Intelligent Reliability Analysis Using Matlab And Ai
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Intelligent Reliability Analysis Using MATLAB and AI
Author: Dr. Cherry Bhargava
language: en
Publisher: BPB Publications
Release Date: 2021-06-21
How to minimize the global problem of e-waste KEY FEATURES ● Explore core concepts of Reliability Analysis, various smart models, different electronic components, and practical use of MATLAB. ● Cutting edge coverage on building intelligent systems for reliability analysis. ● Includes numerous techniques and methods to identify failure and reliability parameters. DESCRIPTION Intelligent Reliability Analysis using MATLAB and AI explains a roadmap to analyze and predict various electronic components’ future life and performance reliability. Deeply narrated and authored by reliability experts, this book empowers the reader to deepen their understanding of reliability identification, its significance, preventive measures, and various techniques. The book teaches how to predict the residual lifetime of active and passive components using an interesting use case on electronic waste. The book will demonstrate how the capacity of re-usability of electronic components can benefit the consumer to reuse the same component, with the confidence of successful operations. It lists key attributes and ways to design experiments using Taguchi’s approach, based on various acceleration factors. This book makes it easier for readers to understand reliability modeling of active and passive components using the Artificial Neural Network, Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System (ANFIS). The book keeps you engaged with a systematic and detailed explanation of step-wise MATLAB-based implementation of electronic components. These explanations and illustrations will help the readers to predict fault and failure well before time. WHAT YOU WILL LEARN ● Optimize various acceleration factors for exploring the residual life of components experimentally. ● Design an intelligent model to predict the upcoming faults and failures of electronic components and make provision for timely replacement of the fault components. ● Design experiments using Taguchi’s approach. ● Understand reliability modeling of active and passive components using the Artificial Neural Network and Fuzzy Logic. WHO THIS BOOK IS FOR This book is for current and aspiring emerging tech professionals, researchers, students, and anyone who wishes to understand and diagnose the product life of electronic components using the power of artificial intelligence and various experimental techniques. TABLE OF CONTENTS 1. RELIABILITY FUNDAMENTALS 2. RELIABILITY MEASURES 3. REMAINING USEFUL LIFETIME ESTIMATION TECHNIQUES 4. INTELLIGENT MODELS FOR RELIABILITY PREDICTION 5. ACCELERATED LIFE TESTING 6. EXPERIMENTAL TESTING OF ACTIVE AND PASSIVE COMPONENTS 7. INTELLIGENT MODELING FOR RELIABILITY ASSESSMENT USING MATLAB
Data Analytics: Principles, Tools, and Practices
A Complete Data Analytics Guide for Learners and Professionals. KEY FEATURES ● Learn Big Data, Hadoop Architecture, HBase, Hive and NoSQL Database. ● Dive into Machine Learning, its tools, and applications. ● Coverage of applications of Big Data, Data Analysis, and Business Intelligence. DESCRIPTION These days critical problem solving related to data and data sciences is in demand. Professionals who can solve real data science problems using data science tools are in demand. The book “Data Analytics: Principles, Tools, and Practices” can be considered a handbook or a guide for professionals who want to start their journey in the field of data science. The journey starts with the introduction of DBMS, RDBMS, NoSQL, and DocumentDB. The book introduces the essentials of data science and the modern ecosystem, including the important steps such as data ingestion, data munging, and visualization. The book covers the different types of analysis, different Hadoop ecosystem tools like Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. It also includes the different machine learning techniques that are useful for data analytics and how to visualize data with different graphs and charts. The book discusses useful tools and approaches for data analytics, supported by concrete code examples. After reading this book, you will be motivated to explore real data analytics and make use of the acquired knowledge on databases, BI/DW, data visualization, Big Data tools, and statistical science. WHAT YOU WILL LEARN ● Familiarize yourself with Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. ● Learn to manage data warehousing with real time transaction processing. ● Explore various machine learning techniques that apply to data analytics. ● Learn how to visualize data using a variety of graphs and charts using real-world examples from the industry. ● Acquaint yourself with Big Data tools and statistical techniques for machine learning. WHO THIS BOOK IS FOR IT graduates, data engineers and entry-level professionals who have a basic understanding of the tools and techniques but want to learn more about how they fit into a broader context are encouraged to read this book. TABLE OF CONTENTS 1. Database Management System 2. Online Transaction Processing and Data Warehouse 3. Business Intelligence and its deeper dynamics 4. Introduction to Data Visualization 5. Advanced Data Visualization 6. Introduction to Big Data and Hadoop 7. Application of Big Data Real Use Cases 8. Application of Big Data 9. Introduction to Machine Learning 10. Advanced Concepts to Machine Learning 11. Application of Machine Learning
Kecerdasan Buatan
Author: Pastima Simanjuntak
language: id
Publisher: Yayasan Tri Edukasi Ilmiah
Release Date: 2024-11-07
Buku Ajar Kecerdasan Buatan ini disusun untuk menjadi panduan yang lengkap bagi mahasiswa, dosen, dan praktisi dalam memahami berbagai konsep dan penerapan kecerdasan buatan (AI). Buku ini dimulai dengan pengenalan dasar AI, mencakup pengertian, tujuan, serta perbedaannya dengan kecerdasan alami. Selanjutnya, pembaca diajak memahami peran penting algoritma dalam AI dan langkah-langkah analisis data yang menjadi inti dari proses pengembangan kecerdasan buatan. Pembahasan kemudian berlanjut ke berbagai teknik pencarian (searching) dan metode representasi pengetahuan, termasuk logika fuzzy dan sistem pakar yang membantu AI dalam mengambil keputusan. Di dalam buku ini, juga dijelaskan konsep dan jenis jaringan saraf tiruan serta algoritma genetika, yang merupakan pendekatan AI yang meniru pola biologis untuk menghasilkan solusi optimal. Tidak hanya teori, buku ini mengupas aspek aplikasi AI dalam dunia nyata, seperti di bidang bisnis, sistem operasi, dan e-commerce. Pembahasan mengenai tantangan, etika, serta masa depan AI juga diulas untuk memberikan wawasan kepada pembaca mengenai perkembangan teknologi ini di masa depan. Dengan struktur yang sistematis dan bahasa yang mudah dipahami, buku ini diharapkan menjadi referensi yang bermanfaat bagi para pembaca yang ingin mendalami dunia kecerdasan buatan.