• Journal of IKEEE
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• v.23 no.4
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• pp.1321-1327
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• 2019

This paper describes an efficient hardware implementation of modular square root (MSQR) computation over GF(p), which is the operation needed to map plaintext messages to points on elliptic curves for elliptic curve (EC)-ElGamal public-key encryption. Our method supports five sizes of elliptic curves over GF(p) defined by the National Institute of Standards and Technology (NIST) standard. For the Koblitz curves and the pseudorandom curves with 192-bit, 256-bit, 384-bit and 521-bit, the Euler's Criterion based on the characteristic of the modulo values was applied. For the elliptic curves with 224-bit, the Tonelli-Shanks algorithm was simplified and applied to compute MSQR. The proposed method was implemented using the finite field arithmetic circuit with 32-bit datapath and memory block of elliptic curve cryptography (ECC) processor, and its hardware operation was verified by implementing it on the Virtex-5 field programmable gate array (FPGA) device. When the implemented circuit operates with a 50 MHz clock, the computation of MSQR takes about 18 ms for 224-bit pseudorandom curves and about 4 ms for 256-bit Koblitz curves.

• Journal of information and communication convergence engineering
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• v.4 no.4
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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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• v.18 no.2
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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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• v.51 no.3
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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.