C++程序设计教学中的技能培养

本文阐述了知识与技能的关系,以及C++程序设计教学中知识和技能的双重性要求。以选择语句为例,演示了"先知识后技能"的教学过程。实践证明,这样的教学安排行之有效。     

认知定位系统的自适应精度研究

为了能够实现定位精度的自适应,本文分析了认知无线电定位系统(cognitive positioning systems,CPS)的原理。该系统利用频谱及环境感知等模块中的传感器单元来获取所需的定位参数,并将其与到达时间(time of arrival,TOA)定位估计算法相结合,以克拉美罗下界(Cramer-Rao lower bound,CRLB)为准则分析最大似然(maximum likelihood,ML)定位精度自适应算法。本文还对单径和多径环境条件下定位精度自适应性能进行了比较。仿真结果表明,该算法能够实现定位精度的自适应,并能通过改善系统可用的频带带宽、分散频谱的数目、信号的调制方式等因素来提高定位精度。     

基于中值滤波和梯度锐化的边缘检测

实现一种基于中值滤波和梯度锐化的边缘检测方法。首先采用既能过滤噪声又能保护边缘信息的中值滤波对图像进行平滑处理。然后采用梯度锐化加强边缘像素强度,通过简单的二值化获得图像的初始边缘,系统采用连通区域标记的方法去除位于图像封闭边界内的残留黑块。最后通过实验结果与人工提取的结果比较,进行误差分析,找出本文算法的不足之处,以便对该算法进行改进。实践证明,本文的边缘检测方法对含有噪声点且灰度分布比较均匀的图像有很好的效果。     

模型桥墩局部冲刷瞬时地形数据自动获取装备研制

模型试验中冲刷地形测绘装备相对落后,致使桥墩周围局部冲刷瞬时地形数据无法实时获取,冲刷动态发展过程及机理的模型试验研究工作难以开展。研制了一种模型桥墩局部冲刷瞬时地形数据等值线自动绘制装备,并利用室内水工模型试验优化了各组成的性能指标,量化了绘制装备的设计参数,分析了监测系统在模型应用中的测绘精度及在工程原型中的适用性。模型试验研究表明,该装备结构简单,操作方便,加工制作成本较低,精度及灵敏性较高,能够快速获取动态过程中局部冲刷瞬时地形等值线,适用于局部冲刷发展影响流场特性研究中的水下地形快速测绘,也可按放大比尺制作成型后应用于涉水建筑物周围水下地形监测。     

江阴市"十四五"水利高质量发展规划的思考

总结江阴市"十三五"水利发展规划在水安全、水资源、水生态、农村水利、水管理服务及水利发展保障等方面的完成情况.分析江阴市水利发展存在的主要短板、问题.围绕建设高质量的水安全保障体系、水资源配置体系、水生态治理体系、水利管理体系、水利服务体系,系统提出江阴市"十四五"水利高质量发展规划思路.     

上覆水磷浓度对沉积物孔隙水中磷垂向分布的影响

为探究上覆水磷浓度对沉积物孔隙水中磷垂向分布的影响,通过室内实验,利用薄膜扩散平衡技术(DET)对孔隙水中磷浓度进行测量,分析孔隙水磷浓度的垂向变化规律,并揭示平衡时上覆水磷浓度与孔隙水磷浓度以及上覆水-沉积物界面孔隙水磷浓度的关系。结果表明:上覆水磷浓度越高,则沉积物孔隙水中磷垂向分布达到平衡所需的时间越长,相同垂向位置处孔隙水磷浓度及其浓度梯度越大;孔隙水磷浓度及其浓度梯度随深度增加而降低,表层沉积物孔隙水中磷的扩散作用最强,下层沉积物孔隙水磷浓度受上覆水磷浓度变化影响较小;吸附平衡后,沉积物孔隙水磷浓度和上覆水-沉积物界面孔隙水磷浓度均与上覆水磷浓度呈幂函数关系,而留存在沉积物孔隙水中的磷浓度仅占2.26%～3.80%,上覆水中减少的磷大部分被沉积物吸附。     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

Image classification method on class imbalance datasets using multi-scale CNN and two-stage transfer learning

Neural Computing and Applications - Image classification tasks widely exist in many actual scenarios, including medicine, security, manufacture and finance. A major problem that hurts algorithm...     

Granular multiple kernel learning for identifying RNA-binding protein residues via integrating sequence and structure information

Neural Computing and Applications - RNA-binding proteins play an important role in the biological process. However, the traditional experiment technology to predict RNA-binding residues is...     

A deep multi-source adaptation transfer network for cross-subject electroencephalogram emotion recognition

Neural Computing and Applications - In real-world application of affective brain–computer interface (aBCI), individual differences across subjects and non-stationary characteristics of...