考虑磨损问题的圆柱齿轮减速器多目标可靠优化

将应力和强度等均视为随机变量,综合考虑斜齿圆柱齿轮减速器结构紧凑和摩擦磨损等问题,以机构的体积和磨损量最小为目标函数,建立有别于现有模型的多目标可靠性优化设计模型。在简要介绍MATLAB优化工具箱中的序列二次规划法后,采用该算法求出设计结果。     

河北省水资源优化配置及效果评价研究

以河北省为研究对象,在确定南水北调调水规模的基础上,根据水资源系统的特征建立了多目标、多水源、多用户的水资源优化配置模型并对各规划水平年的水资源进行优化配置.同时建立区域可持续发展评价指标体系,对配置结果进行效果评价分析,为水资源优化配置决策提供依据.     

水资源多目标决策评价模型及其应用

水资源系统规划的方案优选问题是一个具有多层次结构的系统优化问题,为了较好地评价规划方案的优劣,建立了水资源多目标决策评价模型.该模型采用AHP法确定各层次指标的权重,并进一步运用密切度法达到评价规划方案优劣的目的.     

冲压车间调度的动态拥挤度NSGA-Ⅱ多目标优化方法

为了降低冲压车间总能耗、缩短冲压件完成时间,提出了基于动态拥挤度NSGA-Ⅱ算法的冲压车间调度优化方法。分析了冲压机各工作状态,针对多制件、多工序调度问题,建立了以能耗最低、完成时间最短为优化目标的多目标优化模型。针对NSGA-Ⅱ算法中拥挤度比较算子会降低基因的多样性的问题,给出了动态拥挤度策略,该策略在删除多余染色体的同时,动态更新各染色体拥挤度,从而提出了基于动态拥挤度NSGA-Ⅱ算法的模型求解方法。经实例验证,NSGA-Ⅱ算法搜索的Pareto前沿解集不是真正的前沿解,而是陷入了局部最优,动态拥挤度NSGA-Ⅱ算法搜索出了Pareto前沿解集,其优化结果在能耗和完成时间上均优于NSGA-Ⅱ算法。     

基于多目标遗传算法的传感器优化布点研究

针对目前桥梁模态试验中传感器布点优化都是基于单一准则下的研究的局限性,提出采用双准则或多准则下的传感器优化布点研究,该方法改进了以往布点优化中单一目标最优的限制,转而寻求多准则下的优化布点的满意解,而非真正严格数学意义上的最优解。采用Pareto遗传算法,设计了相应的遗传算子和编码方案,成功求解了双准则下传感器最优布点问题,优化结束时,成功给出了一组pareto最优解的前沿面,进而证明了提出的方法是可行的。     

An Elevator Button Recognition Method Combining YOLOv5 and OCR

Fast recognition of elevator buttons is a key step for service robots to ride elevators automatically. Although there are some studies in this field, none of them can achieve real-time application due to problems such as recognition speed and algorithm complexity. Elevator button recognition is a comprehensive problem. Not only does it need to detect the position of multiple buttons at the same time, but also needs to accurately identify the characters on each button. The latest version 5 of you only look once algorithm (YOLOv5) has the fastest reasoning speed and can be used for detecting multiple objects in real-time. The advantages of YOLOv5 make it an ideal choice for detecting the position of multiple buttons in an elevator, but it’s not good at specific word recognition. Optical character recognition (OCR) is a well-known technique for character recognition. This paper innovatively improved the YOLOv5 network, integrated OCR technology, and applied them to the elevator button recognition process. First, we changed the detection scale in the YOLOv5 network and only maintained the detection scales of 40 * 40 and 80 * 80, thus improving the overall object detection speed. Then, we put a modified OCR branch after the YOLOv5 network to identify the numbers on the buttons. Finally, we verified this method on different datasets and compared it with other typical methods. The results show that the average recall and precision of this method are 81.2% and 92.4%. Compared with others, the accuracy of this method has reached a very high level, but the recognition speed has reached 0.056 s, which is far higher than other methods.