• Title/Summary/Keyword: Information generating function

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GENERALIZED 'USEFUL' INFORMATION GENERATING FUNCTIONS

  • Hooda, D.S.;Sharma, D.K.
    • Journal of applied mathematics & informatics
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    • v.27 no.3_4
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    • pp.591-601
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    • 2009
  • In the present paper, one new generalized 'useful' information generating function and two new relative 'useful' information generating functions have been defined with their particular and limiting cases. It is interesting to note that differentiations of these information generating functions at t=0 or t=1 give some known and unknown generalized measures of useful information and 'useful' relative information. The information generating functions facilitates to compute various measures and that has been illustrated by applying these information generating functions for Uniform, Geometric and Exponential probability distributions.

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AIT: A method for operating system kernel function call graph generation with a virtualization technique

  • Jiao, Longlong;Luo, Senlin;Liu, Wangtong;Pan, Limin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.5
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    • pp.2084-2100
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    • 2020
  • Operating system (OS) kernel function call graphs have been widely used in OS analysis and defense. However, most existing methods and tools for generating function call graphs are designed for application programs, and cannot be used for generating OS kernel function call graphs. This paper proposes a virtualization-based call graph generation method called Acquire in Trap (AIT). When target kernel functions are called, AIT dynamically initiates a system trap with the help of a virtualization technique. It then analyzes and records the calling relationships for trap handling by traversing the kernel stacks and the code space. Our experimental results show that the proposed method is feasible for both Linux and Windows OSs, including 32 and 64-bit versions, with high recall and precision rates. AIT is independent of the source code, compiler and OS kernel architecture, and is a universal method for generating OS kernel function call graphs.

Performance Analysis of Space-Time Codes in Realistic Propagation Environments: A Moment Generating Function-Based Approach

  • Lamahewa Tharaka A.;Simon Marvin K.;Kennedy Rodney A.;Abhayapala Thushara D.
    • Journal of Communications and Networks
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    • v.7 no.4
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    • pp.450-461
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    • 2005
  • In this paper, we derive analytical expressions for the exact pairwise error probability (PEP) of a space-time coded system operating over spatially correlated fast (constant over the duration of a symbol) and slow (constant over the length of a code word) fad­ing channels using a moment-generating function-based approach. We discuss two analytical techniques that can be used to evaluate the exact-PEPs (and therefore, approximate the average bit error probability (BEP)) in closed form. These analytical expressions are more realistic than previously published PEP expressions as they fully account for antenna spacing, antenna geometries (uniform linear array, uniform grid array, uniform circular array, etc.) and scattering models (uniform, Gaussian, Laplacian, Von-mises, etc.). Inclusion of spatial information in these expressions provides valuable insights into the physical factors determining the performance of a space-time code. Using these new PEP expressions, we investigate the effect of antenna spacing, antenna geometries and azimuth power distribution parameters (angle of arrival/departure and angular spread) on the performance of a four-state QPSK space-time trellis code proposed by Tarokh et al. for two transmit antennas.

Hypothesis Testing for New Scores in a Linear Model

  • Park, Young-Hun
    • Communications for Statistical Applications and Methods
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    • v.10 no.3
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    • pp.1007-1015
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    • 2003
  • In this paper we introduced a new score generating function for the rank dispersion function in a general linear model. Based on the new score function, we derived the null asymptotic theory of the rank-based hypothesis testing in a linear model. In essence we showed that several rank test statistics, which are primarily focused on our new score generating function and new dispersion function, are mainly distribution free and asymptotically converges to a chi-square distribution.

Stationary analysis of the surplus process in a risk model with investments

  • Lee, Eui Yong
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.4
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    • pp.915-920
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    • 2014
  • We consider a continuous time surplus process with investments the sizes of which are independent and identically distributed. It is assumed that an investment of the surplus to other business is made, if and only if the surplus reaches a given sufficient level. We establish an integro-differential equation for the distribution function of the surplus and solve the equation to obtain the moment generating function for the stationary distribution of the surplus. As a consequence, we obtain the first and second moments of the level of the surplus in an infinite horizon.

