基于参数化统计模型的雷达HRRP目标识别方法综述

现代战争日趋信息化和智能化,雷达自动目标识别技术(RATR)在国家安全防卫和战略预警等军事应用方面发挥着更加重要的作用。高分辨距离像(HRRP)反映了目标散射点沿雷达视线方向的分布情况,包含了目标丰富的结构信息,对目标识别十分有价值,已成为RATR领域的研究热点。参数化统计建模旨在构建参数化数学模型表征观测数据的分布特性,是估计数据概率分布和挖掘数据隐含信息的重要手段。基于参数化统计模型的雷达HRRP目标识别就是在对HRRP参数化统计建模的基础上,直接利用估计的概率分布进行统计识别或将获取的隐含信息输入分类器进行识别。由于模型具有可融入一定的先验知识、扩展灵活、提供待求参数的不确定性评价以及能结合贝叶斯理论实现自动定阶等优势,基于参数化统计模型的HRRP识别方法整体识别性能优于其他方法,是目前HRRP识别的重点研究方向。该文从浅层和深层参数化统计建模两方面,对近15年的雷达HRRP目标识别方法进行了归纳总结,并分析了各类方法的特点和存在的问题,最后对基于HRRP参数化统计建模的雷达目标识别发展方向进行了展望。      

Industrial applications of soft computing: a review

Fuzzy logic, neural networks, and evolutionary computation are the core methodologies of soft computing (SC). SC is causing a paradigm shift in engineering and science fields since it can solve problems that have not been able to be solved by traditional analytic methods. In addition, SC yields rich knowledge representation, flexible knowledge acquisition, and flexible knowledge processing, which enable intelligent systems to be constructed at low cost. This paper reviews applications of SC in several industrial fields to show the various innovations by TR, HMIQ, and low cost in industries that have been made possible by the use of SC. Our paper intends to remove the gap between theory and practice and attempts to learn how to apply soft computing practically to industrial systems from examples/analogy, reviewing many application papers     

Predictive subset testing: optimizing IC parametric performancetesting for quality, cost, and yield

A formal methodology for IC parametric performance testing, called predictive subset testing, is presented. It is based on a statistical model of parametric process variation. In this Monte-Carlo-based approach, a statistical process simulation is used together with circuit simulation to determine the joint probability distribution of a set of circuit performances. By evaluating the joint probability distribution, rather than assuming the performances to be independent, correlations that exist between them are used to reduce the number of performances that need to be explicitly tested. Once a subset of performances for explicit testing has been identified, regression models are constructed for the untested performances, and from the confidence intervals test limits are assigned for the tested performances. In this manner, the values of the untested performances within desired quality levels are predicted, reducing test complexity and cost     

Variational Bayes inference of spatial mixture models for segmentation

Mixture models are commonly used in the statistical segmentation of images. For example, they can be used for the segmentation of structural medical images into different matter types, or of statistical parametric maps into activating and nonactivating brain regions in functional imaging. Spatial mixture models have been developed to augment histogram information with spatial regularization using Markov random fields (MRFs). In previous work, an approximate model was developed to allow adaptive determination of the parameter controlling the strength of spatial regularization. Inference was performed using Markov Chain Monte Carlo (MCMC) sampling. However, this approach is prohibitively slow for large datasets. In this work, a more efficient inference approach is presented. This combines a variational Bayes approximation with a second-order Taylor expansion of the components of the posterior distribution, which would otherwise be intractable to Variational Bayes. This provides inference on fully adaptive spatial mixture models an order of magnitude faster than MCMC. We examine the behavior of this approach when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging statistical parametric maps.     

基于车辆行为分析的智能车联网关键技术研究

车联网通信系统中通信节点的高移动性、移动行为的复杂性,使得此场景下通信业务呈现数据实时交互性强、空时分布不均、尺度多变、规律复杂的特征,导致传统的车联网网络部署、资源调配难以有效满足用户的差异化服务质量需求。因此,迫切需要设计“车-人-路-云”泛在互联的智能异构车联网网络,通过充分挖掘车辆行为数据的潜在价值,精准预测、刻画车辆行为的空时分布特性,以提升车联网资源利用率、改善车联网服务性能。该文全面梳理了国内外在车辆行为分析、网络部署与接入以及资源优化方面的相关工作,重点阐述了智能车联网关键使能技术,即如何借助先进的人工智能、数据分析技术,探索车联网中车辆行为的空时分布特性,建立车辆行为预测模型,进行智能化网络部署与多网接入、动态资源优化管理,实现高容量、高效率的智能车联网通信。     

