• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1673-1679
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• 2010

A photovoltaic (PV) generator is significantly regarded as one important alternative of renewable energy systems recently. Fault detection and diagnosis of engineering dynamic systems is a fundamental issue to timely prevent unexpected damages in industry fields. This paper presents an intelligent monitoring approach and fault detection technique for PV generator systems by means of artificial neural network and statistical signal detection theory. We devise a multi-Fourier neural network model for representing dynamics of PV systems and apply a general likelihood ratio test (GLRT) approach for investigating our decision making algorithm in fault detection and diagnosis. We make use of a test-bed of ubiquitous sensor network (USN) based PV monitoring systems for testing our proposed fault detection methodology. Lastly, a real-time experiment is accomplished for demonstrating its reliability and practicability.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1227-1233
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• 2013

The new engineering challenges lead to a control of a vehicle more and more complex. To tackle this issue, Hardware-In-the-Loop (HIL) simulation is used in the development of real-time embedded systems. In this paper, the control of a double parallel hybrid vehicle is validated using a reduced power HIL simulation. A graphical description is used in order to organize the emulation and control. Some experimental results of a versatile testbed are given for the Peugeot $3{\infty}8$ HYbrid4.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1103-1120
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• 2015

A self-sensing magnetorheological (MR) damper with embedded piezoelectric force sensor has recently been devised to facilitate real-time close-looped control of structural vibration in a simple and reliable manner. The development and characterization of the self-sensing MR damper are presented based on experimental work, which demonstrates its reliable force sensing and controllable damping capabilities. With the use of experimental data acquired under harmonic loading, a nonparametric dynamic model is formulated to portray the nonlinear behaviors of the self-sensing MR damper based on NARX modeling and neural network techniques. The Bayesian regularization is adopted in the network training procedure to eschew overfitting problem and enhance generalization. Verification results indicate that the developed NARX network model accurately describes the forward dynamics of the self-sensing MR damper and has superior prediction performance and generalization capability over a Bouc-Wen parametric model.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.312-319
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• 2009

This paper proposes a low-complexity indoor localization method of mobile robot under the dynamic environment by fusing the landmark image information from an ordinary camera and the distance information from sensor nodes in an indoor environment, which is based on sensor network. Basically, the sensor network provides an effective method for the mobile robot to adapt to environmental changes and guides it across a geographical network area. To enhance the performance of localization, we used an ordinary CCD camera and the artificial landmarks, which are devised for self-localization. Experimental results show that the real-time localization of mobile robot can be achieved with robustness and accurateness using the proposed localization method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.2
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• pp.259-267
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• 1997

The advent of B-ISDN using ATM(asynchronous transfer mode) made possible a variety of new multimedia services, however it also created a problem of congestion control due to bursty nature of various traffic sources. To tackle this problem, UPC/NPC(user parameter control/network parameter control) have been actively studied and DRLB(dynamic rate leaky bucket) algorithm, in which the token generation rate is changed according to states of data source andbuffer occupancy, is a good example of the UPC/NPC. However, the DRLB algorithm has drawbacks of low efficiency and difficult real-time implementation for bursty traffic sources because the determination of token generation rate in the algorithm is based on the present state of network. In this paper, we propose a more plastic and effective congestion control algorithm by combining the DRLB algorithm and neural network based prediction to remedy the drawbacks of the DRLB algorithm, and verify the efficacy of the proposed method by computer simulations.

• Proceedings of the KSR Conference
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• 2004.06a
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• pp.1495-1500
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• 2004

The interface link for railway signaling systems is generally point-to-point communication scheme, but there have been several efforts to apply the network technology to these interface link in overseas country. However general Ethernet scheme has several drawbacks, so this scheme cannot meet the very high reliability, safety and real-time properties. To overcome these drawbacks, there have been studies reduction and modeling of data collision probability for this ethernet at the industrial and academic world. Therefore, in this paper we analyzed describe a interface trend using network technology between railway signaling equipments and will describe a major technology and framework of a network for railway signaling system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2221-2225
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• 2005

Various types of fieldbus are used in factories in order to achieve the communication between the parts of process. But the protocol of the fieldbus doesn't have the standardized unique protocol. Thus, it is hard to exchange information each other with real time base when the different type protocols are adopted in the same network. In this paper, we implement two types of gateway for CAN and Profibus-DP: PC-based gateway and stand-alone gateway using the 80186 core based Dstni-LX network processor. The performance of proposed PC-based and stand-alone gateway is verified experimentally.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.08a
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• pp.37-44
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• 2004

Continuous depth data are often required in applications of both onboard systems and maritime simulation. But data available are usually discrete and irregularly distributed. Based on the neuron network technique, methods of interpolation to the charted depth are suggested in this paper. Two algorithms based on Levenberg-Marquardt back-propaganda and radial-basis function networks are investigated respectively. A dynamic neuron network system is developed which satisfies both real time and mass processing applications. Using hyperbolic paraboloid and typical chart area, effectiveness of the algorithms is tested and error analysis presented. Special process in practical applications such as partition of lager areas, normalization and selection of depth contour data are also illustrated.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.88-92
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• 1999

This paper aims at developing remote contro system to control and monitor distributed various devices through internet or information communication network. SNMP (Simple Network Management Protocol) and rectifier operated in a row are adopted for network management protocol and applied device, respectively. For controlling and monitoring distributed devices in real-time java-environment software is constructed. Also general-use interface controller between network device and applied device is proposed. Finally, seria communication such as RS-232 and RS-485 is used between controller and applied device.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.690-696
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• 2004

This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.