• Title/Summary/Keyword: Moving-average

Search Result 1,339, Processing Time 0.029 seconds

A COMPLETE CONVERGENCE FOR LINEAR PROCESS UNDER ρ-MIXING ASSUMPTION

  • Kim, Hyun-Chull;Ryu, Dae-Hee
    • Journal of the Chungcheong Mathematical Society
    • /
    • v.23 no.1
    • /
    • pp.127-136
    • /
    • 2010
  • For the maximum partial sum of linear process generated by a doubly infinite sequence of identically distributed $\rho$-mixing random variables with mean zeros, a complete convergence is obtained under suitable conditions.

DEPENDENCE IN M A MODELS WITH STOCHASTIC PROCESSES

  • KIM, TAE-SUNG;BAEK, JONG-IL
    • Honam Mathematical Journal
    • /
    • v.15 no.1
    • /
    • pp.129-136
    • /
    • 1993
  • In this paper we present of a class infinite M A (moving-average) sequences of multivariate random vectors. We use the theory of positive dependence to show that in a variety of cases the classes of M A sequences are associated. We then apply the association to establish some probability bounds and moment inequalities for multivariate processes.

  • PDF

A Small-area Hardware Implementation of EGML-based Moving Object Detection Processor (EGML 기반 이동객체 검출 프로세서의 저면적 하드웨어 구현)

  • Sung, Mi-ji;Shin, Kyung-wook
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.21 no.12
    • /
    • pp.2213-2220
    • /
    • 2017
  • This paper proposes an efficient approach for hardware implementation of moving object detection (MOD) processor using effective Gaussian mixture learning (EGML)-based background subtraction method. Arithmetic units used in background generation were implemented using LUT-based approximation to reduce hardware complexity. Hardware resources used for both background subtraction and Gaussian probability density calculation were shared. The MOD processor was verified by FPGA-in-the-loop simulation using MATLAB/Simulink. The MOD performance was evaluated by using six types of video defined in IEEE CDW-2014 dataset, which resulted the average of recall value of 0.7700, the average of precision value of 0.7170, and the average of F-measure value of 0.7293. The MOD processor was implemented with 882 slices and block RAM of $146{\times}36kbits$ on Virtex5 FPGA, resulting in 60% hardware reduction compared to conventional design based on EGML. It was estimated that the MOD processor could operate with 75 MHz clock, resulting in real-time processing of $800{\times}600$ video with a frame rate of 39 fps.

A High-precision AC Power Control System for Variable Loads Application (가변부하 적용을 위한 고정밀 교류전원 제어시스템)

  • Han, Wun-Dong;Shon, Jin-Geun
    • The Journal of The Korea Institute of Intelligent Transport Systems
    • /
    • v.7 no.3
    • /
    • pp.74-81
    • /
    • 2008
  • The control system of high-precision AC power is important in traffic management lighting and beaconing of aerodromes, etc. To control AC power supply in these load characteristics, inverter systems of AC/DC/AC conversion are widely used in high-precision current control. Therefore, in this paper, a inverter system of constant current regulation using improved measuring technique of feedback current is proposed. Proposed measuring techniques improve response and precision in that it use moving average method of instantaneous RMS for measuring current sensing. Results of the computer simulation and experiment prove the effects of proposed system.

  • PDF

Predictive Resource Allocation Scheme based on ARMA model in Mobile Cellular Networks (ARMA 모델을 이용한 모바일 셀룰러망의 예측자원 할당기법)

  • Lee, Jin-Yi
    • Journal of Advanced Navigation Technology
    • /
    • v.11 no.3
    • /
    • pp.252-258
    • /
    • 2007
  • There has been a lot of research done in scheme guaranteeing user's mobility and effective resources management to satisfy the requested by users in the wireless/mobile networks. In this paper, we propose a predictive resource allocation scheme based on ARMA(Auto Regressive Moving Average) prediction model to meet QoS requirements(handoff dropping rate) for guaranteeing users' mobility. The proposed scheme predicts the demanded amount of resource in the future time by ARMA time series prediction model, and then reserves it. The ARMA model can be used to take into account the correlation of future handoff resource demands with present and past handoff demands for provision of targeted handoff dropping rate. Simulation results show that the proposed scheme outperforms the existing RCS(Reserved channel scheme) in terms of handoff connection dropping rate and resource utilization.

  • PDF

Optimal Adjustment of Misestimated Control Model for a Process with Shift and White Noise (백색잡음과 Shift가 존재하는 공정에서 제어식이 부정확한 경우의 최적 보정)

  • Hwang, Ji-Bin;Kim, Ji-Hyun;Lee, Jae-Hyun;Kim, Sung-Shick
    • Journal of the Korea Society for Simulation
    • /
    • v.16 no.4
    • /
    • pp.43-55
    • /
    • 2007
  • Moving average(MA) and exponentially weighted moving average(EWMA) are the two most popular control methods in manufacturing. Both methods are optimized under the assumption that the exact control equation is known. This paper focuses on the problems rising from estimation errors. Based on the accuracy of the estimated parameter and the range of the weight parameter $\lambda$, the limitations are identified and the performance of methods are evaluated. Optimal adjustment for process shift with misestimated control model and its application control methods to actual process is researched. The efficiency of proposed method is evaluated through simulation.

  • PDF

ARMA-based data prediction method and its application to teleoperation systems (ARMA기반의 데이터 예측기법 및 원격조작시스템에서의 응용)

  • Kim, Heon-Hui
    • Journal of Advanced Marine Engineering and Technology
    • /
    • v.41 no.1
    • /
    • pp.56-61
    • /
    • 2017
  • This paper presents a data prediction method and its application to haptic-based teleoperation systems. In general, time delays inevitably occur during data transmission in a network environment, which degrades the overall performance of haptic-based teleoperation systems. To address this situation, this paper proposes an autoregressive moving average (ARMA) model-based data prediction algorithm for estimating model parameters and predicting future data recursively in real time. The proposed method was applied to haptic data captured every 5 ms while bilateral haptic interaction was carried out by two users with an object in a virtual space. The results showed that the prediction performance of the proposed method had an error of less than 1 ms when predicting position-level data 100 ms ahead.