基于LabVIEW软件的磨削力信号采集与提取程序设计

磨削力信号的采集和提取是研究磨削力的必要环节.文中在分析磨削力信号测量及提取原理的基础上,依据模块化原则设计了磨削力信号采集与提取程序的系统框架和操控流程.用LabVIEW虚拟仪器软件编写了程序,并通过磨削测力实验验证了程序设计的可靠性,以功率法为参考,其数据处理的绝对误差率为4.8%,满足测试要求.     

2.1 μmCo:MgF2脉冲激光器

报导了2.1 μmCo:MgF2激光器在室温下的实验结果,得到了0.279 J的2.1 μm激光,器件效率为14.5%.测定了Co浓度为3%(wt)的MgF2晶体对1.341 4 μm线的偏振吸收系数,测量结果表明,对该谱线π偏振和σ偏振的吸收系数分别为0.804 cm-1和0.516 cm-1.激光实验结果计算得到该晶体2.1 μm线的激光跃迁截面为2.61×10-21 cm2.     

在临界情形下非整数滞后离散系统的稳定性(英文)

本文采用了刘永清教授建立的频率等价法,结合复变函数中的路歇,建立在第一临界情形下非整数滞后离散系统的稳定性。     

甲胺磷中P-S键键能的估算

甲胺磷中Ｐ－Ｓ键键能至今尚未有文献数据可查．本文根据甲胺磷的红外光谱,利用多原子分子中部分原子间形成的化学键的伸缩振动频率与键离解能之间的关系式,推测了甲胺磷中Ｐ－Ｓ键的单键键能为２４３．８３ｋＪ／ｍｏｌ．并通过精胺转化成甲胺磷的反应热的测定,证实了红外光谱法推测的Ｐ－Ｓ键键能的可靠性．     

几种稠环芳烃荧光光谱的量子化学研究

采用量子化学半经验方法ＲＨＦ／ＡＭ１对苯、萘、芴和菲进行了构型优化和振动分析,并用ＲＨＦ／ＣＩＳ方法,分别计算了它们的荧光光谱,所得结果与实验值基本吻合     

平移同态的正规带及其膨胀

给出了任意正规带（从而任意正则半群）和它们的膨胀为平移同态半群的充要条件．作为推论,证明了每左平移与每右平移都相关联的半群恰为矩形带或零半群     

小城镇总体规划理论实践及问题研究──江苏省如东县长沙镇总体规划剖析

小城镇是农村经济的中心,是城市联系乡村的主要节点。小城镇的合理有序发展是中国社会主义初级阶段城市化和工业化的需要,加强小城镇建设,推动农村商品经济和社会发展,加快城乡一体化进程是中国特色社会主义建设的一个主要组成部分。小城镇总体规划则是推动小城镇合理发展的重要手段。笔者通过江苏省如东县长沙镇总体规划实践,就小城镇规划研究中的若干问题做了一些有意义的探讨。     

汽车侧倾过程的计算机模拟分析

本文根据美国密执安大学运输研究院开发的汽车静态侧模型（ＳＲＭ）,用计算机模拟了汽车侧倾过程,并对影响汽车侧倾的敏感参数作了进一步的分析,为汽车操纵稳定性的研究提供了参考。     

脆性材料超声钻孔振动系统的实验研究

对超声能量的传递和超声加工过程进行初步的实验研究,设计了一些简单的测试电路来测定两套超声振动系统的有关参数,分析对振动的影响,得到一些相关结论。     

Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH-MIDAS and deep learning models

Empirical experiments have shown that macroeconomic variables can affect the volatility of stock market. However, the frequencies of macroeconomic variables are low and different from the stock market volatility, and few literature considers the low-frequency macroeconomic variables as input indicators for deep learning models. In this paper, we forecast the stock market volatility incorporating low-frequency macroeconomic variables based on a hybrid model integrating the deep learning method with generalized autoregressive conditional heteroskedasticity and mixed data sampling (GARCH-MIDAS) model to process the mixing frequency data. This paper firstly takes macroeconomic variables as exogenous variables then uses the GARCH-MIDAS model to deal with the problem of different frequencies between the macroeconomic variables and stock market volatility and to forecast the short-term volatility and finally takes the predicted short-term volatility as the input indicator into machine learning and deep learning models to forecast the realized volatility of stock market. It is found that adding macroeconomic variables can significantly improve the forecasting ability in the comparison of the forecasting effects of the same model before and after adding the macroeconomic variables. Additionally, in the comparison of the forecasting effects among different models, it is also found that the forecasting effect of the deep learning model is the best, the machine learning model is worse, and the traditional econometric model is the worst.