• Journal of Advanced Marine Engineering and Technology
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• v.2 no.1
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• pp.3-14
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• 1978

The authors have developed a calculating method of propeller shaft alignment by the finite element method. The propeller shaft is divided into finite elements which can be treated as uniform section bars. For each element, the nodal point equation is derived from the stiffness matrix, the external force vector and the section force vector. Then the overall nodal point equation is derived from the element nodal point equation. The deflection, offset, bending moment and shearing force of each nodal point are calculated from the overall nodal point equation by the digital computer. Reactions and deflections of supporting points of straight shaft are calculated and also the reaction influence number is derived. With the reaction influence number the optimum alignment condition that satisfies all conditions is calculated by the simplex method of linear programming. All results of calculation are compared with those of Det norske Veritas, which has developed a computor program based on the three-moment theorem of the strength of materials. The authors finite element method has shown good results and will be used effectively to design the propeller shaft alignment.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.363-368
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• 2005

This paper proposes an LOS(line-of-sight) guidance system of a ship for path following. From the viewpoint of a control configuration, guidance is a special type of compensation algorithm that is placed in front of the controller to accomplish navigational objects. A guidance system generates a reference trajectory for trajectory tracking or path control and decides the desired velocity, position and heading angle. A control system executes commands based on a reliable guidance law during navigation. An LOS vector from the vessel to a point on the path between two way-points in straight-line navigation or a point among turning circle in turning navigation is selected, and then a heading angle is calculated to converge the desired path based on the LOS vector. The LOS guidance law is defined for the straight-line and the turning circle, respectively. The effectiveness of the suggested LOS guidance system is assured through computer simulation.

• Journal of Magnetics
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• v.8 no.4
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• pp.164-168
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• 2003

In high density recording system, the recording head field on a medium should be focused in small bit area and should have a sufficient value to overcome the medium coercivity, which resulted in head saturation. In this paper, an efficient method to access the head field and field gradient considering head saturation is presented. The magnetic vector potential on the head surface is pre-calculated considering head saturation in several cases and accumulated into database. The head field on the recording media is easily produced solving Laplace equation using accessed magnetic vector potential boundaries. The computed head field is compared with a quantified magnetic force microscopy measurement.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.255-262
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• 1998

This paper presents a translation, rotation and scale invariant methodology for the recognition of closed planar shape images using the bispectrum of a contour sequence and the learning vector quantization(LVQ) neural classifier. The contour sequences obtained from the closed planar images represent the Euclidean distance between the centroid and all boundary pixels of the shape, and are related to the overall shape of the images. The higher order spectra based on third order cumulants is applied to tihs contour sample to extract fifteen bispectral feature vectors for each planar image. There feature vector, which are invariant to shape translation, rotation and scale transformation, can be used to represent two0dimensional planar images and are fed into a neural network classifier. The LVQ architecture is chosen as a neural classifier because the network is easy and fast to train, the structure is relatively simple. The experimental recognition processes with eight different hapes of aircraft images are presented to illustrate the high performance of this proposed method even the target images are significantly corrupted by noise.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1715-1723
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• 2018

This work investigates the performance of fully complex- valued radial basis function network(FC-RBF) and complex extreme learning machine (CELM) based neural approaches for classification of power quality disturbances. This work engages the use of S-Transform to extract the features relating to single and combined power quality disturbances. The performance of the classifiers are compared with their real valued counterparts namely extreme learning machine(ELM) and support vector machine(SVM) in terms of convergence and classification ability. The results signify the suitability of complex valued classifiers for power quality disturbance classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.3
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• pp.469-480
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• 1997

For moving image coding, the variable size of region coding based on local motion is more efficient than fixed size of region coding. It can be applied well to complex motions and is more stable for wide motions because images are segmented according to local motions. In this paper, new image coding method using the segmentation of motion vectors is proposed. First, motion vector field is smoothed by filtering and segmented by smoothed motion vectors. The region growing method is used for decomposition of regions, and merging of regions is decided by motion vector and prediction errors of the region. Edge of regions is excluded because of the correlation of image, and neighbor motion vectors are used evaluation of current block and construction of region. The results of computer simulation show the proposed method is superior than the existing methods in aspect of coding efficiency.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.42-53
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• 1996

The paper presents a speed control system of vector controlled induct- ion motor using neural networks. The main feature of proposed speed control system is a Neural Network Controller(NNC) which supplies torque current to induction motor and Neural Network Emulator(NNE) which captures the forward dynamics of induction motor. A back propagation training algorithm is employed to train the NNE and NNC. In order to determine the NNC output error, plant(induction motor) output error can be back propagated through the NNE. The NNC and NNE for speed control of vector controlled induction motor is carried out by TMS320C30 DSP and IGBT current regulated PWM inverter. Through computer simulation and experimental results, it is verified that proposed speed control system is robust to the load variation. (author). refs., figs.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.311-313
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• 1996

In case of field weakening region, the dynamic behavior of the speed controller depends on the rotor flux level. In this region, the flux is decreased inversely proportional to the rotor speed. As the rotor flux is decreased, as the magnetizing inductance is increased. In this paper, the effect of this increased magnetizing inductance to the performance of vector control is illustrated. The stationary reference frame torque not including the magnetizing inductance is calculated by stationary stator flux, and the rotating reference frame torque including the magnetizing inductance is calculated by rotating rotor flux. If the magnetizing inductance value is constant, two torque values are same regardless of the flux-component current. However, if the magnetizing inductance is varied, those two values are different. The paper presents the new tuning scheme of the magnetizing inductance using the difference between the stationary and rotating torque. Computer simulation demonstrates the efficacy of the proposed scheme.

• Journal of Satellite, Information and Communications
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• v.5 no.1
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• pp.85-89
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• 2010

This paper proposes a new QRD-LS adaptive algorithm with computational complexity of O(N). The main idea of proposed algorithm(D-QR-RLS) is based on the fact that the computation for the unit vector of is made from the process during Givens Rotation. The performance of the algorithm is evaluated through computer simulation of FIR system identification problem. As verified by simulation results, this algorithm exhibits a good performance. And, we can see the proposed algorithm converges to optimal coefficient vector theoretically.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.325-341
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• 2015

This paper describes the Tezpur University dataset of online handwritten Assamese characters. The online data acquisition process involves the capturing of data as the text is written on a digitizer with an electronic pen. A sensor picks up the pen-tip movements, as well as pen-up/pen-down switching. The dataset contains 8,235 isolated online handwritten Assamese characters. Preliminary results on the classification of online handwritten Assamese characters using the above dataset are presented in this paper. The use of the support vector machine classifier and the classification accuracy for three different feature vectors are explored in our research.