• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.5
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• pp.48-55
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• 2007

A bushing component of a vehicle suspension system is tested to capture the nonlinear behavior of rubber bushing element using the MTS 3-axes rubber test machine. The results of the tests are used to model the artificial neural network bushing model. The performances from the neural network model usually are dependent on the structure of the neural network. In this paper, maximum error, peak error, root mean square error, and error-to-signal ratio are employed to evaluate the performances of the neural network bushing model. A simple simulation is carried out to show the usefulness of the developed procedure.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.275-280
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• 2005

The principal context of this research is the approach to an artificial neural network algorithm which solves multivariable nonlinear equation systems by estimating the state of line power flow. First a dynamical neural network with feedback is used to find the minimum value of the objective function at each iteration of the state estimator algorithm. In second step a two-layer neural network structures is derived to implement all of the different matrix-vector products that arise in neural network state estimator analysis. For hardware requirements, as they relate to the total number of internal connections, the architecture developed here preserves in its structure the pronounced sparsity of power networks for which state the estimator analysis is to be carried out. A principal feature of the architecture is that the computing time overheads in solution are independent of the dimensions or structure of the equation system. It is here where the ultrahigh-speed of massively parallel computing in neural networks can offer major practical benefit.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.260-264
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• 1998

In this paper, we implemented the neuro-computer called MY-NEUPOWER in our research to carry out the artificial neural networks (ANN) calculating. An application software was developed based on a neural network using back-propagation (BP) algorithm under the UNIX platform by the specified computer language named MYPARAL. This neural network model was used as an auxiliary controller in the temperature control of sinter cooler system in steel plant which is a nonlinear system. The neural controller was trained off-line using the real input-output data as training pairs. We also made the system description of adaptive neural controller on the same temperature control system. We will carry out the whole system simulation to verify the suitability of neural controller in improving the system features.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.119-125
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• 1999

This paper gives an overview of the various approaches to designing statistical pattern recognition scheme based on Bayes discrimination rule and the artificial neural networks for rotating machine condition classification. Concerning to Bayes discrimination rule, this paper contains the linear discrimination rule applied to classification into several multivariate normal distributions with common covariance matrices, the quadratic discrimination rule under different covariance matrices. Also we discribes k-nearest neighbor method to directly estimate a posterior probability of each class. Five features are extracted in time domain vibration signals. Employing these five features, statistical pattern classifier and neural networks have been established to detect defects on rotating machine. Four different cases of rotation machine were observed. The effects of k number and neural networks structures on monitoring performance have also been investigated. For the comparison of diagnosis performance of these two method, their recognition success rates are calculated form the test data. The result of experiment which classifies the rotating machine conditions using each method presents that the neural networks shows the highest recognition rate.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.727-734
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• 1997

In practice, the design of forging processes is performed based on an experience-oriented technology, that is designer's experience and expensive trial and errors. Using the finite element simulation and the artificial neural network, we propose an optimal die geometry satisfying the design conditions of final product. A three-layer neural network is used and the back propagation algorithm is employed to train the network. An optimal die geometry that satisfied the same between inner extruded rib and outer extruded one is determined by applying the ability of function approximation of neural network. The neural networks may reduce the number of finite element simulation for determine the optimal die geometry of forging products and further they are usefully applied to physical modelling for the forging design.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.1
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• pp.33-51
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• 2002

A fully connected Artificial Neural Network (ANN) contains many connections. Compared to the pruned ANN with fewer connections, the fully connected ANN takes longer time to produce solutions end may not provide appropriate solutions to new unseen date. Therefore, by reducing the sloe of ANN, we can overcome the overfitting problem and increase the computing speed. In this research, we reduced the size of ANN by destroying the connections. In other words, we investigated the performance change of the reduced ANN by systematically destroying the connections. Then we found the acceptable level of connection-destruction on which the resulting ANN Performs as well as the original fully connected ANN. In the previous researches on the sloe reduction of ANN, the reduced ANN had to be retrained every time some connections were eliminated. Therefore, It tool lolly time to obtain the reduced ANN. In this research, however, we provide the acceptable level of connection-destruction according to the size of the fully connected ANN. Therefore, by applying the acceptable level of connection-destruction to the fully connected ANN without any retraining, the reduced ANN can be obtained efficiently.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.26-31
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• 2015

Success of the construction companies is based on the successful completion of projects within the agreed cost and time limits. Artificial neural networks (ANN) have recently attracted much attention because of their ability to solve the qualitative and quantitative problems faced in the construction industry. For the estimation of cost and duration different ANN models were developed. The database consists of data collected from completed projects. The same data is normalised and used as inputs and targets for developing ANN models. The models are trained, tested and validated using MATLAB R2013a Software. The results obtained are the ANN predicted outputs which are compared with the actual data, from which deviation is calculated. For this purpose, two successfully completed highway road projects are considered. The Nftool (Neural network fitting tool) and Nntool (Neural network/ Data Manager) approaches are used in this study. Using Nftool with trainlm as training function and Nntool with trainbr as the training function, both the Projects A and B have been carried out. Statistical analysis is carried out for the developed models. The application of neural networks when forming a preliminary estimate, would reduce the time and cost of data processing. It helps the contractor to take the decision much easier.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.10
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• pp.1479-1485
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• 2015

Robot's technology was very simple and repetitive in the past. Nowadays, robots are required to perform intelligent operation. So, path planning has been studied extensively to create a path from start position to the goal position. In this paper, potential field algorithm was used for path planning in dynamic environments. It is used for a path plan of mobile robot because it is elegant mathematical analysis and simplicity. However, there are some problems. The problems are collision risk, avoidance path, time attrition. In order to resolve path problems, we amalgamated potential field algorithm with the artificial neural network system. The input of the neural network system is set using relative velocity and location between the robot and the obstacle. The output of the neural network system is used for the weighting factor of the repulsive potential function. The potential field algorithm problem of mobile robot's path planning can be improved by using artificial neural network system. The suggested algorithm was verified by simulations in various dynamic environments.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.6
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• pp.213-223
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• 2006

In this study, artificial neural network is applied to the evaluation of bearing capacity of the PHC auger-drilled piles at sites of domestic decomposed granite soils. For the verification of applicability of error back propagation neural network, a total of 168 data of in-situ test results for PHC auger-drilled plies are used. The results show that the estimation of error back propagation neural network provide a good matching with pile test results by training and these results show the confidence of utilizing the neural networks for evaluation of the bearing capacity of piles.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.106.5-106
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• 2002

1. Introduction 2. Artificial Immune Networks and Navigation Algorithm 3. Obstacle Avoidance and Goal Approach Behavior 4. Weights Adjustment Using Neural Network 5. Velocity Control and Local Minimum Avoidance 6. Simulation 7. Conclusion