Access数据库的三个教学案例设计

针对初学者在学习Access数据库中遇到的困难,设计了三个教学案例,介绍报表中计算控件的使用、if语句的语法格式和通配符“？”和“’,的使用．对于提高Access数据库的教学效果有一定的参考价值。     

微温传感器系统的研制

为评价云南天文台抚仙湖太阳望远镜的圆顶视宁度,研制一种能测量常温、微温的仪器。在天文建筑物附近,安放两个微温传感器,可以测量影响天文观测的湍流强度的分布情况。介绍微温传感器的工作原理、硬件设计、软件设计以及仪器的测试。实验结果表明,该传感器具有较高的分辨率和较快的热响应时间,它将对望远镜建设物的设计具有实际意义。     

The parameterized level set method for structural topology optimization with shape sensitivity constraint factor

In recent years, the parameterized level set method (PLSM) has attracted widespread attention for its good stability, high efficiency and the smooth result of topology optimization compared with the conventional level set method. In the PLSM, the radial basis functions (RBFs) are often used to perform interpolation fitting for the conventional level set equation, thereby transforming the iteratively updating partial differential equation (PDE) into ordinary differential equations (ODEs). Hence, the RBFs play a key role in improving efficiency, accuracy and stability of the numerical computation in the PLSM for structural topology optimization, which can describe the structural topology and its change in the optimization process. In particular, the compactly supported radial basis function (CS-RBF) has been widely used in the PLSM for structural topology optimization because it enjoys considerable advantages. In this work, based on the CS-RBF, we propose a PLSM for structural topology optimization by adding the shape sensitivity constraint factor to control the step length in the iterations while updating the design variables with the method of moving asymptote (MMA). With the shape sensitivity constraint factor, the updating step length is changeable and controllable in the iterative process of MMA algorithm so as to increase the optimization speed. Therefore, the efficiency and stability of structural topology optimization can be improved by this method. The feasibility and effectiveness of this method are demonstrated by several typical numerical examples involving topology optimization of single-material and multi-material structures.

     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

Method for discrimination of false targets in multistation radar systems based on the deep neural network

For the existing jamming discrimination methods for multistation radar systems,only the single feature of target echo space correlation is utilized as the metric,which leads to insufficient comprehensiveness of feature extraction,so that effectiveness and universality are insufficient for the discrimination algorithm.In this paper,an identification method in multistatic radar systems based on the deep neural network is proposed.This method combines the characteristics of multistatic radar systems cooperative detection technology,which has many available resources and strong scheduling ability in space,time and frequency domain,with the strong model learning and feature representation ability in the process of information processing on the deep neural network,so that it can effectively apply to the field of anti-deception jamming.Full use is made of unknown information about echo data to obtain more multi-dimensional,more comprehensive,more complete and deeper feature differences besides correlation,so as to achieve a better jamming discrimination effect.Simulation results show that the proposed method can effectively reduce the influence of noise and pulse number on the jamming discrimination performance.At the same time,the limitation of the target echo correlation coefficient on anti-jamming technology under nonideal conditions is alleviated,which broadens the boundary conditions of the application process.     

短视频对服装服饰类用户购买意愿的影响

短视频营销已成为当前助推消费升级的重要新生力量,研究其对用户购买意愿的影响对提升产品销量具有重要的理论意义和实践价值.通过文献总结分析,借助SOR理论模型,提出研究假设,构建探究服装服饰类短视频对用户的购买意愿影响模型,并通过结构方程模型进行验证分析.结果表明:短视频的有用性、易用性显著正向影响用户的购买意愿,而娱乐性影响不显著;感知匹配度在有用性、易用性、娱乐性刺激变量和购买意愿反应变量间存在中介作用.针对上述结论,建议服装服饰类短视频运营企业商家注重在有用性、易用性、感知匹配度方面进行改进,促使短视频营销更快更好的发展.     

银基电接触材料改性及制备工艺

概述了银基电接触材料的发展,介绍了Ag-Ni、Ag-C、Ag-WC和Ag-MO4个主要系列的银基电接触材料的特点及其改性方法,比较了不同银基电接触材料改性前后的优缺点。同时,叙述了粉末冶金法、合金内氧化法及预氧化合粉末法等银基电接触材料的制备工艺。最后指出,提高银基电接触材料的性能、发展新型的节银触头材料和复合触头材料是今后银基电接触材料的发展方向。     

关于输气管线内衬聚乙烯管可防止其腐蚀的商榷

本文参照德国一实例，即利用多种类型PE衬层，在一定温差及有气体扩散阻挡层和无气体扩散阻挡层的条件下，详细地叙述了高压酸性气体对其进行长期渗透作用的试验。结果显示：水蒸气和腐蚀性气体（如H2S）的渗透作用是不能忽视的，甚至当PE层为5mm时亦有一定渗透性；此外，在衬层底部尚产生了化学腐蚀作用，从而促使人们必须考虑须正确选择PE下边的防腐底层。     

超低硫X65管线钢中非金属夹杂物研究

对本钢超低硫X65管线钢中夹杂物的研究结果表明,LF精炼初期,钢中夹杂物主要是球形、多边形和大量簇状的Al2O3夹杂物,99.1%的夹杂物尺寸小于20μm。微合金化后,部分Al2O3夹杂物中出现TiO、SiO2、MnS等成分。钙处理后,夹杂物的平均直径达到8.58μm,大量夹杂物为含有微量MgO、SiO2或MnS等的2~50μm的球形mCaO.nAl2O3,部分为Al2O3、MnS、CaS或CaS-MnS。铸坯中夹杂物数量最少且尺寸最小,96.9%的夹杂物尺寸小于10μm,未发现大于50μm的夹杂物,平均直径为3.71μm。铸坯中大部分为球形的以mCaO.nAl2O3为主要成分的复合夹杂物,还有少量的Al2O3、MnS和CaS夹杂物等。     

基于时间序列模式表示的异常检测算法*

提出了一种基于时间序列的模式表示提取时间序列异常值的异常检测算法(PREOV).时间序列的模式表示本身就具有压缩数据、保持时间序列基本形态的功能,并且具有一定的除噪能力.在时间序列模式表示的基础上提取异常值,可以大大提高算法的效率和准确性,达到事半功倍的效果.在本算法中,还使用了一定的剪枝策略,使得算法的时间复杂度进一步降低.该算法计算简单、实现方便、无须训练,可以支持时间序列的动态增长.