greysystemdecision

window 7操作系统的系统服务项的详细介绍，想优化以下系统服务的可以看看(operating system window 7 A detailed description of services, would like to optimize the following system services can look at)

安捷伦频谱仪 编程测试纲要 安捷伦频谱仪 编程测试纲要(Aigent test)

说明：  电力系统可靠性教材，电力系统可靠性评估分析(Power System Reliability Textbook, Power System Reliability Assessment and Analysis)

说明：  一本介绍ArcObject组件开发的书，介绍GIS二次开发(Copies of the book ArcObject component development, secondary development of GIS introduction)

计算机网络自自顶向下的方法 第五版原版 英文书籍。Computer Networking A Top-Down Approach 5th Edition(Computer Networking A Top-Down Approach 5th Edition)

一本以python为学习语言的机器学习书，内容详细，很适合python初学者学习(A machine learning book based on Python for learning language is detailed and suitable for Python beginners to learn.)

关于关联向量机应用的最新文献！ 提出了一种核主元分析(KPCA)和关联向量机(RVM)相结合的组合建模方法。KPCA-RVM采用KPCA对原始自变量进行非线性变换并提取主成分,形成特征自变量 采用RVM,对KPCA变换后的样本数据进行回归建模,并根据模型的预报能力自适应的确定参与回归的最佳特征变量个数,消除冗余信息干扰,获得强非线性表达能力且预报性能良好的模型。并将KPCA-RVM应用于PTA装置对羧基苯甲醛(4-CBA)含量的软测量建模,结果表明该方法预测精度高于PCA-RVM和RVM。(A novel modeling method integrated KPCA with RVM was proposed. The kernel primary component analysis (KPCA) was employed to identify the principal components from the nonlinear transform data of independent variables, which were regarded as character variables. Regression between character variables and dependent variables was done based on RVM, and the optimal number of the character variables was adaptively determined according to the generalization performance of the regression model. Thus, KPCA-RVM meth...)

一个关于用python语言写abaqus的脚本的好的教程(LearnAbaqusScriptInOneHour)

EMIEMC 设计讲座3，值得学习的好资料(emi)

说明：  本文提出了一种用于通信系统中无线电信号检测的自动调制识别框架。该框架考虑了深度卷积神经网络（CNN）和长期短期记忆网络（LSTM）。 此外，我们提出了一种预处理信号表示，其组合了调制信号的同相，正交和四阶统计。所提供的数据表示允许我们的CNN和LSTM模型对我们的测试数据集实现8％的改进。(Automatic Modulation Recognition using Deep Learning Architectures, 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))