2016年6月22日-26日,国际技术史学会（Society for the History of Technology,SHOT）第59届年会在新加坡国立大学举行。法国哲学家和科学社会学家拉图尔（Bruno Latour）、美国技术史家露丝·史瓦兹·柯望（Ruth Schwartz Cowa...2016年6月22日-26日,国际技术史学会（Society for the History of Technology,SHOT）第59届年会在新加坡国立大学举行。法国哲学家和科学社会学家拉图尔（Bruno Latour）、美国技术史家露丝·史瓦兹·柯望（Ruth Schwartz Cowan）和英国爱丁堡大学社会人类学教授、本届SHOT主席白馥兰（Francesca Bray）女士,分别做了主旨报告。展开更多
针对机器人无序工件分拣过程中在遮挡情况下不同外形工件识别率差异较大的问题,对全局特征描述子和局部特征描述子的描述及投票过程进行详细地分析,分别选取其中实时性较好的PPF(Point Pair Feature)和SHOT(Signature of Histograms of ...针对机器人无序工件分拣过程中在遮挡情况下不同外形工件识别率差异较大的问题,对全局特征描述子和局部特征描述子的描述及投票过程进行详细地分析,分别选取其中实时性较好的PPF(Point Pair Feature)和SHOT(Signature of Histograms of OrienTations)描述子在不同程度遮挡情况下使用不同外形工件进行识别实验,有针对性地对机器人无序分拣中不同外形工件适用的算法进行实验对比和总结,使在不同外形工件上的识别率达到最大化,实验结果表明:PPF全局描述子对工件点云模型的外形特征和遮挡较为敏感,特征不明显工件的识别率为0%,对于外形特征明显、无遮挡的点云模型工件识别速度较快(1 s内)且准确率较高,SHOT局部描述子对遮挡的鲁棒性较强,并且对外形特征不明显工件有一定的识别能力,特征明显与特征不明显工件的识别率均保持在90%以上。展开更多
Taking the real part and the imaginary part of complex sound pressure of the sound field as features,a transfer learning model is constructed.Based on the pre-training of a large amount of underwater acoustic data in ...Taking the real part and the imaginary part of complex sound pressure of the sound field as features,a transfer learning model is constructed.Based on the pre-training of a large amount of underwater acoustic data in the preselected sea area using the convolutional neural network(CNN),the few-shot underwater acoustic data in the test sea area are retrained to study the underwater sound source ranging problem.The S5 voyage data of SWellEX-96 experiment is used to verify the proposed method,realize the range estimation for the shallow source in the experiment,and compare the range estimation performance of the underwater target sound source of four methods:matched field processing(MFP),generalized regression neural network(GRNN),traditional CNN,and transfer learning.Experimental data processing results show that the transfer learning model based on residual CNN can effectively realize range estimation in few-shot scenes,and the estimation performance is remarkably better than that of other methods.展开更多
文摘2016年6月22日-26日,国际技术史学会（Society for the History of Technology,SHOT）第59届年会在新加坡国立大学举行。法国哲学家和科学社会学家拉图尔（Bruno Latour）、美国技术史家露丝·史瓦兹·柯望（Ruth Schwartz Cowan）和英国爱丁堡大学社会人类学教授、本届SHOT主席白馥兰（Francesca Bray）女士,分别做了主旨报告。
文摘针对机器人无序工件分拣过程中在遮挡情况下不同外形工件识别率差异较大的问题,对全局特征描述子和局部特征描述子的描述及投票过程进行详细地分析,分别选取其中实时性较好的PPF(Point Pair Feature)和SHOT(Signature of Histograms of OrienTations)描述子在不同程度遮挡情况下使用不同外形工件进行识别实验,有针对性地对机器人无序分拣中不同外形工件适用的算法进行实验对比和总结,使在不同外形工件上的识别率达到最大化,实验结果表明:PPF全局描述子对工件点云模型的外形特征和遮挡较为敏感,特征不明显工件的识别率为0%,对于外形特征明显、无遮挡的点云模型工件识别速度较快(1 s内)且准确率较高,SHOT局部描述子对遮挡的鲁棒性较强,并且对外形特征不明显工件有一定的识别能力,特征明显与特征不明显工件的识别率均保持在90%以上。
基金supported by the National Natural Science Foundation of China(1197428611904274)+1 种基金the Shaanxi Young Science and Technology Star Program(2021KJXX-07)the fundamental research funding for characteristic disciplines(G2022WD0235)。
文摘Taking the real part and the imaginary part of complex sound pressure of the sound field as features,a transfer learning model is constructed.Based on the pre-training of a large amount of underwater acoustic data in the preselected sea area using the convolutional neural network(CNN),the few-shot underwater acoustic data in the test sea area are retrained to study the underwater sound source ranging problem.The S5 voyage data of SWellEX-96 experiment is used to verify the proposed method,realize the range estimation for the shallow source in the experiment,and compare the range estimation performance of the underwater target sound source of four methods:matched field processing(MFP),generalized regression neural network(GRNN),traditional CNN,and transfer learning.Experimental data processing results show that the transfer learning model based on residual CNN can effectively realize range estimation in few-shot scenes,and the estimation performance is remarkably better than that of other methods.
