A deep learning model for predicting greenhouse film degradation
General Information:
MASTER |
Level |
A deep learning model for predicting greenhouse film degradation |
Title |
software engineer |
Specialty |
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Outline:
General Introduction CHAPTER I Aging of greenhouse films. I.1 Introduction. I.2 Definitions I.2.1 Polymer. I.2.2 Monomers I.3 Polymer polymerization…. I.4 Properties of polymers. I.4.1 Physical properties. I.4.2. Thermal properties I.4.3 Mechanical properties. I.5 Polyethylene (PE) . I.5.1 Definition (EP) I.5.2 Types of Polyethylene….. I.5.3 The properties of LDPE low-density polyethylene I.6.1 General Aspects of aging I.6.2 Physical aging. I.6.3 Chemical aging I.7 Stress-strain curve…………….. I.8 The engineering measures of stress and strain.. I.9 True stress and strain measurements I.10 Area under stress-strain curve… I.11 Conclusion……………………….. CHAPTER II Deep Learning…….. II.1 Introduction II.2 Artificial Intelligence.. II.3 Distinction between AI, ML and DL II.4 Machine Learning…. II.4.1 Types of machine learning.. II.5 Real-world applications.. II.6 DEEP LEARNING. II.6.1 Artificial Neural Network. II.6.2 Activation Function II.6.3 LOSS FUNCTIONS II.6.4 Deep Learning Technique……. II.6.5 Deep learning models II.6.6 Convolutional Neural Network [25]. II.6.7 Training… II.7 Conclusion….. CHAPTER III Implementation… III.1 Introduction……….. III.2 Technical choices…… III.2.1 Python.. III.2.2 Anaconda.. III.2.3. Pycharm. III.2.4.Additional Libraries III.3 Experimental Source of the database.. III.3.1 Materials. III.4 Training and Parameterization of Models III.4.1 Optimization Function…….. III.4.2 Error Function III.4.3 Activation Function III.4.4 Number of Iterations (Epochs).. III.4.5 Force stopping “early stopping”. III.4.6 A callback. III.5 The suggested model. The convolutional neural network submodel. The Support Vector Machine (SVM) submodel. III.5.1 Findings and Analysis. III.6 Software III.6.1 User interaction and design.. III.7 Conclusion……………
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