• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.877-887
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• 2015

Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.520-527
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• 2008

This work focuses on the evaluation of active power filter which is controlled by fuzzy logic and neural network based controller for harmonic mitigation and power factor enhancement. The APF consists of a variable DC voltage source and a DC/AC inverter. The task of an APF is to make the line current waveform as close as possible to a sinusoid in phase with the line voltage by injecting the compensation current. The compensation current is estimated using adaptive neural network. Using the estimated current, the proposed APF is controlled using neural network and fuzzy logic. Computer simulations of the proposed APF are performed using MATLAB. The results show that the proposed techniques for the evaluation of APF can reduce the total harmonic distortion less than 3% and improve the power factor of the system to almost unity.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.483-497
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• 2023

Depth estimation is one of the most complicated and difficult problems to deal with in the light field. In this paper, a compound attention convolutional neural network (CAttNet) is proposed to extract depth maps from light field images. To make more effective use of the sub-aperture images (SAIs) of light field and reduce the redundancy in SAIs, we use a compound attention mechanism to weigh the channel and space of the feature map after extracting the primary features, so it can more efficiently select the required view and the important area within the view. We modified various layers of feature extraction to make it more efficient and useful to extract features without adding parameters. By exploring the characteristics of light field, we increased the network depth and optimized the network structure to reduce the adverse impact of this change. CAttNet can efficiently utilize different SAIs correlations and features to generate a high-quality light field depth map. The experimental results show that CAttNet has advantages in both accuracy and time.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.135-139
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• 1988

This paper presents a new algorithm using power flow solution which is given by the polar form Newton-Raphson method in a transient stability analysis. The computation time to solve network equations can be much saved by a decoupled power flow method. In addition, the time is much saved in performing a approximate stability analysis by linearizing the differential equations and using a voltage and angle sensitivity matrix given in network equations.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.236-238
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• 1992

This paper proposes a new algorithm to estimate time-varying parameters by combining KFSM(Kalman Filter with Shift Matrix) with neural network compansator. While the time varying parameters are estimated from KFSM, the error coverence of system, R(k) are compansated by neural network concurrently. The casestudy using computer simulation proves the usefullness and advantages of the proposed algorithm in this paper.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.761-763
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• 1997

As the power system network is enlarged and complicated gradually, the automation of power system network is essential to enhance the reliability and performance of the operation of power system. Distribution restoration is a major part of the automation. This paper proposes a new distribution restoration system to restore healthy blackout regions by the hierarchical cooperation with the substation automation systems. In proposed system, the range of restoration is extended to substations and the performance as well as the switching efficiency of restoration system is enhanced.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2290-2292
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• 1998

This paper introduces Two-stage neural network that is capable of recognizing spatio-temporal patterns. First stage takes a spatio-temporal pattern as input and compress it into sparse spatio-temporal pattern. Second stage is for temporal pattern recognition with nonuniform inhibitory connections and different cell sizes. These are basic properties for detecting a embeded pattern in a larger pattern. The network is evaluated by computer simulation.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1501-1504
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• 2004

We describe a Bluetooth wireless sensor network for security systems, which includes the implementation issues about system architecture, power management, self-configuration of network, and routing. We think that the methods or algorithms described in this paper can be easily applied to other embedded Bluetooth applications for wireless networks.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.2
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• pp.109-116
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• 2017

In this paper, we propose an Internet of Things (IoT) platform for ocean observation buoys. The proposed system consists of various sensor modules, a gateway, and a remote monitoring site. In order to integrate sensor modules with various communications interfaces, we propose a controller area network (CAN)-based sensor data packet and a protocol for the gateway. The proposed scheme supports the registration and management of sensor modules so as to make it easier for the buoy system to manage various sensor modules. Also, in order to extend communication coverage between ocean observation buoys and the monitoring site, we implement a multi-hop relay network based on a mesh network that can provide greater communication coverage than conventional buoy systems. In addition, we verify the operation of the implemented multi-hop relay network by measuring the received signal strength indication between buoy nodes and by observing the collected data from the deployed buoy systems via our monitoring site.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.608-619
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• 2018

As distributed generation (DG) is connected to grid, there is new node-type occurring in distribution network. An efficient algorithm is proposed in this paper to calculate power flow for weakly meshed distribution network with DGs in different load models. The algorithm respectively establishes mathematical models focusing on the wind power, photovoltaic cell, fuel cell, and gas turbine, wherein the different DGs are respectively equivalent to PQ, PI, PQ (V) and PV node-type. When dealing with PV node, the algorithm adopts reactive power compensation device to correct power, and the reactive power allocation principle is proposed to determine reactive power initial value to improve convergence of the algorithm. In addition, when dealing with the weakly meshed network, the proposed algorithm, which builds path matrix based on loop-analysis and establishes incident matrix of node voltage and injection current, possesses good convergence and strong ability to process the loops. The simulation results in IEEE33 and PG&G69 node distribution networks show that with increase of the number of loops, the algorithm's iteration times will decrease, and its convergence performance is stronger. Clearly, it can be effectively used to solve the problem of power flow calculation for weakly meshed distribution network containing different DGs.