Technological Concept
Turning the existing technology's impossible into the possible
The AI developed originally by AISing can achieve unprecedented levels
of machine efficiency and performance enhancement.
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About AI in AISing
Edge AI (edge AI/ endpoint AI) refers to AI that performs predictions on edge devices rather than on the cloud side as in conventional AI. By executing predictions on the edge, it effectively addresses issues such as communication delays that are common in conventional AI. This AI is ideal for integration into production equipment in manufacturing processes, where even slight communication delays can be critical.
The difference between conventional AI and AISing's edge AI
Three unique features of AISing's edge AI
Low memory usage Can be installed on microcontrollers
It offers accuracy that is equal to or greater than conventional algorithm while being memory-efficient, so it can be installed on microcontrollers that have KB-sized memory. It can be incorporated into production equipment and products.
High-speed Operates in the microsecond to millisecond range
It is possible to make predictions within the range of microseconds to milliseconds, even when using microcontrollers that have limited memory capacity. Equipment and products that demand high-speed control can also guarantee real-time performance.
Following AI that becomes smarter and smarter while operating on-site
It continuously learns on the device, adapting to the aging of equipment and environmental changes. During model updates, there is no need to communicate with the cloud, eliminating communication costs and security risks.
AI-PID provided by AISing
AI-PID is a technology that corrects control input using AI for control processes in devices and production equipment (such as motor control and hydraulic control). For example, it can be utilized for vibration suppression in production equipment. PID control, a type of feedback control, that adjusts the reference signal after vibration occurs. This makes it difficult to avoid problems such as the time it takes to stabilize at startup, the effects of sudden external disturbances, and subtle deviations due to individual differences in equipment. These issues often cannot be dealt with using standard control blocks alone. By adding AI to PID control, it becomes possible to predict the future state of the controlled object and proactively adjust the control variables, thereby enhancing the stability of the control system. Therefore, by utilizing AI-PID technology and enhancing existing controls, it can achieve improvements in product quality and reductions in defective products. For example, it can be used to suppress vibrations generated in productionequipment. In PID control, the amount of control is adjusted after vibration occurs, so it is difficult to avoid shortering the time to stabilize at start-up, the effects of sudden disturbances, and subtle deviations due to individual differences in equipment that cannot be deal with by ordinary control blocks alone.