边云智能数据分析与应用

定　　价：55 元

• 作者：沈钧戈 等
• 出版时间：2023/8/1
• ISBN：9787121460425
• 出 版 社：电子工业出版社

• 中图法分类：TP274 
• 页码：216
• 版次：01
• 开本：16开

内容简介

随着“十四五”规划纲要中提出“协同发展云服务与边缘计算服务”的观点，边云智能已成为未来发展的重要趋势。本书依托于政策大背景，旨在向读者介绍边云智能的基础知识和应用。书中分为四个篇章，第一篇章介绍了边云架构的骨架和基础概念，第二篇章介绍了人工智能算法和深度学习模型，第三篇章介绍了云端决策算法和边缘端轻量化算法，第四篇章介绍了边云智能在智慧教育领域的应用。本书可以使读者了解边云计算的基本概念和原理逻辑，熟悉基本的人工智能计算方法和数据分析的逻辑和运用场景。通过数据科学的思路和方法，读者可以将无人系统的数据智能化应用提升，并培养数据导向思维方式，为未来学习智能无人系统科学与技术学科打下基础。 本书目标明确，技术先进，强调课程思政和润物无声的教育理念，旨在提高学生的数据科学素养和“用数据”的能力。本书面向智能无人系统科学与技术专业的研究生，涵盖人工智能、大数据分析、数据挖掘和边云计算等学科，具有交叉性的特点。同时，资深从业者也可将其作为参考书籍。

