Passive Fourier transform infrared （FTIR） remote sensing measurement of chemical gas cloud is a vital technology. It takes an important part in many fields for the detection of released gases. The principle of conce...Passive Fourier transform infrared （FTIR） remote sensing measurement of chemical gas cloud is a vital technology. It takes an important part in many fields for the detection of released gases. The principle of concentration measurement is based on the Beer-Lambert law. Unlike the active measurement, for the passive remote sensing, in most cases, the difference between the temperature of the gas cloud and the brightness temperature of the background is usually a few kelvins. The gas cloud emission is almost equal to the background emission, thereby the emission of the gas cloud cannot be ignored. The concentration retrieval algorithm is quite different from the active measurement. In this paper, the concentration retrieval algorithm for the passive FTIR remote measurement of gas cloud is presented in detail, which involves radiative transfer model, radiometric calibration, absorption coefficient calculation, et al. The background spectrum has a broad feature, which is a slowly varying function of frequency. In this paper, the background spectrum is fitted with a polynomial by using the Levenberg-Marquardt method which is a kind of nonlinear least squares fitting algorithm. No background spectra are required. Thus, this method allows mobile, real-time and fast measurements of gas clouds.展开更多
With the development of power grid, as one of the key equipment, the transformer’s condition assessment method has always receive attention from experts, scholars concern more and more about the method’s practicalit...With the development of power grid, as one of the key equipment, the transformer’s condition assessment method has always receive attention from experts, scholars concern more and more about the method’s practicality and reliability. In the traditional condition assessment method, due to the characteristics of the transformer’s complex structure, the assessment system is not comprehensive enough, or the assessment system is too complex, the indexes are not easy to quantify, such problems are emerging. The traditional method is complex and the degree of quantification is not enough. Therefore it is necessary to propose a condition assessment method that is easy to carry out the condition assessment work and does not affect the assessment results. In this paper, we propose a method to assess the state of the transformer’s complex structure. First, we establish a comprehensive assessment system, then apply the method of principal component analysis to optimize the index system, and then use the theory of cloud-matter-element. Finally the reliability and rationality of the method are verified by an example.展开更多
【背景】传统方法因静态感受野设计较难适配城市自动驾驶场景中汽车、行人及骑行者等目标的显著尺度差异,且跨尺度特征融合易引发层级干扰。【方法】针对自动驾驶场景中多类别、多尺寸目标的3D检测中跨尺度表征一致性的关键挑战,本研究...【背景】传统方法因静态感受野设计较难适配城市自动驾驶场景中汽车、行人及骑行者等目标的显著尺度差异,且跨尺度特征融合易引发层级干扰。【方法】针对自动驾驶场景中多类别、多尺寸目标的3D检测中跨尺度表征一致性的关键挑战,本研究提出基于均衡化感受野的3D目标检测方法VoxTNT,通过局部-全局协同注意力机制提升检测性能。在局部层面,设计了PointSetFormer模块,引入诱导集注意力模块(Induced Set Attention Block,ISAB),通过约简的交叉注意力聚合高密度点云的细粒度几何特征,突破传统体素均值池化的信息损失瓶颈;在全局层面,设计了VoxelFormerFFN模块,将非空体素抽象为超点集并实施跨体素ISAB交互,建立长程上下文依赖关系,并将全局特征学习计算负载从O(N^(2))压缩至O(M^(2))(M<<N,M为非空体素数量),规避了复杂的Transformer直接使用在原始点云造成的高计算复杂度。该双域耦合架构实现了局部细粒度感知与全局语义关联的动态平衡,有效缓解固定感受野和多尺度融合导致的特征建模偏差。【结果】实验表明,该方法在KITTI数据集单阶段检测下,中等难度级别的行人检测精度AP(Average Precision)值达到59.56%,较SECOND基线提高约12.4%,两阶段检测下以66.54%的综合指标mAP(mean Average Precision)领先次优方法BSAODet的66.10%。同时,在WOD数据集中验证了方法的有效性,综合指标mAP达到66.09%分别超越SECOND和PointPillars基线7.7%和8.5%。消融实验进一步表明,均衡化局部和全局感受野的3D特征学习机制能显著提升小目标检测精度(如在KITTI数据集中全组件消融的情况下,中等难度级别的行人和骑行者检测精度分别下降10.8%和10.0%),同时保持大目标检测的稳定性。【结论】本研究为解决自动驾驶多尺度目标检测难题提供了新思路,未来将优化模型结构以进一步提升效能。展开更多
