• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.77-86
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• 2024

Using random numbers to represent uncertainty and unpredictability is essential in many industries. This is crucial in disciplines like computer science, cryptography, and statistics where the use of randomness helps to guarantee the security and dependability of systems and procedures. In computer science, random number generation is used to generate passwords, keys, and other security tokens as well as to add randomness to algorithms and simulations. According to recent research, the hardware random number generators used in billions of Internet of Things devices do not produce enough entropy. This article describes how raw data gathered by IoT system sensors can be used to generate random numbers for cryptography systems and also examines the results of these random numbers. The results obtained have been validated by successfully passing the FIPS 140-1 and NIST 800-22 test suites.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.105-112
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• 1996

Softwares including random number generation are abundant in modern informative society. But it's hard to get directly multivariate multinomial random numbers from existing softwares. Multivariate multinomial random numbers are greatly used in social and medical sciences. In this paper, we show that desired multivariate multinomial random numbers can be easily generated by the aids of existing random number generating software. Some characteristics of multivariate multinomial distribution are surveyd. Measures of association for the generated random numbers were computed and compared with population ones via simulation study.

• 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.

• 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.

• ETRI Journal
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• v.42 no.4
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• pp.518-526
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• 2020

This paper presents an efficient hardware random-number generator based on a beta source. The proposed generator counts the values of "0" and "1" and provides a method to distinguish between pseudo-random and true random numbers by comparing them using simple cumulative operations. The random-number generator produces labeled data indicating whether the count value is a pseudo- or true random number according to its bit value based on the generated labeling data. The proposed method is verified using a system based on Verilog RTL coding and LabVIEW for hardware implementation. The generated random numbers were tested according to the NIST SP 800-22 and SP 800-90B standards, and they satisfied the test items specified in the standard. Furthermore, the hardware is efficient and can be used for security, artificial intelligence, and Internet of Things applications in real time.

• Structural Engineering and Mechanics
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• v.90 no.1
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• pp.41-49
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• 2024

In recent years, China's high-speed railway (HSR) line continues to expand into seismically active regions. Analyzing the features of earthquake rail irregularity is crucial in this situation. This study first established and experimentally validated a finite element (FE) model of bridge-track. The FE model was then combined with earthquake record database to generate the earthquake rail irregularity library. The sample library was used to construct a model of desired earthquake rail irregularity based on signal processing (SFT) and hypothesis principle. Finally, the effects of random pier height and random span number on desired irregularity were analyzed. Herein, an equivalent method of calculating earthquake rail irregularities for random structures was proposed. The results of this study show that the amplitude of desired irregularity is found to increase with increasing pier height. When calculating the desired irregularity of a structure with unequal pier heights, the structure can be regarded as that with equal pier heights (taking the largest pier height). For a structure with the span number large than 9, its desired irregularity can be considered equal to that of a 9-span structure. For the structures with both random pier heights and random span number, their desired irregularities are obtained by equivalent calculations for pier height and span number, respectively.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.415-420
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• 2010

Cryptographic keys for security should be generated by true random number generators that apply irreversible hashing algorithms to initial values taken from a random source. As DRAM shows randomness in its access latency, it can be used as a random source. However, systems with synchronous DRAM (SDRAM) do not easily expose such randomness resulting in highly clustered random numbers. We resolved this problem by using the xor instruction. Statistical testing shows that the generated random bits have the quality comparable to true random bit sequences. The performance of bit generation is at the order of 100 Kbits/sec. Since the proposed random number generation requires neither external devices nor any special circuits, this method may be used in any computing device that employs DRAM.

• ETRI Journal
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• v.42 no.6
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• pp.951-964
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• 2020

This paper presents a lightweight true random number generator (TRNG) using beta radiation that is useful for Internet of Things (IoT) security. In general, a random number generator (RNG) is required for all secure communication devices because random numbers are needed to generate encryption keys. Most RNGs are computer algorithms and use physical noise as their seed. However, it is difficult to obtain physical noise in small IoT devices. Since IoT security functions are required in almost all countries, IoT devices must be equipped with security algorithms that can pass the cryptographic module validation programs of each country. In this regard, it is very cumbersome to embed security algorithms, random number generation algorithms, and even physical noise sources in small IoT devices. Therefore, this paper introduces a lightweight TRNG comprising a thin-film beta-radiation source and integrated circuits (ICs). Although the ICs are currently being designed, the IC design was functionally verified at the board level. Our random numbers are output from a verification board and tested according to National Institute of Standards and Technology standards.

• Korean Journal of Mathematics
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• v.9 no.1
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• pp.21-28
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• 2001

We find bounds for the domination number of a tournament and investigate the sharpness of these bounds. We also find the domination number of a random tournament.

• 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.