Comparison of software-reliability-growth predictions: neuralnetworks vs parametric-recalibration

This paper compares empirically the predictive performance of two different methods of software reliability prediction: `neural networks' and `recalibration for parametric models'. Both methods were claimed to predict as good or better than the conventional parametric models that have been used-with limited results so far. Each method applied its own predictability measure, impeding a direct comparison. To be able to compare, this study uses a common predictability measure and common data-sets. This study reveals that neural networks are not only much simpler to use than the recalibration method, but that they are equal or better trend (variable term) predictors. The neural network prediction is further improved by preparing the data with a running average, instead of the traditionally used averages of grouped data points. Neural network predictions do not depend on prior known models. Off-the-shelf neural network software tools make it easy to apply the method     

Statistical modeling of MOS devices for parametric yield prediction

In the manufacturing of VLSI circuits, engineering designs should take into consideration random variations arising from processing. In this paper, statistical modeling of MOS devices is reviewed, and effective and practical models are developed to predict the performance spread (i.e., parametric yield) of MOS devices and circuits due to the process variations. To illustrate their applications, the models are applied to the 0.25 μm CMOS technology, and measured data are included in support of the model calculations.     

Fourier series based nonminimum phase model for statistical signalprocessing

In this paper, a parametric Fourier series based model (FSBM) for or as an approximation to an arbitrary nonminimum-phase linear time-invariant (LTI) system is proposed for statistical signal processing applications where a model for LTI systems is needed. Based on the FSBM, a (minimum-phase) linear prediction error (LPE) filter for amplitude estimation of the unknown LTI system together with the Cramer-Rao (CR) bounds is presented. Then, an iterative algorithm for obtaining the optimum LPE filter with finite data is presented that is also an approximate maximum-likelihood algorithm when data are Gaussian. Then three iterative algorithms using higher order statistics (HOS) with finite non-Gaussian data are presented to estimate parameters of the FSBM followed by some simulation results as well as some experimental results with real speech data to support the efficacy of the proposed algorithms using the FSBM. Finally, we draw some conclusions     

Big Data Aided Vehicular Network Feature Analysis and Mobility Models Design

Vehicular networks play a pivotal role in intelligent transportation system (ITS) and smart city (SC) construction, especially with the coming of 5G. Mobility models are crucial parts of vehicular network, especially for routing policy evaluation as well as traffic flow management. The big data aided vehicle mobility analysis and design attract researchers a lot with the acceleration of big data technology. Besides, complex network theory reveals the intrinsic temporal and spatial characteristics, considering the dynamic feature of vehicular network. In the following content, a big GPS dataset in Beijing, and its complex features verification are introduced. Some novel vehicle and location collaborative mobility schemes are proposed relying on the GPS dataset. We evaluate their performance in terms of complex features, such as duration distribution, interval time distribution and temporal and spatial characteristics. This paper elaborates upon mobility design and graph analysis of vehicular networks.     

基于云平台的智能家居气象站的研究与设计

针对日益恶化的生活环境,提出家居环境监测站、专业的网络管理平台、云、智能手机及智能家居统一组网的模式,完成室内外家居环境的监测与云管理,进而可远程控制和调整相关智能家居设备.运用MCU(Micro-Control Unit)系统构建室内与室外监测站,利用GPRS(General Packet Radio Service)进行数据通信,微博、微信及手机短信平台实现监测结果与报警显示及相关控制信号的发送;设计了太阳能供电电路,保证了系统稳定运行及降低了布线与运行成本;运用3D(Three Dimensions)打印技术模拟构建了智能家居环境及相关设备模型;运用电机代替和模拟相关执行机构.经过实际模型、网络和控制系统的执行验证表明,该系统模式和模型能够实现家居环境的监测和控制,云、网络与智能手机通信系统的合理设计使得操作非常简单和便捷.     

