scholarly journals Machine Learning for Smart Electronic Systems

2021 ◽  
Vol 67 (4) ◽  
pp. 224-225
Author(s):  
Fernando Pescador ◽  
Saraju P. Mohanty
Keyword(s):  
Machine Learning ◽  
Electronic Systems

(Keynote) Machine Learning and High-Speed Circuitry in Thin Film Transistors for Sensor Interfacing in Hybrid Large-Area Electronic Systems

2019 ◽  
Vol 92 (4) ◽  
pp. 121-134
Author(s):  
James Sturm ◽  
Yoni Mehlman ◽  
Levent E. Aygun ◽  
Can Wu ◽  
Z Zheng ◽  
...  
Keyword(s):  
Thin Film ◽  
Machine Learning ◽  
Thin Film Transistors ◽  
High Speed ◽  
Electronic Systems ◽  
Large Area

scholarly journals Machine Learning for Malware Detection: Beyond Accuracy Rates

2019 ◽  
Author(s):  
Lucas Galante ◽  
Marcus Botacin ◽  
André Grégio ◽  
Paulo De Geus
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Malware Detection ◽  
Detection Accuracy ◽  
Electronic Systems ◽  
Dynamic Features ◽  
Daily Lives ◽  
Feedback Information ◽  
The Cost ◽  
Accuracy Rates

Today's world is supported by connected, electronic systems, thus ensuring their secure operation is essential to our daily lives. A major threat to system's security is malware infections, which cause financial and image losses to corporate and end-users, thus motivating the development of malware detectors. In this scenario, Machine Learning (ML) has been demonstrated to be a powerful technique to develop classifiers able to distinguish malware from goodware samples. However, many ML research work on malware detection focus only on the final detection accuracy rate and overlook other important aspects of classifier's implementation and evaluation, such as feature extraction and parameter selection. In this paper, we shed light to these aspects to highlight the challenges and drawbacks of ML-based malware classifiers development. We trained 25 distinct classification models and applied them to 2,800 real x86, Linux ELF malware binaries. Our results shows that: (i) dynamic features outperforms static features when the same classifiers are considered; (ii) Discrete-bounded features present smaller accuracy variance over time in comparison to continuous features, at the cost of some time-localized accuracy loss; (iii) Datasets presenting distinct characteristics (e.g., temporal changes) impose generalization challenges to ML models; and (iv) Feature analysis can be used as feedback information for malware detection and infection prevention. We expect that our work could help other researchers when developing their ML-based malware classification solutions.

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 Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vahid Samavatian ◽  
Mahmud Fotuhi-Firuzabad ◽  
Majid Samavatian ◽  
Payman Dehghanian ◽  
Frede Blaabjerg
Keyword(s):  
Machine Learning ◽  
Thermal Cycling ◽  
Solder Joints ◽  
Electronic Materials ◽  
Electronic Devices ◽  
Reliability Assessment ◽  
Electronic Systems ◽  
Learning Framework ◽  
Useful Lifetime

Abstract The quantity and variety of parameters involved in the failure evolutions in solder joints under a thermo-mechanical process directs the reliability assessment of electronic devices to be frustratingly slow and expensive. To tackle this challenge, we develop a novel machine learning framework for reliability assessment of solder joints in electronic systems; we propose a correlation-driven neural network model that predicts the useful lifetime based on the materials properties, device configuration, and thermal cycling variations. The results indicate a high accuracy of the prediction model in the shortest possible time. A case study will evaluate the role of solder material and the joint thickness on the reliability of electronic devices; we will illustrate that the thermal cycling variations strongly determine the type of damage evolution, i.e., the creep or fatigue, during the operation. We will also demonstrate how an optimal selection of the solder thickness balances the damage types and considerably improves the useful lifetime. The established framework will set the stage for further exploration of electronic materials processing and offer a potential roadmap for new developments of such materials.

Nonintrusive Monitoring of Mental Fatigue Status Using Epidermal Electronic Systems and Machine-Learning Algorithms

ACS Sensors ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 1305-1313 ◽  
Author(s):  
Zhikang Zeng ◽  
Zhao Huang ◽  
Kangmin Leng ◽  
Wuxiao Han ◽  
Hao Niu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Mental Fatigue ◽  
Machine Learning Algorithms ◽  
Electronic Systems ◽  
Nonintrusive Monitoring

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>

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