• Korean Journal of Mathematics
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• v.14 no.1
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• pp.85-93
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• 2006

A good arithmetic random number generator should possess full period, uniformity and independence, etc. To obtain the excellent random number generator, many researchers have found good parameters. Also an initial seed is the important factor in random number generator. But, there is no theoretical guideline for using the initial seeds. Therefore, random number generator is usually used with the arbitrary initial seed. Through the empirical tests, we show that the choice of the initial values for the seed is important to generate good random numbers.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.464-467
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• 2001

A good arithmetic random number generator should possess full period, uniformity and independence, etc. To obtain the excellent random number generator, many researchers have found good parameters. Also an initial seed is the important factor in random number generator. But, there is no theoretical guideline for using the initial seeds. Therefore, random number generator is usually used with the arbitrary initial seed. Through the empirical tests, we show that the choice of the initial values for the seed is important to generate good random numbers.

• The Journal of Society for e-Business Studies
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• v.6 no.2
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• pp.167-178
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• 2001

Key is generally formed using the Random Number. How to make the Random Number is to cast coin or dice as classical method, to form the Real Random Number with Hardware and to make the Pseudo Random Number by means of utilizing mathematical algorithm. This thesis presented NRNG(New Random Number Generator) which put self-development Hardware to use as Key Generation Method and inspected to compare the Real Random Number with the Pseudo Random Number and special properties which PRNG(Pseudo-Random Number Generator) creates.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.541-546
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• 2003

We present a random bit generator that uses fingerprint image as the source of random, and the random bit generator is the first generator in the world that uses biometric information for the source of random in the world. The generator produces, on the average, 9,334 bits a fingerprint image in 0.03 second, and the produced bit sequence passes all 16 statistical tests that are recommended by NIST for testing the randomness.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.05a
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• pp.164-168
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• 2001

The Multiple Recursive Generator(MRG) has been considered by many scholars as a very good Random Number generator. For the long period and excellent statistical properties, the method of the combination with random number generators are used. In this paper, for two-combined MRGs, we examine the statistical properties and show the importance of the seeds likewise other random number generators. And we modify the two-combined MRGs and verify the statistical superiority.

• Journal of the Korea Society for Simulation
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• v.10 no.2
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• pp.25-32
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• 2001

The Multiple Recursive Generator(MRG) has been considered by many scholars as a very good random number generator. For the long period md excellent statistical properties, the method of the combination with random number generators is used. In this paper, we thought the two-combined MRGs. Using the frequency and serial test, and runs test, we studied the importance of the initial seeds likewise other random number generators.

• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.223-228
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• 2007

This paper deals with the secondary excited induction generator applied to random wave input generation system. As it is preferred to stabilize the output voltage and frequency in the constant level, microcomputer controlled CSI connected to the secondary windings supplies the secondary current with slip frequency. For testing this method, the input torque simulator is constructed, according to the power flow analysis. The experimental and numerical results show the advantage of secondary excited induction generator system for the random input wave generation system.

• Journal of information and communication convergence engineering
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• v.8 no.5
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• pp.539-543
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• 2010

We present a random bit generator that uses fingerprint image for the source of random, and random bit generator using fingerprint image for the randomness has not been presented as yet. Fingerprint image is affected by the operational environments including sensing act, nonuniform contact and inconsistent contact, and these operational environments make FPI to be used for the source of random possible. Our generator produces, on the average, 9,334 bits a fingerprint image in 0.03 second. We have used the NIST SDB14 test suite consisting of sixteen statistical tests for testing the randomness of the bit sequence generated by our generator, and as the result, the bit sequence passes all sixteen statistical tests.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.878-881
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• 2009

As for the technology developed for network security, there is little difference of design ability between the domestic and the foreign studies. Although the development of 2048 RSA processor has been undergone, the processing speed does not meet the requirement due to its long width. These days, an RSA processor architecture with higher speed comsuming less resource is necessary. As for the development of RNG (Random Number Generator), the technology trend is moving from PRNG (Pseudo Random Number Generator) to TRNG (True Random Number Generator), also requiring less area and high speed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.803-806
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• 2007

Since the different characteristics from the PRNG (Pseudo Random Number Generator) or various deterministic devices such as arithmetic processing units, new concepts and test methods should be suggested in order to test TRNG (Ture Random Number Generator). Deterministic devices can be covered by ATPG (Automatic Test Pattern Generation), which uses patterns generated by cyclic shift registers due to its hardware oriented characteristics, pure random numbers are not possibly tested by automatic test pattern generation due to its analog-oriented characteristics. In this paper, we studied and analyzed a hardware/software combined test method named Diehard test, in which we apply continuous pattern variation to check the statistics. We also point out the considerations when making random number tests.