materials [material]的复数形式 CLASS =Canberra Laboratories Automation Software System 堪培拉工业公司实验室自动化软件系统[美] Capacity Loading and Schedul class n. 1.[C]班,班级 2.[C,U]课,上课 3.[C]某科目的系列课程 4.[C]同届毕业生 5.[C]阶级,阶层 6.[U]社会等级 7.[C]种类,类别,等...
It is found that all the three methods categorise the materials into the correct polymer groups irrespective of their complexity. Methods based on the correlation structure in the data prove more beneficial than methods based on distance due to particular characteristics in the data. Best results ...
(PLS-DA) and Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA), were applied to verify the main source of raw materials used in the ... Geronimo, Diego Macielde Oliveira, Sheila CatarinaSoares, Frederico Luis FelipePeralta-Zamora, PatricioNagata, Noemi - 《Microchemical Journal...
Fewer training examples of the class of interest further complicates the learning process. One commonly-accepted relationship between sample size n and number of features or dimensions d to avoid overfit is for d<\sqrt{n} [6]. For the datasets used in this work (see Table 1), d>\sqrt{...
LBP is highly regarded by scientists due to its distinctive merits including ease to train with a small amount of data, implementation simplicity, suitability to solve high-class texture problems with real-time applications due to its relative fast calculation, invariability to monotonic illumination ...
aThese tables are especially MT respond to the classification "metal-enclosed type compartmentalized according to the requirements of IEC 60298 in addition to the main circuits are individually wrapped in MT solid insulating materials Class B according to IEC 60466". 这些桌是特别是MT反应分类“根据IEC...
First, The RBF kernel nonlinearly maps samples into a higher dimensional space, so it can handle the relationship between class labels and nonlinear attributes; where the linear kernel is a special case of RBF [116]. Second, the RBF kernel has less hyper parameters than a polynomial kernel, ...
These tasks involve identifying and categorizing defects in products or materials. Deep learning-based defect classification involves identifying types of defects in a product or simply identifying wether a product is defective or not. Detection involves localization and classification of defects, while ...
Worthy of mentioning, morphology-based family-level classifi- cations (Siphoviridae, Myoviridae and Podoviride) are abolished in the latest 2021 taxonomy release, unlike in the 2020 release. A significant fraction of BSPs do not appear in the 2021 taxonomy release as they became family-level-...
In the case of missing values for well-characterized commercial materials (e.g., Aeroxide P25 of Degussa-Evonik), they were replaced with the manufacturers’ characterization data of NPs that had the same brand and product number as the NPs of interest, assuming that the same NP products ...