Yokogawa and DOCOMO Successfully Conduct Test of Remote Control Technology Using 5G, Cloud, and AI
Yokogawa Electric Corporation and NTT DOCOMO, INC. announced today that they have conducted a proof-of-concept test (PoC) of a remote control technology for industrial processing. The PoC test involved the use in a cloud environment of an autonomous control AI, the Factorial Kernel Dynamic Policy Programming (FKDPP) algorithm developed by Yokogawa and the Nara Institute of Science and Technology, and a fifth-generation (5G) mobile communications network provided by DOCOMO. The test, which successfully controlled a simulated plant processing operation, demonstrated that 5G is suitable for the remote control of actual plant processes.
Industrial Internet of Things: Real-time remote control of smart factory between Korea and Finland
The Electronics and Telecommunications Research Institute (ETRI) announced that it has succeeded in demonstrating the Industrial Internet of Things service that controls and monitors smart factory facilities and robots in real-time at home and abroad at the same time.
The core of successfully demonstrating the technology is ultra-low latency communication technology. The communication delay between a distance of over 10,000 km is less than 0.3 seconds. It has been demonstrated that factory facilities in Gyeongsan, Gyeongsangbuk-do can be controlled in real-time seamlessly from the University of Oulu in Finland.
“We see AI as a disruptive technology that will in the long run eliminate, and in the near term reduce the need for verification,” says Anupam Bakshi, CEO and founder of Agnisys. “We have had some early successes in using machine learning to read user specifications in natural language and directly convert them into SystemVerilog Assertions (SVA), UVM testbench code, and C/C++ embedded code for test and verification.”
There is nothing worse than spending time and resources to not get the desired result, or for it to take longer than necessary. “In formal, we have multiple engines, different algorithms that are working on solving any given property at any given time,” says Pete Hardee, director for product management at Cadence. “In effect, there is an engine race going on. We track that race and see for each property which engine is working. We use reinforcement learning to set the engine parameters in terms of which engines I’m going to use and how long to run those to get better convergence on the properties that didn’t converge the first time I ran it.”
BMW Creates Fully Automated Production Lines for 3D Printed Car Parts
By utilizing systems made up of laser powder bed fusion (LPBF) platforms, combined with AI and robotics, that it has developed, the IDAM consortium can print 50,000 series parts a year, as well as 10,000 new and individual parts. Opened in 2020, BMW’s campus at Oberschleißheim has 50 3D printers for both metal and plastics. Aside from investing in a variety of 3D printing startups, including Desktop Metal and Xometry, the company also employs HP MultiJet Fusion (MJF) and EOS machines, among other brands.
Smart factories need smarter cyber defence
From a ransomware perspective, manufacturers are quite exposed to time-driven critical processes, Heppenstall notes. “So, if you can cause a disruption, manufacturers are perceived to be more prone and therefore more likely to pay a ransom. Companies don’t run dual manufacturing processes.”
Engine block assembly line for Scania's trucks of tomorrow
Detecting dangerous gases to improve safety and reduce emissions
The primary advantage of differential optical absorption spectroscopy is its scalability. Two elements are required: a calibrated light source tuned to emit a specific wavelength, and a receiver able to read the same wavelength. In some cases, the receiver must also read a reference source for comparison. The two elements can be within the same housing to function as a point detector, but the source and receiver can also be separated, sending a beam across an open path, looking for a cloud of the target gas to move into its field of view.
A Step by Step Guide to Robot Arm Demo
Assuming we are operating a smart warehouse optimized for an e-commerce company. In the warehouse, we employ several, “intelligent robots mover” to help us to move objects from spot to spot. In this demonstration, we have used a miniaturized, “intelligent robots mover” powered by Qeexo AutoML to determine whether the robot griped an object.
This blog is intended to show you how to use Qeexo AutoML to build your own, “intelligent robots mover” from end to end, including data collection, data segmentation, model training and evaluation, and live testing.
The US baby formula shortage continues as Abbott ramps back up production for specialty formulas. China is dominating the collaborative robot market while industrial robot orders in the US rose by a record 40%. Ford is creating 6200 jobs in the Midwest while Tesla orders employees back to the office and plans to cut 10% of salaried staff. Rivian faces ‘production hell’.