Technique Comparison(技术比较)研究综述
Technique Comparison 技术比较 - The study goes through different comparative studies such as technique comparison and controller comparison to defend superiority of proposed MVO algorithm and suggested tilt fuzzy controller. [1] The inter-technique comparison was undertaken at a patient and segment level. [2] This workflow and technique comparison can be applied to other plant canopy models, such as the vertical distribution of nitrogen, carbohydrates, photosynthesis, etc. [3] Additionally an inter-technique comparison was included for the bulk chemical composition obtained by means of the X-ray fluorescence PIXE and XRF techniques. [4] Multi-technique comparisons between emulsion and pure squalane revealed that a hydrocarbon only based fluid could not replicate the traction promoting properties of the emulsion. [5] Material comparison (19%) and technique comparison (16%) were the 2 most popular themes. [6] Future directions are proposed, including suggestions on data collection, technique comparison, industrial participation, cost-benefit analyses and the future of mineral engineering training. [7] To show the accuracy and reliability of the technique comparisons are made between the variational iteration algorithm-I with an auxiliary parameter and classic variational iteration algorithm-I. [8] Inter-technique comparisons included linear regression and Bland–Altman analyses. [9] The inter-technique comparisons showed good correlations(r-values: LVEDV 0. [10] This article proposes a two-technique comparison, a proposed deep-convolution neural network and transfer learning with the pre-trained model mobilnetv2 , for hand gestures recognition of American sign language. [11]该研究通过技术比较和控制器比较等不同的比较研究来捍卫提出的MVO算法和提出的倾斜模糊控制器的优越性。 [1] 技术间比较是在患者和部门级别进行的。 [2] 这种工作流程和技术比较可以应用于其他植物冠层模型,例如氮、碳水化合物、光合作用等的垂直分布。 [3] 此外,还对通过 X 射线荧光 PIXE 和 XRF 技术获得的本体化学成分进行了技术间比较。 [4] 乳液和纯角鲨烷之间的多技术比较表明,仅基于碳氢化合物的流体无法复制乳液的牵引力促进特性。 [5] 材料比较 (19%) 和技术比较 (16%) 是两个最受欢迎的主题。 [6] 提出了未来的方向,包括数据收集、技术比较、工业参与、成本效益分析和矿物工程培训的未来等方面的建议。 [7] 为了显示技术的准确性和可靠性,对带有辅助参数的变分迭代算法-I和经典的变分迭代算法-I进行了比较。 [8] 技术间比较包括线性回归和 Bland-Altman 分析。 [9] 技术间比较显示出良好的相关性(r值:LVEDV 0. [10] 本文提出了两种技术比较,一种提出的深度卷积神经网络和使用预训练模型 mobilnetv2 的迁移学习,用于美国手语的手势识别。 [11]