• 전기전자학회논문지
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• 제23권4호
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• pp.1321-1327
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• 2019

본 논문에서는 GF(p) 상에서 모듈러 제곱근 (MSQR) 연산의 효율적인 하드웨어 구현에 대해 기술한다. MSQR 연산은 타원곡선 기반의 EC-ElGamal 공개키 암호를 위해 평문 메시지를 타원곡선 상의 점으로 매핑하기 위해 필요하다. 본 논문의 방법은 NIST 표준으로 규정된 5가지 크기의 GF(p) 타원곡선을 지원하며, 192-비트, 256-비트, 384-비트 그리고 521-비트 크기의 Kobliz 곡선과 슈도 랜덤 곡선들은 모듈러 값의 특성을 기반으로 오일러 판정법을 적용하고, 224-비트 크기의 경우에는 Tonelli-Shanks 알고리듬을 간략화시켜 적용하였다. 제안된 방법을 ECC 프로세서의 32-비트 데이터 패스를 갖는 유한체 연산회로와 메모리 블록을 이용하여 구현하였으며, FPGA 디바이스에 구현하여 하드웨어 동작을 검증하였다. 구현된 회로가 50 MHz 클록으로 동작하는 경우에, 224-비트 슈도 랜덤 곡선의 경우에는 MSQR 계산에 약 18 ms가 소요되고, 256-비트 Kobliz 곡선의 경우에는 약 4 ms가 소요된다.

• Journal of information and communication convergence engineering
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• 제4권4호
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• pp.166-169
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• 2006

Almost all network systems provide an authentication mechanism based on user ID and password. In such system, it is easy to obtain the user password using a sniffer program with illegal eavesdropping. The one-time password and challenge-response method are useful authentication schemes that protect the user passwords against eavesdropping. In client/server environments, the one-time password scheme using time is especially useful because it solves the synchronization problem. In this paper, we present a new identification scheme: OPI(One Pass Identification). The security of OPI is based on the square root problem, and OPI is secure: against the well known attacks including pre-play attack, off-line dictionary attack and server comprise. A number of pass of OPI is one, and OPI processes the password and does not need the key. We think that OPI is excellent for the consuming time to verify the prover.

• Journal of Power Electronics
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• 제18권2호
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• pp.558-572
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• 2018

In addition to its modular nature, a Hybrid Modular Multilevel Converter (HMMC) assembled from half-bridge and full-bridge sub-modules, is able to block DC faults with a minimum number of switching devices, which makes it attractive for high power applications. This paper introduces a control strategy based on the Root-Least Square (RLS) algorithm to estimate the capacitor voltages instead of using direct measurements. This action eliminates the need for voltage transducers in the HMMC sub-modules and the associated communication link with the central controller. In addition to capacitor voltage balancing and suppression of circulating currents, a fault tolerant control unit (FTCU) is integrated into the proposed strategy to modify the parameters of the HMMC controller. On advantage of the proposed FTCU is that it does not need extra components. Furthermore, a fault detection unit is adapted by utilizing a hybrid estimation scheme to detect sub-module faults. The behavior of the suggested technique is assessed using PSCAD offline simulations. In addition, it is validated using a real-time digital simulator connected to a real time controller under various normal and fault conditions. The proposed strategy shows robust performance in terms of accuracy and time response since it succeeds in stabilizing the HMMC under faults.

• Nuclear Engineering and Technology
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• 제51권3호
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• pp.723-730
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• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.