首页Java By Comparison - Become a Java Craftsman in 70 Examples
a Java Craftsman
in 70 Examples
Foreword by Venkat
Edited by Andrea Stewart
Jorg Lenhard, Linus Dietz
Java by Comparison
Become a Java Craftsman in 70 Examples
by Simon Harrer, Jörg Lenhard, Linus Dietz
Version: P1.0 (March 2018)
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Table of Contents
Who Should Read This Book
Why Read This Book?
How to Read This Book
Get Ready for Your Mission to Mars
1. Start Cleaning Up
Avoid Unnecessary Comparisons
Return Boolean Expressions Directly
Simplify Boolean Expressions
Avoid NullPointerException in Conditionals
Avoid Switch Fallthrough
Always Use Braces
Ensure Code Symmetry
What Have You Learned?
2. Level Up Your Code Style
Replace Magic Numbers with Constants
Favor Enums Over Integer Constants
用中文翻译：As is mentioned above, the wavy flow channels with identical phase for anode and cathode sides are studied by literatures with single flow channel models . In this section, the parallel and inverse wavy flow field designs with multiple flow channels are compared to study the interaction effects between the neighbored and intercrossed channels. The detailed water content and heat distributions under 1 A/cm2 for case 2 are shown in Fig. 12 as an example for investigation. Fig. 12 (a1) and (b1) present the different geometries with mesh building for the parallel and inverse wavy flow fields. For comparison, the geometric dimensions of the MEA and flow channels are the same for the two models with identical channel numbers. The gas flow channel numbers are both 5 for anode and cathode while the coolant flow channel number is 6 for each layer. The reactant flow directions are also indicated in the two-dimensional schematic of the overlayered H2 and air flow channels. The RH distributions in gas flow channels are shown in Fig. (a2) and (b2). In the parallel wavy flow field design, each anode channel corresponds to the cathode channel on the other side of the membrane with similar mass transfer processes to those of the single channel model.
根据这个框架写一篇关于《自主学习中基于多模态数据的学习风格高精度识别》的文章框架如下：一、介绍。 A.研究背景 B.研究目的 C.研究意义 二、文献综述 A.学习风格概述 B.以往关于学习风格识别的研究 C.多模态数据概述 D.多模态数据集成和学习风格识别 三、方法 A.数据收集 B.数据预处理 C.特征提取 D.模型选择 E.模型评价 四、结果 A.结果概述 B.与以往研究的准确性比较 C.分析不同模式对识别精度的影响 D.讨论结果 五、结论 A.调查结果摘要 B.本研究的局限性 C.研究的意义 D.对未来研究的建议 六。参考文献
Traditional network security situation prediction methods depend on the accuracy of historical situation value. Moreover, there are differences in correlation and importance among various network security factors. In order to solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO) was proposed. This model was first decomposed and reconstructed into a series of subsequences through the SSA of network security situation data. Next, a prediction model of TCAN-BiGRU was established for each subsequence, respectively. The TCN with relatively simple structure was used in the TCAN to extract features from the data. Besides, the improved channel attention mechanism (CAM) was used to extract important feature information from TCN. Afterwards, the before-after status of the learning situation value of the BiGRU neural network was used to extract more feature information from sequences for prediction. Meanwhile, an improved IQPSO was proposed to optimize the hyper-parameter of the BiGRU neural network. Finally, the prediction results of subsequence were superimposed to obtain the final predicted value. In the experiment, on the one hand, the IQPSO was compared with other optimization algorithms; and the results showed that the IQPSO has better optimization performance; on the other hand, the comparison with traditional prediction methods was performed through the simulation experiment and the established prediction model; and the results showed that the combined prediction model established has higher prediction accuracy.
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