• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.10
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• pp.1121-1232
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• 1990

A hierarchical neural network structure is described for robot PTP trajectory planning. In the first level, the multi-layered Perceptron neural network is used for the inverse kinematics with the back-propagation learning procedure. In the second level, a saccade generation model based joint trajectory planning model in proposed and analyzed with several features. Various simulations are performed to investigate the characteristics of the proposed neural networks.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.897-903
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• 1991

In this paper we propose a hybrid position/force controller of a robot manipulator using feedback error learning rule and neural networks. The neural network is constructed from inverse dynamics. The weighting value of each neuron is trained by using a feedback force as an error signal. If the neural networks are sufficiently trained well, it does not require the feedback-loop with error signals. The effectiveness of the proposed hybrid position/force controller is demonstrated by computer simulation using PUMA 560 manipulator.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.1077-1088
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• 2013

High-dimensional data analysis arises from almost all scientific areas, evolving with development of computing skills, and has encouraged penalized estimations that play important roles in statistical learning. For the past years, various penalized estimations have been developed, and the least absolute shrinkage and selection operator (LASSO) proposed by Tibshirani (1996) has shown outstanding ability, earning the first place on the development of penalized estimation. In this paper, we first introduce a number of recent advances in high-dimensional data analysis using the LASSO. The topics include various statistical problems such as variable selection and grouped or structured variable selection under sparse high-dimensional linear regression models. Several unsupervised learning methods including inverse covariance matrix estimation are presented. In addition, we address further studies on new applications which may establish a guideline on how to use the LASSO for statistical challenges of high-dimensional data analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.2 no.1
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• pp.45-60
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• 1976

In this study, Multiple Factor Analysis was undertaken for the purpose of substituting General Vocational Aptitude tester for paper tests according to the standardized and partially modified norm, and compared and analyzed these aptitude tests YK Type Working Character test for a test battery. In this analysis, four basis aptitude cluster of AQE was utilized as aptitude cluster, the study for skill was carried out by the method of sampling electronic aptitude cluster in four basis ones, and the parts needed in the process of its analysis were investigated by means of Video-Tape Recording. This paper was performed with sample test by application of the inverse variation curve from learning theory and induced learning rate as a measure of the degree of proficient of technicians, and from the obtained results illustrated optimum newly-production plan of ability program and load program by the use of computer program.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.8
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• pp.816-824
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• 1991

It is well known that dynamic control is needed for fast and accurate control. Neural networks are ideal for representing the strongly nonlinear relationship in the dynamic equations including complex unmodeled effects. It thus creates many advantages over conventional methods such as simple, fast and accurate control through neural network's inherent learning and massive parallelism. In this paper, dynamic control of the full six degrees of freedom of an industrial robot arm will be presented using neural networks. Moreover, through application to a real robot the usefulness of neurocontrol is demonstrated. The back propagation and feedback-error learning is used to train the neurocontroller. Simulated control of a PUMA 560 arm demonstrates that it moves at high speed with good accuracy and generalizes over untrained trajectories as well as adapt to unforseen load changes and sensor noise.

• Journal of the Korean School Mathematics Society
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• v.2 no.1
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• pp.93-104
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• 1999

The subject of this study was the graph of relation of direct inverse proportion, linear function and quadratic function in the 1st, 2nd and 3rd grade of current middle school mathematics curriculum. GSP materials were developed to simplify the principle, trait and characteristics of graphs and make them easier to understand. The overall aim of the materials is to improve the effectiveness of teaching and learning through the utilization of enhanced students' practice. Additionally, the use of the GSP will be useful in the development of mere effective materials. The effectiveness of the GSP materials will be as followings. 1. The step by step approach of GSP materials through computer interaction will enhance students motivation and interest in mathematics. 2. By presenting the subject matter simply and in a variety of ways, difficult concepts can be understood without the use of complex mathematical calculation. 3. The GSP program is different from CAI and other software programs. It should be used only after learning how to input and output data.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.11 no.9
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• pp.475-485
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• 2001

A study on the structural analysis of the composite laminated cylindrical shell which has simply supported boundary conditions at both ends, was performed. The results were used into the neural networks. Neural networks identify the load characteristics of the composite shells. Momentum Backpropagation which the learning rate can be varied was developed. Input patterns consist of strains at 9 side points which is divided equally. Output layers are the load characteristics. Developed program was used for the training. The training with variable learning rate was converged close to real oad characteristics. Inverse engineering can be applicable to the composite laminated cylindrical shells with developed neural networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.9
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• pp.2456-2469
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• 1996

A digital satellite communication channel has a nonlinearity with memory due to saturation characeristis of the high poer amplifier in the satellite and transmitter/receiver linear filter used in the overall system. In this paper, we propose a complex radial basis function network(CRBFN) based adaptive equalizer for compensation of nonlinearities in digital satellite communication channels. The proposed CRBFN untilizes a complex-valued hybrid learning algorithm of k-means clustering and LMS(least mean sequare) algorithm that is an extension of Moody Darken's algorithm for real-valued data. We evaluate performance of CRBFN in terms of symbol error rates and mean squared errors nder various noise conditions for 4-PSK(phase shift keying) digital modulation schemes and compare with those of comples pth order inverse adaptive Volterra filter. The computer simulation results show that the proposed CRBFN ehibits good equalization, low computational complexity and fast learning capabilities.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.9
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• pp.739-746
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• 1991

This paper develops a modified multilayer perceptron architecture which speeds up learning as well as the net's mapping accuracy. In Phase I, a cluster partitioning algorithm like the Kohonen's self-organizing feature map or the leader clustering algorithm is used as the front end that determines the cluster to which the input data belongs. In Phase II, this cluster selects a subset of the hidden layer nodes that combines the input and outputs nodes into a subnet of the full scale backpropagation network. The proposed net has been applied to two mapping problems, one rather smooth and the other highly nonlinear. Namely, the inverse kinematic problem for a 3-link robot manipulator and the 5-bit parity mapping have been chosen as examples. The results demonstrate the proposed net's superior accuracy and convergence properties over the original backpropagation network or its existing improvement techniques.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.204-210
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• 2018

As the 4th industrial revolution and artificial intelligence technology develop, it is expected that there will be a revolutionary changes in the security robot. However, artificial intelligence system requires enormous hardwares for tremendous computing loads, and there are many challenges that need to be addressed more technologically. This paper introduces precise tracking control technique of autonomous system that need to move repetitive paths for security purpose. The input feedforward signal is generated by using the inverse based iterative learning control theory for the 2 input 2 output nonminimum-phase system which was difficult to overcome by the conventional feedback control system. The simulation results of the input signal generation and precision tracking of given path corresponding to the repetition rate of extreme, such as bandwidth of the system, shows the efficacy of suggested techniques and possibility to be used in military security purposes.