• Journal of the Korean Data and Information Science Society
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• 제7권1호
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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.

• 로봇학회논문지
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• 제4권1호
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• pp.56-61
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• 2009

A probability-based data generation is a typical context-generation method that is a not only simple and strong data generation method but also easy to update generation conditions. However, the probability-based context-generation method has been found its natural-born ambiguousness and confliction problems in generated context data. In order to compensate for the disadvantages of the probabilistic random data generation method, a situation propagation network is proposed in this paper. The situation propagating network is designed to update parameters of probability functions are included in probability-based data generation model. The proposed probability-based context-generation model generates two kinds of contexts: one is related to independent contexts, and the other is related to conditional contexts. The results of the proposed model are compared with the results of the probabilitybased model with respect to performance, reduction of ambiguity, and confliction.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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• pp.848-853
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• 2005

3D geometric modeling of an object of interest has been intensively investigated in many fields including CAD/CAM and computer graphics. Traditionally, CAD and geometric modeling tools are widely used to create geometric models that have nearly the same shape of 3D real objects or satisfy designers intent. Recently, with the help of the reverse engineering (RE) technology, we can easily acquire 3D point data from the objects and create 3D geometric models that perfectly fit the scanned data more easily and fast. In this paper, we present 3D geometric model generation based on a stereo vision system (SVS) using random pattern projection. A triangular mesh is considered as the resulting geometric model. In order to obtain reasonable results with the SVS-based geometric model generation, we deal with many steps including camera calibration, stereo matching, scanning from multiple views, noise handling, registration, and triangular mesh generation. To acquire reliable stere matching, we project random patterns onto the object. With experiments using various random patterns, we propose several tips helpful for the quality of the results. Some examples are given to show their usefulness.

• International Journal of Computer Science & Network Security
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• 제24권4호
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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 Information Processing Systems
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• 제19권1호
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• pp.1-16
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• 2023

Synthetic data generation is generally used in performance evaluation and function tests in data-intensive applications, as well as in various areas of data analytics, such as privacy-preserving data publishing (PPDP) and statistical disclosure limit/control. A significant amount of research has been conducted on tools and languages for data generation. However, existing tools and languages have been developed for specific purposes and are unsuitable for other domains. In this article, we propose a regular expression-based data generation language (DGL) for flexible big data generation. To achieve a general-purpose and powerful DGL, we enhanced the standard regular expressions to support the data domain, type/format inference, sequence and random generation, probability distributions, and resource reference. To efficiently implement the proposed language, we propose caching techniques for both the intermediate and database queries. We evaluated the proposed improvement experimentally.

• 전자통신동향분석
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• 제39권5호
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• pp.21-30
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• 2024

Memory can refer to a storage device that collects data, and it has evolved to increase the reading/writing speed and reduce the power consumption. As large amounts of data are processed by artificial intelligence services, the memory data capacity requires expansion. Dynamic random-access memory (DRAM) is the most widely used type of memory. In particular, graphics double date rate and high-bandwidth memory allow to quickly transfer large amounts of data and are used as memory solutions for artificial intelligence semiconductors. We analyze development trends in DRAM from the perspectives of processing speed and power consumption. We summarize the characteristics required for next-generation memory by comparing DRAM and other types of memory implementations. Moreover, we examine the shortcomings of DRAM and infer a next-generation memory for their compensation. We also describe the operating principles of spin-torque transfer magnetic random access memory, which may replace DRAM in next-generation devices, and explain its characteristics and advantages.

• JSTS:Journal of Semiconductor Technology and Science
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• 제17권1호
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• pp.147-155
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• 2017

A new endurance test-pattern generation on NAND-flash memory is proposed to improve test cost. We mainly focus on the correlation between the data-pattern and the device error-rate during endurance testing. The novelty is the development of testing method using quasi-random pattern based on device architectures in order to increase the test efficiency during time-consuming endurance testing. It has been proven by the experiments using the commercial 32 nm NAND flash-memory. Using the proposed method, the error-rate increases up to 18.6% compared to that of the conventional method which uses pseudo-random pattern. Endurance testing time using the proposed quasi-random pattern is faster than that of using the conventional pseudo-random pattern since it is possible to reach the target error rate quickly using the proposed one. Accordingly, the proposed method provides more low-cost testing solutions compared to the previous pseudo-random testing patterns.

• 전기전자학회논문지
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• 제24권1호
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• pp.52-58
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• 2020

정보보안 응용을 위한 참난수 발생기(true random number generator; TRNG)의 하드웨어적 구현에 대하여 기술한다. 셀룰러 오토마타에 무작위 천이규칙을 도입하고, 매 시간단계마다 다른 천이규칙이 적용되는 새로운 방법을 제안하였다. 설계된 참난수 발생기를 Spartan-6 FPGA 소자에 구현하고, 100 MHz 동작 주파수에서 난수 생성동작을 검증하였다. FPGA 소자에 구현된 참난수 발생기로부터 2×10⁷ 비트의 난수 데이터를 추출하여 NIST SP 800-22 테스트를 통해 생성된 난수 데이터의 무작위 성능을 검증하였으며, 15개의 테스트 항목 모두 기준을 충족하는 것으로 확인되었다. 본 논문의 참난수 발생기는 Spartan-6 FPGA 소자의 139 슬라이스로 구현되었고, 100 MHz 동작 주파수에서 600 Mbps의 참난수 생성 성능을 갖는다.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1999년도 추계학술대회 논문집
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• pp.75-80
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• 1999

This paper presents a new method to find a optimal solution for stochastic system. This method uses Genetic Algorithm(GA) and simulation. GA is used to search for new alternative and simulation is used to evaluate alternative. The stochastic system has one or more random variables as inputs. Random inputs lead to random outputs. Since the outputs are random, they can be considered only as estimates of the true characteristics of they system. These estimates could greatly differ from the corresponding real characteristics for the system. We need multiple replications to get reliable information on the system. And we have to analyze output data to get a optimal solution. It requires too much computation to be practical. We address the problem of reducing computation. The procedure on this paper use GA character, an iterative process, to reduce the number of replications. The same chromosomes could exit in post and present generation. Computation can be reduced by using the information of the same chromosomes which exist in post and present current generation.

• Journal of Animal Science and Technology
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• 제48권2호
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• pp.143-150
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• 2006

본 연구는 현재 유전평가에 사용하는 모델보다 많은 장점을 지니고 있는 임의회귀 검정일 모형(Random regression test-day model)을 이용할 때 마지막 세대의 불완전한 검정기록이 유전능력에 어떤 영향을 주는지 알아보고자 실시하였다.이용된 재료는 유우군능력검정사업을 통하여 수집된 2000년 1월부터 2005년 6월까지의 825,157개의 초산의 검정일 자료를 이용하였으며, 유전모수와 종모우의 육종가 추정은 REMLF90, BLUPF90을 이용하였다.