• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.130-138
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• 1995

In this paper, a Novel Approach is Proposed which Identifies linear system Parameters Using a multilayer feedforward neural network trained with backpropagation algorithm. The parameters of linear system can be represented by x9t)/x(t) and x(t)/u(t). Thud, its parameters can be represented in terms of the derivative of output with respect to input of parameters can be represented in terms of the derivative of output with respect to input of trained neural network which is a function of weights and output of neurons. Mathematical representation of the proposed approach is derived, and its validity is shown by simulation results on 2-layer and 3-layer neural network.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.522-532
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• 1995

Multilayer neural networks with backpropagation learning algorithm are widely used for pattern classification problems. For many real applications, it is more important to reduce the misclassification rate than to increase the rate of successful classification. But multilayer perceptrons(MLP's) have drawbacks of slow learning speed and false convergence to local minima. In this paper, we propose a new method for character recognition problems with a single-layer network and double rejection mechanisms, which guarantees a very low misclassification rate. Comparing to the MLP's, it yields fast learning and requires a simple hardware architecture. We also introduce a new coding scheme to reduce the misclassification rate. We have prepared two databases: one with 135,000 digit patterns and the other with 117,000 letter patterns, and have applied the proposed method for printed character recognition, which shows that the method reduces the misclassification rate significantly without sacrificing the correct recognition rate.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.25-28
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• 2002

The path planning of autonomous mobile robot use two method. One is global path planning and another is local path planning. In this paper, We study the local path planning of autonomous mobile robot move in unknown enviroment. This local path planning is based on neural network using the direction indicator rules learning. also the system is made up of sensor system. The motion control system for real-time execution. The experimental results show that the developed direction indicator system operates properly and strongly at circumstance.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2020-2023
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• 2003

In this paper, our proposed system uses the regional Gabor wavelet and Neural Network to implement face recognition similar to human face recognition system, because the Gator wavelet expresses visual recognition system of human mathematically and the regional Neural Network is robust to white noise and partial illumination. This system consists of two stages of building database and recognizing face. One is composed by using the supervised learning of Neural Network. At this time, the Neural Network is applied to the upper and the lower part of face images respectively. The Backpropagation algorithm is used to learn Neural Network. Another consists of calibration of slope of face image, measurement of illumination variant using deviation with average face image and similarity comparison using Euclidean distance measure.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.1
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• pp.80-87
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• 1998

In this study, a multi-element ultrasound transducer has been developed aiming at basic experiment of three-dimension endovascular ultrasound endscopy for clinical diagnos, and experimental results of two-dimensional object imaging in the water are presented by the ultrasound tranducer and neural network. Each ultrasound echo received by thirty-six angular transducer elements is inputed to the eural network, and then backpropagation is used as a learning algorithm. A three-layer artificial neural network is used for learning and imaging of targetw placed in front of the transducer. The object shape of imaging is restricted to rectangular shapes by considering experimental restraint conditions. As a result, rough visualization can be realized even for objects with unlearned shapes through the training by primitive patterns of a various sized rectangular targets.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.293-295
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• 1995

In this article a fuzzy controller and neural network adaptive observer is proposed and applied to the case of induction motor control. The proposed observer which comprises neural network flux observer and neural network torque observer is trained to learn the flux dynamics and torque dynamics and subjected to further on-line training by means of a backpropagation algorithm. Therefore it has been shown that the robust control of induction motor neglects the rotor time constant variations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.6 s.237
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• pp.809-814
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• 2005

The goal of this paper is to minimize the weight of the T-structure supporting steering system in reducing the vibration level on steering wheel which could be amplified by the resonance. Presently, requirements for reducing noise, vibration and harshness (NVH) in automotive area are more stringent than ever. One of them is the vibration of steering system which occurs sometimes at high speeds or when the engine is idling. Besides, the reduction of weight is also one of requirements for improvement of vehicle performance. This paper used the micro genetic algorithm as an optimization method to satisfy above two requirements. The whole T-structure assembly including steering column was used for frequency analysis.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.171-175
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• 2000

Neural networks are in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.894-899
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• 2008

Measured sensor datum from a quadruped robotics is commonly used for recognizing physical environment information which controls the posture of robotics. We can advance the ambulation with this sensed information and need to synthesize various sensors for obtaining accurate data, but most of these sensors are expensive and require excessive load for the operation. Those defects can be serious problem when it comes to the prototype's practicality and mass production, and maintenance of the system. This paper suggests virtual sensor technology for avoiding previous defects and presents ways to apply a theory to a walking robotics through virtual sensor information which is trained with several kinds of actual sensor information from the prototype system; the general algorithm is initially based on the neural network theory of back propagation. In specific, we verified a possibility of replacing the virtual sensor with the actual one through a reaction force measurement experiment.

• Geomechanics and Engineering
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• v.20 no.5
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• pp.385-397
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• 2020

This paper presents an application of artificial neural networks (ANNs) in settlement prediction of a foundation on sandy soil. In order to train the ANN model, a wide experimental database about settlement of foundations acquired from available literatures was collected. The data used in the ANNs model were arranged using the following five-input parameters that covered both geometrical foundation and sandy soil properties: breadth of foundation B, length to width L/B, embedment ratio D_f/B, foundation net applied pressure q_net, and average SPT blow count N. The backpropagation algorithm was implemented to develop an explicit predicting formulation. The settlement results are compared with the results of previous studies. The accuracy of the proposed formula proves that the ANNs method has a huge potential for predicting the settlement of foundations on sandy soils.