Earticle

본 연구에서는 확률형 아이템 위험 사용의 변화 과정에 영향을 미치는 기질, 정서, 동기요인 을 탐색하고자 하였다. 이를 위해 성인 인터넷 패널을 대상으로 3개월 간격의 3회의 조사가 실시 되었으며 3회에 모두 응답한 400명의 자료가 분석에 사용되었다. 시간의 흐름에 따른 변화과정 과 이에 영향을 미치는 요인을 탐색하기 위해 잠재성장모형분석을 실시하였다. 분석 결과 확률 형 아이템 위험 사용 점수는 시간의 흐름에 따라 감소하는 것으로 나타났다. 기질 특성 중 행동 억제체계는 확률형 아이템 위험 사용의 초기값과 변화율에 미치는 영향이 유의하지 않았으며 행 동접근체계는 확률형 아이템 사용의 초기값과 변화율에 미치는 영향이 유의하였다. 정서특성 중 우울과 불안 모두 확률형 아이템 사용의 초기값에 미치는 영향이 유의하였다. 확률형 아이템 사 용 동기의 경우 유희동기와 금전동기는 초기값에 미치는 영향만 유의하였으며 회피동기는 초기 값과 변화율에 미치는 영향이 모두 유의하였다. 끝으로 본 연구에서 나타난 결과를 바탕으로 심 리적 요인을 활용한 개입 및 상담 전략에 대해 논의하였다.

In this study, we tried to explore temperament, emotion, and motivation factors that affect the process of change in the risky usage random item. To this end, three surveys were conducted on adult Internet panels every three months, and data from 400 people who answered all three times were used for analysis. A latent growth model analysis was conducted to explore the process of change over time and the factors affecting it.. As a result of the analysis, it was found that the risky usage of random item score decreased over time. Among the substrate characteristics, the behavioral inhibition system did not have a significant effect on the initial value and rate of change of the risky usage of random item, and the behavioral activation system had a significant effect on the initial value and rate of change of random items. Among the emotional characteristics, both depression and anxiety had a significant effect on the initial value of random items. In the case of the motivation to use random item, only the effect of play motivation and money motivation on the initial value was significant, and the avoidance motivation was significant in both the initial value and the rate of change. Finally, based on the results shown in this study, intervention and counseling strategies using psychological factors were discussed.

This paper puts towards the improvement of the HMM algorithm, then proposed the HMM state identification model of the electronic equipment. It is verified by examples of the experiment, which results show that the HMM state recognition effect is better, recognition rate can reach 94%.Finally, and the experimental results were analyzed, and found the number of hidden states and have bigger influence on the model of training samples.

In this paper, we propose a new dithering method which uses random weight assignment. We use 3-by-3 window for dithering process and four coefficient values are randomly determined, where right, left-down, down, and right-down weights are determined accordingly. The sum of all weights is one. Block diagram and the pseudo code are provided. Experiments were conducted on two natural and two artificial images. Simulation results show that the proposed approach gives the best visual quality among all result images.

The spectrum estimation methods of random processes are expressed in this paper. Beginning with the basic theory, non-parametric and parametric methods are overviewed. As to non-parametric method, numerical calculation method is also discussed. As to parametric method, AR model is a very famous and effective model representing random process. Estimation methods of AR parameters which have been proposed are mentioned here. Wavelet analysis is a recently interested technique in signal processing. An application of wavelet analysis is also shown.

신호처리와 analysis를 위한 여러 기초적인 기술이 소개되었다. 이들은 먼저 불확정성순리의 개입에 의하여 특히 교환불가능한 operator 들이 작용한 결과의 등호는 tolerance가 있을 수 있음과 random process 처리방법과 manmum entropy estimate적인 ,사고방식을 통하여 재래식 확정론적 사고방식으로부터의 이탈을 길잡았다. 마지막으로 검출, 추정 및 함수추정의 여러 기법과 covariance functron의 posltive semi-definite-ness 그리고 Karhunen-Loeve 전개, 한 화상의 SVD 등이 설명됐다.

In this paper several basic techniques for signal processing and analysis are surveyed. Firstly by the intervention of the uncertainty principle, an equality sign may have different degree of precision if non commutable operators are applied. Seconds y maximum entropy estimate and randam process based viewpoint must be enhanced to get rid of the well established and reigning deterministic image of science. Thirdly techniques for the analysis of a signal namely detection. ess]motion and modulation are explained as well as the positive definiteness of a covariance function, Karhunen-Loeve expansion and SVD of an image.

In the nonlinear dynamic structural analysis the given ground excitation as an input should be well defined. Because of the lack of recorded accelerograms in Korea it is required to generate an artificial earthquake by a stochastic model of ground excitation with various dynamic properties rather than recorded accelerograms. It is well known that earthquake motions are generally non-stationary with time-varying intensity and frequency content. Many researchers have proposed non-stationary random process models. Yeh and Wen (1990) proposed a non-stationary modulation function and a power spectral density function to describe such non-stationary characteristics. Satio and Wen(1994) proposed a non-stationary stochastic process model to generate earthquake ground motions which are compatible with design reponse spectrum at sites in Japan. this paper shows the process to modify power spectrum compatible with target design response spectrum for generating of nonstationary artificial earthquake ground motions. Target reponse spectrum is chosen by ATC14 to calibrate the response spectrum according to a give recurrence period.

This paper proposes a cost-efficient and automatic method for large data acquisition from a test chip without expensive equipment to characterize random process variation in an integrated circuit. Our method requires only a test chip, a personal computer, a cheap digital-to-analog converter, a controller and multimeters, and thus large volume measurement can be performed on an office desk at low cost. To demonstrate the proposed method, we designed a test chip with a current model logic driver and an array of 128 current mirrors that mimic the random process variation of the driver's tail current mirror. Using our method, we characterized the random process variation of the driver's voltage due to the random process variation on the driver's tail current mirror from large volume measurement data. The statistical characteristics of the driver's output voltage calculated from the measured data are compared with Monte Carlo simulation. The difference between the measured and the simulated averages and standard deviations are less than 20% showing that we can easily characterize the random process variation at low cost by using our cost-efficient automatic large data acquisition method.

