• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.64-71
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• 2023

If an impedance mismatch occurs between the source and load in a Radio Frequency transmission system, reflected power is generated. This results in incomplete power transmission and the generation of Reflected Power, which returns to the Radio Frequency generator. To minimize this Reflected Power, Impedance matching is performed. Fast and efficient Impedance matching, along with converging reflected power towards zero, is advantageous for achieving desired plasma characteristics in semiconductor processes. This paper explores Impedance matching by adjusting the Vacuum Variable Capacitor of an L-type Matching Module based on the trends observed in the voltage of the Phase Sensor and Electromotive Force voltage. After assessing the impedance matching characteristics, the findings are described.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.80-87
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• 1999

This paper proposed a high impedance fault detection method using a neural network on distribution lines. The $\upsilon-i$ characteristic curve was obtained by high impedance fault data tested in various soil conditions. High impedance fault was simulated using EMTP. The pattern of High Impedance Fault on high density pebbles was taken as the learning model, and the neural network was evaluated on various soil conditions. The average values after analyzing fault current by FFT of even.odd harmonics and fundamental rms were used for the neural network input. Test results were verified the validity of the proposed method .ethod .

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.17-25
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• 2014

This paper presents the formulation of an impedance controller for regulating the contact force with the environment. To achieve an accurate force tracking control, uncertainties in both robot dynamics and the environment require to be addressed. As part of the framework of the proposed force tracking formulation, a neural network is introduced at the desired trajectory to compensate for all uncertainties in an on-line manner. Compensation at the input trajectory leads to a remarkable structural advantage in that no modifications of the internal force controllers are required. Minimizing the objective function of the training signal for a neural network satisfies the desired force tracking performance. A neural network actually compensates for uncertainties at the input trajectory level in an on-line fashion. Simulation results confirm the position and force tracking abilities of a robot manipulator.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1015-1017
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• 1997

A high impedance fault on the multi-grounded three-phase four-wire distribution system can not be detected by conventional overcurrent sensing devices. In this paper, the neural network is used to detect high impedance faults. The proposed algorithm using back - propagation neural network is demonstrated by simulation with the staged fault test data. The harmonic components of current and the phase of voltage are used as the inputs of neural network. Results of the simulation can be used as a reference for the development of a high impedance fault detector.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.3
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• pp.105-111
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• 2001

The research presented in this paper focuses on a method for the detection of High Impedance Fault(HIF). The method will use the wavelet transform and neural network system. HIF on the multi-grounded three-phase four-wires primary distribution power system cannot be detected effectively by existing over current sensing devices. These paper describes the application of discrete wavelet transform to the various HIF data. These data were measured in actual 22-9kV distribution system. Wavelet transform analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate HIF from the normal status by a gradient descent method. The proposed method performed very well by proving the right state when it was applied staged fault data and normal load mimics HIF, such as arc-welder.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.811-813
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• 1996

High impedance fault can not be easily detected by conventional method. But if it would not be detected and cleared quickly, it can result in fires, and electric shock. In this paper, neural network, which has learning capability, is used for high impedance fault detector. The potential of the neural network approach is demonstrated by simulation using KEPCO's measured data. The instantaneous values and frequency spectrum of current are respectively used as the inputs of neural networks. Also, the methods using combined data to exploit the advantage of each data are proposed. In this paper, back-propagation network(BPN) is used for high impedance fault detector and can use for high speed relay because it detects faults within 1 cycle.

• Journal of Power Electronics
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• v.16 no.6
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• pp.2338-2349
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• 2016

This paper presents an impedance-matching-based control scheme for the harmonic resonance damping of multiple grid-connected-converters (GCCs) with LCL filters. As indicated in this paper, harmonic resonance occurs if a GCC possesses an output impedance that is not matched with the rest of the network in some specific frequency bands. It is also revealed that the resonance frequency is associated with the number of GCCs, the grid impedance and even the capacitive loads. By controlling the grid-side current instead of the converter-side current, the critical LCL filter is restricted as an internal component. Thus, the closed-loop output impedance of the GCC within the filter can be configured. The proposed scheme actively regulates the output impedance of the GCC to match the impedance of the external network, based on the detected resonance frequency. As a result, the resonance risk of multiple GCCs can be avoided, which is beneficial for the plug-and-play property of the GCCs in microgrids. Simulation and experimental results validate the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.548-551
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• 1997

Performance of force tracking impedance control of robot manipulators is degraded by the uncertainties in the robot and environment dynamic model. The purpose of this paper is to improve the controller robustness by applying neural network. Neural networks are designed to learn the uncertainties in robot and environment model for compensating the uncertainties. The proposed scheme is verified through the simulation of 20DOF robot manipulator.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.1
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• pp.1-6
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• 2011

The Underground pipeline facility is a general but most important facility in modern world, but its maintainability has been left behind. An automated and intelligent management technology is needed to prevent the wast of social resource and security. In this paper, we introduce Pipeline Health Monitoring(PHM) with Ubiquitous Sensor Network(USN) for inexpensive structure safety monitoring system, and improve its utility by inventing the advanced impedance converter.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.4
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• pp.687-690
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• 1994

This paper presents a method of obtaining transmission network equivalents from the network's response to the pulse excitation signal. Proposed method is base on Prony signal analysis and jtransfer function identification technique. As a result Thevenin-type of discrete-time filter model can be generated. It can reproduce the driving point impedance characteristic of the network.