisons have been made for our improved BP-NN control method and the conventional PI control method to verify the proposed method's effectiveness.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.913-916
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• 2005

This paper presents the development of Artificial Neural Network models for forecasting the cost and contract duration of school reconstruction projects to assist the planners' decision-making in the early stage of the projects. 132 schools reconstruction projects in central Taiwan, which received the most serious damage from the Chi-Chi Earthquake, were collected. The developed Artificial Neural Network prediction models demonstrate good prediction abilities with average error rates under 10% for school reconstruction projects. The analytical results indicate that the Artificial Neural Network model with back-propagation learning is a feasible method to produce accurate prediction results to assist planners' decision-making process.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.425-430
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• 2015

This paper is a study to improve the classification efficiency of rotating objects by using deep neural networks to which a deep learning algorithm was applied. For the classification experiment of rotating objects, COIL-20 is used as data and total 3 types of classifiers are compared and analyzed. 3 types of classifiers used in the study include PCA classifier to derive a feature value while reducing the dimension of data by using Principal Component Analysis and classify by using euclidean distance, MLP classifier of the way of reducing the error energy by using error back-propagation algorithm and finally, deep learning applied DBN classifier of the way of increasing the probability of observing learning data through pre-training and reducing the error energy through fine-tuning. In order to identify the structure-specific error rate of the deep neural networks, the experiment is carried out while changing the number of hidden layers and number of hidden neurons. The classifier using DBN showed the lowest error rate. Its structure of deep neural networks with 2 hidden layers showed a high recognition rate by moving parameters to a location helpful for recognition.