H.264中树状结构运动估计的连续消除算法

为减少视频编码标准 H.2 6 4中树状结构运动估计的高运算复杂度 ,将连续消除算法 (SEA )应用于树状结构的运动估计中。在性能完全相同的条件下 ,与 H.2 6 4整像素运动矢量全搜索算法相比 ,其块匹配运算量减少到原来的 1%～ 2 0 %。利用不同大小块之间因部分重叠 ,其运动矢量具有更强相关性的特点。提出了一种简单有效地确定运动矢量搜索初值的方法 ,并应用两种快速的目标运动矢量判定方法 ,改进了 SEA算法。在性能损失可以忽略的情况下 ,进一步将基本 SEA运算量降低到全搜索块匹配运算量的 0 .3%～4 %。     

基于超螺旋精度提升机制的OFDM网络信道噪声消除算法

为解决当前OFDM网络信道噪声消除算法难以规避信道中的窄带莱斯噪声,且抗信道衰落能力不强,以及信道间普遍存在严重的频率干涉问题,提出了一种基于超螺旋精度提升机制的OFDM网络信道噪声消除算法.首先,根据各个传输子信道具有差异化的频率载波特性,将传输子信道等价为接收背景中的接收点,从而构建了超螺旋接收结构,有效消除不同传输子信道间存在的频率干扰特性,降低信道窄带莱斯噪声对信号预发射过程的衰落影响;随后,基于角度识别方式进行锐-钝识别,并结合拐点思想,采用序列重排技术,将待发射信号进行消波处理,并进行自旋精度提升,从而提高了其抗窄带莱斯噪声的性能.仿真实验表明:与常见的超高频精度自适应机制(UHF adaptive,UHFA)、共线度自旋精度提升机制(the degree of accuracy of collinear spin,DACS)相比,本文算法与理想状态下的OFDM功率谱密度曲线最为接近,具有更高的信号增益水平与更低的信道误码率.     

回采过断层采用深孔爆破技术的研究

介绍了3807回采工作面的地质构造情况,论述了采用深孔爆破技术解决回采工作面过断层的工艺,指出该工艺技术对于生产条件较差、岩石较多的回采工作面提高单产具有很好的借鉴作用。     

具有非线性恢复力和外力激励的Duffing-Van der Pol系统的谐波解

应用动力系统的分支理论和二阶平均方法,研究带非线性恢复力和外力激励的Duffing-Van der Pol系统随系统参数变化的谐波解分支,得到了谐波解和其分支存在的条件.     

小城镇住宅排水系统存在问题调查分析

随着社会的发展,国民对居住环境质量的要求越来越高,对建筑排水系统中排水噪声大、排水器具返臭气、漏水、排水不畅等诸多问题的反映也越来越强烈。根据对台州市椒江区调查的住宅排水系统实态资料,对国内小城镇住宅排水系统存在的主要问题进行了统计分析,并提出相关建议。     

石圪台煤矿71203工作面过断层分析

介绍了71203工作面断层情况及前期准备工作,阐述了综采过断层的工艺,指出在断层部分进行回采卧底留顶快速直推法通过断层,可取得很好的经济效益.     

痕量PBBs/PBDEsGC/MS分析法中PCB及其衍生物干扰消除方法

电子电气产品中痕量阻燃剂聚溴联苯/聚溴联苯醚(PBBs/PBDEs)的检测通常采用气相色谱/质谱(GC-MS)联用法,但该法是建立在持久性污染物多氯化苯/多氯化萘(PCBs/PCNs)基础上,因此此类物质干扰不可避免。为了解决此类问题,提出了2种解决方法:1)阴离子化学电离-气相色谱/质谱联用法(GC/MS-NICI)和电子轰击-气相色谱/质谱联用法(GC/MS-EI)联合使用;2)以YMC ODS-C18为载体,基质固相分散法(MSPD)吸附分离。结果显示,前者可以同时洞察聚溴联苯和联苯醚的分子链断裂后阴离子碎片[Br]-,[HBr2]-和分子链段碎片[M+2]+,[M+4]+,[M+6]+,[M+8]+的滞留峰,增加了对溴系阻燃剂的选择性。后者可以有效分离多种多氯化苯与聚溴联苯醚的色谱峰,这为溴系阻燃剂气相色谱/质谱联用分析法中多氯化苯及其衍生物干扰提供了新的解决方法。采用此方法对8类电子电气产品中10种聚溴联苯醚和多氯化苯进行加标回收实验及精密度分析,回收率在60%～98%,相对误差在9.5%以内,符合国际电工委员会(IEC)对溴系阻燃剂检测精度要求,说明方法具有较高的可靠性,也为我国电子电气业更好遵守...     

Dynamic Latent Class Model Averaging for Online Prediction

We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called dynamic latent class model averaging, which combines a state‐space model for the parameters of each of the candidate models of the system with a Markov chain model for the best model. We propose a polychotomous regression model for the transition weights to assume that the probability of a change in time depends on the past through the values of the most recent time periods and spatial correlation among the regions. The evolution of the parameters in each submodel is defined by exponential forgetting. This structure allows the ‘correct’ model to vary over both time and regions. In contrast to existing methods, the proposed model naturally incorporates clustering and prediction analysis in a single unified framework. We develop an efficient Gibbs algorithm for computation, and we demonstrate the value of our framework on simulated experiments and on a real‐world problem: forecasting IBM's corporate revenue. Copyright © 2014 John Wiley & Sons, Ltd.     

Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.     

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Four methods of model selection—equally weighted forecasts, Bayesian model‐averaged forecasts, and two models produced by the machine‐learning algorithm boosting—are applied to the problem of predicting business cycle turning points with a set of common macroeconomic variables. The methods address a fundamental problem faced by forecasters: the most useful model is simple but makes use of all relevant indicators. The results indicate that successful models of recession condition on different economic indicators at different forecast horizons. Predictors that describe real economic activity provide the clearest signal of recession at very short horizons. In contrast, signals from housing and financial markets produce the best forecasts at longer forecast horizons. A real‐time forecast experiment explores the predictability of the 2001 and 2007 recessions. Copyright © 2015 John Wiley & Sons, Ltd.