Intro
Preface
Contents
About the Editors
1 Prediction Based on Sentiment Analysis and Deep Learning
1.1 First Section
1.2 Benchmark Prediction
1.2.1 Capture Data of Stock Comments from www.guba.eastmoney.com [1]
1.2.2 Build a Model for False News Judgment
1.2.3 Test Stock Comments for False News
1.2.4 Build a Sentiment Classification Model for Stock Comment
1.2.5 Build an Index from the Analysis Results
1.2.6 Capture and Load Data
1.2.7 A Subsection Sample
1.3 Conclusion
References
2 A Survey on Time Series Forecasting
2.1 Introduction

2.2 Traditional Machine Learning-Based Method
2.2.1 Feature Extraction
2.2.2 Feature Selection
2.2.3 Model Training
2.2.4 Rolling Time Series Forecasting
2.3 Deep Learning-Based Method
2.3.1 RNN
2.3.2 LSTM
2.3.3 GRU
2.4 Experiment Results
2.4.1 Machine Learning Results
2.4.2 Deep Learning Results
2.5 Conclusion
References
3 Research and Development of Visual Interactive Performance Test Methods and Equipment for Intelligent Cockpit
3.1 Introduction
3.2 System Overview
3.2.1 Visual Bionic Robot
3.2.2 Main Case of Bionic Robot

3.2.3 Binocular High-Frame Camera
3.2.4 Software
3.3 Head Visual Tracking
3.3.1 Self-Stabilizing Function of the Head
3.3.2 High-Precision Servo Motor
3.3.3 Servo Encoder
3.3.4 Self-Stabilizing PID Algorithm for the Cradle Head
3.3.5 Precision Test of Self-Stabilizing Function of Head
3.4 System Effect
3.5 Conclusion
References
4 Design and Validation of Automated Inspection System Based on 3D Laser Scanning of Rocket Segments
4.1 Introduction
4.2 3D Scanning Measurement Principle
4.2.1 Three-Dimensional Laser Scanning Equipment

4.2.2 Lifting Platform System
4.2.3 Checking Standard Device
4.2.4 Measurement Software Design
4.3 Measurement System Accuracy Verification
4.3.1 Verification of Length Splicing Accuracy
4.3.2 Verification of Geometric Element Detection Accuracy
4.4 Conclusion
4.5 Discussion
References
5 Research and Implementation of Electric Equipment Connectivity Data Analysis Model Based on Graph Database
5.1 Introduction
5.2 Related Work
5.3 Research on the Method and Algorithm of Electric Data Modeling
5.3.1 Electric Data
5.3.2 Electric Data Modeling

5.4 Implementation of Electric Data Model Based on Graph Database
5.4.1 Electric Data Relation Processing
5.4.2 Implementation and Construction of Power Data Model
5.4.3 Electric Equipment Connectivity Analysis Based on Power Grid Data Model
5.5 Application Results
References
6 Improving CXR Self-Supervised Representation by Pretext Task and Cross-Domain Synthetic Data
6.1 Introduction
6.2 Related Works
6.2.1 Overview of CXR Classification
6.2.2 Self-Supervision and Contrastive Learning
6.2.3 Pretext Task and Data Augmentation
6.3 Problem Definition