• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.764-767
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• 1999

In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• Proceedings of the KAIS Fall Conference
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• 2008.11a
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• pp.191-196
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• 2008

The intent of this paper is to describe a neural network structure called dynamic neural processor(DNP), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning using the DNP.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.6
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• pp.1143-1156
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• 2013

Power control system like SCADA(Supervisory Control And Data Acquisition) is gathering information using RS232C and low-speed analog communication network. In general, these methods are known as secure because of the secure characteristics from the analog based communication network and serial communication. In this study, first we build DNP communication environment using commercial power control simulator and find some vulnerabilities by testing from the viewpoint of confidentiality, integrity and availability. Consequently, we see the necessity of a valid method for authentication and data encryption when gathering information, even though that is known as secure so far. Discussion of needs of DNP authentication and data encryption is started about several years ago, but there is still nowhere applied that on real environment because the current methods can not fully meet the security requirements of the real environment. This paper suggests a solution to the vulnerabilities, and propose some considerations for enhancing power control system's security level by applying DNP authentication and data encryption.

• Journal of the Korean Magnetic Resonance Society
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• v.21 no.4
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• pp.114-118
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• 2017

Here, I report solid state Dynamic Nuclear Polarization (DNP) of $^1H$ nuclear spins at 0.3 T and 4.2 K. The DNP polarizer was developed based on a commercial X-band Electron Spin Resonance (ESR) modified for DNP, in combination with a NMR console and a liquid-Helium cryostat. By detuning magnetic field, DNP spectrum was measured to find the optimal condition. At +3 mT detuned from on-resonance field, $^1H$ NMR signal of 60:40 glycerol/water frozen solution doped with 20 mM perdeuterated-Tempone was amplified 43 times. The $^1H$ spin polarization obtained at 4.2 K is over 3100 times higher than that at 300 K. The width of the DNP spectrum, which is five times broader than ESR spectrum, is inconsistent with solid effect or thermal mixing, and presumably suggests a different DNP mechanism.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.480-484
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• 2008

DNP3.0은 산업 분야에서 SCADA(Supervisory Control And Data Aquisition) system의 개방형 프로토콜로 사용되어지고 있다. 본 논문에서는 개발하거나 개발된 Module들 사이에서 송 수신되는 DNP3.0 PDU를 분석할 수 있는 기능을 제공하는 DPA(DNP3.0 Protocol Analyzer) Module을 설계 및 구현하였다. 해당 Master Station에서 Request 메시지를 생성하여 Outstation으로 전송하고 Outstation에서 수신된 Request 메시지를 분석하여 Response 메시지를 생성하여 Response 한다. 또한 Master Station 과 Outstation에서 DNP3.0 프로토콜을 사용하여 통신하는 중간에서 송 수신하는 메시지를 Monitoring한다.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.4
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• pp.371-376
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• 2004

Distributed Network Protocol Version 3.0 (DNP3.0) is the communication protocol developed for the interoperability between a RTU and a central control station of SCADA in the power utility industry. In this paper DNP3.0 is implemented by using HDL with FPGA and C program on Hitachi H8/532 processor. DNP3.0 is implemented from physical layer to network layer in hardware level to reduce the computing load on a CPU. Finally, the ASIC for DNP3.0 has been manufactured from Hynix Semiconductor. The commercial feasibility of the hardware through the communication test with ASE2000 and DNP Master Simulator is performed. The developed protocol becomes one of IP, and can be used to implement SoC for the terminal device in SCADA systems. Also, the result can be applicable to various industrial controllers because it is implemented in HDL.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.121-126
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• 1999

In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. In order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment systems is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.265-267
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• 2002

In this paper, the communication between Over-Current Relay agents is realized using DNP(Distributed Network Protocol), which is the standard communication protocol of distribution automation system in KEPKO. The key words in OCR agent communication are defined and represented by use of DNP application function code. And the DNP index for OCR agent is defined. The proposed communication scheme is tested by use of Communication Test Harness, a test tool for DNP protocol to show its soundness.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.187-187
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• 1999

in order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment system is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulation are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• Journal of Environmental Health Sciences
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• v.36 no.3
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• pp.222-228
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• 2010

Biological degradation of nitrogen-containing aromatic compounds was investigated in activated sludge previously adapted to mineralize low concentrations of nitrogen-containing aromatic compounds. Normally, the time required for 95% degradation of 10 mg/l dinitrophenol (DNP) under aerobic conditions was less than 4 hours without any lag, and with mixed liquor suspended solid (MLSS) levels from 600 to 1,000 mg/l. However, when the initial DNP concentration was increased to 75 mg/l, lags and even complete inhibition of DNP degradation were observed. The length of the lag was found to increase proportionally with decreasing MLSS levels. When dilute activated sludge was incubated for extended periods (192 hours), degradation of 75 mg/l DNP did eventually occur after lag periods of 37 to 144 hours, depending on the MLSS concentration. DNP was degradable in high concentrations if MLSS concentrations were sufficiently high to allow growth of bacteria resistant to the toxic effects of DNP.