Abstract—This article presents quick analytical prediction meth- ods of switching loss, turn-ON/OFF overvoltage, di/dt, and dv/dt for SiC metal–oxide–semiconductor field-effect transistor based on device datasheet. First, the switching process is analyzed and the simplification principles are discussed based on charge con- servation and flux conservation. Second, the analytical equations of switching loss, turn-ON/OFF overvoltage, di/dt, and dv/dt are derived. Third, experimental results under different operating con- ditions are presented to validate the proposed methods. It is found that the average prediction error is 10.11%. Finally, relationships between switching performance and different parameters, such as parasitic capacitance, parasitic inductance, dc-voltage, and load current, are analyzed and summarized based on the proposed prediction methods.请将这一段进行以下要求，Move analysis 语步（内容成分）分析； Language devices和实现该功能的语言手段（某些关键专有名词提供汉语翻译）

Move analysis:

该段落为一篇论文的摘要，主要介绍了基于器件数据手册的SiC金属-氧化物-半导体场效应晶体管的快速分析预测方法。首先，对开关过程进行了分析，并基于电荷守恒和通量守恒原则讨论了简化原则。其次，推导了开关损耗、开关过压、di/dt和dv/dt的分析方程式。第三，提供了不同工作条件下的实验结果来验证所提出的方法。发现平均预测误差为10.11%。最后，基于所提出的预测方法，分析和总结了开关性能与不同参数（例如寄生电容、寄生电感、直流电压和负载电流）之间的关系。

Language devices:

- 该段落使用了较为专业的术语和专有名词，如SiC、metal-oxide-semiconductor field-effect transistor等，用以准确描述论文的研究对象和内容。
- 使用了一些分析性的语言手段，如分析、讨论、推导、验证、分析和总结，突出了论文的研究方法和成果。
- 使用了一些形容词和副词，如快速、简化、不同等，用以修饰和描述所提出的方法和实验结果。

实现该功能的语言手段：

- 使用了专业术语和专有名词，准确描述研究对象和内容。
- 使用了分析性的语言手段，如分析、讨论、推导、验证、分析和总结，突出了研究方法和成果。
- 使用了形容词和副词，用以修饰和描述所提出的方法和实验结果。

相关问题

4 Experiments This section examines the effectiveness of the proposed IFCS-MOEA framework. First, Section 4.1 presents the experimental settings. Second, Section 4.2 examines the effect of IFCS on MOEA/D-DE. Then, Section 4.3 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on 19 test problems. Finally, Section 4.4 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on four real-world application problems. 4.1 Experimental Settings MOEA/D-DE [23] is integrated with the proposed framework for experiments, and the resulting algorithm is named IFCS-MOEA/D-DE. Five surrogate-based MOEAs, i.e., FCS-MOEA/D-DE [39], CPS-MOEA [41], CSEA [29], MOEA/DEGO [43] and EDN-ARM-OEA [12] are used for comparison. UF1–10, LZ1–9 test problems [44, 23] with complicated PSs are used for experiments. Among them, UF1–7, LZ1–5, and LZ7–9 have 2 objectives, UF8–10, and LZ6 have 3 objectives. UF1–10, LZ1–5, and LZ9 are with 30 decision variables, and LZ6–8 are with 10 decision variables. The population size N is set to 45 for all compared algorithms. The maximum number of FEs is set as 500 since the problems are viewed as expensive MOPs [39]. For each test problem, each algorithm is executed 21 times independently. For IFCS-MOEA/D-DE, wmax is set to 30 and η is set to 5. For the other algorithms, we use the settings suggested in their papers. The IGD [6] metric is used to evaluate the performance of each algorithm. All algorithms are examined on PlatEMO [34] platform.

4 实验
本节将研究所提出的IFCS-MOEA框架的有效性。首先，在第4.1节中介绍实验设置。其次，在第4.2节中研究IFCS对MOEA/D-DE的影响。然后，在第4.3节中，将IFCS-MOEA/D-DE与19个测试问题上的五种最先进的MOEA进行比较。最后，在第4.4节中，将IFCS-MOEA/D-DE与四种真实世界应用问题上的五种最先进的MOEA进行比较。

4.1 实验设置
实验中将MOEA/D-DE [23]与所提出的框架集成，得到的算法称为IFCS-MOEA/D-DE。比较使用了五种基于代理的MOEA，即FCS-MOEA/D-DE [39]，CPS-MOEA [41]，CSEA [29]，MOEA/D-EGO [43]和EDN-ARM-OEA [12]。使用了具有复杂PS的UF1-10、LZ1-9测试问题[44，23]。其中，UF1-7、LZ1-5和LZ7-9具有2个目标，UF8-10和LZ6具有3个目标。UF1-10、LZ1-5和LZ9具有30个决策变量，LZ6-8具有10个决策变量。所有比较算法的种群大小N都设置为45。由于这些问题被视为昂贵的MOP [39]，所以最大FE的数量设置为500。对于每个测试问题，每个算法都独立运行21次。对于IFCS-MOEA/D-DE，将wmax设置为30，将η设置为5。对于其他算法，我们使用其论文中建议的设置。使用IGD [6]指标评估每个算法的性能。所有算法都在PlatEMO [34]平台上进行检验。

翻译成中文：Extraction and energy consumption are two important indicators of the milling process and whether they meet standards will affect the smooth operation and economic benefits of sugar production. This study presents a novel modeling method to predict these indicators based on the deep feature extraction and deep kernal extreme learning machine. These methods provided a good reference for end-point control and judgment of quick direct tapping. In addition, it a new concept for the design of soft sensors for such complex industrial processes.

提取和能量消耗是碾糖过程的两个重要指标，是否符合标准将影响碾糖生产的顺利运行和经济效益。本研究介绍了一种基于深度特征提取和深度内核极限学习机的新型建模方法来预测这些指标。这些方法为端点控制和快速直接开口判断提供了很好的参考。此外，它也为这类复杂工业过程的软传感器设计提供了新的概念。