• ETRI Journal
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• v.39 no.6
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• pp.841-850
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• 2017

We investigate neural network image reconstruction for magnetic particle imaging. The network performance strongly depends on the convolution effects of the spectrum input data. The larger convolution effect appearing at a relatively smaller nanoparticle size obstructs the network training. The trained single-layer network reveals the weighting matrix consisting of a basis vector in the form of Chebyshev polynomials of the second kind. The weighting matrix corresponds to an inverse system matrix, where an incoherency of basis vectors due to low convolution effects, as well as a nonlinear activation function, plays a key role in retrieving the matrix elements. Test images are well reconstructed through trained networks having an inverse kernel matrix. We also confirm that a multi-layer network with one hidden layer improves the performance. Based on the results, a neural network architecture overcoming the low incoherence of the inverse kernel through the classification property is expected to become a better tool for image reconstruction.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.3
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• pp.184-191
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• 2018

Orthogonal frequency division multiplexing(OFDM) is a multicarrier modulation(MCM) system that enables high-speed communications using multiple carriers and has advantages of power and spectral efficiency. Therefore, this study aims to complement the existing shortcomings and to design an efficient MCM system. The proposed system uses the inverse discrete wavelet transform(IDWT) operation instead of the inverse fast Fourier transform(IFFT) operation. The bit error rate(BER), spectral efficiency, and peak-to-average power ratio(PAPR) performance were compared with the conventional OFDM system through the OFDM system design based on wavelet transform. Our results showed that the conventional OFDM and Wavelet-OFDM exhibited the same BER performance, and that the Wavelet-OFDM using the discrete Meyer wavelet had the same spectral efficiency as the conventional OFDM. In addition, all systems of Wavelet-OFDM based on various wavelets confirm a PAPR performance lower than that of conventional OFDM.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1711-1716
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• 2004

This paper presents identification and control of a 10-m antenna via accelerometers and angle encoder data. Artificial Neural Networks can be used effectively for the identification and control of nonlinear dynamical system such as a large flexible antenna. Some identification results are shown and compared with the results of conventional prediction error method. And we use a neural network inverse model for control the large flexible antenna. In the neural network inverse model, a neural network is trained, using supervised learning, to develop an inverse model of the antenna. The network input is the process output, and the network output is the corresponding process input. The control results show the validation of the ANN approach for identification and control of the 10-m flexible antenna.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.383-388
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• 2004

A piezoelectric actuator yields hysteresis effect due to its composed ferroelectric. Hysteresis nonlinearty is neglected when a piezoelectric actuator moves with short stroke. However when it moves with long stroke and high frequency, the hysteresis nonlinearty can not be neglected. The hysteresis nonlinearty of piezoelectric actuator degrades the control performance in precision position control. In this paper, in order to improve the control performance of piezoelectric actuator, an inverse modeling scheme is proposed to compensate the hysteresis nonlinearty problem. And feedforward - feedback controller is proposed to give a good tracking performance. The Feedforward controller is inverse hysteresis model, Nueral network and PID control is used as a feedback controller. To show the feasibility of the proposed controller and hysteresis modeling, some experiments have been carried out. It is concluded that the proposed control scheme gives good tracking performance

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.2
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• pp.202-208
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• 2008

A 6 DOF Multi axis simulation table (MAST) is used to perform vibration and fatigue tests for parts or assemblies of automobiles, aircraft, or other systems. It consists of a table and 6 linear actuators. For its attitude control, we have to adjust the lengths of 6 actuators properly. The system is essentially a parallel mechanism. Three actuators are connected to the table directly and other three actuators are connected indirectly. Because of these, the MAST shows also a serial mechanism#s property: the inverse kinematics is more complicated than a pure parallel mechanism and each actuator can operate independently. The authors have performed a kinematics analysis of the 6 DOF MAST. We have presented an analytical and a numerical solution for the inverse and forward kinematics, and we have verified the solutions by a 3D CAD software.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.402-405
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• 1997

Based on the relative velocity method and the inverse kinematics theory, this paper presents an automated system for designing and manufacturing of an open type cylindrical cam with a rotating follower(OCRF). In the first part, this paper defines the relative velocity method for OCRF and calculates the contact point by using the coordinate transformation technique. In the second part, it generates NC Code of a CNC machine center for inverse kinematics by using the cutter location and the cutter orientation of OCRF. Finally, the automated CADICAM program developed in the paper shows an example on the desip and manufacture process of OCRF.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.05a
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• pp.29-36
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• 2004

In this paper, a study of damage detection for 2-Dimensional Truss Structures using the parametric projection filter theory is presented. Many researchers are interested in inverse problem and one of solution procedures for inverse problems that are very effective is the approach using the filtering algorithm in conjunction with numerical solution methods. In filtering algorithm, the Kalman filtering algorithm is well known and have been applied to many kind of inverse problems. In this paper, the Parametric projection filtering in conjunction with structural analysis is applied to the identification of damages in 2-D truss structures. The natural frequency and modes of damaged truss model are adopted as the measurement data. The effectiveness of proposed method is verified through the numerical examples.

• Journal of Institute of Control, Robotics and Systems
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• v.1 no.2
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• pp.88-93
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• 1995

This paper presents a study using neural networks in the design of the tracking controller of robotic systems. Our strategy is to put to use the available knowledge about the robot manipulator, such as estimation models, in the contoller design via the computed torque method, and then to add the neural network to control the remaining uncertainty. The neural network used here learns to provide the inverse dynamics of the plant uncertainty, and acts as an inverse controller. In the simulation study, we verify that the proposed neural network controller is robust not only to structured uncertainties, but also to unstructured uncertainties such as friction models.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.519-523
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• 2016

This paper proposes a method of estimating the actuator faults of a hexacopter without using encoders when one or more of six actuators do not operate normally. In the case of the hexacopter, a Pseudo-Inverse matrix is generally used to obtain the rotational speed of the actuators because the matrix that transforms the rotational speed of the actuators into the thrust and torque of the body coordinate system is not a square matrix. However, the method based on the Pseudo-Inverse matrix cannot detect the actuator faults correctly because the Pseudo-Inverse matrix is approximate. In the proposed method, the actuator faults are estimated by modifying the transform matrix using the property that the actuators of the hexacopter are symmetrical. The simulation results show the effectiveness of the proposed method when faults occur in one or more of the six actuators.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1670-1679
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• 1995

In this paper, an optimal variable structure controller with a multilayer neural inverse identifier is proposed. A multilayer neural network with error back propagation learning algorithm is used for construction the neural inverse identifier which is an observer of the external disturbances and the parameter variations of the system. The variable structure controller with the multilayer neural inverse identifier not only needs a small part of a priori knowledge of the bounds of external disturbances and parameter variations but also alleviates the chattering magnitude of the control input. Also, an optimal sliding line is designed by the optimal linear regulator technique and an integrator is introduced for solving the reaching phase problem. Computer simulation results show that the proposed approach gives the effective control results by reducing the chattering magnitude of control input.