一种基于改进 Surendra背景更新算法的运动目标检测

建立背景模型时,采用动态阈值的方法,以减少噪声和光照强度变化等外界环境的影响;在运动目标检测中,针对传统方法检测到的运动目标偏大的问题,提出一种改进的三帧差分检测方法.在形态学处理过程中,采用自适应方法选择结构元素的大小,对二值图像进行去噪处理,以得到平滑图像.实验证明,改进后的方法与传统背景模型建立方法相比,能更准确地检测到运动目标区域,并实时更新背景模型     

实时视频检测系统中背景更新及复杂目标定位算法

本文提出了针对固定背景帧差法的背景更新算法和多目标定位算法。在背景更新中，通过建立目标、噪声矩阵来保存图像的特征信息，利用分类的思想优化更新策略，基于像素对背景进行更新；在目标定位中，提出了基于目标对角线的连通判断模板，该模板不断生长进而检测目标的中心点和体积，可实现实时场景中多目标和形态差异目标的定位：户外实验证明了算法的健壮性和鲁棒性，可以实现背景更新和复杂目标识别．     

面向场景变化的动态自适应同时定位与地图构建

在动态变化的复杂环境中,如何使同时定位与地图构建(SLAM)系统根据场景变化持续可靠地进行定位和地图构建,成为其能否走向实际应用的关键所在,也是目前SLAM研究领域中重点研究和待解决的问题之一.针对这一问题,本文提出了一个可扩展的、面向场景变化的同时定位与地图构建框架—SceneSLAM,支持场景检测和SLAM算法的有效复用和自动化调度;根据当前场景检测结果,动态自适应地调用依赖不同传感器数据的SLAM算法,从而提高SLAM系统适应环境变化和持续稳定工作的能力;在SceneSLAM框架基础之上,提出了能够有效检测室内、室外、黑暗场景的场景检测模型,以及应对光线变化的室内、室外场景的动态自适应SLAM模型;在模型切换时自动地进行坐标转换和尺度转换,从而获得具备全局一致性和尺度一致性的定位与地图构建结果.最后,基于ROS平台实现了一个动态适应场景变化的SLAM原型系统,实验中利用Turtlebot机器人,在室内、室外、黑暗场景中进行实验,验证了本文所提出方法的有效性.     

自动目标检测的形态学神经网络与模拟退火学习算法

提出了一种具有实用意义的形态滤波神经网络模型及其网络参数的模拟退火优化算法. 通过分析指出, 形态滤波网络的优化设计过程实际上就是网络参数(结构元素)不断调整、逐步适应图像环境的优化学习过程, 从而将目标客体的特征规律反映到网络结构上来, 赋予结构元素特定的知识, 使形态滤波过程融入特有的智能, 以实现对复杂变化的图像具有良好的滤波性能和稳健的适应能力. 为结合运动图像目标的检测需要, 采用了渐近收缩误差、适时校正网络权值的动态跟踪学习算法. 通过实验结果可以看出, 该算法不仅能适应复杂多样的背景环境, 而且对运动目标的持续检测能力具有位移不变、伸缩不变和旋转不变的特性.     

解决非可微凸优化问题的次梯度反馈神经网络

发展了Leung等人所提出的解决非线性凸规划问题的动态反馈神经网络模型, 引入基于次梯度动态反馈神经网络模型解决非可微凸优化问题. 对于无约束非可微凸优化问题, 假定目标函数是强迫性的凸函数, 证明了由投影次梯度构造的反馈神经网络轨道从任意初值点出发都收敛于一个渐近稳定的平衡点, 该平衡点为原无约束问题的最优解. 对于约束非可微凸优化问题, 在目标函数是强迫性的凸函数, 约束函数也具有凸性的假定下, 依次造构能量函数序列和相应的基于次梯度的动态反馈子网络的模型, 建立了收敛定理并给出了停时条件. 最后, 设计了两种有效的算法并结合一些实例进行了仿真验证.     

