Engineering mathematics (1版)
其他會員也一起購買
【簡介】
本書特色
1. 全面表格化整理、論述圖像化。
2. 習題區分基礎、進階題,有效掌握學習情況。
3. 每章附範例解題影音。
4. 新增線上教學頻道,包含作者已出版教科書之現場教學錄影,幫助讀者建立行動教室自我學習。
內容簡介
本教材內容相當豐富,在建立為工程與電資領域所用之數學為基礎的前提下分成「常微分方程式」、「線性代數」、「向量函數分析」、「傅立葉分析與偏微分方程式」及「複變分析」等五大部分,適合四年制大學部學生一學年上下兩學期各三學分,共六學分之工程數學課程,其重點分別簡介如下。
第一部分:常微分方程式(第一到四章)
幾乎所有大學中工程與電資領域相關系所在工程數學第一學期的教材內容都是以「常微分方程式」為主要內容,其內容包含有「一階常微分方程式」、「高階線性常微分方程式」、「拉氏轉換」與「常微分方程式之冪級數解」等四大主題重點。
第二部分:線性代數(第五到七章)
此部分教材內容包含了「向量運算與向量空間」、「矩陣分析」及「線性微分方程式系統」等三大重點。
第三部分:向量函數分析(第八章)
此部分內容包含向量微分、Del運算、線積分、面積分與積分三大定理(格林定理、高斯散度定理與史托克定理),其觀念與計算被大量應用在電磁學(電資領域)與流體力學(工程領域),是非常重要的單元。
第四部分:傅立葉分析與偏微分方程式(第九到十章)
本書中第九章介紹了正交函數集合與傅立葉分析,此單元在工程領域會用來求解第十章的偏微分方程式,除此之外,電資領域更大量應用到訊號分析與處理上。
第五部分:複變分析(第十一章)
本章共包含複數運算、複變函數與微分、複變函數積分、泰勒與洛朗展開式、留數定理及實變函數定積分等單元,本章節在工程與電資領域會用在一般物理學、熱力學、流體力學、自動控制、電路(子)學、訊號與系統與電機機械與控制等專業課程中。
目錄
【目錄】
Chapter 1 First-Order Ordinary Differential Equations
1-1 Introduction to Differential Equations
1-2 Separable First-Order ODEs
1-3 Exact ODEs and Integration Factor
1-4 Linear ODEs
1-5 Solving First-order ODEs with the Grouping Method
1-6 Application of First-Order ODEs
Chapter 2 High-Order Linear Ordinary Differential Equation
2-1 Basic Theories
2-2 Solving Higher-Order ODE with the Reduction of Order Method
2-3 Homogeneous Solutions of Higher-Order ODEs
2-4 Finding Particular Solution Using the Method of Undetermined Coefficients
2-5 Finding Particular Solution Using the Method of Variation of Parameters
2-6 Finding Particular Solution Using the Method of Inverse Differential Operators
2-7 Equidimensional Linear ODEs
2-8 The Applications of Higher-Order ODEs in Engineering
Chapter 3 Laplace Transform
3-1 The Definition of Laplace Transform
3-2 Basic Characteristic and Theorems
3-3 Laplace Transform of Special Functions
3-4 Laplace Inverse Transform
3-5 The Application of Laplace Transform
Chapter 4 Power Series Solution of Ordinary Differential Equations
4-1 Expansion at a Regular Point for Solving ODE
4-2 Regular Singular Point Expansion for Solving ODE (Selected Reading)
Chapter 5 Vector Operations and Vector Spaces
5-1 The Basic Operations of Vector
5-2 Vector Geometry
5-3 Vector Spaces Rn
Chapter 6 Matrix Operations and Linear Algebra
6-1 Matrix Definition and Basic Operations
6-2 Matrix Row (Column) Operations and Determinant
6-3 Solution to Systems of Linear Equations
6-4 Eigenvalues and Eigenvectors
6-5 Matrix Diagonalization
6-6 Matrix Functions
Chapter 7 Linear differential equation system
7-1 The Solution of a System of First-Order Simultaneous Linear Differential Equations
7-2 The Solution of a Homogeneous System of Simultaneous Differential Equations
7-3 Diagonalization of Matrix for Solving Non-Homogeneous System of Simultaneous Differential Equations
Chapter 8 Vector Function Analysis
8-1 Vector Functions and Differentiation
8-2 Directional Derivative
8-3 Line Integral
8-4 Multiple Integral
8-5 Surface Integral
8-6 Green’s Theorem
8-7 Gauss's Divergence Theorem
8-8 Stokes’ Theorem
Chapter 9 Orthogonal Functions and Fourier Analysis
9-1 Orthogonal Functions
9-2 Fourier Series
9-3 Complex Fourier Series and Fourier Integral
9-4 Fourier Transform
Chapter 10 Partial Differential Equation
10-1 Introduction to Partial Differential Equation (PDE)
10-2 Solving Second-Order PDE Using the Method of Separation of Variables
10-3 Solving Non-Homogeneous Partial Differential Equation
10-4 Solving PDE Using Integral Transformations
10-5 Partial Differential Equations in Non-Cartesian Coordinate System
Chapter 11 Complex Analysis
11-1 Basic Concepts of Complex Number
