时间序列分析——The main focus of this book is on a systematic development of the theory of sequential hypothesis testing (Part I) and changepoint detection (Part II). In Part III, we briefly describe certain important applications where theoretical results can be used efficiently, perhaps with some reasonable modifications. We review recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. The emphasis is not only on more traditional binary hypotheses but also on substantially more difficult multiple decision problems. Scenarios with simple hypotheses and more realistic cases of (two and finitely many) composite hypotheses are considered and treated in detail. While our major attention is on more practical discrete-time models, since we strongly believe that life is discrete in nature??? (not only due to measurements obtained from devices and sensors with discrete sample rates), certain continuous-timemodels are also considered once in a while, especially when general results can be obtained very similarly in both cases. It should be noted that although we have tried to provide rigorous proofs of the most important results, in some cases we included heuristic argument instead of the real proofs as well as gave references to the sources where the proofs can be found.

Exchange2016是一款功能强大、高速、安全的CAD格式转换软件，他可与Delcam的其它软件模块集成运行，也可独立运行。Delcam Exchange的用户界面新颖、流畅，转换操作轻松、简便，支持广泛的CAD数据格式，可以将CATIA、Pro-E、SolidWorks以及Rhino等工具制作的CAD格式换为其它机械制图软件所需要的格式。Delcam Exchange还提供独特的模型预览功能

egret长按实现交换图片位置，并且顺序刷新其他图片位置， 完整demo。

OTX Open Test sequence eXchange的解释，助你快速掌握OTX ISO 13209标准

c#使用StackExchange.Redis组件来访问redis数据库，最低需要.net4.5的支持

In the development of the basic stress equations for tension, compression, bending, and torsion, it was assumed that no geometric irregularities occurred in the member under consideration. But it is quite difficult to design a machine without permitting some changes in the cross sections of the members. Rotating shafts must have shoulders designed on them so that the bearings can be properly seated and so that they will take thrust loads; and the shafts must have key slots machined into them for securing pulleys and gears. A bolt has a head on one end and screw threads on the other end, both of which account for abrupt changes in the cross section. Other parts require holes, oil grooves, and notches of various kinds. Any discontinuity in a machine part alters the stress distribution in the neighborhood of the discontinuity so that the elementary stress equations no longer describe the state of stress in the part at these locations. Such discontinuities are called stress raisers, and the regions in which they occur are called areas of stress concentration. Stress concentrations can also arise from some irregularity not inherent in the member, such as tool marks, holes, notches, grooves, or threads. 1 in T 18 in Figure 3–28 The cross-section of a thin strip of steel subjected to a torsional moment T. bud29281_ch03_071-146.qxd 11/24/09 3:02PM Page 110 ntt 203:MHDQ196:bud29281:0073529281:bud29281_pagefiles: Load and Stress Analysis 111 A theoretical, or geometric, stress-concentration factor Kt or Kts is used to relate the actual maximum stress at the discontinuity to the nominal stress. The factors are defined by the equations K t = σ max σ0 K ts = τ max τ0 (3–48) where Kt is used for normal stresses and Kts for shear stresses. The nominal stress σ0 or τ0 is the stress calculated by using the elementary stress equations and the net area, or net cross section. Sometimes the gross cross section is used instead, and so it is always wise to double check the source of Kt or

本书为约翰·克雷格（John Craig）所著《机器人学导论》的中文版系统讲解了机器人学的理论知识，主要内容包括：空间位姿的描述和变换、操作臂的正运动学和逆运动学、操作臂的雅可比、操作臂动力学、轨迹规划、操作臂的机构设计、操作臂的线性和非线性控制、操作臂的力控制、机器人编程语言和离线编程。此外，各章末包括不同难度的习题、编程练习和MATLAB练习。本书可作为高等院校相关专业的教材和参考书，也可供相关技术人员参考。

Change Detection 一个用 PyTorch 编写的，专门针对变化检测 (Change Detection) 任务的模型框架。 结果可视化（部分） Siamese_unet_conc + Szada TODO 参考 写在前面 为什么写这个项目？ 变化检测（Change Detection，CD）任务与其他任务，如语义分割，目标检测等相比，有其特有的特性（坑），如数据集少（少到可怜那种，尤其是异源，我**），公开的模型也很少，输入常常是成对的（导致一些在 PyTorch 中常用的函数，如Random系列等需要做出一些改变），给初学者带来了很大的困扰（对，没错就是我），所以我将毕设期间写的一些代码，仿照 maskrcnn-benchmark 整理一下发布出来。 特性 边训练边测试（可选） 由于数据集数量较少，以及 CD 只是一个 “二分类” 问题，所以模型一般较为简单，因此边训练边

Sakurai J.J., Napolitano J.J写的高等量子力学习题答案

Keras注意机制 在Keras中为以下层实现了简单的关注机制： 密集（注意2D块） LSTM，GRU（注意3D块） 示例：注意块 致密层 inputs = Input(shape=(input_dims,)) attention_probs = Dense(input_dims, activation='softmax', name='attention_probs')(inputs) attention_mul = merge([inputs, attention_probs], output_shape=input_dims, name='attention_mul', mode='mul') 让我们考虑这个“ Hello World”示例： 32个值的向量v作为模型的输入（简单前馈神经网络）。 v [1] =目标。 目标是二进制（0或1）。 向量v的所有其他值（