基于邻近值的HEVC帧内预测优化

高性能视频编码(High Efficiency Video Coding,HEVC)标准中的帧内编码模式利用当前帧中像素点之间的空间相关性作出有效预测。为了解决待编码像素远离参考像素时预测不准确的问题,提出了一种基于邻近值的HEVC帧内预测优化算法。该算法的主要思想是,对于当前像素,先根据传统HEVC帧内编码方法得到其预测值,再使用该像素点左边、左上、上边位置的修正值以及该像素本身的预测值对该预测值进行修正。因为将当前像素与周围像素的相关性进行了有效的数学建模,所以HEVC帧内编码性能得到了提升。实验结果显示,所提算法与HEVC标准相比,最高节省了2.7%的码率,平均节省的码率为1.3%。     

Real exchange rate misalignments

This paper investigates episodes of real exchange rate appreciations and depreciations for a sample of 85 countries from 1960 to 1998. A Markov Switching Model is used to characterize real exchange rate misalignment series as stochastic autoregressive processes governed by two states corresponding to different means and variances. Our main findings are: first, some countries present no evidence of distinct misalignment regimes; second, for some countries there is no RER misalignment in one of the regimes; and, third, for the countries with two misalignment regimes, the appreciated regime has higher persistence than the depreciated one.     

农村连锁超市运营模式创新研究

在农村连锁超市现行运营模式中,连锁总部处于中间经销商的地位,收益来源主要为供应商的销售返点.这一模式存在连锁总部经营负担过重、整体供应链上信息传递不畅、交易成本太高等弊端;而在新型的"B2B连锁网络与物流共配"综合服务运营模式中,连锁总部处于中间服务运营商的地位,收益来源为网站广告费用和中介服务费用.这一模式具有减轻连锁总部经营负担、节省加盟店和连锁总部信息搜寻成本、缓解农村连锁超市供应链上各方之间矛盾、降低连锁总部物流配送成本等优势,弥补了现行农村连锁超市运营模式的不足;要实现这一新型运营模式.连锁总部必须建立完善的信息系统平台,具有较强分拣配送能力的配送中心以及比较密集的农村连锁超市加盟店实体网络,并不断提高加盟零售商的计算机应用水平.     

信用迁移矩阵特性实证分析

借鉴了马尔可夫链的统计原理,在商业银行评级体系的基础上,利用大量实际数据,统计得到多种信用等级迁移矩阵,对其特性进行了深入分析,并为开展后继研究奠定基础。     

基于时空的土地利用变化预测及其对策

以湖北省武汉市江夏区为例,通过对土地利用动态度、土地利用新增度和土地利用耗减度的计算,研究该区域的土地利用变化速度;运用马尔柯夫模型,构建土地利用结构转移矩阵,对土地利用动态变化及演变趋势进行预测分析.根据研究结果,提出加强耕地保护动力机制建设;优化土地利用结构,提高土地利用效率;建立土地利用规划信息系统以及进行综合治理,控制水土流失的对策.     

夫妻联合长期护理保险的定价模型与应用

在人口老龄化和人口长寿背景下,社会对老年长期护理保障的需求不断增长,基于家庭保障的理念,本文将多元寿险模型推广到夫妻联合长期护理保险,构建了健康、轻度失能、重度失能和死亡的马尔科夫四状态转移模型,并在联合个体状态转移相互独立的假设下给出了夫妻联合长期护理保险定价模型.最后基于中国老年健康影响因素跟踪调查微观数据,实现了...     

自主创新与全球价值链嵌入产业升级

中国嵌入全球价值链的产业处于一种从属、被支配的地位。这需要推动产业升级以改善产业的价值链地位,提升产业竞争力。嵌入产业的升级离不开自主创新,自主创新促进产业升级,积极采取措施推动自主创新实施才能逐步市场化,从而全面打破全球价值链对中国的低端锁定。     

利率期限结构的马尔科夫区制转移模型与实证分析

本文在利率期限结构中通过纳入马尔科夫(Markov)区制转移,将传统CKLS模型推广到更为一般的状态相依的CKLS模型,并将之应用于对我国1996年1月至2006年3月银行间同业拆借市场六组不同到期日之月度加权平均利率的研究。通过模型估计和检验分析,我们发现在不同区制下不同到期日利率漂移函数和扩散函数均呈现非线性,其中漂移函数表现为强烈的随机游走过程或均值回归过程,而扩散函数表现为低波动状态或高波动状态。此外,结果表明不同到期日利率期限结构可由缩压的马尔科夫区制转移CKLS模型获得。     

De-industrialization and comparative advantage in the global value chain

We investigate the causes of de-industrialization and potential for re-industrialization using trade-linked input–output data from WIOD. By introducing a new global value chain measure of comparative advantage, we relate a sector's share in domestic final demand to that in production and separate the direct effect of trade on its income share. This method identifies the declining share of manufacturing value added in domestic final expenditures to be the main cause of de-industrialization. Differences in comparative advantage between countries do matter, especially in the case of employment shares, but have a limited impact via the direct trade effect on value added. The findings point to a peculiar paradox of industrial policy: precisely when it is successful in raising competitiveness and hence productivity growth of manufacturing, it also furthers the global decline of relative prices in manufacturing. In contrast to the national objectives of re-industrialization, effective industrial policies accelerate de-industrialization in the global economy.     

Probabilistic forecasting of industrial electricity load with regime switching behavior

This paper suggests a novel inhomogeneous Markov switching approach for the probabilistic forecasting of industrial companies’ electricity loads, for which the load switches at random times between production and standby regimes. The model that we propose describes the transitions between the regimes using a hidden Markov chain with time-varying transition probabilities that depend on calendar variables. We model the demand during the production regime using an autoregressive moving-average (ARMA) process with seasonal patterns, whereas we use a much simpler model for the standby regime in order to reduce the complexity. The maximum likelihood estimation of the parameters is implemented using a differential evolution algorithm. Using the continuous ranked probability score (CRPS) to evaluate the goodness-of-fit of our model for probabilistic forecasting, it is shown that this model often outperforms classical additive time series models, as well as homogeneous Markov switching models. We also propose a simple procedure for classifying load profiles into those with and without regime-switching behaviors.