How To Learn Scada
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Spartan Focus and The Feynman Technique
THOSE WHO LEARN NEW SKILLS DON’T DO SO BECAUSE THEY HAVE THE TIME; THEY DO SO BECAUSE THEY HAVE THE TECHNIQUE This book provides very actionable principles and techniques that can be used to learn new skills amidst your busy schedule. In today’s fast-paced world, learning new skills is an essential part of personal and professional growth. However, many people, though painfully aware of their need, struggle to find the time to learn these skills that they have identified as crucial to the overall improvement of their lives and careers. The reality we must confront as we try to deal with this situation, however, is that we would never have more time than we presently do; this is because, often times, what we call “more time” comes as a result of learning certain skills that put us in a position where we can do the things we want to do with our time, but these are often the very skills we currently don’t have the time to learn. This book was written to address this situation by teaching a very simple concept which readers can apply to learn new skills more efficiently and effectively when they simply don’t have the time. Whether you’re a busy professional wanting to acquire new job skills, or a student wanting to learn an important subject, or a busy stay-at-home mom desiring to attend to your personal development, SPARTAN FOCUS AND THE FEYNMAN TECHNIQUE will help you learn faster, better, and more efficiently in an interesting way, even when you don't have the time.
Application of Machine Learning and Deep Learning Methods to Power System Problems
Author: Morteza Nazari-Heris
language: en
Publisher: Springer Nature
Release Date: 2021-10-20
This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.
Explainable AI Within the Digital Transformation and Cyber Physical Systems
Author: Moamar Sayed-Mouchaweh
language: en
Publisher: Springer Nature
Release Date: 2021-10-30
This book presents Explainable Artificial Intelligence (XAI), which aims at producing explainable models that enable human users to understand and appropriately trust the obtained results. The authors discuss the challenges involved in making machine learning-based AI explainable. Firstly, that the explanations must be adapted to different stakeholders (end-users, policy makers, industries, utilities etc.) with different levels of technical knowledge (managers, engineers, technicians, etc.) in different application domains. Secondly, that it is important to develop an evaluation framework and standards in order to measure the effectiveness of the provided explanations at the human and the technical levels. This book gathers research contributions aiming at the development and/or the use of XAI techniques in order to address the aforementioned challenges in different applications such as healthcare, finance, cybersecurity, and document summarization. It allows highlighting the benefits and requirements of using explainable models in different application domains in order to provide guidance to readers to select the most adapted models to their specified problem and conditions. Includes recent developments of the use of Explainable Artificial Intelligence (XAI) in order to address the challenges of digital transition and cyber-physical systems; Provides a textual scientific description of the use of XAI in order to address the challenges of digital transition and cyber-physical systems; Presents examples and case studies in order to increase transparency and understanding of the methodological concepts.