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401.
针对移动机器人基于超声波传感器的地图创建,提出了一种双重环境模型。该模型同时包涵以直线段表示的几何模型和以概率形式表示的栅格模型,结合了几何模型与栅格模型的优点。给出了该模型的建立及更新算法,并基于该模型提出了一种主动探测策略,在保证探测效率的同时减小了超声波传感器的镜面反射产生的不良影响。在室内环境下进行移动机器人实验,验证了基于双重环境模型的探测策略的有效性。 相似文献
402.
基于企业集群理论的中小企业发展战略研究 总被引:1,自引:0,他引:1
本文简要介绍了企业集群的概念、特征,分析了企业集群对于提升集群内企业竞争力的作用,在分析我国中小企业的特点的基础上,提出了我国中小企业应该走一条集群化发展战略之路. 相似文献
403.
Stephan Ulamec Jens Biele 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2009,44(7):847-858
The investigation of small bodies, comets and asteroids, can contribute substantially to our understanding of the formation and history of the Solar System. In-situ observations by Landers play a prominent role in this field.The Rosetta Lander – Philae – is currently on its way to comet 67P/Churyumov–Gerasimenko. It will land in November 2014 and perform numerous experiments with a suite of 10 scientific instruments.Philae has been designed to cope with a wide range of possible comet properties. The considerations taken during its development are relevant for future Lander missions to small bodies in the Solar System.In addition the paper provides a review of alternative concepts, studied or developed for various missions like Phobos, Hayabusa/Minerva or Géocroiseur/Leonard.Various missions to small bodies in the Solar System, including Landers, are currently studied (e.g., Marco Polo). The paper will address the mission options and compare applicable technologies with the solutions chosen for Philae. 相似文献
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残余应力是引起零件加工变形和失效的重要因素,残余应力场的重构有助于消除和控制零件在设计、制造、评价和使用过程中的残余应力。针对已有方法难以准确重构残余应力场的问题,提出了一种通过零件加工过程中的变形力数据实现零件残余应力场重构的方法。该方法首先提取了由于材料去除引起的应力重新分布带来的装夹力变化数据,即变形力。残余应力到变形力之间通过协方差矩阵自适应进化策略(CMA-ES算法)反复迭代求解出映射关系,从而构建出零件的残余应力场,最后在仿真环境下对该方法进行了验证。验证结果表明:该方法能较好地预测残余应力场,重构出的零件的初始残余应力场与原始施加的应力场平均误差值约0.38 MPa,为零件残余应力场的重构提供了一种新思路。 相似文献
407.
Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community. 相似文献
408.
An aero-engine life-cycle maintenance policy optimization algorithm: Reinforcement learning approach
An aero-engine maintenance policy plays a crucial role in reasonably reducing maintenance cost. An aero-engine is a type of complex equipment with long service-life. In engineering,a hybrid maintenance strategy is adopted to improve the aero-engine operational reliability. Thus,the long service-life and the hybrid maintenance strategy should be considered synchronously in aero-engine maintenance policy optimization. This paper proposes an aero-engine life-cycle maintenance policy optimization algorithm that synchronously considers the long service-life and the hybrid maintenance strategy. The reinforcement learning approach was adopted to illustrate the optimization framework, in which maintenance policy optimization was formulated as a Markov decision process. In the reinforcement learning framework, the Gauss–Seidel value iteration algorithm was adopted to optimize the maintenance policy. Compared with traditional aero-engine maintenance policy optimization methods, the long service-life and the hybrid maintenance strategy could be addressed synchronously by the proposed algorithm. Two numerical experiments and algorithm analyses were performed to illustrate the optimization algorithm in detail. 相似文献
409.
基于MDO策略的民用航空发动机概念设计研究 总被引:1,自引:1,他引:0
以某型民用涡扇航空发动机概念设计为例,集成了总体方案(包括热力循环分析、质量评估、噪声评估、氮氧化物排放评估)、风扇(包括气动设计、强度分析)、低压涡轮(包括气动设计、强度分析)3个子系统,以设计点耗油率、整机质量、飞越状态总声功率级、进近状态总声功率级和燃烧室氮氧化物排放量为优化目标,进行了基于多目标的整机多学科设计优化(MDO)的研究。对比研究了单学科可行(IDF)、协作优化(CO)和不对称子空间优化 (ASO)3种典型的优化策略及其在整机MDO中的应用,探讨了整机 MDO的建模方法、子系统间数据传递及解耦方式。结果表明:3种策略优化效率相当,优化时间最大相差742s;CO策略与ASO策略在整机MDO应用中优化效果较好,综合评价指标分别降低了0.82%和0.86%。 相似文献
410.