Recently, Zhao et al. [Fuzzy Optimization and Decision Making (2007) 6, 279-295] characterized the interarrival times as fuzzy random variables and presented a fuzzy random elementary renewal theorem on the limit value of the expected renewal rate of the process in the fuzzy random renewal process. They also depicted both the interarrival times and rewards are depicted as fuzzy random variables and provided fuzzy random renewal reward theorem on the limit value of the long run expected reward per unit time in the fuzzy random renewal reward process. In this note, we simplify the proofs of two main results of the paper.

Recently, Zhao et.al [European Journal of Operational Research 169 (2006) 189-201] discussed a random fuzzy renewal process based on random fuzzy theory. They considered the rate of the random fuzzy renewal process and presented a random fuzzy elementary renewal theorem. They also established Blackwell's theorem in random fuzzy sense. But all these results are invalid. We give a counter example in this note.

The aim of this paper is to establish a functional central limit theorem for multivariate moving average process generated by negatively associated random vectors under the finite second moments.

Let $\{A_u,\;u=0,\;1,\;2,\;{\cdots}\}$ be a sequence of coefficient matrices such that ${\sum}_{u=0}^{\infty}{\parallel}A_u{\parallel}<{\infty}$ and ${\sum}_{u=0}^{\infty}\;A_u{\neq}O_{m{\times}m}$, where for any $m{\times}m(m{\geq}1)$, matrix $A=(a_{ij})$, ${\parallel}A{\parallel}={\sum}_{i=1}^m{\sum}_{j=1}^m{\mid}a_{ij}{\mid}$ and $O_{m{\times}m}$ denotes the $m{\times}m$ zero matrix. In this paper, a functional central limit theorem is derived for a stationary m-dimensional linear process ${\mathbb{X}}_t$ of the form ${\mathbb{X}_t}={\sum}_{u=0}^{\infty}A_u{\mathbb{Z}_{t-u}}$, where $\{\mathbb{Z}_t,\;t=0,\;{\pm}1,\;{\pm}2,\;{\cdots}\}$ is a stationary sequence of linearly positive quadrant dependent m-dimensional random vectors with $E({\mathbb{Z}_t})={{\mathbb{O}}$ and $E{\parallel}{\mathbb{Z}_t}{\parallel}^2<{\infty}$.

In this article we show that a class of random coefficient autoregressive processes including the NEAR (New exponential autoregressive) process has the strong mixing property in the sense of Rosenblatt with mixing order decaying to zero. The result can be used to construct model free prediction interval for the future observation in the NEAR processes.

Let {${\xi}_k,\;k\;{\in}\;{\mathbb{Z}}$} be a strictly stationary associated sequence of H-valued random variables with $E{\xi}_k\;=\;0$ and $E{\parallel}{\xi}_k{\parallel}^2\;<\;{\infty}$ and {$a_k,\;k\;{\in}\;{\mathbb{Z}}$} a sequence of linear operators such that ${\sum}_{j=-{\infty}}^{\infty}\;{\parallel}a_j{\parallel}_{L(H)}\;<\;{\infty}$. For a linear process $X_k\;=\;{\sum}_{j=-{\infty}}^{\infty}\;a_j{\xi}_{k-j}$ we derive that {$X_k} fulfills the functional central limit theorem.

A central limit theorem is obtained for a stationary multivariate linear process of the form (equation omitted), where { $Z_{t}$} is a sequence of strictly stationary m-dimensional associated random vectors with E $Z_{t}$ = O and E∥ $Z_{t}$∥$^2$ < $\infty$ and { $A_{u}$} is a sequence of coefficient matrices with (equation omitted) and (equation omitted).ted)..ted).).

A new technique is presented to construct predictive models of plasma etch processes. This was accomplished by combining a backpropagation neural network (BPNN) and a random generator (RC). The RG played a critical role to control neuron gradients in the hidden layer, The predictive model constructed in this way is referred to as a randomized BPNN (RG-BPNN). The proposed scheme was evaluated with a set of experimental plasma etch process data. The etch process was characterized by a 2$^3$ full factorial experiment. The etch responses modeled are 4, including aluminum (Al) etch rate, profile angle, Al selectivity, and do bias. Additional test data were prepared to evaluate model appropriateness. The performance of RC-BPNN was evaluated as a function of the number of hidden neurons and the range of gradient. for given range and hidden neurons, 100 sets of random neuron gradients were generated and among them one best set was selected for evaluation. Compared to the conventional BPNN, the proposed RC-BPNN demonstrated about 50％ improvements in all comparisons. This illustrates that the RG-BPNN of multi-valued gradients is an effective way to considerably improve the predictive ability of current BPNN of single-valued gradient.

A simple proof for the random central limit theorem is given for a family of stationary linear lattice processes, which belogn to a class of 2 dimensional random fields, applying the Beveridge and Nelson decomposition in time series context. The result is an extension of Fakhre-Zakeri and Fershidi (1993) dealing with the linear process in time series to the case of the linear lattice process with 2 dimensional indices.

Let｛Ｘt｝be an m-dimensional linear process of the form (equation omitted), where｛Zt｝is a sequence of stationary m-dimensional weakly associated random vectors with EZt = O and E∥Zt∥$^2$＜$\infty$. We Prove central limit theorems for multivariate linear processes generated by weakly associated random vectors. Our results also imply a functional central limit theorem.