High dimensional data clustering,with the inherent sparsity of data and the existence of noise,is a serious challenge for clustering algorithms.A new linear manifold clustering method was proposed to address this prob...High dimensional data clustering,with the inherent sparsity of data and the existence of noise,is a serious challenge for clustering algorithms.A new linear manifold clustering method was proposed to address this problem.The basic idea was to search the line manifold clusters hidden in datasets,and then fuse some of the line manifold clusters to construct higher dimensional manifold clusters.The orthogonal distance and the tangent distance were considered together as the linear manifold distance metrics. Spatial neighbor information was fully utilized to construct the original line manifold and optimize line manifolds during the line manifold cluster searching procedure.The results obtained from experiments over real and synthetic data sets demonstrate the superiority of the proposed method over some competing clustering methods in terms of accuracy and computation time.The proposed method is able to obtain high clustering accuracy for various data sets with different sizes,manifold dimensions and noise ratios,which confirms the anti-noise capability and high clustering accuracy of the proposed method for high dimensional data.展开更多
造斜率的准确预测是进行井眼轨迹调控的基础,直接影响定向井钻井效率,但由于井下力学行为的复杂性,传统预测方法存在一定限制,难以实现精确预测。为此,提出了一种力学-智能模型融合的造斜率预测方法。利用力学模型计算钻头侧向力、钻头...造斜率的准确预测是进行井眼轨迹调控的基础,直接影响定向井钻井效率,但由于井下力学行为的复杂性,传统预测方法存在一定限制,难以实现精确预测。为此,提出了一种力学-智能模型融合的造斜率预测方法。利用力学模型计算钻头侧向力、钻头转角和极限造斜率并作为主控因素,通过自动化机器学习框架联合其他参数进行拟合预测,从而取代传统方法反演经验系数的过程,使其充分发挥力学模型宏观规律描述准确和智能模型非线性拟合能力强的优势。利用新疆玛湖区块14口井数据进行训练和测试。结果显示,融合力学参数后,模型造斜率最大误差、均方根误差和平均绝对误差分别下降了17%、12%和8%,其中均方根误差和平均绝对误差均小于每30 m 1.00°,表明该方法能够有效提升造斜率预测精度,尤其在造斜率急剧变化的井段表现出更优的预测性能。研究结果可为造斜率的准确预测提供新的思路,同时也可为井眼轨迹的精确调控提供一定的技术支撑。展开更多
当前,步态识别的主流方法常依赖堆叠卷积层来逐步扩大感受野,以融合局部特征,这种方法大多采用浅层网络,在提取步态图像的全局特征时存在一定的局限性,并缺乏对时序周期特征信息的关注。因此提出一种融合Transformer和3D卷积的深层神经...当前,步态识别的主流方法常依赖堆叠卷积层来逐步扩大感受野,以融合局部特征,这种方法大多采用浅层网络,在提取步态图像的全局特征时存在一定的局限性,并缺乏对时序周期特征信息的关注。因此提出一种融合Transformer和3D卷积的深层神经网络算法(3D convolutional gait recognition network based on adaptFormer and spect-conv,3D-ASgaitNet)。首先,初始残差卷积层将二进制轮廓数据转换为浮点编码特征图,以提供密集的低级结构特征;在此基础上,光谱层通过频域和时域的联合处理增强特征提取能力,并使用伪3D残差卷积模块进一步提取高级时空特征;最后融合AdaptFormer模块,通过轻量级的下采样-上采样网络结构,以适应不同的数据分布和任务需求,提供灵活的特征变换能力。3D-ASgaitNet分别在4个公开的室内数据集(CASIA-B、OU-MVLP)、室外数据集(GREW、Gait3D)上进行,分别取得99.84%、87.83%、45.32%、72.12%的识别准确率。实验结果表明,所提出方法在CASIA-B、Gait3D数据集中的识别准确率接近SOTA性能。展开更多
基金Project(60835005) supported by the National Nature Science Foundation of China
文摘High dimensional data clustering,with the inherent sparsity of data and the existence of noise,is a serious challenge for clustering algorithms.A new linear manifold clustering method was proposed to address this problem.The basic idea was to search the line manifold clusters hidden in datasets,and then fuse some of the line manifold clusters to construct higher dimensional manifold clusters.The orthogonal distance and the tangent distance were considered together as the linear manifold distance metrics. Spatial neighbor information was fully utilized to construct the original line manifold and optimize line manifolds during the line manifold cluster searching procedure.The results obtained from experiments over real and synthetic data sets demonstrate the superiority of the proposed method over some competing clustering methods in terms of accuracy and computation time.The proposed method is able to obtain high clustering accuracy for various data sets with different sizes,manifold dimensions and noise ratios,which confirms the anti-noise capability and high clustering accuracy of the proposed method for high dimensional data.
文摘造斜率的准确预测是进行井眼轨迹调控的基础,直接影响定向井钻井效率,但由于井下力学行为的复杂性,传统预测方法存在一定限制,难以实现精确预测。为此,提出了一种力学-智能模型融合的造斜率预测方法。利用力学模型计算钻头侧向力、钻头转角和极限造斜率并作为主控因素,通过自动化机器学习框架联合其他参数进行拟合预测,从而取代传统方法反演经验系数的过程,使其充分发挥力学模型宏观规律描述准确和智能模型非线性拟合能力强的优势。利用新疆玛湖区块14口井数据进行训练和测试。结果显示,融合力学参数后,模型造斜率最大误差、均方根误差和平均绝对误差分别下降了17%、12%和8%,其中均方根误差和平均绝对误差均小于每30 m 1.00°,表明该方法能够有效提升造斜率预测精度,尤其在造斜率急剧变化的井段表现出更优的预测性能。研究结果可为造斜率的准确预测提供新的思路,同时也可为井眼轨迹的精确调控提供一定的技术支撑。
文摘当前,步态识别的主流方法常依赖堆叠卷积层来逐步扩大感受野,以融合局部特征,这种方法大多采用浅层网络,在提取步态图像的全局特征时存在一定的局限性,并缺乏对时序周期特征信息的关注。因此提出一种融合Transformer和3D卷积的深层神经网络算法(3D convolutional gait recognition network based on adaptFormer and spect-conv,3D-ASgaitNet)。首先,初始残差卷积层将二进制轮廓数据转换为浮点编码特征图,以提供密集的低级结构特征;在此基础上,光谱层通过频域和时域的联合处理增强特征提取能力,并使用伪3D残差卷积模块进一步提取高级时空特征;最后融合AdaptFormer模块,通过轻量级的下采样-上采样网络结构,以适应不同的数据分布和任务需求,提供灵活的特征变换能力。3D-ASgaitNet分别在4个公开的室内数据集(CASIA-B、OU-MVLP)、室外数据集(GREW、Gait3D)上进行,分别取得99.84%、87.83%、45.32%、72.12%的识别准确率。实验结果表明,所提出方法在CASIA-B、Gait3D数据集中的识别准确率接近SOTA性能。
