• 정보통신설비학회논문지
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• 제2권2호
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• pp.32-35
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• 2003

This paper presents a simple method to show that the committed bit based on pseudo-random sequence can be predicted with a probability very close to.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권3호
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• pp.209-215
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• 2014

Sequence tagging is the task of predicting frame-wise labels for a given input sequence and has important applications to diverse domains. Conventional methods such as maximum likelihood (ML) learning matches global features in empirical and model distributions, rather than local features, which directly translates into frame-wise prediction errors. Recent probabilistic sequence models such as conditional random fields (CRFs) have achieved great success in a variety of situations. In this paper, we introduce a novel discriminative CRF learning algorithm to minimize local feature mismatches. Unlike overall data fitting originating from global feature matching in ML learning, our approach reduces the total error over all frames in a sequence. We also provide an efficient gradient-based learning method via gradient forward-backward recursion, which requires the same computational complexity as ML learning. For several real-world sequence tagging problems, we empirically demonstrate that the proposed learning algorithm achieves significantly more accurate prediction performance than standard estimators.

• Journal of applied mathematics & informatics
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• 제12권1_2호
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• pp.299-305
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• 2003

The central limit theorems are obtained for stationary linear processes of the form Xt = (equation omitted), where {$\varepsilon$t} is a strictly stationary sequence of random variables which are either linearly positive quad-rant dependent or associated and {aj} is a sequence of .eat numbers with (equation omitted).

• Journal of applied mathematics & informatics
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• 제25권1_2호
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• pp.425-434
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• 2007

We iteratively generate a sequence of measurable mappings and study necessary conditions for its convergence to a random fixed point of random nonexpansive operator. A random fixed point theorem for random nonexpansive operator, relaxing the convexity condition on the underlying space, is also proved. As an application, we obtained random fixed point theorems for Caristi type random operators.

• ETRI Journal
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• 제30권4호
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• pp.495-505
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• 2008

This paper presents a parallel processing searcher structure for the initial synchronization of a direct sequence ultra-wideband (DS-UWB) system, which is suitable for the digital implementation of baseband functionalities with a 1.32 Gsample/s chip rate analog-to-digital converter. An initial timing acquisition algorithm and a data demodulation method are also studied. The proposed searcher effectively acquires initial symbol and frame timing during the preamble transmission period. A hardware efficient receiver structure using 24 parallel digital correlators for binary phase-shift keying DS-UWB transmission is presented. The proposed correlator structure operating at 55 MHz is shared for correlation operations in a searcher, a channel estimator, and the demodulator of a RAKE receiver. We also present a pseudo-random noise sequence generated with a primitive polynomial, $1+x^2+x^5$, for packet detection, automatic gain control, and initial timing acquisition. Simulation results show that the performance of the proposed parallel processing searcher employing the presented pseudo-random noise sequence outperforms that employing a preamble sequence in the IEEE 802.15.3a DS-UWB proposal.

• 인지과학
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• 제18권1호
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• pp.1-14
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• 2007

목적 지향 대화에서 사용자의 의도는 화행과 개념열의 쌍으로 구성된 영역 행위로 표현될 수 있다. 그러므로 지능적인 대화 시스템을 구성하기 위해서는 영역 행위를 정확히 파악하는 것이 매우 중요하다. 본 논문에서는 CRFs (Conditional Random Fields)를 이용하여 화행과 개념열을 동시에 결정하는 통계 모델을 제안한다. 편향 학습 문제를 피하기 위하여 제안한 모델은 어휘와 품사 같은 낮은 수준의 언어 자질을 입력 자질로 사용하며, 카이 제곱 통계량을 이용하여 불필요한 자질들을 제거한다. 일정 관리 영역에서 실험을 수행한 결과, 제안한 모델은 화행 분류 정착률에서 93.0%, 개념열 분류 정확률에서 90.2%의 좋은 성능을 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.240-244
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• 1992

A new method for generation of binary random sequences, called random sampling method, has been proposed by the authors. However, the random sampling method has the defect that binary random sequence can not be rapidly generated. In this paper, two methods based on the random sampling method are proposed for fast generation of binary random sequences. The optimum conditions for obtaining ideal binary random sequences are derived.

• 대한수학회보
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• 제38권4호
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• pp.763-772
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• 2001

The rate of convergence for an almost surely convergent series $S_n={\Sigma^n}_{i-1}X_i$ of independent random variables is studied in this paper. More specifically, when S$_{n}$ converges almost surely to a random variable S, the tail series $T_n{\equiv}$ S - S_{n-1} = {\Sigma^\infty}_{i-n} X_i$ is a well-defined sequence of random variables with T$_{n}$ $\rightarrow$ 0 almost surely. Conditions are provided so that for a given positive sequence {$b_n, n {\geq$ 1}, the limit law sup$_{\kappa}\geqn | T_{\kappa}|/b_n \rightarrow$ 0 holds. This result generalizes a result of Nam and Rosalsky [4].

• Journal of information and communication convergence engineering
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• 제8권5호
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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.

• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.129-139
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• 2002

Default Bayesian method for detecting the changes in sequences of independent exponential random variates and independent Poisson random variates is considered. Noninformative priors are assumed for all the parameters in both of change models. Default Bayes factors, AIBF, MIBF, FBF, to check whether there is any change or not on each sequence and the posterior probability densities of change at each time point are derived. Theoretical results discussed in this paper are applied to some numerical data.