交叉敏感情况下多传感器系统的动态特性研究

讨论了具有交叉敏感的传感器组成的多传感器系统的动态特性及其对系统检测精度的影响. 提出了将该多传感器系统看作一个线性滤波器与一个无记忆非线性函数的串联系统, 即Wiener系统, 把后续的信息融合系统看作一个无记忆非线性函数与一个线性滤波器的串连, 即Hammerstein系统. 在静态标定的基础上, 用盲解卷积技术求得Hammerstein的线性滤波器系数矩阵. 针对盲解卷积技术复现信号幅值不确定性的缺点, 提出了一种根据逆滤波器系数矩阵与静态标定的线性矩阵的同一性校正滤波器系数矩阵的方法, 以获得多传感器系统的近似逆向动态模型, 提高了多传感器系统在实际检测过程中的检测精度, 降低了动态检测结果失真度, 满足了实际情况的需要. 最后的仿真结果表明, 输入信号频率接近采样频率1/10时, 用盲解卷积技术获得的幅值复现误差是未经过动态补偿的幅值复现误差的1/20, 是各传感器单独动态补偿后幅值复现误差的1/2, 快速性提高了2倍. 金属氧化物半导体甲烷传感器的动态补偿结果表明, 在温度阶跃响应下, 补偿后的甲烷检测误差小于补偿前的1/2, 因此这种方法拓宽了多传感器系统的带宽.     

基于服务质量的导游多期动态声誉机制研究

由于旅行社与导游之间的信息不对称,旅行社作为委托人将承受导游提供低服务质量的道德风险。在旅游服务供应链中旅行社和导游的多期合作背景下,建立了导游多周期动态声誉机制模型,分析了该模型对导游提供高服务质量的隐性激励作用,并揭示了导游声誉机制模型演化机理,最后对结论进行了数值仿真和验证分析。结果表明导游多期动态声誉机制对导游服务质量能产生有效的激励作用,同时能缓解导游的道德风险。     

倾转旋翼机在转换飞行时的旋翼尾迹弯曲非定常动态入流模型

倾转旋翼机在旋翼倾转过程（即转换飞行）中的气动力、动力学响应及气弹耦合问题异常复杂. 传统的准定常假设模型已经不能反映旋翼倾转过程中的非定常气动问题, 基于涡流理论的CFD方法虽然能够得到较好的结果, 但需要耗费大量的计算资源. 本文以直升机的Peters-He旋翼动态入流模型为基础, 首先建立了倾转旋翼机的旋翼尾迹弯曲动态入流模型, 然后结合ONERA非定常气动力模型, 建立了一个倾转旋翼机在旋翼倾转过程中的旋翼尾迹弯曲非定常动态入流模型, 并用孤立旋翼大机动上仰飞行的实验数据验证了本模型的正确性. 采用本文建立的倾转旋翼动态入流模型计算了一个倾转旋翼机模型在旋翼倾转过程中旋翼的动力学响应, 响应规律符合物理解释.     

三维、动态、实时数字化锅炉技术发展探讨

锅炉作为发电厂的一个庞然大物,是一个典型的三维空间分布系统;而燃烧和热力过程又是一个处于外界需求不断变化、各种扰动持续影响的动态系统,对其信息化、智能化技术的研发应建立三维、动态、实时数字化的框架思路.三维设计技术目前仅仅用于实现火电厂高效的设计和技术移交,其建立的电站设备和系统的三维数据库可作为三维、动态、实时火电机组技术研发平台.炉内燃烧过程三维数值模拟研究已经发展到相当的程度,一方面数值模拟需要研发更加精细的模型以更加准确地反映炉内实际燃烧工况,另一方面数值模拟分析技术有待嵌入到发电机组实际运行分析和优化中.基于辐射图像处理的炉内三维燃烧过程可视化检测技术正在从炉内三维温度场在线检测向炉内煤粉燃烧过程(煤粉浓度、燃尽度和燃烧放热量空间分布)三维全面可视化检测的阶段过渡,为三维、动态、实时数字化锅炉技术发展奠定了良好的基础.炉内三维燃烧过程可视化检测技术将为分析、诊断炉内燃烧过程故障和燃烧经济性,以及锅炉受热面安全运行状况提供具有空间分辨能力的精细三维信息,是锅炉信息化、智能化发展的一个重要方向.     

