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作战仿真想定是作战方案的剧本,是作战仿真系统的前提和重要组成部分。目前各个仿真团体采用不同的仿真想定描述方法,导致想定的可读性和可重用性降低。文章以基于Agent的空军作战仿真为研究背景,分析空军作战的特殊形势,制定出了一套适合空军空战的想定组成结构,同时采用XML(eXtensible Markup Language)对想定各组成部分进行描述,并制定好XML Schema进行语法约束,从而实现想定的规范性和可重用性。设计了一种基于XML的想定生成系统,系统以空军空战为例,实现了想定的快速生成。 相似文献
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目的 卷积神经网络结合U-Net架构的深度学习方法广泛应用于各种医学图像处理中,取得了良好的效果,特别是在局部特征提取上表现出色,但由于卷积操作本身固有的局部性,导致其在全局信息获取上表现不佳。而基于Transformer的方法具有较好的全局建模能力,但在局部特征提取方面不如卷积神经网络。为充分融合两种方法各自的优点,提出一种基于分组注意力的医学图像分割模型(medical image segmentation module based on group attention,GAU-Net)。方法 利用注意力机制,设计了一个同时集成了Swin Transformer和卷积神经网络的分组注意力模块,并嵌入网络编码器中,使网络能够高效地对图像的全局和局部重要特征进行提取和融合;在注意力计算方式上,通过特征分组的方式,在同一尺度特征内,同时进行不同的注意力计算,进一步提高网络提取语义信息的多样性;将提取的特征通过上采样恢复到原图尺寸,进行像素分类,得到最终的分割结果。结果 在Synapse多器官分割数据集和ACDC (automated cardiac diagnosis challenge)数据集上进行了相关实验验证。在Synapse数据集中,Dice值为82.93%,HD(Hausdorff distance)值为12.32%,相较于排名第2的方法,Dice值提高了0.97%,HD值降低了5.88%;在ACDC数据集中,Dice值为91.34%,相较于排名第2的方法提高了0.48%。结论 本文提出的医学图像分割模型有效地融合了Transformer和卷积神经网络各自的优势,提高了医学图像分割结果的精确度。 相似文献
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2007年9月10日,大庆油田矿区事业服务部成立,一是要全面落实科学发展观,努力建设社会主义和谐矿区,二是要持续推进集团化,专业化运作,提高整体运行质量,三是要创新服务模式提高矿区服务水平,四是要加强队伍建设,提升全员素质,为百年油田建设,搞好二次创业做出应有的贡献。 相似文献
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随着科学技术的不断进步,电子电工工业技术不断更新,汽车行业高速发展,自动化、智能化的不断革新,这给汽车电子中的自动控制领域带来新的挑战和机遇。如何让人们更好地掌握汽车电子、电工技术,以便适应日新月异的发展需要。本文主要结合汽车电子的现状和当前汽车电子发展所存在的问题,阐述了自动控制系统对汽车电子技术的机遇与发展。 相似文献
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Mining Frequent Generalized Itemsets and Generalized Association Rules Without Redundancy 总被引:1,自引:0,他引:1 下载免费PDF全文
This paper presents some new algorithms to efficiently mine max frequent generalized itemsets (g-itemsets) and essential generalized association rules (g-rules). These are compact and general representations for all frequent patterns and all strong association rules in the generalized environment. Our results fill an important gap among algorithms for frequent patterns and association rules by combining two concepts. First, generalized itemsets employ a taxonomy of items, rather than a flat list of items. This produces more natural frequent itemsets and associations such as (meat, milk) instead of (beef, milk), (chicken, milk), etc. Second, compact representations of frequent itemsets and strong rules, whose result size is exponentially smaller, can solve a standard dilemma in mining patterns: with small threshold values for support and confidence, the user is overwhelmed by the extraordinary number of identified patterns and associations; but with large threshold values, some interesting patterns and associations fail to be identified. Our algorithms can also expand those max frequent g-itemsets and essential g-rules into the much larger set of ordinary frequent g-itemsets and strong g-rules. While that expansion is not recommended in most practical cases, we do so in order to present a comparison with existing algorithms that only handle ordinary frequent g-itemsets. In this case, the new algorithm is shown to be thousands, and in some cases millions, of the time faster than previous algorithms. Further, the new algorithm succeeds in analyzing deeper taxonomies, with the depths of seven or more. Experimental results for previous algorithms limited themselves to taxonomies with depth at most three or four. In each of the two problems, a straightforward lattice-based approach is briefly discussed and then a classificationbased algorithm is developed. In particular, the two classification-based algorithms are MFGI_class for mining max frequent g-itemsets and EGR_class for mining essential g-rules. The classification-based algorithms are featured with conceptual classification trees and dynamic generation and pruning algorithms. 相似文献
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