• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.4
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• pp.229-235
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• 2017

Until now, a study on a ubiquitous sensor network has been mainly concentrated in the areas of sensor nodes, and as a results, technologies related with sensor node were greatly developed. Despite of many achievements on research and development for a sensor node, a ubiquitous sensor network may failed to establish the actual service environment because variety of restrictions. In order to provide a actual service using a ubiquitous sensor networks applied to many results on research and development for a sensor nodes, a study on a wired/wireless composite router must be carried out. However a study on a wired/wireless composite router is relatively very slow compared with the sensor node. In this study, developed a high-performance router platform supporting IPv6 that can provide high-speed wired/wireless interface and QoS, and it can provide the multimedia service Interlocking the wireless sensor network and the Internet network. To analysis a given network environment and to develop the appropriate hardware and software in accordance with this requirement.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.9
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• pp.33-43
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• 2011

Since the fuzzy neural network(FNN) is universal approximators, the development of FNN control systems have also grown rapidly to deal with non-linearities and uncertainties. However, the major drawback of the existing FNNs is that their processor is limited to static problems due to their feedforward network structure. This paper proposes the recurrent FNN(RFNN) for high performance and robust control of SynRM. RFNN is applied to speed controller for SynRM drive and model reference adaptive fuzzy controller(MFC) that combine adaptive fuzzy learning controller(AFLC) and fuzzy logic control(FLC), is applied to current controller. Also, this paper proposes speed estimation algorithm using artificial neural network(ANN). The proposed method is analyzed and compared to conventional PI and FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.560-562
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• 2005

The electronic transmission of VLBI data(dubbed e-VLBI) presents a special challenge to the use of high-speed global network. with long-term requirements for simultaneous or near-simultaneous Gbps data streams from antennas worldwide converging in a single processing center, e-VLBI is both a useful and highly synergetic application for global high-speed networksAs broband access to high speed research and education networks has become increasingly available to radio telescopes around the world the use of e-VLBI has also increased. High bandwidth e-VLBI experiments have been achieved across wide areas e-VLBI has also been used for the transfer of data from "production"exoeriments

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.89-97
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. 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 IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.57-68
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• 2020

This paper reviews the recent trends in optical access technologies and their future directions. As the number of hyper-connected, ultra-low-latency, and hyper-realistic services increases, the wireless path has become shorter and the optical access network has become deeply penetrated into the end user. The optical access network continues to evolve through endless innovation via high-speed, ultra-low-latency, and abstraction/virtualization technologies.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.227-230
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• 2009

In this paper, the results of field testing of high-speed power line in-home networking in Korea are reported. When multiple networks share a common channel, they can interfere destructively and reduce system capacity. This is major problem in-home power line communications, especially in MDU deployments. The effects of neighboring network interference in power line communications are reported.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.413-417
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• 2001

High speed linear motor feed system has been simulated using neural network technique. Due to the limited resources, control gain tuning has been the most troublesome part in controller design. Regardless of the system structure, conventional control gain could be adjusted minimizing the resulting error using the proposed method. Slight performance deterioration was observed at the small value of training epoch.

• Proceedings of the KSR Conference
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• 2003.05a
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• pp.185-189
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• 2003

According to opening Seoul-Busan high speed rail line in 2004, we are studying to offer speedily for the parcel service of high speed rail-combined transport as it does, tender the quality freight services within a one and the same high speed train formation. Therefore, we reviewed for various types of HSR-combined transport and suggested to application methods in Korean high speed rail line. Through these kinds of approaches, we are able to expect the fulfillment of customer needs for high speed rail market segments, the profit maximization of future railway operating companies, and enhancement for cost-effective rail network operation of the mixed traffic on infrastructure cost-reduction in national logistics.

• Journal of Power Electronics
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• v.6 no.1
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• pp.38-44
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• 2006

In this paper, a speed sensorless control for an ultrasonic motor (USM) using a neural network (NN) is presented. In the proposed method, rotor speed is estimated by a three-layer NN which adapts nonlinearities associated with load torque and motor temperature into control. The intrinsic properties of a USM, such as high torque for low speeds, high static torque, compact size, etc., offer great advantages for industrial applications. However, the speed property of a USM has strong nonlinear properties associated with motor temperature and load torque, which make accurate speed control difficult. These properties are considered in designing a control method through the application of mathematical models. In these strategies, a detailed speed model of the USM is required which makes actual applications impractical. In the proposed method, a three-layer NN estimates the speed of the USM from the drive frequency, the root mean square value of input voltage and the surface temperature of the USM, where no mechanical speed sensor is needed. The NN speed based estimator enables inclusion of variations in driving conditions due to input signals of the NN involved during the driving state of the USM. The disuse of sensors offers many advantages on both the cost and maintenance front. Moreover, the model free sensorless control method offers practical controller construction within a small number of parameters. To validate the proposed speed sensorless control method for a USM, experiments have been executed under several conditions.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.34-50
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• 2023

Quantum error correction is a key technology for achieving fault-tolerant quantum computation. Finding the best decoding solution to a single error syndrome pattern counteracting multiple errors is an NP-hard problem. Consequently, error decoding is one of the most expensive processes to protect the information in a logical qubit. Recent research on quantum error decoding has been focused on developing conventional and neural-network-based decoding algorithms to satisfy accuracy, speed, and scalability requirements. Although conventional decoding methods have notably improved accuracy in short codes, they face many challenges regarding speed and scalability in long codes. To overcome such problems, machine learning has been extensively applied to neural-network-based error decoding with meaningful results. Nevertheless, when using neural-network-based decoders alone, the learning cost grows exponentially with the code size. To prevent this problem, hierarchical error decoding has been devised by combining conventional and neural-network-based decoders. In addition, research on quantum error decoding is aimed at reducing the spacetime decoding cost and solving the backlog problem caused by decoding delays when using hardware-implemented decoders in cryogenic environments. We review the latest research trends in decoders for quantum error correction with high accuracy, neural-network-based quantum error decoders with high speed and scalability, and hardware-based quantum error decoders implemented in real qubit operating environments.