室内目标物方向发射率的被动测量方法

１９９１年Ｌａｂｅｄ等人提出了一个室内目标物方向发射率的被动测量公式．这个公式是以黑体背景辐射及目标温度（Ｔｔ）与背景目标（Ｔｂ）不同为前提条件,Ｔｔ与Ｔｂ的差越大,则越有利于提高测量精度．然而现实世界中只有近似黑体,尤其当Ｔｔ≠Ｔｂ时,“黑体背景”的要求就很难满足．在放弃“黑体背景”的前提条件下,引入有效发射率概念,重新推导了目标物方向发射率的被动测量公式,并为 Ｍｏｎｔｅ Ｃａｒｌｏ模拟和实测数据所证实．对 Ｌａｂｅｄ等人所提供的实验结果重新予以解释,阐明了造成比,随θ增加而减少得更快的内在原因．     

Detection of regime switches between stationary and nonstationary processes and economic forecasting

It often occurs that no model may be exactly right, and that different portions of the data may favour different models. The purpose of this paper is to propose a new procedure for the detection of regime switches between stationary and nonstationary processes in economic time series and to show its usefulness in economic forecasting. In the proposed procedure, time series observations are divided into several segments, and a stationary or nonstationary autoregressive model is fitted to each segment. The goodness of fit of the global model composed of these local models is evaluated using the corresponding information criterion, and the division which minimizes the information criterion defines the best model. Simulation and forecasting results show the efficacy and limitations of the proposed procedure. Copyright © 2005 John Wiley & Sons, Ltd.     

Identifying the time‐effect factors of multiple time series

The Peña–Box model is considered for finding the time‐effect factors of a multiple time series. This paper first establishes the connection between the Peña–Box model and the vector ARMA model. According to the Peña–Box model, some series can be ignored while modelling the vector ARMA model. A consistent estimator is then proposed to identify the model for nonlinear and nonstationary time series. Finally, the finite‐sample behaviour of the estimator is illustrated via simulations. Copyright © 2005 John Wiley & Sons, Ltd.     

Dynamic harmonic regression

This paper describes in detail a flexible approach to nonstationary time series analysis based on a Dynamic Harmonic Regression (DHR) model of the Unobserved Components (UC) type, formulated within a stochastic state space setting. The model is particularly useful for adaptive seasonal adjustment, signal extraction and interpolation over gaps, as well as forecasting or backcasting. The Kalman Filter and Fixed Interval Smoothing algorithms are exploited for estimating the various components, with the Noise Variance Ratio and other hyperparameters in the stochastic state space model estimated by a novel optimization method in the frequency domain. Unlike other approaches of this general type, which normally exploit Maximum Likelihood methods, this optimization procedure is based on a cost function defined in terms of the difference between the logarithmic pseudo‐spectrum of the DHR model and the logarithmic autoregressive spectrum of the time series. The cost function not only seems to yield improved convergence characteristics when compared with the alternative ML cost function, but it also has much reduced numerical requirements. Copyright © 1999 John Wiley & Sons, Ltd.     

A class of periodic trend models for seasonal time series

Trend and seasonality are the most prominent features of economic time series that are observed at the subannual frequency. Modeling these components serves a variety of analytical purposes, including seasonal adjustment and forecasting. In this paper we introduce unobserved components models for which both the trend and seasonal components arise from systematically sampling a multivariate transition equation, according to which each season evolves as a random walk with a drift. By modeling the disturbance covariance matrix we can encompass traditional models for seasonal time series, like the basic structural model, and can formulate more elaborate ones, dealing with season specific features, such as seasonal heterogeneity and correlation, along with the different role of the nonstationary cycles defined at the fundamental and the harmonic frequencies in determining the shape of the seasonal pattern.     

