讲述静电放电(ESD)基本原理和防护， 包括静电的产生及防护， 以及实用的案例如工作区的ESD, 包装及标签， 非常实用值得大家参考， 还是PPT版本， 大家可以根据实际情况修改使用。

此，为 “ About Python A ” 系列的最后版本。以后的更新，会在 “ About Python B 系列 ” 。

Morgan Kaufmann - Knowledge Representation and Reasoning Excellent book from masters

How can knowledge be represented symbolically and manipulated in an automated way by reasoning programs

This book provides a definition and study of a knowledge representation and reasoning formalism stemming from conceptual graphs, while focusing on the computational properties of this formalism.

Review on the Knowledge Graph in Robotics Domain--知识图谱在机器人领域的应用论文

Guerci教授的认知雷达入门书籍， Cognitive Radar：The Knowledge-Aided Fully Adaptive Approach

Knowledge Sharp This is a knowledge repository made for college student to share their own: Project experience higher degree persuit Course information 这个专案分享台湾国内各大专院校有毕业专题的校系，各组学生的个人与组经验, 目的是希望透过传承经验，提供毕业校友一个作品的线上查询管到, 并让学弟妹有相关于专题制作上的经验可以咨询 More Provision Programming and Web Design Tutorial Team Original Team: CCUMISKM @ National Cung Chen University * [Veck Hsiao](fbukevin@gmail.com) * [Ra

Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict what drugs are likely to target proteins involved with both diseases X and Y?—a query that requires reasoning about all possible proteins that might interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries—a flexible but tractable subset of first-order logic—on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum.

该书详细地介绍了无线通信的基本概念