首页|China Jiliang University Researchers Discuss Research in Machine Learning (Gas-L iquid Two-Phase Flow Measurement Based on Optical Flow Method with Machine Learn ing Optimization Model)
China Jiliang University Researchers Discuss Research in Machine Learning (Gas-L iquid Two-Phase Flow Measurement Based on Optical Flow Method with Machine Learn ing Optimization Model)
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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 originating from Hangzhou, Peo ple's Republic of China, by NewsRx correspondents, research stated, "Gas-Liquid two-phase flows are a common flow in industrial production processes." Financial supporters for this research include Science And Technology Department of Zhejiang Province. The news reporters obtained a quote from the research from China Jiliang Univers ity: "Since these flows inherently consist of discrete phases, it is challenging to accurately measure the flow parameters. In this context, a novel approach is proposed that combines the pyramidal Lucas-Kanade (L-K) optical flow method wit h the Split Comparison (SC) model measurement method. In the proposed approach, videos of gas-liquid two-phase flows are captured using a camera, and optical fl ow data are acquired from the flow videos using the pyramid L-K optical flow det ection method. To address the issue of data clutter in optical flow extraction, a dynamic median value screening method is introduced to optimize the corner poi nt for optical flow calculations. Machine learning algorithms are employed for t he prediction model, yielding high flow prediction accuracy in experimental test s. Results demonstrate that the gradient boosted regression (GBR) model is the m ost effective among the five preset models, and the optimized SC model significa ntly improves measurement accuracy compared to the GBR model, achieving an * * R * * 2 value of 0.97, RMSE of 0.74 m3/h, MAE of 0.52 m3/h, and MAPE of 8.0%."
China Jiliang UniversityHangzhouPeop le's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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