An overview of artificial intelligence applications for power electronics

2020 ◽  
Author(s):  
Shuai Zhao ◽  
Frede Blaabjerg ◽  
Huai Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fuzzy Logic ◽  
Data Structure ◽  
Power Electronics ◽  
Practical Implementation ◽  
Electronic Systems ◽  
Power Electronic ◽  
Implementation Challenges ◽  
The Common

<div>This is a preprint version of the manuscript submitted to IEEE on June 4, 2020.</div><div><br></div><div>This paper gives an overview of the Artificial Intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics.<br></div>

scholarly journals An overview of artificial intelligence applications for power electronics

2020 ◽  
Author(s):  
Shuai Zhao ◽  
Frede Blaabjerg ◽  
Huai Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fuzzy Logic ◽  
Data Structure ◽  
Power Electronics ◽  
Practical Implementation ◽  
Electronic Systems ◽  
Power Electronic ◽  
Implementation Challenges ◽  
The Common

<div>This is a preprint version of the manuscript submitted to IEEE on June 4, 2020.</div><div><br></div><div>This paper gives an overview of the Artificial Intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics.<br></div>

scholarly journals An overview of artificial intelligence applications for power electronics

2020 ◽  
Author(s):  
Shuai Zhao ◽  
Frede Blaabjerg ◽  
Huai Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fuzzy Logic ◽  
Data Structure ◽  
Power Electronics ◽  
Practical Implementation ◽  
Electronic Systems ◽  
Power Electronic ◽  
Implementation Challenges ◽  
The Common

<div>This is a preprint version of the manuscript submitted to IEEE on June 4, 2020.</div><div><br></div><div>This paper gives an overview of the Artificial Intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics.<br></div>

scholarly journals The Interplay of Knowledge Representation with Various Fields of Artificial Intelligence in Medicine

2019 ◽  
Vol 28 (01) ◽  
pp. 027-034 ◽  
Author(s):  
Laszlo Balkanyi ◽  
Ronald Cornet
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Knowledge Representation ◽  
Expert Systems ◽  
Frequency Analysis ◽  
Medical Subject Headings ◽  
Subject Headings

Introduction: Artificial intelligence (AI) is widespread in many areas, including medicine. However, it is unclear what exactly AI encompasses. This paper aims to provide an improved understanding of medical AI and its constituent fields, and their interplay with knowledge representation (KR). Methods: We followed a Wittgensteinian approach (“meaning by usage”) applied to content metadata labels, using the Medical Subject Headings (MeSH) thesaurus to classify the field. To understand and characterize medical AI and the role of KR, we analyzed: (1) the proportion of papers in MEDLINE related to KR and various AI fields; (2) the interplay among KR and AI fields and overlaps among the AI fields; (3) interconnectedness of fields; and (4) phrase frequency and collocation based on a corpus of abstracts. Results: Data from over eighty thousand papers showed a steep, six-fold surge in the last 30 years. This growth happened in an escalating and cascading way. A corpus of 246,308 total words containing 21,842 unique words showed several hundred occurrences of notions such as robotics, fuzzy logic, neural networks, machine learning and expert systems in the phrase frequency analysis. Collocation analysis shows that fuzzy logic seems to be the most often collocated notion. Neural networks and machine learning are also used in the conceptual neighborhood of KR. Robotics is more isolated. Conclusions: Authors note an escalation of published AI studies in medicine. Knowledge representation is one of the smaller areas, but also the most interconnected, and provides a common cognitive layer for other areas.

scholarly journals Investigation of reliability assessement in power electronics circuits using machine learning

2021 ◽  
Vol 12 (1) ◽  
pp. 558
Author(s):  
Soumya Rani Mestha ◽  
Pinto Pius A.J
Keyword(s):  
Machine Learning ◽  
Power Electronics ◽  
New Technologies ◽  
Remaining Useful Life ◽  
Power Electronic ◽  
Electronic Components ◽  
Recent Advances ◽  
Useful Life ◽  
Comprehensive Study