Computing the variance of the forecast error for the holt-winters seasonal models

This paper presents expressions for the variance of the forecast error for arbitrary lead times for both the additive and multiplicative Holt-Winters seasonal forecasting models. It is shown that even when the smoothing constants are chosen to have values between zero and one, when the period is greater than four, the variance may not be finite for some values of the smoothing constants. In addition, the regions where the variance becomes infinite are almost the same for both models. These results are of importance for practitioners, who may choose values for the smoothing constants arbitrarily, or by searching on the unit cube for values which minimize the sum of the squared errors when fitting the model to a data set. It is also shown that the variance of the forecast error for the multiplicative model is nonstationary and periodic.     

Forecasting the FOMC's interest rate setting behavior: a further analysis

We develop a model to forecast the Federal Open Market Committee's (FOMC's) interest rate setting behavior in a nonstationary discrete choice model framework by Hu and Phillips (2004). We find that if the model selection criterion is strictly empirical, correcting for nonstationarity is extremely important, whereas it may not be an issue if one has an a priori model. Evaluating an array of models in terms of their out‐of‐sample forecasting ability, we find that those favored by the in‐sample criteria perform worst, while theory‐based models perform best. We find the best model for forecasting the FOMC's behavior is a forward‐looking Taylor rule model. Copyright © 2008 John Wiley & Sons, Ltd.     

A Bayesian time-varying autoregressive model for improved short-term and long-term prediction

Motivated by the application to German interest rates, we propose a time-varying autoregressive model for short-term and long-term prediction of time series that exhibit a temporary nonstationary behavior but are assumed to mean revert in the long run. We use a Bayesian formulation to incorporate prior assumptions on the mean reverting process in the model and thereby regularize predictions in the far future. We use MCMC-based inference by deriving relevant full conditional distributions and employ a Metropolis-Hastings within Gibbs sampler approach to sample from the posterior (predictive) distribution. In combining data-driven short-term predictions with long-term distribution assumptions our model is competitive to the existing methods in the short horizon while yielding reasonable predictions in the long run. We apply our model to interest rate data and contrast the forecasting performance to that of a 2-Additive-Factor Gaussian model as well as to the predictions of a dynamic Nelson-Siegel model.     

Optimal prediction with nonstationary ARFIMA model

We propose two methods to predict nonstationary long‐memory time series. In the first one we estimate the long‐range dependent parameter d by using tapered data; we then take the nonstationary fractional filter to obtain stationary and short‐memory time series. In the second method, we take successive differences to obtain a stationary but possibly long‐memory time series. For the two methods the forecasts are based on those obtained from the stationary components. Copyright © 2007 John Wiley & Sons, Ltd.     

基于IRS—P6AWIFS遥感影像和GIS的湖北省生态环境评价

以IRS-P6 AWIFS遥感影像为主要数据源,利用监督分类方法进行土地利用/覆被分类,在GIS技术支持下,通过空间分析提取出生物丰度指数、植被覆盖指数、水网密度指数、土地退化指数、环境质量指数等评价信息.依据国家环境监测总站2006年颁布的〈生态环境状况评价技术规范〉（HJ/192-2006）建立生态环境质量评价模型,通过模型的空间叠加进行湖北省生态环境状况评价. 结果 表明湖北省各地市生态环境状况总体较好,这与湖北各地的自然条件、气候条件、资源开发与利用等因素具有较好的相关性,表明评价模型是科学客观的.本文探索了IRS-P6 AWIFS数据和GIS技术相结合的生态环境状况评价方法,对区域生态环境的保护和改善具有积极意义.     

Prediction in a Generalized Spatial Panel Data Model with Serial Correlation

This paper considers the generalized spatial panel data model with serial correlation proposed by Lee and Yu (Spatial panels: random components versus fixed effects. International Economic Review 2012; 53 : 1369–1412.), which encompasses many of the spatial panel data models considered in the literature, and derives the best linear unbiased predictor (BLUP) for that model. This in turn provides valuable BLUP for several spatial panel models as Special Cases. Copyright © 2016 John Wiley & Sons, Ltd.