Analytics

Most-viewed university articles in 2019

The most-viewed articles from university sources included stories on drag-and-drop analytics, robotic actuators, nanocrystals, and prolonging power plant life with AI. Links to each article below.
By Chris Vavra February 13, 2020
For years, researchers from MIT and Brown University have been developing an interactive system that lets users drag-and-drop and manipulate data on any touchscreen, including smartphones and interactive whiteboards. Now, they’ve included a tool that instantly and automatically generates machine-learning models to run prediction tasks on that data. Courtesy: Melanie Gonick, MIT

Hot topics from university sources published in 2019 at Control Engineering included drag-and-drop analytics, robotic actuators, nanocrystals, and prolonging power plant life with AI. Catch up on what you may have missed using the links below.

1. Drag-and-drop data analytics using machine learning, Massachusetts Institute of Technology (MIT), June 28: MIT researchers have developed a system that lets nonspecialists use machine-learning (ML) models to make predictions for medical research, sales, and more.

2. Automated system generates robotic actuators, MIT, July 18: MIT researchers have developed an automated system that designs and 3-D prints robotic actuators, which are optimized and automatically created, which is almost impossible for a human to do.

3. Diamond-based quantum sensor fabricated on a silicon chip, MIT, Sept. 26: MIT researchers have fabricated a diamond-based quantum sensor on a silicon chip. The advance, called a nitrogen-vacancy (NV) center, could pave the way toward low-cost, scalable hardware for quantum computing, sensing, and communication.

4. Nanocrystals improve quantum dot manufacturing for process monitoring applications, North Carolina State University, March 27: North Carolina State University researchers have developed a system for synthesizing perovskite quantum dots to reduce manufacturing costs for real-time process monitoring to help ensure quality control.

In-flow QD anion exchange. Three UV-illuminated snapshots of the continuous room-temperature anion exchange reactions of the pristine CsPbBr3 QDs (middle spiral) with 7.5 × 10−3 m ZnCl2 (left spiral) and 10 × 10−3 m ZnI2 (right spiral) with corresponding perovskite nanostructure illustrations. Courtesy: Milad Abolhasani/North Carolina State University

In-flow QD anion exchange. Three UV-illuminated snapshots of the continuous room-temperature anion exchange reactions of the pristine CsPbBr3 QDs (middle spiral) with 7.5 × 10−3 m ZnCl2 (left spiral) and 10 × 10−3 m ZnI2 (right spiral) with corresponding perovskite nanostructure illustrations. Courtesy: Milad Abolhasani/North Carolina State University

5. Prolonging power plant life through artificial intelligence, West Virginia University, Sept. 25: A West Virginia University chemical engineer is tapping into artificial intelligence (AI) to prolong the lives of power plant boilers.

6. Supersonic jet injector accelerates nanoscale additive manufacturing, Georgia Institute of Technology, July 19: Georgia Tech researchers are using a supersonic jet injector to accelerate nanoscale additive manufacturing used for applications such as electronic circuitry and superconducting materials.

7. Low-power hybrid chip makes small robots more capable, Georgia Institute of Technology, March 8: Researchers from the Georgia Institute of Technology demonstrated robotic cars that use an ultra-low power hybrid chip to give palm-sized robots the ability to collaborate and learn from their experiences like the human brain does. See video.

A robotic car controlled by an ultra-low power hybrid chip is placed into an arena to demonstrate its ability to learn and collaborate with another robot. Courtesy: Allison Carter, Georgia Tech

A robotic car controlled by an ultra-low power hybrid chip is placed into an arena to demonstrate its ability to learn and collaborate with another robot. Courtesy: Allison Carter, Georgia Tech

8. Machine learning used to determine warehouse ergonomics for worker safety, University of Washington, Aug. 23: Researchers at the University of Washington used machine learning to develop a system that monitors factory and warehouse workers and tell them, in real time, how risky their behaviors are.

9. Mobile process sensor technology developed to identify plant diseases, North Carolina State University, July 31: North Carolina State University researchers have developed a mobile process sensor that uses analytics to identify plant diseases in the field by sampling airborne volatile organic compounds (VOCs) plants release through their leaves with a mobile instrumentation device.

10. Control your career with a personal board of directors, Georgia Institute of Technology, Aug. 16: A personal board of directors is a broader version of a mentorship with many people assisting your engineering career journey from a variety of viewpoints. See four ways to begin.

Chris Vavra, associate editor, CFE Media, cvavra@cfemedia.com.


Chris Vavra
Author Bio: Chris Vavra is an associate editor for Control Engineering and has worked for the magazine since 2011. He edits articles on all automation topics and has written on topics including robotics, power generation, IIoT, AI/machine learning, and more. He has a Bachelor of Arts in English Literature degree from North Central College and is also a self-published crime/mystery novelist on Amazon.