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ISF Series Ring-type Industrial Ethernet Switch
Reliable and cost-effective solution for simple ring network
Building ring networks usually requires managed switches. However, for simple ring networking, customer may use one managed switch coupled with a certain number of ISF switches. Price of ISF ring-type switches is much lower than that of managed switches, thus will significantly cut down equipment investment while not compromising network performance.
Best Value Simple Ring Solution
To build a simple ring network, instead of using multiple managed switches, customer may use one managed switch coupled with a certain number of ISF switches.
Price of ISF ring-type switches is much lower than that of managed switches, thus will largely cut down the equipment investment.  Reliable Communications
With iRing protocol, the ISF ring-type switch can build up self-healing redundant ring in<30ms@100 sets, ensuring reliable and stable communications on industrial sites.
● Real-time monitoring of each node on iRing network, ensuring reliable network operation. ● VLAN network partitioning, enhancing bandwidth efficiency and network security  Rugged for Industrial Environments
The ISF switch is also rugged to withstand unforgiving industrial conditions.
● Wide operating temperature: -40°C~+85°C ● Passed EMC level 3 + Solid aluminum alloy shell, IP40 ● Industrial-grade redundant power supply ● MTBF (Mean Time between Failures) > 35 years  Efficient Deployment and Management
The ISF switch is plug and play, enabling fast network deployment or device replacement when needed. The auto relay alarms can help engineers detect faults promptly and manage the network efficiently.
● Auto relay alarms of power failure and port down for fast fault detection ● Standard DIN rail and wall mounting, compact-sized to fit easily in racks and cabinets  |
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Background Metro systems, the veins of modern metropolises, have changed the way of urban transit. The construction and operation of metro systems brought about good business opportunities. But accidents happen, casualties in metro accidents have increased significantly in recent years. Metro construction and operation companies need up-to-date safety management means and technologies to prevent accidents and to minimize casualties when they happen. A comprehensive metro station environment monitoring solution is developed by Schneider Electric to provide real-time monitoring of the environment of metro stations, pre-alert to accidents and emergencies, and safety protection of the metro system. InHand Networks is honored to contribute a part in this solution. System Requirements ● Support multiple ring protocols, including RSTP, G.8032 and iRing ● Instant ring response is within 50ms ● VLAN partitioning and data isolation ● Support DRSTP ● Operate reliably under extreme temperatures, e.g. down to -45℃ or up to +85℃ ● EMC reaches class 4
Network Topology
How It Works The InHand ISM2008D managed switches form a reliable ring network in the metro station that connects all the Schneider PLCs and I/O nodes to conduct comprehensive monitoring of the environment of the metro station and related controlling:
Advantages ● Using highly-reliable and stable ISM2008D series switches ● Support RSTP/STP for fast ring recovery, self-recovery of ring is less than 50ms ● May build ring mixing with products of other brands (that support RSTP/STP), meeting the compatibility requirement for networking ● ISM2008D is highly-reliable and can maintain long hours of stable operation under harsh operating conditions, featuring operating temperature of -40℃~+85℃, IP40 protection, seamless metal housing, PCB protective coating (optional), input voltage tolerance of 18~36VDC, and power terminal block with reverse connection and over-current protection. The product passed EMC testing and safety certification of China Electric Power Research Institute. |
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There are two ways to teach a robot to do something: Demonstrate the task and make it replicate it, or program the task directly into its software. The first method usually means the robot is limited to performing a very specific task and isn’t able to transfer its skill to different situations, while the second method is usually time-consuming and requires skilled programmers to carry it out. But what if it was possible to combine these two techniques? Researchers at the Massachusetts Institute of Technology (MIT) have developed a system that bridges both methods, enabling robots to transfer skills and knowledge amongst themselves. While it may sound like the beginning of a robot takeover, the new approach may prove useful in urgent situations, such as bomb disposal and disaster response. The C-LEARN technique—described by the researchers in a paper recently accepted to the IEEE International conference on Robotics and Automation (ICRA)—involves providing a robot with a knowledge base of information of how to perform a task. A 3D interface is then used to show the robot a single demonstration of a specific task, allowing it to understand the motions it is being taught in the real world. Tech & Science Emails and Alerts - Get the best of Newsweek Tech & Science delivered to your inbox “This approach is actually very similar to how humans learn in terms of seeing how something’s done and connecting it to what we already know about the world,” said Claudia Pérez-D’Arpino, a PhD student who has written about the C-LEARN technique. “We can’t magically learn from a single demonstration, so we take new information and match it to previous knowledge about our environment.” The teaching approach is similar to how humans learn. MIT CSAIL “Having a knowledge base is fairly common, but what’s not common is integrating it with learning from demonstration,” said Dmitry Berenson, an assistant professor of computer science at the University of Michigan, who was not involved in the research. “That’s very helpful, because if you are dealing with the same objects over and over again, you don’t want to then have to start from scratch to teach the robot every new task.” A robot is therefore able to figure out how to perform a task even if there are obstacles in the way, as it does not simply learn one specific way to perform the action. “It’s good for the field that we’re moving away from directly imitating motion, towards actually trying to infer the principles behind the motion,” Berenson said. “By using these learned constraints in a motion planner, we can make systems that are far more flexible than those which just try to mimic what’s being demonstrated.” |