Parametric manufacturing yield modeling of GaAs/AlGaAs multiplequantum well avalanche photodiodes

GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiodes (APD's) are of interest as an ultra-low noise image capture mechanism for high-definition systems. Since literally millions of these devices must be fabricated for imaging arrays, it is critical to evaluate potential performance variations of individual devices in light of the realities of semiconductor manufacturing. Specifically, even in a defect-free manufacturing environment, random variations in the fabrication process will lead to varying levels of device performance, Accurate device performance prediction requires precise characterization of these variations. This paper presents a systematic methodology for modeling the parametric performance of GaAs MQW APD's. The approach described requires a model of the probability distribution of each of the relevant process variables, as well as a second model to account for the correlation between this measured process data and device performance metrics. The availability of these models enables the computation of the joint probability density function required for predicting performance using the Jacobian transformation method. The resulting density function can then be numerically integrated to determine parametric yield. Since they have demonstrated the capability of highly accurate function approximation and mapping of complex, nonlinear data sets, neural networks are proposed as the preferred tool for generating the models described above. In applying this methodology to MQW APD's, it is shown that using a small number of test devices with varying active diameters, barrier and well widths, and doping concentrations enables prediction of the expected performance variation of APD gain and noise in larger populations of devices. This approach compares favorably with Monte Carlo techniques and allows device yield prediction prior to high volume manufacturing in order to evaluate the impact of both design decisions and process capability     

Generalized linear models in software reliability: parametric andsemi-parametric approaches

The penalized likelihood method is used for a new semi-parametric software reliability model. This new model is a nonparametric generalization of all parametric models where the failure intensity function depends only on the number of observed failures, viz. number-of-failures models (NF). Experimental results show that the semi-parametric model generally fits better and has better 1-step predictive quality than parametric NF. Using generalized linear models, this paper presents new parametric models (polynomial models) that have performances (deviance and predictive-qualities) approaching those of the semi-parametric model. Graphical and statistical techniques are used to choose the appropriate polynomial model for each data-set. The polynomial models are a very good compromise between the nonvalidity of the simple assumptions of classical NF, and the complexity of use and interpretation of the semi-parametric model. The latter represents a reference model that we approach by choosing adequate link and regression functions for the polynomial models     

基于宽度学习的注塑产品质量预测方法

在注塑成型工业中,产品质量自动监测一直是注塑工业智能化发展的核心问题。高品质和大规模的产品质量数据采集成本高昂,导致数据样本量少、不同类别样本数据不平衡,为注塑产品质量预测提出了更高的挑战。为此,该文提出一种基于宽度学习方法的注塑产品质量预测模型,以产品的3维尺寸为预测目标,在普通的宽度学习系统(BLS)中加入最小p范数来改进得到模型p范数宽度学习系统(pN-BLS),解决小样本和不平衡数据的问题,提高模型对离群点的检测性能。在第4届工业大数据竞赛任务2《注塑成型工艺的虚拟量测和调机优化》数据集中,将192个参数特征与预测目标进行相关分析,提取相关性高的基础特征17个,衍生特征4个和调机参数2个作为模型的输入。将16600条数据平均分为训练集和测试集各8300条,与支持向量机 (SVM)、最近邻算法 (KNN)、多层感知机 (MLP)和BLS进行对比实验,实验结果显示pN-BLS具有更快速和更准确的预测效果。在实际缺陷检测应用中,pN-BLS能更准确地预测异常数据,具有更高的鲁棒性。     

Spectral properties of signal fading and Doppler spectra distribution in urban mobile communication links

In this work, we continue the analysis of a probabilistic approach and the corresponding stochastic multi‐parametric model of wave propagation, in built‐up areas with randomly distributed buildings. We have concentrated on the spectral properties of signal strength spatial variations and on Doppler spread spectrum distribution of signal power. The analysis is based on a unified stochastic approach of radio wave propagation above the built‐up terrain with applications to mobile communications. We analyze the signal power spectrum of spatial frequencies and the signal power distribution in the Doppler domain for moving vehicles, taking into account a Doppler shift proportional to the vehicle antenna speed relative to the base station. The comparison between the theoretical prediction and experimental data was motivated by the proposed stochastic model and other existing statistical models to verify the signal power distribution in the Doppler domain for various urban environments and terminal heights with respect to building rooftops. New effects of terrain features on signal spectrum are obtained, examined and compared with existing models. Copyright © 2006 John Wiley & Sons, Ltd.     

