Maria Valtorta (1897-1961, Italian mystic)—bedridden since 1934 because paralyzed—wrote in Italian 13,193 pages of 122 school notebooks concerning alleged mystical visions on Jesus’ life, during World War II and fe...Maria Valtorta (1897-1961, Italian mystic)—bedridden since 1934 because paralyzed—wrote in Italian 13,193 pages of 122 school notebooks concerning alleged mystical visions on Jesus’ life, during World War II and few following years. The contents—about 2.64 million words—are now scattered in different books. She could write from 2 to 6 hours without pausing, with steady speed, and twice in the same day. She never made corrections and was very proficient in Italian. We have studied her writing activity concerning her alleged mystical experience with the main scope of establishing the time sequence of daily writing. This is possible because she diligently annotated the date of almost every text. We have reconstructed the time series of daily words and have converted them into time series of writing time, by assuming a realistic speed of 20 words per minute, a reliable average value of fast handwriting speed, applicable to Maria Valtorta. She wrote for 1340 days, about 3.67 years of equivalent contiguous writing time, mostly concentrated in the years 1943 to 1948. This study is a first approach in evaluating the effort done, in terms of writing time, by a mystic turned out to be a very effective literary author, whose texts are interesting to read per se, beyond any judgement—not of concern here—on her alleged visions.展开更多
This paper has proposed a new methodology extracting stability classes of field association words depending on automatically power link analysis to enhance the precision of decision tree. In this paper, we have studie...This paper has proposed a new methodology extracting stability classes of field association words depending on automatically power link analysis to enhance the precision of decision tree. In this paper, we have studied the effects of the time variation based on the frequencies of specific words called field association words that connected to documents using power link in a specific period. The stability classes have referred to the popularity of field association words based on the change of time in a given period. The new approach has evaluated by conducting experiments simulating results of 1575 files (about 5.16 MB). Based on these experiments, it has turned out that, the F-measure for ascending, stable and descending classes have achieved 93.6%, 99.8% and 75.7%, respectively. These results mean that F-measure was increasing by 12%, 4% and 34% than traditional methods because of the power link analysis.展开更多
Digital broadcasting system has become a high-light of research on computer application. To respond to the changes of the playbill in the broadcasting system in real time, the response time of the system must be studi...Digital broadcasting system has become a high-light of research on computer application. To respond to the changes of the playbill in the broadcasting system in real time, the response time of the system must be studied. There is scarcely the research on this area currently. The influence factors in the response time are analyzed; the model on the response time of the system service is built; how the influence factors affect the response time of the system service is validated; and four improvement measures are proposed to minimize the response time of system service.展开更多
针对室外大范围场景移动机器人建图中,激光雷达里程计位姿计算不准确导致SLAM(simultaneous localization and mapping)算法精度下降的问题,提出一种基于多传感信息融合的SLAM语义词袋优化算法MSW-SLAM(multi-sensor information fusion...针对室外大范围场景移动机器人建图中,激光雷达里程计位姿计算不准确导致SLAM(simultaneous localization and mapping)算法精度下降的问题,提出一种基于多传感信息融合的SLAM语义词袋优化算法MSW-SLAM(multi-sensor information fusion SLAM based on semantic word bags)。采用视觉惯性系统引入激光雷达原始观测数据,并通过滑动窗口实现了IMU(inertia measurement unit)量测、视觉特征和激光点云特征的多源数据联合非线性优化;最后算法利用视觉与激光雷达的语义词袋互补特性进行闭环优化,进一步提升了多传感器融合SLAM系统的全局定位和建图精度。实验结果显示,相比于传统的紧耦合双目视觉惯性里程计和激光雷达里程计定位,MSW-SLAM算法能够有效探测轨迹中的闭环信息,并实现高精度的全局位姿图优化,闭环检测后的点云地图具有良好的分辨率和全局一致性。展开更多
