Ladder logic stems from the history of relays. At one time, relays were the primary control for most automatic systems. These electromechanical devices consisted of coils and contacts that they moved. Energized coils moved their contacts from their resting position to their active position (either closed to open or open to closed.)
Ladder logic is shaped like an actual ladder, and is read from top to bottom/left to right.
In practical application, a ladder diagram showed how to wire relays together. This diagram looked like a drawing of a standard household ladder with uprights and rungs. Modern-day ladder logic still follows these conventions. Relays did (and still do) their job well, but can be cumbersome due to the sheer size of multiple relays wired together. This is where programmable logic controllers using ladder logic can be advantageous: able to do the same kind of job but in significantly less space.
Machine learning, or rather the idea machines can learn to ‘do’ without an explicit set of instructions (programming), has been the basis of many movies where humans end up getting the short end of the deal. But is machine learning truly that dire?
Unlikely. Machine learning, which is a subcategory of artificial intelligence, is simply a way for machines to imitate intelligent human behavior. It’s a type of data analysis that allows programs to learn via experience in order to complete complex tasks, much like humans problem-solve. This type of learning typically breaks down into two specific types: deep learning and reinforcement learning. But what’s the difference?
Deep Learning
Deep learning is essentially what you see in any young child as they start to understand that while chickens are birds, not all large birds are chickens. It is based upon the ability to classify both the common features (in this case: feathers, beaks, wings, etc) as well as the uncommon features that separate each grouping from each other (sound, size, feather pattern, beak length). This kind of hierarchical feature learning stacks multiple layers of learning nodes as observed data from one layer produces new outputs that are then fed to a higher level.
In deep learning, the machine begins with raw data that must then be sorted into relevant and irrelevant subsets. The machine, exposed to more data, improves over time. This is similiar to how a baby learns.
Reinforcement Learning
Meanwhile, reinforcement learning relies more on trying out slight variations of a problem. As results occur (favorable and unfavorable) data sets change until the best outcome emerges. This is reminiscent of “The Good Place” as Michael tries to create a better version of his neighborhood.
Reinforcement learning uses a closed-loop algorithm where each action receives feedback in a trial-in-error process until the best action is determined.
Sourcing from PPE manufacturers for products related to the safety increased by almost 12,000%, according to recent data released by ThomasNet.com. (1) This included items like respirators, face masks, nitrile gloves, and hand sanitizer. Demand is expected to remain elevated as manufacturing tries to keep up with demand caused by the COVID-19 crisis.
Other areas of growth include in automation engineering services. This sector exceeded the anticipated growth of 6-9%. Actual sector growth was 16%. Robotic welding services also exceeded predictions, growing by 8%.
The benefits of automation have come to the fore in the wake of the pandemic and associated labor shortages, allowing manufacturers to optimize their plant efficiency and better utilize the human workforce available.
Thomas Index Q3 predictions include continued sustained sourcing activity for products related to PPE and other COVID-19 products. This includes plastic barriers, isopropyl alcohol, UV lamps, and cleaning products. Sourcing for farm equipment also increased as producers worked to keep up with food supply chain changes. Additionally, plastic injection molding services serving both the medical and food and beverage markets have increased.
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