Moments of the ruin time and the total amount of claims until ruin in a diffusion risk process

  • Kim, Jihoon;Ahn, Soohan
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.1
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    • pp.265-274
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    • 2016
  • In this paper, we consider a diffusion risk process, in which, its surplus process behaves like a Brownian motion in-between adjacent epochs of claims. We assume that the claims occur following a Poisson process and their sizes are independent and exponentially distributed with the same intensity. Our main goal is to derive the exact formula of the joint moment generating function of the ruin time and the total amount of aggregated claim sizes until ruin in the diffusion risk process. We also provide a method for computing the related first and second moments using the joint moment generating function and the augmented matrix exponential function.

Center estimation of the n-fold engineering parts using self organizing neural networks with generating and merge learning (뉴런의 생성 및 병합 학습 기능을 갖는 자기 조직화 신경망을 이용한 n-각형 공업용 부품의 중심추정)

  • 성효경;최흥문
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.34C no.11
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    • pp.95-103
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    • 1997
  • A robust center estimation tecnique of n-fold engineering parts is presented, which use self-organizing neural networks with generating and merging learning for training neural units. To estimate the center of the n-fold engineering parts using neural networks, the segmented boundaries of the interested part are approximated to strainght lines, and the temporal estimated centers by thecosine theorem which formed between the approximaged straight line and the reference point, , are indexed as (.sigma.-.theta.) parameteric vecstors. Then the entries of parametric vectors are fed into self-organizing nerual network. Finally, the center of the n-fold part is extracted by mean of generating and merging learning of the neurons. To accelerate the learning process, neural network uses an adaptive learning rate function to the merging process and a self-adjusting activation to generating process. Simulation results show that the centers of n-fold engineering parts are effectively estimated by proposed technique, though not knowing the error distribution of estimated centers and having less information of boundaries.

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Asymptotic Relative Efficiency for New Scores in the Generalized F Distribution

  • Choi, Young-Hun
    • Communications for Statistical Applications and Methods
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    • v.11 no.3
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    • pp.435-446
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    • 2004
  • In this paper we introduced a new score generating function for the rank dispersion function in a multiple linear model. Based on the new score function, we derived the asymptotic relative efficiency, ARE(11, rs), of our score function with respect to the Wilcoxon scores for the generalized F distributions which show very flexible distributions with a variety of shape and tail behaviors. We thoroughly explored the selection of r and s of our new score function that provides improvement over the Wilcoxon scores.

Some applications for the difference of two CDFs

  • Hong, Chong Sun;Son, Yun Hwan
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.1
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    • pp.237-244
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    • 2014
  • It is known that the dierence in the length between two location parameters of two random variables is equivalent to the difference in the area between two cumulative distribution functions. In this paper, we suggest two applications by using the difference of distribution functions. The first is that the difference of expectations of a certain function of two continuous random variables such as the differences of two kth moments and two moment generating functions could be defined by using the difference between two univariate distribution functions. The other is that the difference in the volume between two empirical bivariate distribution functions is derived. If their covariance is estimated to be zero, the difference in the volume between two empirical bivariate distribution functions could be defined as the difference in two certain areas.

A Study on the Generating Algorithm and the Correlation Function of Kumar Code Sequences for the Frequency Hopping Patterns (주파수 경로 패턴을 위한 Kumar 부호계열 발생 알고리즘 및 상관함수 특성에 관한 연구)

  • 이정재;한영렬
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.26 no.12
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    • pp.1886-1902
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    • 1989
  • In this paper we discuss the generating algorithm of Kumar code sequences which are based on generalized bent sequences for FH-CDMA. The code sequence generator was constructed for the shift register stages n=4 over GF (5). Finally we analyze the characteristics fo Hamming correlation between two code sequences and the time-frequency correlatins of the complete waveform with the sinusoidal chips as the elemental waveforms.

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