基于分形理论的磨粒磨损预测模型

为了求解在粗糙表面上磨粒磨损的磨损率,在分形接触模型的基础上,利用塑性变形磨损理论推导了基于分形参数的磨损率模型,并建立了磨损率与分形维数、材料性能常数、磨屑概率之间的关系,从而反映出材料的磨损规律。从分析结果可以看出,当分形维数处在某一区间内时,随着分形维数的变化,磨损率先减小后增大;最优分形维数为1.5,此时磨损率最小。当分形维数保持不变时,随着尺度常数、概率常数的增大磨损率也随之增大;而随着材料性能常数的增大磨损率减小。当其他各参数都保持恒定时,接触面积越大磨损率也变大。该模型的建立为进一步研究粗糙表面的摩擦、磨损与润滑具有重要意义。     

综合类期刊2012-2014年载文作者群统计分析--以地方科学院主办的科技期刊为例

应用文献计量学的方法，对?河南科学?、?山东科学?、?广西科学?、?甘肃科学学报?2012—2014年的载文量、基金论文比、作者地域分布和所属单位的分布、论文作者数、合著率进行了统计和对比分析，为提高?甘肃科学学报?今后在综合类期刊中的排名、促进自身发展提供参考。     

高主动支护性与高材料强度利用率锚杆支护技术的研究与应用

论述了巷道锚杆支护的作用,介绍了锚杆支护的主动性和材料强度利用率的特点,基于施工实践,指出采用高主动支护性、高材料强度利用率锚杆支护技术能使巷道支护达到快速、优质、安全、高效的目的.     

多级提水高扬程泵站高压变频节能的计算与分析

针时平定县供水管理局娘子关各站机电水泵在直接启动时站与站之间存在流量不匹配问题而造成扬程损失,提出了改进措施,重点对多级提水高扬程泵站的高压变频节能进行了计算与分析.     

多用户正交多级差分混沌键控通信系统

针对传统差分混沌键控(differential chaos shift keying, DCSK)采用多电平方式传输多进制信息导致误比特率(bit error rate, BER)高的缺点,提出一种多用户正交多级DCSK(multi-user orthogonal multi-level DCSK, MOM-DCSK)通信系统。该系统将传输的多进制信息映射为不同的传输系数,多个传输系数分别乘以对应的Walsh码后调制在信息承载信号上,并且通过不同的延时传输多用户信息。在加性高斯白噪声(additive white Gaussian noise, AWGN)信道和多径Rayleigh衰落信道中推导了系统的理论BER公式并进行了仿真分析。结果显示,该系统在传输多进制信息时拥有较好的误码性能,并且在多用户传输领域中有较好的实际应用价值。     

基于区间直觉模糊数ANP的体系贡献率计算方法

体系贡献率主要用于体系建设中度量装备对体系的贡献程度。为解决目前体系贡献率计算存在的一些问题,提出一种“任务–能力–指标–装备”的多层次装备指标体系结构。考虑了装备指标体系结构、网络分析法和区间直觉模糊数的特点,提出了一种基于区间直觉模糊数的网络分析法以得到更准确的体系贡献率。实验表明,该方法不仅能够解决体系贡献率计算公式现存的问题,而且与AHP(analytic hierarchy process)和ANP(analytic network process)相比,可以得到更准确的体系贡献率,为体系建设提供更有效的支持。     

引入微元法思想的混合测控资源联合调度方法

针对混合测控资源联合调度中存在的资源属性繁多、偏好差异大以及可能出现的调度弧段冲突等问题,借鉴单体制测控网调度研究中的任务需求描述方法,对混合测控任务需求的特点进行分析,给出其任务需求的规范化描述。以测控调度收益值最大为目标,建立混合资源联合调度模型;引入微元法思想,提出了可用弧段扩展复用方法,以此为基础提出了基于改进遗传算法的求解策略。仿真结果表明：所建模型和相应的解冲突算法能有效提高测控需求满足率和测控收益。     

Robust model rankings of forecasting performance

This paper investigates robust model rankings in out‐of‐sample, short‐horizon forecasting. We provide strong evidence that rolling window averaging consistently produces robust model rankings while improving the forecasting performance of both individual models and model averaging. The rolling window averaging outperforms the (ex post) “optimal” window forecasts in more than 50% of the times across all rolling windows.     

Exchange rate forecasting and the performance of currency portfolios

We examine the potential gains of using exchange rate forecast models and forecast combination methods in the management of currency portfolios for three exchange rates: the euro versus the US dollar, the British pound, and the Japanese yen. We use a battery of econometric specifications to evaluate whether optimal currency portfolios implied by trading strategies based on exchange rate forecasts outperform single currencies and the equally weighted portfolio. We assess the differences in profitability of optimal currency portfolios for different types of investor preferences, two trading strategies, mean squared error‐based composite forecasts, and different forecast horizons. Our results indicate that there are clear benefits of integrating exchange rate forecasts from state‐of‐the‐art econometric models in currency portfolios. These benefits vary across investor preferences and prediction horizons but are rather similar across trading strategies.     

Google Trends and the forecasting performance of exchange rate models

In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out‐of‐sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends‐based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends‐based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.