دانلود کتاب Unsupervised Pattern Discovery in Automotive Time Series: Pattern-based Construction of Representative Driving Cycles

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Danksagung
Zusammenfassung
Abstract
Contents
Abbreviations
Symbols
List of Figures
List of Tables
1 Introduction
1.1 Motivation
1.2 Structure of the Thesis
2 Related Work
2.1 Pattern Discovery in Time Series
2.1.1 Terminology
2.1.2 Matrix Profile
2.1.3 Discretization-based Approaches
2.1.4 Other Approaches
2.2 Representative Cycles
2.2.1 Possible Applications
2.2.2 Representativeness
2.2.3 Existing Construction Methods
2.3 The Idea of Pattern-based Cycles
3 Development of Pattern Discovery Algorithms for Automotive Time Series
3.1 Suitability of Approaches
3.1.1 Special Requirements in Automotive Time Series
3.1.2 Discussion of Suitability
3.2 Framework Introduction
3.3 Preprocessing—Reduction of Complexity
3.3.1 Problem of Symbolic Toggling
3.3.2 Problem of Multidimensionality
3.3.3 Problem of Time Warping
3.4 Creating a Dictionary
3.4.1 Adapted Sequitur Compression Algorithm
3.4.2 Pattern Enumeration Algorithm
3.5 Postprocessing—Selection of Relevant Patterns
3.5.1 Filtering of Irrelevant Patterns
3.5.2 Selection of Representative Patterns
4 Pattern-based Representative Cycles
4.1 Pattern-based Statistics
4.1.1 Characteristic Parameters
4.1.2 Use Case Differentiation
4.1.3 Pattern-based Distance Measure
4.1.4 Influence of Pattern Discovery
4.2 Identification of Full Trip RDC
4.2.1 Trips and Pattern Sets
4.2.2 Selection of RDC
4.3 Construction of RDC
4.3.1 Selection of Absolute Pattern Frequency
4.3.2 Solving a Directed Graph Problem
4.3.3 Design of Transitions between Patterns
5 Evaluation
5.1 Experimental Evaluation of Unsupervised Pattern Discovery
5.1.1 Synthetic Randomized Data Set
5.1.2 Real-Life Data Set
5.1.3 Dimensionality
5.1.4 Postprocessing
5.2 Representativeness of Driving Cycles
5.2.1 Basics
5.2.2 Identification of Full Trip RDC
5.2.3 Construction of RDC
5.2.4 Evenly Distributed Pattern Frequency RDCs
6 Conclusion
A References