Statistical inference on stationary random fields

Statistical methods are developed to model random processes on multidimensional Euclidean space from observed data. Statistical inference techniques are used to estimate model parameters and test hypotheses concerning stationarity, isotropy, and number of parameters. Algorithms are described for fitting parametric models and testing between alternative model structures. Stochastic partial difference equation models of multidimensional processes are discussed in detail. Computer generated data from a known model are used to directly demonstrate the statistical procedures.     

Analysis of electrical parameters of InGaN-based LED packages with aging

As the light-emitting diode (LED) becomes a mature technology in the general illumination space, there is a tendency to operate LEDs at high current densities and temperatures in order to gain higher light output at lower cost. Further, there is interest among intelligent-lighting platform developers to offer predictive maintenance capabilities to users. The existing useful life prediction model defines LED lifetime based on parametric failure; however, there is a need for a useful life prediction model based on catastrophic failure, which can occur with the degradation of components in an LED package. Electrical parameters, especially package series resistance, are good indicators of LED package health (i.e., remaining useful life) and could potentially be sensed real-time in an application. In this study, the series resistance variation pattern until catastrophic failure was measured at different current and temperature stress conditions. The degradation mechanisms at each phase of variation were explained and, using available models, activation energies and exponents were extracted. The experimental data suggest electromigration-induced metal migration from the contact metallization layer to the semiconductor is the cause of short circuit catastrophic failure of LED packages. The variation patterns of ideality factor and reverse leakage current support this hypothesis. The information presented can be used to develop a catastrophic life estimation model for LED packages under current and temperature stress.     

Reliability modeling on a MOSFET power package based on embedded die technology

Embedding of discrete semiconductors into substrates has the advantages of achieving high degree of miniaturization, good electrical performance and possible low cost. A MOSFET power package based on the embedded die technology was developed and the demonstrators were built. To reduce cost and time-to-market, thermo-mechanical virtual prototyping is applied to support the package development. 2D and 3D parametric FE models were established to conduct numerical simulations to investigate the thermo-mechanical reliability performance under packaging processes and test conditions. The package design and material variations, such as the thicknesses of the Cu layer and the resin in the RCC foil, the Bond Line Thickness (BLT), the thickness and material properties of prepreg, via dimensions and via-filling, were included in the parametric models. The root cause for die cracking, delamination between the interface die/RCC foil, and cracking of Cu vias were analyzed based on the simulation results. Verification of the modeling results was conducted through comparison with the test results. The results indicate that the prediction from the FE modeling matches reasonably well with the test results.     

遗传算法优化支持向量机的交通流量预测

交通流量预测是智能交通系统中的关键技术,针对当前交通流量预测模型存在不足,提出一种遗传算法优化支持向量机的交通流量预测模型。首先收集交通流量历史数据,并基于混沌理想对其进行相空间重构,然后将训练样本输入到支持向量机中进行学习,并采用遗传算法优化支持向量机参数,建立交通流量预测模型,最后采用测试样本对模型的性能进行测试。结果表明,相对于经典交通流量预测模型,本文模型可以更加准确描述交通流量预测复杂的变化趋势,提高了交通流量的单步和多步预测精度。     

Symbolic Macromodeling for Statistical Simulation of Operational Amplifiers

Symbolic circuit simulator is traditionally applied to the small-signal analysis of analog circuits. This paper establishes a symbolic behavioral macro-modeling method applicable to both small-signal and large-signal analysis of general two-stage operational amplifiers (op-amps). The proposed method creates a two-pole parametric macromodel whose parameters are analytical functions of the circuit element parameters generated by a symbolic circuit simulator. A moment matching technique is used in deriving the analytical model parameter. The created parametric behavioral model can be used for op-amps performance simulation in both frequency and time domains. In particular, the parametric models are highly suited for fast statistical simulation of op-amps in the time-domain. Experiment results show that the statistical distributions of the op-amp slew and settling time characterized by the proposed model agree well with the transistor-level results in addition to achieving significant speedup.     

Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data

Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segment using models of just the histogram of intensity values. Spatial mixture models have also been developed which augment this histogram information with spatial regularization using Markov random fields. However, these techniques have control parameters, such as the strength of spatial regularization, which need to be tuned heuristically to particular datasets. We present a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields. This means that the amount of spatial regularization does not have to be tuned heuristically but is adaptively determined from the data. We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.