回环检测作为同步建图与定位(Simulation Localization and Mapping,SLAM)算法中的基本组成部分,能有效关联相同场景之间的特征信息,提供全局一致性的位姿估计。基于词袋(Bag of Words,BoW)模型的回环检测算法在视觉SLAM领域有着显著成...回环检测作为同步建图与定位(Simulation Localization and Mapping,SLAM)算法中的基本组成部分,能有效关联相同场景之间的特征信息,提供全局一致性的位姿估计。基于词袋(Bag of Words,BoW)模型的回环检测算法在视觉SLAM领域有着显著成效,但对于激光雷达SLAM算法,主流的方法无法实时有效地识别回环场景,且通常无法校正完整的六自由度(6 Degree of Freedom,6-DOF)环路姿态。针对以上问题,文章提出了一种基于线性关键点特征表示的词袋模型,用于激光雷达SLAM中的实时回环检测。该词袋模型计算性能高效,可满足自动驾驶实时性要求。同时,算法具有稳定的姿态校正能力,可用于精确的点对点匹配。在公开数据集上,将文章提出的方法嵌入激光SLAM算法中进行闭环性能评估。结果表明,基于词袋模型的回环检测算法在激光SLAM领域优于现有的主流方法。展开更多
文摘Maria Valtorta (1897-1961, Italian mystic)—bedridden since 1934 because paralyzed—wrote in Italian 13,193 pages of 122 school notebooks concerning alleged mystical visions on Jesus’ life, during World War II and few following years. The contents—about 2.64 million words—are now scattered in different books. She could write from 2 to 6 hours without pausing, with steady speed, and twice in the same day. She never made corrections and was very proficient in Italian. We have studied her writing activity concerning her alleged mystical experience with the main scope of establishing the time sequence of daily writing. This is possible because she diligently annotated the date of almost every text. We have reconstructed the time series of daily words and have converted them into time series of writing time, by assuming a realistic speed of 20 words per minute, a reliable average value of fast handwriting speed, applicable to Maria Valtorta. She wrote for 1340 days, about 3.67 years of equivalent contiguous writing time, mostly concentrated in the years 1943 to 1948. This study is a first approach in evaluating the effort done, in terms of writing time, by a mystic turned out to be a very effective literary author, whose texts are interesting to read per se, beyond any judgement—not of concern here—on her alleged visions.
文摘This paper has proposed a new methodology extracting stability classes of field association words depending on automatically power link analysis to enhance the precision of decision tree. In this paper, we have studied the effects of the time variation based on the frequencies of specific words called field association words that connected to documents using power link in a specific period. The stability classes have referred to the popularity of field association words based on the change of time in a given period. The new approach has evaluated by conducting experiments simulating results of 1575 files (about 5.16 MB). Based on these experiments, it has turned out that, the F-measure for ascending, stable and descending classes have achieved 93.6%, 99.8% and 75.7%, respectively. These results mean that F-measure was increasing by 12%, 4% and 34% than traditional methods because of the power link analysis.
文摘Digital broadcasting system has become a high-light of research on computer application. To respond to the changes of the playbill in the broadcasting system in real time, the response time of the system must be studied. There is scarcely the research on this area currently. The influence factors in the response time are analyzed; the model on the response time of the system service is built; how the influence factors affect the response time of the system service is validated; and four improvement measures are proposed to minimize the response time of system service.
文摘针对室外大范围场景移动机器人建图中,激光雷达里程计位姿计算不准确导致SLAM(simultaneous localization and mapping)算法精度下降的问题,提出一种基于多传感信息融合的SLAM语义词袋优化算法MSW-SLAM(multi-sensor information fusion SLAM based on semantic word bags)。采用视觉惯性系统引入激光雷达原始观测数据,并通过滑动窗口实现了IMU(inertia measurement unit)量测、视觉特征和激光点云特征的多源数据联合非线性优化;最后算法利用视觉与激光雷达的语义词袋互补特性进行闭环优化,进一步提升了多传感器融合SLAM系统的全局定位和建图精度。实验结果显示,相比于传统的紧耦合双目视觉惯性里程计和激光雷达里程计定位,MSW-SLAM算法能够有效探测轨迹中的闭环信息,并实现高精度的全局位姿图优化,闭环检测后的点云地图具有良好的分辨率和全局一致性。
文摘回环检测作为同步建图与定位(Simulation Localization and Mapping,SLAM)算法中的基本组成部分,能有效关联相同场景之间的特征信息,提供全局一致性的位姿估计。基于词袋(Bag of Words,BoW)模型的回环检测算法在视觉SLAM领域有着显著成效,但对于激光雷达SLAM算法,主流的方法无法实时有效地识别回环场景,且通常无法校正完整的六自由度(6 Degree of Freedom,6-DOF)环路姿态。针对以上问题,文章提出了一种基于线性关键点特征表示的词袋模型,用于激光雷达SLAM中的实时回环检测。该词袋模型计算性能高效,可满足自动驾驶实时性要求。同时,算法具有稳定的姿态校正能力,可用于精确的点对点匹配。在公开数据集上,将文章提出的方法嵌入激光SLAM算法中进行闭环性能评估。结果表明,基于词袋模型的回环检测算法在激光SLAM领域优于现有的主流方法。
