• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.537-540
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• 1992

This paper presents Samsung CNC (Computer Numerical Controller) system with an intel 80486/487 as the main CPU and a 32 bit floating point DSP(Digital Signal Processor) TMS320C30 as the motion control CPU. The Samsung CNC system diverse user-frienly characteristics such as multi-tasking, powerful menu system, internal PLC system, and 2/3 dimensional graphics in wire and solid mode. The main CPU executes central processing program, user interface program, interpreter, BMI, etc while the motion control CPU carries out some interpolations, acceleration/deceleration, and PID control algorithm with feedforward terms. Complex interpolations except linear and circular ones are performed on the main control CPU. The experimental results for the circular interpolation under linear acceleration/deceleration shows that the proposed CNC system can be widely used in controlling machining centers with good machining accuracy.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.3
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• pp.51-59
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• 2009

This paper presents the design of continuous-time sigma-delta modulator for acoustic sensor. The feedforward structure without summing block is used to reduce power consumption of sigma-delta modulator. A high-linearity active-RC filter is used to improve resolution of sigma-delta modulator. Excess loop delay problem in conventional continuous-time sigma-delta modulators is solved by our proposed architecture. A low power, high resolution fourth-order continuous-time sigma-delta modulator with 1-bit quantization was realized in a 0.13-${\mu}m$ 1-Poly 8-metal CMOS technology, with a core area of $0.58\;mm^2$. Simulation results show that the modulator achieves 91.3-dB SNR over a 25-kHz signal bandwidth with an oversampling ratio of 64, while dissipating $290{\mu}W$ from a 3.3-V supply.

• Journal of Sensor Science and Technology
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• v.12 no.6
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• pp.289-297
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• 2003

In this paper, we propose accurate classification method of an EEG signals during a mental tasks. In the experimental task, subjects achieved through the process of responding to visual stimulus, understanding the given problem, controlling hand motions, and select a key. To recognize the subjects' selection time, we analyzed with 4 types feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, $\theta$, $\gamma$ waves. From the analysed features, we construct specific rules for each subject meta rules including common factors in all subjects. In this system, the architecture of the neural network is a three layered feedforward networks with one hidden layer which implements the error back propagation learning algorithm. Applying the algorithms to 4 subjects show 87% classification success rates. In this paper, the proposed detection method can be a basic technology for brain-computer-interface by combining with discrimination methods.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.1
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• pp.70-79
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• 2005

In this paper, improvement method of control performance by a full-order observer using reduced-order state equation is proposed in the low speed range. Full-order observer using reduced-order state equation is the motor speed and the disturbance torque observer. The proposed algorithm is very stable in the low speed range about 1.9[rpm]. The disturbance torque in the motor drive system degrades speed control performance in the low speed range. The proposed algorithm estimated both motor speed and disturbance torque. The estimated disturbance torque is used as a feedforward value in output of the speed controller, As a result, it improves the response of load torque in the low speed range(1.9rpm).

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.2
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• pp.237-244
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• 2012

This paper studies the design of a two-DOF optimal controller for the strip gauge-tension of cold tandem mill processes, that uses a reference shaping filter and a disturbance observer. First, a mathematical model of the strip gauge and tension system is constructed using the gauge meter equation and Hooke's law, respectively. Next, a two-DOF controller considering of a feedforward controller and a feedback controller is designed. The former is based on the reference shaping filter and the disturbance observer, and the latter is based on the ILQ optimal control algorithm. Finally, it is shown through a computer simulation that the proposed optimal controller is able to improve the strip gauge accuracy and the tension variation more than the conventional MV-AGC controller.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.6
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• pp.805-814
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• 2013

A hydraulic dipod platform is used for tracking and stabilizing an antenna system to designate a satellite on a moving vehicle. The 2-DOF controller is very well suited to this controller design object because it is more flexible than the 1-DOF controller when the design object is not only the consideration between stabilizing and tracking but also the trade-off between performance and robustness. The 2-DOF controller synthesis based on the $H_{\infty}$ framework is divided into two design procedures. In this hydraulic dipod platform example, the single-step method shows better performance whereas the two-step method shows better robustness. The difference between these two synthesis results is compared using the structural property of the interconnection system matrix.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.1
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• pp.10-17
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• 2013

In this paper, a new active damping method of LCL filter without capacitor voltage sensors is proposed for 3 phase PWM Inverter. Normally, L filter or LCL filter is used as an output filter of grid connected PWM inverter. An LCL filter has more excellent performance than L filter to reduce harmonic current, so the small inductance value can be used. However, the resonance problem in LCL filter is happen due to the zero impedance by the addition of LC branch. To solve the resonance problem, the various active damping method has been proposed so far. Generally, the virtual resistor active damping methods is required to additional hardware sensors for measurement of capacitor voltage and current. In this paper, the new active damping method is proposed without any capacitor voltage or current sensors. In the proposed method, the resonance component of the capacitor voltage of LCL filter can be observed by a simple MRAS(Model Reference Adaptive System) observer without additional hardware sensors, and this component is suppressed by feedforward compensation. The validity of the proposed method is proven by simulation and experiment on the 3-phase PWM inverter system.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.1-6
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• 2001

Eigenface method in face recognition is useful due to its insensitivity to large variations in facial expression and facial details. However its low recognition rate necessitates additional researches. In this paper, we present an efficient method for improving the recognition rate in face recognition using eigenface feature. For this, we performs a comparative study of three different classifiers which are i) a single prototype (SP) classifier, ii) a nearest neighbor (NN) classifier, and iii) a standard feedforward neural network (FNN) classifier. By evaluating and analyzing the performance of these three classifiers, we shows that the distribution of eigenface features of face image is not compact and that selections of classifier and sample training data are important for obtaining higher recognition rate. Our experiments with the ORL face database show that 1-NN classifier outperforms the SP and FNN classifiers. We have achieved a recognition rate of 91.0% by selecting sample trainging data properly and using 1-NN classifier.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.33-40
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• 1998

This paper presents an Modified Orthogonal Neural Network(MONN), new modified model of Orthogonal Neural Network(0NN) based on orthogonal functions, and applies it to nonlinear system approximator. ONN proposed by Yang and Tseng, doesn't have the problems of traditional multilayer feedforward neural networks such as the determination of initial weights and the numbers of layers and processing elements. And tranining of ONN converges rapidly. But ONN cannot adapt its orthogonal functions to a given system. The accuracy of ONN, in terms of the minimal possible deviation between system and approximator, is essentially dependent on the choice of basic orthogonal functions. In order to improve ability and effectiveness of approximate nonlinear systems, MONN has an input transformation layer to adapt its basic orthogonal functions to a given nonlinear system. The results show that MONN has the excellent performance of approximate nonlinear systems and the input transfnrmation makes the ability of MONN better than one of ONN.

• Applied Biological Chemistry
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• v.41 no.2
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• pp.160-165
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• 1998

Current discriminating technique of rice variety is known to be not objective till this time because of depending on naked eye of well trained inspector. DNA finger print method based on genetic character of rice has been indicated inappropriate for on-site application, because the method need much labor and skilled expert. The purpose of this study was to develops the identification technique of polished rice varieties using CCD camera images. To minimize the noise of the captured image, thresholding and median filtering were carried out, and edge was extracted from the image data. Image data after pretreatment of normalize and FFT(fast fourier transform) were used for library model and feedforward backpropagation neural network model. Image processing technique using CCD camera could discriminate the variety of rice with high accuracy in case of quite different rice of shape, but the accuracy was reached at 85% in the similar shape of rice.