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Your search keyword '"Chen, Shih-Ming"' showing total 406 results
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406 results on '"Chen, Shih-Ming"'

119. Digital signature with message recovery based on factoring and discrete logarithm.

Author

Hwang, Min-Shiang, Chen, Shih-Ming, and Liu, Chi-Yu

Subjects
*DIGITAL signatures, *LOGARITHMS, *ENCRYPTION protocols
Abstract

There are two applications in digital signature schemes with message recovery based on a discrete logarithm problem. One is an authenticated encryption scheme, and the other is a key agreement scheme. Considering that the cryptographic assumptions will be broken in the future, the digital signature scheme with message recovery should also be designed based on two assumptions. Besides the digital signature scheme with message recovery, the authenticated encryption scheme with message linkages should also be redesigned to deal with the problem when any one of the factoring and discrete logarithm assumptions is broken. In this paper, we propose a digital signature with message recovery based on factoring and discrete logarithm and show that the scheme is secure. In comparison with Zhang ET AL.'s scheme, our proposed scheme is the most efficient one in terms of communication cost and computation complexity. [ABSTRACT FROM PUBLISHER]

Published
2016
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146. Acute nephrotoxicity of aristolochic acids in mice

Author

Sato, Noriko, primary, Takahashi, Daisuke, additional, Tsuchiya, Reiko, additional, Mukoyama, Takuya, additional, Yamagata, Shin-ichi, additional, Satoh, Nobunori, additional, Ueda, Shiro, additional, Chen, Shih-Ming, additional, Ogawa, Makoto, additional, Yoshida, Masaaki, additional, and Kondo, Seizo, additional

Published
2004
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147. The state-of-health diagnosis of Li-Co batteries with fuzzy identification.

Author

Ho-Ta Lin, Tsorng-Juu Liang, and Chen, Shih-Ming

Abstract

This paper employs fuzzy identification for diagnosing battery state of health (SOH) to avoid the inconvenience of battery failure. First, this study performs a life-cycle test of 95 Li-Co batteries with five various discharge currents in order to understand the most important factors that affect battery SOH. The batteries are charged with 0.5C constant-current and constant-voltage hybrid charging, and are discharged with 0.2, 0.4, 0.6, 0.8, and 1 C. The experimental results show that the charging time, the voltage difference between the open circuit and with load, and the voltage change of the battery between the voltage under full discharge and the voltage after the rest for 1 minute can be used to accurately diagnose battery SOH. Since each battery is tested with 300 cycles, the total patterns obtained from the experimental results are 95×300. 1,835 patterns are randomly selected for the fuzzy identification. 60 patterns are also randomly selected for verification. The principle of fuzzy identification as it applies to battery-health diagnosis is based on the principle of closest normal distribution. The average error of the good diagnosis is 1.46%, and the diagnosis standard deviation is 2.36%. The average error with the poor diagnosis and the diagnosis standard deviation are 6.45% and 6.83%, respectively. The results show that the proposed method can accurately diagnose battery health, and thus the state of charge can be more precisely predicted. [ABSTRACT FROM PUBLISHER]

Published
2012
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