• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.8
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• pp.405-411
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• 2000

When faults occur in transmission lines, the classification of faults is very important. If the fault is HIF(High Impedance Fault), it cannot be detected or removed by conventional overcurrent relays (OCRs), and results in fire hazards and causes damages in electrical equipment or personal threat. The fast discrimination of fault needs to effective protection and treatment and is important problem for power system protection. This paper propolsed the fault detection and discrimination algorithm for LIFs(Low Impedance Faults) and HIFs(High Impedance Faults). This algorithm uses artificial neural networks and variation of 3-phase maximum currents per period while faults. A double lines-to-ground and line-to-line faults can be detected using Neural Network. Also, the other faults can be detected using the value of variation of maximum current. Test results show that the proposed algorithms discriminate LIFs and HIFs accurately within a half cycle.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.9 no.1
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• pp.29-34
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• 2010

In this study, we realized the broadband home gateway system of dual mode based upon EDR 2~3Mbps transmission speed and 300Mbps of 802.11n, In broadcasting and telecommunication convergence networks to deliver multimedia services, high-quality home. The bluetooth module is designed to use general low rate data speed service and the 802.11n module also is designed to use high rate speed service at various home network situation. The dual mode home gateway system, bluetooth and 802.l1n module, is well realized for many specification such as emission power, spurious power, impedance matching, frequency channel test.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.3
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• pp.182-187
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• 2018

In this study, 20.8% of a p-type Si bifacial solar cell was used to develop a photovoltaic (PV) module to obtain the maximum power under a limited installation area. The transparent back sheet material was replaced during fabrication with a white one, which is opaque in commercial products. This is very beneficial for the generation of more electricity, owing to the additional power generation via absorption of light from the rear side. A new model is suggested herein to predict the power of the bifacial PV module by considering the backside reflections from the roof and/or environment. This model considers not only the frontside reflection, but also the nonuniformity of the backside light sources. Theoretical predictions were compared to experimental data to prove the validity of this model, the error range for which ranged from 0.32% to 8.49%. Especially, under $700W/m^2$, the error rate was as low as 2.25%. This work could provide theoretical and experimental bases for application to a distributed and microgrid network.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.1
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• pp.49-54
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• 2010

3GPP(Third Generation Partnership Project) LTE system that is the next step forward in cellular 3G services address the issues related to power saving at LTE base station. This is because that most of the energy in a typical telecommunication network is consumed by the wireless network's base station site. Power saving at LTE base station fall in with low-carbon green technology. This thesis proposes a power saving MAC protocol for LTE base station which utilizes different graded DRX/DTX(Discontinuous. Reception/Transmission). Considering traffic type in UE, proposed MAC protocol controls adaptive DRX/DTX cycle. The proposed method is more improve power saving performance than another method which is unchanged DRX/DTX by conditions. In this thesis, I propose an power saving MAC protocol in an environment where LTE base station are communicated with UE and prove improvement in performance through simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.12A
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• pp.1303-1310
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• 2007

In IEEE 802.11b wireless network, stations synchronize themselves to the beacons periodically sent by the access point (AP) when they are running in low power mode. Stations stay awake for enough time to receive beacon because it is delayed in AP if the wireless channel has been being used by other traffic at each scheduled instant. In this paper, we propose a method that measures the delay of received beacons and calculates wake-up interval of station to receive the next one. Beacon transmission delay at the AP is analyzed. The proposed method is simulated and its characteristics are described in the analysis. The result measured in terms of station's wake-up interval shows some enhancement in energy consumption.

• ETRI Journal
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• v.46 no.5
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• pp.829-838
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• 2024

SoCs with analog-circuit-based unsigned weight-accumulating spiking neural networks (UWA-SNNs) are a highly promising solution for achieving lowpower AI-SoCs. This paper addresses the challenges that must be overcome to realize the potential of UWA-SNNs in low-power AI-SoCs: (i) the absence of UWA-SNN learning methods and the lack of an environment for developing applications based on trained SNN models and (ii) the inherent issue of testing and validating applications on the system being nearly impractical until the final chip is fabricated owing to the mixed-signal circuit implementation of UWA-SNN-based SoCs. This paper argues that, by integrating the proposed solutions, the development of an EDA tool that enables the easy and rapid development of UWA-SNN-based SoCs is feasible, and demonstrates this through the development of the SNN eXpress (SNX) tool. The developed SNX automates the generation of RTL code, FPGA prototypes, and a software development kit tailored for UWA-SNN-based application development. Comprehensive details of SNX development and the performance evaluation and verification results of two AI-SoCs developed using SNX are also presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.227-230
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• 2002

The Loose Part Monitoring System(LPMS) has been designed to detect, locate and evaluate detached or loosened parts and foreign objects in the reactor coolant system. In this paper, at first, we presents an application of the back propagation neural network. At the preprocessing step, the moving window average filter is adopted to reject the low frequency background noise components. And then, extracting the acoustic signature such as Starting point of impact signal, Rising time, Half period, and Global time, they are used as the inputs to neural network. Secondly, we applied the neural network algorithm to LPMS in order to estimate the mass of loose parts. We trained the impact test data of YGN3 using the backpropagation method. The input parameter for training is Rising Time, Half Period, Maximum amplitude. The result showed that the neural network would be applied to LPMS. Also, applying the neural network to the Practical false alarm data during startup and impact test signal at nuclear power Plant, the false alarms are reduced effectively. 1.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.8
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• pp.1263-1268
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• 2008

In this paper, the low noise power amplifier for GaAs FET ATF-10136 is designed and fabricated with active bias circuit and self bias circuit. To supply most suitable voltage and current, active bias circuit is designed. Active biasing offers the advantage that variations in the pinch-off voltage($V_p$) and saturated drain current($I_{DSS}$) will not necessitate a change in either the source or drain resistor value for a given bias condition. The active bias network automatically sets a gate-source voltage($V_{gs}$) for the desired drain voltage and drain current. Using resistive decoupling circuits, a signal at low frequency is dissipated by a resistor. This design method increases the stability of the LNA, suitable for input stage matching and gate source bias. The LNA is fabricated on FR-4 substrate with active and self bias circuit, and integrated in aluminum housing. As a results, the characteristics of the active and self bias circuit LNA implemented more than 13 dB and 14 dB in gain, lower than 1 dB and 1.1 dB in noise figure, 1.7 and 1.8 input VSWR at normalized frequency $1.4{\sim}1.6$, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.99-105
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• 2011

Typically, quick and easily deployed communication for a clear disaster management is required in a disaster situation. But it is not easy because lack of backbone network and poor conditions of disaster site. it is possible to obtain a wide service coverage and low power with portable wireless mesh network technology. In this paper, we developed portable wireless mesh network gateway with a network processor and evaluate it's performance including throughput, latency delay, packet loss, etc. The experiments show our wireless mesh network gateway has basic performance and can be used in various environments like disaster site.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.71-74
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• 1991

This paper is described an expert system which performs the network reconfiguration in order to operate distribution systems optimally using heuristic rules. The proposed network reconfiguration is able to not only eliminate abnormal states such as overload of transformers/lines and low voltage violation, but also achieve minimum power loss and optimum load balancing under normal states. In order to apply the network reconfiguration efficiently, an expert system is adopted a best-first tree searching strategy on the basis of heuristics, and is implemented in AI language Turbo PROLOG. Several examples are used to illustrate concepts described above.