针对多目标识别过程中点云分类和分割精度不高的问题,提出了一种基于改进Transformer模型的点云分类与分割方法DRPT(Double randomness Point Transformer),该方法在Transformer模型卷积投影层创建新的点嵌入,利用局部邻域的动态处理在...针对多目标识别过程中点云分类和分割精度不高的问题,提出了一种基于改进Transformer模型的点云分类与分割方法DRPT(Double randomness Point Transformer),该方法在Transformer模型卷积投影层创建新的点嵌入,利用局部邻域的动态处理在数据特征向量中持续增加全局特征属性,从而提高多目标识别中点云分类和分割的精度。实验中采用了标准基准数据集(ModelNet40、ShapeNet部分分割和SemanticKITTI场景语义分割数据集)以验证模型的性能,实验结果表明:DRPT模型的pIoU值为85.9%,比其他模型平均高出3.5%,有效提高了多目标识别检测时点云分类与分割精度,是对智能网联技术发展的有效支撑。展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No 083H311501)the National High Technology Research and Development Program of China (Grant No 073H3f1514)
文摘Passive Fourier transform infrared （FTIR） remote sensing measurement of chemical gas cloud is a vital technology. It takes an important part in many fields for the detection of released gases. The principle of concentration measurement is based on the Beer-Lambert law. Unlike the active measurement, for the passive remote sensing, in most cases, the difference between the temperature of the gas cloud and the brightness temperature of the background is usually a few kelvins. The gas cloud emission is almost equal to the background emission, thereby the emission of the gas cloud cannot be ignored. The concentration retrieval algorithm is quite different from the active measurement. In this paper, the concentration retrieval algorithm for the passive FTIR remote measurement of gas cloud is presented in detail, which involves radiative transfer model, radiometric calibration, absorption coefficient calculation, et al. The background spectrum has a broad feature, which is a slowly varying function of frequency. In this paper, the background spectrum is fitted with a polynomial by using the Levenberg-Marquardt method which is a kind of nonlinear least squares fitting algorithm. No background spectra are required. Thus, this method allows mobile, real-time and fast measurements of gas clouds.
文摘With the development of power grid, as one of the key equipment, the transformer’s condition assessment method has always receive attention from experts, scholars concern more and more about the method’s practicality and reliability. In the traditional condition assessment method, due to the characteristics of the transformer’s complex structure, the assessment system is not comprehensive enough, or the assessment system is too complex, the indexes are not easy to quantify, such problems are emerging. The traditional method is complex and the degree of quantification is not enough. Therefore it is necessary to propose a condition assessment method that is easy to carry out the condition assessment work and does not affect the assessment results. In this paper, we propose a method to assess the state of the transformer’s complex structure. First, we establish a comprehensive assessment system, then apply the method of principal component analysis to optimize the index system, and then use the theory of cloud-matter-element. Finally the reliability and rationality of the method are verified by an example.
文摘【背景】传统方法因静态感受野设计较难适配城市自动驾驶场景中汽车、行人及骑行者等目标的显著尺度差异,且跨尺度特征融合易引发层级干扰。【方法】针对自动驾驶场景中多类别、多尺寸目标的3D检测中跨尺度表征一致性的关键挑战,本研究提出基于均衡化感受野的3D目标检测方法VoxTNT,通过局部-全局协同注意力机制提升检测性能。在局部层面,设计了PointSetFormer模块,引入诱导集注意力模块(Induced Set Attention Block,ISAB),通过约简的交叉注意力聚合高密度点云的细粒度几何特征,突破传统体素均值池化的信息损失瓶颈;在全局层面,设计了VoxelFormerFFN模块,将非空体素抽象为超点集并实施跨体素ISAB交互,建立长程上下文依赖关系,并将全局特征学习计算负载从O(N^(2))压缩至O(M^(2))(M<<N,M为非空体素数量),规避了复杂的Transformer直接使用在原始点云造成的高计算复杂度。该双域耦合架构实现了局部细粒度感知与全局语义关联的动态平衡,有效缓解固定感受野和多尺度融合导致的特征建模偏差。【结果】实验表明,该方法在KITTI数据集单阶段检测下,中等难度级别的行人检测精度AP(Average Precision)值达到59.56%,较SECOND基线提高约12.4%,两阶段检测下以66.54%的综合指标mAP(mean Average Precision)领先次优方法BSAODet的66.10%。同时,在WOD数据集中验证了方法的有效性,综合指标mAP达到66.09%分别超越SECOND和PointPillars基线7.7%和8.5%。消融实验进一步表明,均衡化局部和全局感受野的3D特征学习机制能显著提升小目标检测精度(如在KITTI数据集中全组件消融的情况下,中等难度级别的行人和骑行者检测精度分别下降10.8%和10.0%),同时保持大目标检测的稳定性。【结论】本研究为解决自动驾驶多尺度目标检测难题提供了新思路,未来将优化模型结构以进一步提升效能。
文摘针对多目标识别过程中点云分类和分割精度不高的问题,提出了一种基于改进Transformer模型的点云分类与分割方法DRPT(Double randomness Point Transformer),该方法在Transformer模型卷积投影层创建新的点嵌入,利用局部邻域的动态处理在数据特征向量中持续增加全局特征属性,从而提高多目标识别中点云分类和分割的精度。实验中采用了标准基准数据集(ModelNet40、ShapeNet部分分割和SemanticKITTI场景语义分割数据集)以验证模型的性能,实验结果表明:DRPT模型的pIoU值为85.9%,比其他模型平均高出3.5%,有效提高了多目标识别检测时点云分类与分割精度,是对智能网联技术发展的有效支撑。
