首页|New Machine Learning Data Have Been Reported by Researchers at Chuo University ( Construction of Motion Mode Switching System by Machine Learning for Peristaltic Mixing Conveyor Based on Intestinal Movement)
New Machine Learning Data Have Been Reported by Researchers at Chuo University ( Construction of Motion Mode Switching System by Machine Learning for Peristaltic Mixing Conveyor Based on Intestinal Movement)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news reporting out of Bunkyo ku, Ja pan, by NewsRx editors, research stated, "The high frequency of rocket launches requires low-cost solid rocket fuel. Currently, the fuel manufacturing process f aces increased launch costs caused by the risk of ignition from rotary mixers an d increased equipment and labor costs from batch processes in which mixing and c onveying are separated." Funders for this research include Japan Society For The Promotion of Science Kak enhi Grant-in-aid For Scientific Research on Innovative Areas Through Ministry o f Education, Culture, Sports, Science And Technology of Japan; Chuo University R esearch And Development Initiative. Our news correspondents obtained a quote from the research from Chuo University: "Therefore, this paper proposes and verifies an automatic switching system betw een mixing and conveying modes for a peristaltic mixing conveyor that enables sa fe and continuous mixing and conveying of solid fuel. In a previous study, peris taltic mixing conveyor with low shear force was developed and successfully produ ced solid fuel. However, there was room for improvement for more efficient fuel production because the device was controlled by pre-determined driving pattern. The actual intestine generates movement autonomously by enteric nerves. Therefor e, the development of a sensing function that imitates the enteric nervous syste m and generates movement patterns based on the acquired data is expected to impr ove manufacturing efficiency. In this study, the sensor data of a mixed solid fu el simulant packaged in a bag were acquired, and the degree of mixing (unmixed a nd mixed completely) was discriminated using supervised learning (the k-nearest neighbor method)."
Chuo UniversityBunkyo kuJapanAsiaCyborgsEmerging TechnologiesMachine Learning
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