• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.2 no.2
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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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• v.14 no.3
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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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• v.12 no.1_2
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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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• v.25 no.1_2
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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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• v.30 no.4
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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.

• Korean Journal of Cognitive Science
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• v.18 no.1
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• pp.1-14
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• 2007

In a goal-oriented dialogue, speakers' intentions can be represented by domain actions that consist of pairs of a speech act and a concept sequence. Therefore, if we plan to implement an intelligent dialogue system, it is very important to correctly infer the domain actions from surface utterances. In this paper, we propose a statistical model to determine speech acts and concept sequences using conditional random fields at the same time. To avoid biased learning problems, the proposed model uses low-level linguistic features such as lexicals and parts-of-speech. Then, it filters out uninformative features using the chi-square statistic. In the experiments in a schedule arrangement domain, the proposed system showed good performances (the precision of 93.0% on speech act classification and the precision of 90.2% on concept sequence classification).

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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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.

• Bulletin of the Korean Mathematical Society
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• v.38 no.4
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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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• 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.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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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.