Li Chen

NO.1 Middle School Affiliated to Central China Normal University

Copyright © 2022 by Cayley Nielson Press, Inc.

ISBN: 978-1-957274-10-2

Cayley Nielson Press Scholarly Monograph Series Book Code No.: 212-3-29








With the continuous advancement of the new college entrance examination reform, the responsibility and mission of the senior high school education level has become more and more important in the framework of the entire education system. Senior high school education is not only a key link in achieving basic education for the completion of its own staged education policies and goals, but also for cultivating and conveying students with ideas, abilities, professional aspirations, certain innovation capabilities, and certain professional skills and skills. An important stage for "customized" outstanding talents. However, for a long time, a bilateral independent development state of "high school education ignores university education, and university education does not care about high school education" has formed. There are connection barriers between high school education and university education. Therefore, to explore and establish a scientific, reasonable and efficient way the integration path of high school education and university education has become an important issue facing both the social system and the education system.

The NO.1 Middle School Affiliated to Central China Normal University, where the author is located, has been carrying out many explorations in promoting the effective connection between university education and high school education. In 2021, the NO.1 Middle School Affiliated to Central China Normal University and Huazhong University of Science and Technology will build an "integrated construction pilot zone". Taking artificial intelligence, a technology hotspot currently being vigorously developed in various countries, as a breakthrough, an AI+ Robot Innovation Laboratory will be established. The AI+ Robotics Innovation Laboratory currently adopts the operation mode of "high school teachers are responsible for the management of the laboratory, and university teachers are responsible for the main course teaching work". It needs to be strengthened in the following three aspects: the communication channel between the teaching staff is single, and the exchanges and cooperation need to be further Deepening; the course objectives, content settings, practical links and other aspects of the elective course "AI + Robot" need to be further optimized according to teaching evaluation; laboratory hardware equipment also needs the support of a collaborative and shared platform.

In the following chapters, the book will conduct research and calculations from the aspects of mathematics and computer, life science, pattern matching and remote sensing science, and combine artificial intelligence and deep learning with case applications.

In this way, it can not only improve students' in-depth thinking in various subject areas, but also cultivate students' ability to ask questions, analyze problems, and solve problems. Moreover, students' logical thinking ability, English reading ability and writing ability have been greatly improved. Through these case studies, students' traditional cognition of previous knowledge has also been improved, making them feel that knowledge is vivid and three-dimensional, rather than rigid and monotonous. They are more interested in such research. (4) As for reporting form, ID accounts for the vast majority, for the purpose of convincing readers. However, the overall percentage of ID is in a downward trend, for the purpose of avoiding questioning of its objectivity. The sum of the percentages of DD and DDS rises in general, so as to enhance readers’ trust. Li Chen
NO.1 Middle School Affiliated to Central China Normal University
Wuhan, Hubei, China
March 15, 2022




Preface I
Chapter 1 Construction of AI+Robot Innovational Laboratory from the Perspective of Talent Training Integration 1
1.1 Background Status and Issues 1
1.2 Work Thinking 2
1.3 Work Improvement and Effectiveness 4
Chapter 2 Artificial Intelligence Education and Multidiscip-linary Integration 9
2.1 Artificial Intelligence Education and Artificial Intelligence Technology 9
2.2 Artificial Intelligence and Mathematics Education 9
2.3 Artificial Intelligence and Life Sciences 11
2.4 Artificial Intelligence and Remote Sensing Science 13
2.5 Multidisciplinary Case Teaching Based on Artificial Intelligence 17
Chapter 3 Trailing and Measuring of Vasculature in Fundus Images Using Additive Gaussian Process 19
3.1 Introduction 20
3.1.1 Background 20
3.1.2 Literature Review 21
3.1.3 Section Structure 23
3.2 Additive Gaussian Process 23
3.2.1 Gaussian Process 23
3.2.2 Additive Gaussian Process 24
3.3 Proposed Agp Method for Trailing and Measuring of Vasculature 26
3.3.1 Vessel Centerline Trailing Method 26
3.3.2 Diameter Estimation 31
3.3.3 Algorithm and Process 32
3.4 Experimental Results 35
3.4.1 Datasets 35
3.4.2 Holistic Analysis 38
3.4.3 Comparative Analysis 39
3.5 Conclusions and Future Directions 43
Chapter 4 Wearing Detection of Pedestrian Masks Based on Machine Vision 45
4.1 Introduction 47
4.1.1 Background 47
4.1.2 The Art of State 49
4.1.3 The main research work of this chapter 51
4.2 Related Technology and Theoretical Basis 53
4.2.1 Object Detection Overview 53
4.2.2 Object Detection Related Concepts and Technologies 55 Bounding Box 55 Intersection over Union,IoU 56 Non-Maximum-Suppres-sion,NMS 57 Attention Mechanism 57
4.2.3 YOLO Target Detection Algorithm 58 YOLOv1-YOLOv4 59 YOLOv5 60
4.3 Research and Design of Pedestrian Mask Wearing Detection 63
4.3.1 Research and Design of Pedestrian Mask Wearing Detection 63
4.3.2 Design 65 Adding Feature Layer 65 Fusion Attention Mechanism 68 Using the DIOU-NMS Method 71 Overall Design 72
4.4 Realization of Pedestrian Mask Wearing Detection 73
4.4.1 Experiment Preparation 73 Experiment Environment 73 Data Set 74 Model training parameter configuration 78
4.4.2 Experimental results and performance analysis 80 Experimental evaluation index 80 Performance Analysis 80
Chapter 5 Target Matching of Remote Sensing Images Based on Convolutional Neural Network Fusion of Local Features 86
5.1 Introduction 86
5.1.1 Background technique 86
5.2 Our New Approach 88
5.3 Related Technology 89
5.3.1 Convolutional Neural Networks, CNN 89
5.3.2 VGG-16 92
5.3.3 SIFT 94
5.4 Work Flow 96
References 104



This book should be useful for students, scientists, engineers and professionals working in the areas of optoelectronic packaging, photonic devices, semiconductor technology, materials science, polymer science, electrical and electronics engineering. This book could be used for one semester course on adhesives for photonics packaging designed for both undergraduate and graduate engineering students.


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