11-2 Complex Functions
11-3 Differentiation of Complex Functions
11-4 Integration of Complex Functions
11-5 Taylor Series Expansion and Laurent Series Expansion
11-6 Residue Theorem
11-7 Definite Integral of Real Variable Functions
原價:
1000
售價:
880
現省:
120元
立即查看
Learning Styles and Learning Strategies of University of Science and Technology
類似書籍推薦給您
立即查看
Computing and Technology Ethics: Engaging Through Science Fiction (1版)
類似書籍推薦給您
【簡介】
A new approach to teaching computing and technology ethics using science fiction stories.Should autonomous weapons be legal? Will we be cared for by robots in our old age? Does the efficiency of online banking outweigh the risk of theft? From communication to travel to medical care, computing technologies have transformed our daily lives, for better and for worse. But how do we know when a new development comes at too high a cost? Using science fiction stories as case studies of ethical ambiguity, this engaging textbook offers a comprehensive introduction to ethical theory and its application to contemporary developments in technology and computer science. Computing and Technology Ethics: Engaging through Science Fiction first introduces the major ethical frameworks: deontology, utilitarianism, virtue ethics, communitarianism, and the modern responses of responsibility ethics, feminist ethics, and capability ethics. It then applies these frameworks to many of the modern issues arising in technology ethics including privacy, computing, and artificial intelligence. A corresponding anthology of science fiction brings these quandaries to life and challenges students to ask ethical questions of themselves and their work. Uses science fiction case studies to make ethics education engaging and fun Trains students to recognize, evaluate, and respond to ethical problems as they ariseFeatures anthology of short stories from internationally acclaimed writers including Ken Liu, Elizabeth Bear, Paolo Bacigalupi, and T. C. Boyle to animate ethical challenges in computing technology Written by interdisciplinary author team of computer scientists and ethical theoristsIncludes a robust suite of instructor resources, such as pedagogy guides, story frames, and reflection questions
立即查看
Big Data Science in Finance: Mathematics and Applications (1版)
類似書籍推薦給您
【簡介】
Explains the mathematics, theory, and methods of Big Data as applied to finance and investingData science has fundamentally changed Wall Street--applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data.Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samplesExplains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD)Covers vital topics in the field in a clear, straightforward mannerCompares, contrasts, and discusses Big Data and Small DataIncludes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slidesBig Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.
立即查看
Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making (1版)
類似書籍推薦給您
【簡介】
Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science.Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven resultsTeaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist’s decision-making processCultivates critical thinking throughout the entire data science life cycleProvides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutionsSuitable for advanced undergraduate and graduate students, domain scientists, and practitioners
立即查看
UNDERSTANDING ANIMAL WELFARE - THE SCIENCE IN ITS CULTURAL CONTEXT 2ND EDITION (2版)
類似書籍推薦給您
立即查看