排序方式: 共有42条查询结果,搜索用时 31 毫秒
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针对精炼与非精炼船钢低温冲击值的差异,分析了两种钢的化学成分、夹杂物和显微组织等对冲击值的影响。结果表明,钢的精炼可以增加钢的耐冲击能力,使钢的冲击韧性大大提高,满足实际使用要求。 相似文献
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笔者对安溪某一大型乌龙茶生产企业夏季铁观音精制工艺进行了分析和总结,并取样进行感官审评。中低档乌龙茶精制生产重内质轻外形,产品精制烘焙后,重复了筛分、风选和拣剔工序,提高了品质,保障了安全性。经样品感官审评,符合四级铁观音茶标准。 相似文献
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成品油营销通过判断、适应、改变影响营销结果的因素,利用正确的营销手段,达到营销目的。分析了成品油营销的综合评价指标体系,利用群体AHP模型分析计算各指标权重并进行综合评判,对影响成品油营销的各指标体系进行最终排序,得出其中的关键指标,有利于对营销活动进行整体的控制和改进。 相似文献
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The Default&Refine algorithm is a new rule-based learning algorithm that was developed as an accurate and efficient pronunciation prediction mechanism for speech processing systems. The algorithm exhibits a number of attractive properties including rapid generalisation from small training sets, good asymptotic accuracy, robustness to noise in the training data, and the production of compact rule sets. We describe the Default&Refine algorithm in detail and demonstrate its performance on two benchmarked pronunciation databases (the English OALD and Flemish FONILEX pronunciation dictionaries) as well as a newly-developed Afrikaans pronunciation dictionary. We find that the algorithm learns more efficiently (achieves higher accuracy on smaller data sets) than any of the alternative pronunciation prediction algorithms considered. In addition, we demonstrate the ability of the algorithm to generate an arbitrarily small rule set in such a way that the trade-off between rule set size and accuracy is well controlled. A conceptual comparison with alternative algorithms (including Dynamically Expanding Context, Transformation-Based Learning and Pronunciation by Analogy) clarifies the competitive performance obtained with Default&Refine. 相似文献
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为了减少机器人点对点工作路径长度,提出了个体动态细化分工花授粉算法的路径规划方法。以花授粉算法为基础,将花粉按照适应度划分为精英个体、优等个体、差等个体,并对个体进行动态细化分工。精英个体引领进化方向,优等个体使用改进搜索方式进行寻优,差等个体使用柯西变异逃出局部最优,由此提出了个体动态细化分工花授粉算法。使用个体动态细化分工花授粉算法搜索最优路径结点,依据最优路径结点和三次样条插值法规划出最优路径。在简单环境和复杂环境下进行仿真验证,个体动态细化分工花授粉算法规划的路径长度、收敛速度和寻优稳定性均优于传统花授粉算法和改进蝙蝠算法。 相似文献