<p>Recent advances in power electronics (PE) and machine learning (ML) have prompted the technologists to adapt these new technologies to improve the reliability of PE systems. During the process, a lot of investigations on the performance and reliability of PE systems is carried out. The intention of this paper is to present a comprehensive study of advances in the field of reliability of PE systems using machine learning. Recent publications in this regard are analysed and findings are tabulated. In addition to this, literatures published in the prediction of remaining useful life (RUL) of power electronic components is discussed with emphasis on its limitations.</p>

scholarly journals Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda

2021 ◽  
Vol 13 (4) ◽  
pp. 2025 ◽  
Author(s):  
Fotis Kitsios ◽  
Maria Kamariotou
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Theoretical Model ◽  
Business Strategy ◽  
Management Practices ◽  
Corporate Strategy ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Practical Implementation ◽  
Learning Techniques

In the past decade, current literature and businesses have drawn attention to Artificial Intelligence (AI) tools and in particular to the advances in machine learning techniques. Nevertheless, while the AI technology offers great potential to solve difficulties, challenges remain implicated in practical implementation and lack of expertise in the strategic usage of AI to create business value. This paper aims to implement a systematic literature review analyzing convergence of the AI and corporate strategy and develop a theoretical model incorporating issues based on the existing research in this field. Eighty-one peer-reviewed articles were discussed on the basis of research methodology from Webster and Watson (2002). In addition to gaps in future research, a theoretical model is developed, discussing the four sources of value creation: AI and Machine Learning in organizations; alignment of AI tools and Information Technology (IT) with organizational strategy; AI, knowledge management and decision-making process; and AI, service innovation and value. These outcomes lead to both theoretical and managerial viewpoints, with extensive possibilities to generate new methods and types of management practices.

scholarly journals Artificial intelligence: A cutting edge technology in agriculture

2021 ◽  
Vol 17 (1) ◽  
pp. 114-120
Author(s):  
Sidhant Allawadi ◽  
Jayaty ◽  
Parmod Sharma ◽  
Kapil Rohilla ◽  
Gopal Deokar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Computer Vision ◽  
Cutting Edge ◽  
High Quality ◽  
High Expectations ◽  
Advanced Technologies ◽  
The Common ◽  
Smart Technologies

Attention is currently being paid to the use of smart technologies. Agriculture has provided an important source of food for humans over thousands of years, including the development of appropriate farming methods for the cultivation of different crops. The emergence of new advanced technologies has the potential to monitor the agricultural environment to ensure high-quality produce. In this context, a systematic review that aimsto study the application of various technologies and algorithms in Artificial Intelligence (AI) with the latest solutions to make the farming more efficient remains one of the greatest imperatives. Artificial intelligence can be applied directly in the field of agriculture for various operations. Amid high expectations about how AI will help the common personand transform his mindset, thoughts and attitude towards the benefits that it may bring. There are certain concerns about the ill effects of such sophisticated technologies as well.This review also focuses on the activation of perceptive technologies and application of computer vision and machine learning in agriculture.

Artificial Intelligence

2017 ◽  
pp. 454-474 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Fuzzy Logic ◽  
Polynomial Regression ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Complex Data ◽  
Evolutionary Polynomial Regression

This chapter explains the Artificial Intelligence (AI) techniques in terms of Artificial Neural Networks (ANNs), fuzzy logic, expert systems, machine learning, Genetic Programming (GP), Evolutionary Polynomial Regression (EPR), and Support Vector Machine (SVM); the AI applications in modern education; the AI applications in software engineering development; the AI applications in Content-Based Image Retrieval (CBIR); and the multifaceted applications of AI in the digital age. AI is a branch of science which deals with helping machines find the suitable solutions to complex problems in a more human-like manner. AI technologies bring more complex data-analysis features to the existing applications in various industries and greatly contribute to management's organization, planning, and controlling operations, and will continue to do so with more frequency as programs are refined.

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