高等学校IT技术专业复合型人才培养创新研究

在社会高速发展的今天,IT领域专业技术人员的逐渐饱和使得对IT技术专业复合型人才的需求增加,复合型人才的巨大缺口制约了IT行业的更高速发展,这也逐渐引起了高校教育的重视,并致力于培养IT技术专业复合型人才。多元化的人才需求同时也是社会进步的具体体现。笔者通过对专才教育模式及复合型人才培养模式优劣势分析旨在借鉴国外研究经验并探究我国高校教育IT技术专业复合型人才培养创新模式。     

基于CAN-FD总线的车载网络安全通信

以数据加密和入侵检测为基础,建立了一种基于灵活数据速率控制器局域网络的汽车通信网络信息安全方法.提出了包括安全传输、安全启动、时间同步与密钥分配的汽车网络安全通信协议,并通过建立网络仿真模型验证安全协议的有效性.搭建了基于硬件实例的安全通信节点,测试硬件节点的实际通信性能和加密性能.最后针对汽车通信网络潜在的攻击方式,以Dolev-Yao入侵模型攻击和拒绝服务攻击为测试方法,对安全通信模型进行了安全性攻击测试和入侵检测功能验证,确定了网络入侵检测的判定指标.测试结果验证了该方法的安全性和可用性.     

聋人高等融合教育的学习支持服务探究

聋人高等融合教育有利于构建"宽容、无差异"的校园文化与"平等、融合"的社会环境,有助于聋人融入主流社会。融合教育的实施需要完善的支持系统,在分析我国聋人高等融合教育现状的基础上,从学校角度探讨了无障碍学习环境、差异化教学、人员保障方面的学习支持服务,以期为聋人高等融合教育的发展提供借鉴。     

Model averaging estimation for conditional volatility models with an application to stock market volatility forecast

This paper is concerned with model averaging estimation for conditional volatility models. Given a set of candidate models with different functional forms, we propose a model averaging estimator and forecast for conditional volatility, and construct the corresponding weight-choosing criterion. Under some regulatory conditions, we show that the weight selected by the criterion asymptotically minimizes the true Kullback–Leibler divergence, which is the distributional approximation error, as well as the Itakura–Saito distance, which is the distance between the true and estimated or forecast conditional volatility. Monte Carlo experiments support our newly proposed method. As for the empirical applications of our method, we investigate a total of nine major stock market indices and make a 1-day-ahead volatility forecast for each data set. Empirical results show that the model averaging forecast achieves the highest accuracy in terms of all types of loss functions in most cases, which captures the movement of the unknown true conditional volatility.     

Forecasting stock volatility in the presence of extreme shocks: Short-term and long-term effects

This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH-MIDAS-ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short- and long-term effects caused by extreme events, we extend the standard GARCH-MIDAS model to characterize the different responses of the stock market for short- and long-term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in-sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH-MIDAS-SLES model by including the short- and long-term impacts of extreme shocks for all forecasting horizons. The out-of-sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short- and long-term effects to improve the accuracy of the DJIA volatility forecasts.     

碱激发矿粉-膨润土竖向阻隔屏障在某风险阻控工程中的应用

以某在产农药厂污染场地风险阻控工程为依托,通过三级搅拌工艺制备屏障材料,并利用高压旋喷注浆法和深层搅拌法施工工艺建造周长659m、深度25m的活性氧化镁激发矿粉-膨润土系竖向阻隔屏障(MSB),实现了MSB材料在国内的工程应用.对施工后屏障进行取样,采用芯样表观特征分析、室内无侧限抗压强度和刚性壁渗透试验分析2种施工工艺、屏障深度和工后时间对屏障无侧限抗压强度qu和渗透系数k的影响.结果表明:2种施工工艺均实现了材料沿深度的均匀搅拌,屏障完整性较好;工后40～70d,qu可达1.6-2.6MPa,qu随工后时间增加呈增长趋势;自来水渗透作用下屏障k为4.5×10-8～9.0×10-8cm/s,k随深度变化不明显.     

基于动态阈值的混凝土裂缝高抗噪提取及识别方法

针对表面布有蜂窝麻面等复杂噪声的混凝土裂缝,提出了一种基于动态阈值的混凝土裂缝高抗噪提取方法.首先,通过灰度变换、高斯滤波、信息增强等技术弱化、去除噪声,增强图像中裂缝的亮度,提高裂缝与背景的对比度;然后,采用OTSU阈值分割算法去除伪裂缝,基于裂缝与干扰噪声面积的差异,引入动态阈值,实现各类型裂缝的提取;最后,基于边缘检测和裂缝骨架细化处理,计算裂缝的长度、宽度和面积3项特征参数.基于室内实验和室外实际桥梁拍摄的4组裂缝图像结果表明,4组裂缝的平均宽度分别为2.01、1.07、1.34、0.97 mm,说明该方法适用于不同背景下各类型裂缝的提取,具有较强的鲁棒性.