Design of power magnetic devices : a multi-objective design approach /: a multi-objective design approach. (2021)

• Record Type: Book
• Further Information: Note: Maria C. Mariani, Osei Kofi Tweneboah, Maria Pia Beccar-Varela.
• Authors: Mariani, Maria C; Tweneboah, Osei Kofi, 1988-; Beccar-Varela, Maria Pia
• Contents: List of Figures xvii List of Tables xxi Preface xxiii 1 Background of Data Science 1 1.1 Introduction 1 1.2 Origin of Data Science 2 1.3 Who is a Data Scientist? 2 1.4 Big Data 3 1.4.1 Characteristics of Big Data 4 1.4.2 Big Data Architectures 4 2 Matrix Algebra and Random Vectors 7 2.1 Introduction 7 2.2 Some Basics of Matrix Algebra 7 2.2.1 Vectors 7 2.2.2 Matrices 8 2.3 Random Variables and Distribution Functions 12 2.3.1 The Dirichlet Distribution 15 2.3.2 Multinomial Distribution 17 2.3.3 Multivariate Normal Distribution 18 2.4 Problems 19 3 Multivariate Analysis 21 3.1 Introduction 21 3.2 Multivariate Analysis: Overview 21 3.3 Mean Vectors 22 3.4 Variance–Covariance Matrices 24 3.5 Correlation Matrices 26 3.6 Linear Combinations of Variables 28 3.6.1 Linear Combinations of Sample Means 29 3.6.2 Linear Combinations of Sample Variance and Covariance 29 3.6.3 Linear Combinations of Sample Correlation 30 3.7 Problems 31 4 Time Series Forecasting 35 4.1 Introduction 35 4.2 Terminologies 36 4.3 Components of Time Series 39 4.3.1 Seasonal 39 4.3.2 Trend 40 4.3.3 Cyclical 41 4.3.4 Random 42 4.4 Transformations to Achieve Stationarity 42 4.5 Elimination of Seasonality via Differencing 44 4.6 Additive and Multiplicative Models 44 4.7 Measuring Accuracy of Different Time Series Techniques 45 4.7.1 Mean Absolute Deviation 46 4.7.2 Mean Absolute Percent Error 46 4.7.3 Mean Square Error 47 4.7.4 Root Mean Square Error 48 4.8 Averaging and Exponential Smoothing Forecasting Methods 48 4.8.1 Averaging Methods 49 4.8.1.1 Simple Moving Averages 49 4.8.1.2 Weighted Moving Averages 51 4.8.2 Exponential Smoothing Methods 54 4.8.2.1 Simple Exponential Smoothing 54 4.8.2.2 Adjusted Exponential Smoothing 55 4.9 Problems 57 5 Introduction to R 61 5.1 Introduction 61 5.2 Basic Data Types 62 5.2.1 Numeric Data Type 62 5.2.2 Integer Data Type 62 5.2.3 Character 63 5.2.4 Complex Data Types 63 5.2.5 Logical Data Types 64 5.3 Simple Manipulations – Numbers and Vectors 64 5.3.1 Vectors and Assignment 64 5.3.2 Vector Arithmetic 65 5.3.3 Vector Index 66 5.3.4 Logical Vectors 67 5.3.5 Missing Values 68 5.3.6 Index Vectors 69 5.3.6.1 Indexing with Logicals 69 5.3.6.2 A Vector of Positive Integral Quantities 69 5.3.6.3 A Vector of Negative Integral Quantities 69 5.3.6.4 Named Indexing 70 5.3.7 Other Types of Objects 70 5.3.7.1 Matrices 70 5.3.7.2 List 72 5.3.7.3 Factor 73 5.3.7.4 Data Frames 75 5.3.8 Data Import 76 5.3.8.1 Excel File 76 5.3.8.2 CSV File 76 5.3.8.3 Table File 77 5.3.8.4 Minitab File 77 5.3.8.5 SPSS File 77 5.4 Problems 78 6 Introduction to Python 81 6.1 Introduction 81 6.2 Basic Data Types 82 6.2.1 Number Data Type 82 6.2.1.1 Integer 82 6.2.1.2 Floating-Point Numbers 83 6.2.1.3 Complex Numbers 84 6.2.2 Strings 84 6.2.3 Lists 85 6.2.4 Tuples 86 6.2.5 Dictionaries 86 6.3 Number Type Conversion 87 6.4 Python Conditions 87 6.4.1 If Statements 88 6.4.2 The Else and Elif Clauses 89 6.4.3 The While Loop 90 6.4.3.1 The Break Statement 91 6.4.3.2 The Continue Statement 91 6.4.4 For Loops 91 6.4.4.1 Nested Loops 92 6.5 Python File Handling: Open, Read, and Close 93 6.6 Python Functions 93 6.6.1 Calling a Function in Python 94 6.6.2 Scope and Lifetime of Variables 94 6.7 Problems 95 7 Algorithms 97 7.1 Introduction 97 7.2 Algorithm – Definition 97 7.3 How toWrite an Algorithm 98 7.3.1 Algorithm Analysis 99 7.3.2 Algorithm Complexity 99 7.3.3 Space Complexity 100 7.3.4 Time Complexity 100 7.4 Asymptotic Analysis of an Algorithm 101 7.4.1 Asymptotic Notations 102 7.4.1.1 Big O Notation 102 7.4.1.2 The Omega Notation, Ω 102 7.4.1.3 The Θ Notation 102 7.5 Examples of Algorithms 104 7.6 Flowchart 104 7.7 Problems 105 8 Data Preprocessing and Data Validations 109 8.1 Introduction 109 8.2 Definition – Data Preprocessing 109 8.3 Data Cleaning 110 8.3.1 Handle Missing Data 110 8.3.2 Types of Missing Data 110 8.3.2.1 Missing Completely at Random 110 8.3.2.2 Missing at Random 110 8.3.2.3 Missing Not at Random 111 8.3.3 Techniques for Handling the Missing Data 111 8.3.3.1 Listwise Deletion 111 8.3.3.2 Pairwise Deletion 111 8.3.3.3 Mean Substitution 112 8.3.3.4 Regression Imputation 112 8.3.3.5 Multiple Imputation 112 8.3.4 Identify Outliers and Noisy Data 113 8.3.4.1 Binning 113 8.3.4.2 Box Plot 113 8.4 Data Transformatio
• Edition: Second edition
• Publisher Details: Hoboken : John Wiley & Sons, Inc
• Publication Date: 2021
• Extent: 1 online resource
• Subjects: 621.31042; Electromagnetic devices -- Design and construction; Power electronics; Electric machinery
• Languages: English
• ISBNs: 9781119674733
• Related ISBNs: 9781119674702
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.