作者介绍

沈钧戈，西北工业大学助理教授，陕西省电子学会图形图象专委会委员，主讲课程为智慧城市与计算机视觉，并负责相关慕课建设。

目 录

第 1 章 绪论 ····························································································1
1.1 边云智能产生的大背景····································································1
1.1.1 新一代信息技术的快速发展·····················································2
1.1.2 国家政策的支持与引导···························································6
1.2 边云智能······················································································7
1.3 边云智能的发展·············································································9
1.3.1 边云智能的三大发展阶段························································9
1.3.2 城市大脑··········································································.11
1.4 “智能+”新潮头··········································································.13
1.4.1 “智能+”技术新融合···························································.13
1.4.2 多维度场景应用·································································.14
本章习题··························································································.15
第 2 章 边云架构 ···················································································.16
2.1 系统工程方法论··········································································.17
2.1.1 概述 ················································································.17
2.1.2 基本方法··········································································.17
2.2 边云智能体系架构模型·································································.20
2.2.1 概念框架··········································································.20
2.2.2 层次结构··········································································.22
2.3 协同模式···················································································.23
2.3.1 “云-边”协同 ····································································.24
2.3.2 “边-边”协同 ····································································.25
2.3.3 “边-端”协同 ····································································.27
2.3.4 “云-边-端”协同 ································································.28
2.3.5 度量指标··········································································.28
2.4 边云智能架构应用·······································································.30
2.4.1 “云-边-端”区块链 ·····························································.30
2.4.2 “云-边-端”一体化机器人系统 ··············································.32
本章习题··························································································.33
第 3 章 深度学习 ···················································································.35
3.1 深度学习概念·············································································.36
3.1.1 人工智能与机器学习···························································.36
3.1.2 深度学习··········································································.37
3.1.3 神经网络··········································································.39
3.2 前馈神经网络·············································································.39
3.2.1 感知机模型·······································································.39
3.2.2 反向传播··········································································.42
3.2.3 卷积神经网络····································································.44
3.2.4 几种典型的卷积神经网络·····················································.47
3.3 反馈神经网络·············································································.50
3.3.1 循环神经网络····································································.50
3.3.2 长短期神经网络·································································.53
3.4 Transformer 神经网络 ···································································.56
3.4.1 编码器单元与解码器单元·····················································.58
3.4.2 多头注意力机制·································································.59
3.4.3 非参位置编码····································································.60
本章习题··························································································.61
第 4 章 自然语言处理 ·············································································.62
4.1 自然语言处理概述·······································································.63
4.1.1 自然语言处理简介······························································.63
4.1.2 自然语言处理的发展历史·····················································.74
4.1.3 自然语言处理的应用及面临的挑战·········································.76
4.2 文本挖掘···················································································.79
4.2.1 文本挖掘简介····································································.79
4.2.2 文本挖掘算法····································································.81
4.3 机器翻译···················································································.87
4.3.1 机器翻译简介····································································.87
4.3.2 机器翻译算法····································································.89
4.4 自动问答系统·············································································.93
4.4.1 自动问答系统简介······························································.93
4.4.2 自动问答系统模型······························································.95
4.5 语音识别···················································································101
4.5.1 语音识别简介····································································102
4.5.2 语音识别算法····································································103
本章习题··························································································105
第 5 章 计算机视觉 ················································································107
5.1 计算机视觉概述··········································································107
5.1.1 计算机视觉简介·································································108
5.1.2 计算机视觉的发展历史························································109
5.1.3 计算机视觉的应用及面临的挑战···········································.110
5.2 图像分类··················································································.114
5.2.1 图像分类简介···································································.114
5.2.2 图像分类算法···································································.115
5.3 目标检测··················································································.119
5.3.1 目标检测简介···································································.119
5.3.2 目标检测算法····································································120
5.4 图像分割···················································································123
5.4.1 图像分割简介····································································123
5.4.2 图像分割算法····································································124
5.5 目标跟踪···················································································125
5.5.1 目标跟踪简介····································································126
5.5.2 目标跟踪算法····································································126
本章习题··························································································128
第 6 章 边缘轻量化 ················································································129
6.1 边缘轻量化的简介·······································································129
6.1.1 边缘端对轻量化的需求························································129
6.1.2 什么是边缘轻量化······························································130
6.2 模型压缩方法·············································································131
6.2.1 量化和二值化····································································131
6.2.2 网络剪枝··········································································131
6.2.3 低秩因子分解····································································132
6.2.4 参数共享··········································································133
6.2.5 蒸馏学习··········································································133
6.2.6 加速网络设计····································································134
6.3 模型压缩举例·············································································137
6.3.1 知识蒸馏··········································································137
6.3.2 深度压缩··········································································139
6.3.3 MNASNet ·········································································143
本章习题··························································································145
第 7 章 云端决策 ···················································································146
7.1 云端决策简介·············································································147
7.1.1 云端决策的重要性······························································147
7.1.2 云端决策的特点·································································147
7.2 云端决策——大数据挖掘······························································149
7.2.1 回归分析··········································································149
7.2.2 聚类 ················································································150
7.2.3 关联规则··········································································152
7.3 云端决策——推荐算法·································································154
7.3.1 基于统计的推荐算法···························································155
7.3.2 基于协同过滤的推荐系统·····················································155
7.3.3 基于内容的推荐系统···························································156
7.3.4 基于关联规则的推荐系统·····················································158
7.3.5 基于网络结构的推荐系统·····················································158
本章习题··························································································159
第 8 章 边云智能赋能智慧教室 ·································································160
8.1 智慧教室的形成背景与边云框架·····················································161
8.1.1 智慧教室政策支持与特征分析 ··············································162
8.1.2 基于边云智能的智慧教室框架 ··············································164
8.1.3 基于边云智能建设的智慧教室目标愿景 ··································166
8.2 智慧教室的边缘端感知技术与应用··················································166
8.2.1 无感考勤、表情感知与异常行为识别 ·····································167
8.2.2 边缘端感知模型的压缩与轻量化 ···········································172
8.3 智慧教室的云端决策技术与应用·····················································174
8.3.1 “教育大脑”大数据分析决策方法··········································174
8.3.2 个性化推荐、学习评价与师生互动应用 ··································176
8.4 “边云智能+”前景展望·································································178
8.4.1 边云智能赋能智慧交通························································178
8.4.2 边云智能赋能智慧安防························································185
本章习题··························································································190
习题答案································································································191