一种新型GM(1,1)-AR预测模型在陀螺漂移中的应用

为了对陀螺漂移趋势进行更有效的预测,提出一种基于小波分析的新型GM(1,1)-AR时间序列预测算法。该算法通过运用小波分解算法将陀螺漂移数据中的趋势项和随机项进行分离,然后分别运用GM(1,1)模型和AR时间序列预测模型对趋势项和随机项进行预测,最后用小波重构算法得出最终的预测值。给出了一种算法及具体步骤,最后用某型导弹陀螺漂移数据进行仿真实验,以检验这种算法的有效性和可行性,结果表明这种预测算法应用于陀螺漂移趋势预测是可行的。     

Assessing the Value of the Threshold Parameter in the Weibull Distribution Using Bayes Paradigm

The Weibull distribution represents a wide variety of situations. Usually, the distribution is considered as a two-parameter family with a scale, and a shape parameter. If, however, the given data reflect additional information in the form of a minimum guarantee, a positive value away from zero, it is better to go for a three-parameter model with the additional parameter known as the threshold. The threshold parameter is often very important, but increases the complexity of the model. Arbitrarily going for the three-parameter form is not advisable unless it is really required by the data. This article attempts to make a simulation-based Bayesian study for checking if the threshold parameter can be taken to be zero or positive in situations representing the two models. We study the compatibility of the models for the given data set. We conduct the posterior simulation in each case using Gibbs sampling.      

Blind Receiver Design for OFDM Systems Over Doubly Selective Channels

We develop blind data detectors for orthogonal frequency-division multiplexing (OFDM) systems over doubly selective channels by exploiting both frequency-domain and time-domain correlations of the received signal. We thus derive two blind data detectors: a time-domain data detector and a frequency-domain data detector. We also contribute a reduced complexity, suboptimal version of a time-domain data detector that performs robustly when the normalized Doppler rate is less than 3%. Our frequency-domain data detector and suboptimal time-domain data detector both result in integer least-squares (LS) problems. We propose the use of the V-BLAST detector and the sphere decoder. The time-domain data detector is not limited to the Doppler rates less than 3%, but cannot be posed as an integer LS problem. Our solution is to develop an iterative algorithm that starts from the suboptimal time-domain data detector output. We also propose channel estimation and prediction algorithms using a polynomial expansion model, and these estimators work with data detectors (decision-directed mode) to reduce the complexity. The estimators for the channel statistics and the noise variance are derived using the likelihood function for the data. Our blind data detectors are fairly robust against the parameter mismatch     

Remaining storage life prediction for an electromagnetic relay by a particle filtering-based method

In this paper, we propose a particle filtering-based method for predicting the remaining storage life (RSL) of electromagnetic relays. The RSL prediction problem here addressed has the following three distinctive features: i) limited measurement data available; ii) incomplete run-to-failure data; and iii) no model available for the physical degradation process. Then, to develop the method for RSL prediction, storage testing and degradation mechanism analysis have been carried out to obtain the knowledge and information needed to develop the physical model that supports the RSL prediction procedure. We discuss the three main steps of the proposed prediction method: parameter estimation, model validation and RSL prediction. Data from nine relays are used for estimating the initial parameter values distribution and data from one relay are used for RSL prediction. The RSL prediction results are compared with those obtained by a nonlinear curve-fitting method and a basic particle filtering algorithm. The comparison shows that the proposed method is more effective in predicting the RSL than the other methods.     

基于多维时空的NPCA-PSR-IGM(1,1)组合模型的短时交通流预测

针对城市短时交通流序列非线性和混沌性的特点,为提高短时交通流的预测精度,该文提出一种基于多维时空的非线性主成分分析(NPCA)和相空间重构(PSR)的改进灰色(IGM(1,1))组合预测模型。首先,使用数据相关性的非线性主成分分析算法对多维交通流量序列进行时空降维,同时保留影响预测点的主要交通流量数据,从而提高建模的精确度;其次,利用多维时空交通流量序列相空间重构放大交通流量内部的细微特征,以使其内在规律得以充分展现,进一步提升预测精度;最后,结合背景值改进的灰色模型适应于线性、非线性以及所需数据少的特点,进行短时交通流预测。实验结果表明,NPCA-PSR-IGM(1,1)组合预测模型的平均相对误差相比NPCA-PSR-GM(1,1)组合预测模型减小3.12%,其标准偏差相对PCA-PSR-IGM(1,1)组合预测模型从15.7091下降到2.0589。同时与最新的预测模型相比,该组合预测模型也提高了预测精度,达到了较好的预测效果。     

