Getting Smarter About Smart Machines for Education

2004 ◽  
Vol 49 (6) ◽  
pp. 694-698
Author(s):  
Janet L. Kolodner
Keyword(s):  
Smart Machines

scholarly journals Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 487 ◽  
Author(s):  
Mahmoud Elsisi ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
Mohamed M. F. Darwish
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Industry 4.0 ◽  
Learning Algorithm ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Smart Meters ◽  
Industrial Environment ◽  
Physical Systems ◽  
Smart Machines

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

Multi-functional dielectric elastomer artificial muscles for soft and smart machines

2012 ◽  
Vol 112 (4) ◽  
pp. 041101 ◽  
Author(s):  
Iain A. Anderson ◽  
Todd A. Gisby ◽  
Thomas G. McKay ◽  
Benjamin M. O’Brien ◽  
Emilio P. Calius
Keyword(s):  
Dielectric Elastomer ◽  
Artificial Muscles ◽  
Smart Machines

scholarly journals Smart Machines and Long-Term Misery

2012 ◽  
Author(s):  
Jeffrey Sachs ◽  
Laurence Kotlikoff
Keyword(s):  
Smart Machines

Business Management Learning

2019 ◽  
pp. 307-327 ◽  
Author(s):  
Gürcan Banger
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Robots ◽  
Collaborative Work ◽  
Business Management ◽  
Management Learning ◽  
New Learning ◽  
Management Paradigms ◽  
Learning Platforms ◽  
Smart Machines ◽  
Applications Of Machine Learning

The Transhumanist future will be an age of data dominance, pervasive computing, artificial intelligence, smart machines, and autonomous mobile robots accompanied by a vast speed and ever-increasing acceleration of change. The pervasive and ongoing change requires a fundamental re-invention of business management which should coincide with the conditions of the converging transhumanism age. The main feature of the future management paradigms that differ from the traditional style will undoubtedly be the artificial intelligence with several applications of machine learning and humans' collaborative work with associate-like autonomous robots. Managers at all levels will have to adapt to the world of artificial intelligence and smart environment. The transhumanist manager should learn and get equipped with the necessary management requirements. The new learning platforms, methods, techniques, and media should be researched to get prepared for a transhumanist business management future with a faster alacrity to compensate for the speed of the technological progress.

A Framework of Enhanced Performance

2018 ◽  
Vol 8 (3) ◽  
pp. 1-17 ◽  
Author(s):  
Md. Abul Kalam Siddike ◽  
Jim Spohrer ◽  
Haluk Demirkan ◽  
Youji Kohda
Keyword(s):  
Data Analysis ◽  
Theoretical Framework ◽  
Cognitive Computing ◽  
Daily Lives ◽  
People’S Daily ◽  
Smart Machines ◽  
People's Daily ◽  
The Impact

While cognitive computing-enabled smart computers are growing in people's daily lives, there are not many studies that explain how people interact and utilize these solutions, and the impact of these smart machines to people's performance to do things. In this article, a theoretical framework for boosting people's performance using cognitive assistants (CAs) was developed and explained using the data analysis from 32 interviews. The results show that people interaction with CAs enhance their levels of cognition and intelligence that help them to enhance their capabilities. Enhanced capabilities help people to enhance their performance.

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