基于多维时空的NPCA-PSR-IGM(1,1)组合模型的短时交通流预测

针对城市短时交通流序列非线性和混沌性的特点,为提高短时交通流的预测精度,该文提出一种基于多维时空的非线性主成分分析(NPCA)和相空间重构(PSR)的改进灰色(IGM(1,1))组合预测模型。首先,使用数据相关性的非线性主成分分析算法对多维交通流量序列进行时空降维,同时保留影响预测点的主要交通流量数据,从而提高建模的精确度;其次,利用多维时空交通流量序列相空间重构放大交通流量内部的细微特征,以使其内在规律得以充分展现,进一步提升预测精度;最后,结合背景值改进的灰色模型适应于线性、非线性以及所需数据少的特点,进行短时交通流预测。实验结果表明,NPCA-PSR-IGM(1,1)组合预测模型的平均相对误差相比NPCA-PSR-GM(1,1)组合预测模型减小3.12%,其标准偏差相对PCA-PSR-IGM(1,1)组合预测模型从15.7091下降到2.0589。同时与最新的预测模型相比,该组合预测模型也提高了预测精度,达到了较好的预测效果。     

VDMOSFET HEF degradation modelling considering turn-around phenomenon

Gate oxide failure of power VDMOSFET has been researched for a long time. For BTI parameter degradation, some models are proposed. However, the degradation modelling of HEF still have challenges, one of which is the turn-around phenomenon. Due to the existence of the turn-around point, the threshold voltage degradation model under HEF cannot be described using classical models. Aiming at this problem, the experimental study and the argument are proposed in this paper. First, the theoretical model assumption is discussed based on the degradation mechanism. Second, the HEF stress experiments are carried out to acquire experimental data. Then the model fitting is processed. A three-phase model is proposed to describe threshold voltage degradation under HEF stress.     

From destination prediction to route prediction

A prediction of a vehicle's route would be useful for giving the driver advance warnings and alerts about upcoming situations and opportunities. This paper presents a new algorithm for predicting a driver's route based on a probabilistic prediction of the driver's destination. For each candidate destination, our route prediction algorithm plans a route to that destination. Roads on these routes accumulate the probabilities of their respective destinations, giving higher probabilities to roads along the way to higher probability destinations. The algorithm is based on a single parameter that characterises how efficiently a driver drives. Once this parameter is computed, it does not require storing a history of trips, and it works in places a driver has never visited. We test the algorithm on 100 routes recorded with the Global Positioning System (GPS) and show that the route prediction quickly narrows down the future route to a small fraction of the road network. We also compare our algorithm to two other algorithms to show how it performs better.     

A smart handover prediction system based on curve fitting model for Fast Mobile IPv6 in wireless networks

Seamless handover process is essential in order to provide efficient communication between mobile nodes in wireless local area networks. Despite the importance of a signal strength prediction model to provide seamless handovers, it is not embedded in standard mobility management protocols. In this article, we propose a smart handover prediction system based on curve fitting model to perform the handover (CHP) algorithm. The received signal strength indicator parameter, from scanning phase, is considered as an input to the CHP in order to provide a prediction technique for a mobile node to estimate the received signal strength value for the access points in the neighborhood and to select the best candidate access point from them in an intelligent way. We implemented the proposed approach and compared it with standard protocols and linear regression‐based handover prediction approach. Simulation results in complex wireless environments show that our CHP approach performs the best by predicting the received signal strength value with up to 800 ms in advance from real obtained value via scanning phase. Moreover, our CHP approach is the best in terms of layer 2 and overall handover latency, in comparison with standard protocols and linear regression approach, respectively. Copyright © 2012 John Wiley & Sons, Ltd.     

Pilot-symbol-assisted LDPC coded BICM over correlated Rayleigh fading channels

Pilot-symbol-assisted low-density parity-check (LDPC)-coded bit-interleaved coded modulation (BICM) is analyzed using the density evolution (DE) and the extrinsic information-transfer (EXIT) chart for correlated Rayleigh fading channels. The key parameter (the power correlation coefficient) is identified, and the threshold degradation is quantified. The optimal tradeoff of energy allocation between pilots and coded symbols is found to be sensitive to the normalized Doppler spread of the channel, the interpolation filter, the modulation scheme, and the pilot selection. In addition, a simple upper bound on the performance of any receiver that performs joint iterative decoding and channel estimation is derived. Extension to irregular code design is also discussed.