Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study intr...Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.展开更多
A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes...A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes of variable sample morphological characteristics,low contrast and complex background texture.Firstly,by analyzing the spectral component distribution and spatial contour feature of the image,a salient feature model is established in spatial-frequency domain.Then,the salient object detection method based on Gaussian band-pass filter and the design criterion of adaptive convolution kernel are proposed to extract the salient contour feature of the target in spatial and frequency domain.Finally,the selection and growth rules of seed points are improved by integrating the gray level and contour features of the target,and the target is segmented by seeded region growing.Experiments have been performed on Berkeley Segmentation Data Set,as well as sample images of online detection,to verify the effectiveness of the algorithm.The experimental results show that the Jaccard Similarity Coefficient of the segmentation is more than 90%,which indicates that the proposed algorithm can availably extract the target feature information,suppress the background texture and resist noise interference.Besides,the Hausdorff Distance of the segmentation is less than 10,which infers that the proposed algorithm obtains a high evaluation on the target contour preservation.The experimental results also show that the proposed algorithm significantly improves the operation efficiency while obtaining comparable segmentation performance over other algorithms.展开更多
Geographic visualization is essential for explaining and describing spatiotemporal geographical processes in flow fields.However,due to multi-scale structures and irregular spatial distribution of vortices in complex ...Geographic visualization is essential for explaining and describing spatiotemporal geographical processes in flow fields.However,due to multi-scale structures and irregular spatial distribution of vortices in complex geographic flow fields,existing two-dimensional visualization methods are susceptible to the effects of data accuracy and sampling resolution,resulting in incomplete and inaccurate vortex information.To address this,we propose an adaptive Line Integral Convolution(LIC)based geographic flow field visualization method by means of rotation distance.Our novel framework of rotation distance and its quantification allows for the effective identification and extraction of vortex features in flow fields effectively.We then improve the LIC algorithm using rotation distance by constructing high-frequency noise from it as input to the convolution,with the integration step size adjusted.This approach allows us to effectively distinguish between vortex and non-vortex fields and adaptively represent the details of vortex features in complex geographic flow fields.Our experimental results show that the proposed method leads to more accurate and effective visualization of the geographic flow fields.展开更多
由于行人交互的复杂性和周围环境的多变性,行人轨迹预测仍是一项具有挑战性的任务.然而,基于图结构的方法建模行人之间的交互时,存在着网络感受野小、成对行人间的相互交互对称、固定的图结构不能适应场景变化的问题,导致预测轨迹与真...由于行人交互的复杂性和周围环境的多变性,行人轨迹预测仍是一项具有挑战性的任务.然而,基于图结构的方法建模行人之间的交互时,存在着网络感受野小、成对行人间的相互交互对称、固定的图结构不能适应场景变化的问题,导致预测轨迹与真实轨迹偏差较大.为了解决这些问题,本文提出一种基于全局自适应有向图的行人轨迹预测方法(pedestrian trajectory prediction method based on Global Adaptive Directed Graph,GADG).设计全局特征更新(Global Feature Updating,GFU)和全局特征选择(Global Feature Selection,GFS)分别提升空间域和时间域的网络感受范围,以获取全局交互特征.构建有向特征图,定义行人间的不对称交互,提高网络建模的方向性.建立自适应图模型,灵活调整行人间的交互关系,减少冗余连接,增强图模型的自适应能力.在ETH和UCY数据集上的实验结果表明,与最优值相比,平均位移误差降低14%,最终位移误差降低3%.展开更多
在自适应光学（AO）系统中，成像是不可或缺的一部分。AO仿真系统中的探测器和哈特曼-夏克波前传感器的成像过程一般用二维的离散卷积来计算，而通常它的数值算法用快速傅立叶变换（FFT）实现。但是随着矩阵维数的增加，卷积的运算量会...在自适应光学（AO）系统中，成像是不可或缺的一部分。AO仿真系统中的探测器和哈特曼-夏克波前传感器的成像过程一般用二维的离散卷积来计算，而通常它的数值算法用快速傅立叶变换（FFT）实现。但是随着矩阵维数的增加，卷积的运算量会急剧增大，成为制约整个AO仿真效率的一个瓶颈。利用图形处理器（GPU）的强大计算能力，可以使成像系统运行速度大幅提高。在NVIDIA Tesla C2050 GPU上，针对不同分辨率的图像，获得了相对于串行程序5-24倍的加速比。展开更多
In this work,we revisit the adaptive L1 time-stepping scheme for solving the time-fractional Allen-Cahn equation in the Caputo’s form.The L1 implicit scheme is shown to preserve a variational energy dissipation law o...In this work,we revisit the adaptive L1 time-stepping scheme for solving the time-fractional Allen-Cahn equation in the Caputo’s form.The L1 implicit scheme is shown to preserve a variational energy dissipation law on arbitrary nonuniform time meshes by using the recent discrete analysis tools,i.e.,the discrete orthogonal convolution kernels and discrete complementary convolution kernels.Then the discrete embedding techniques and the fractional Gronwall inequality are applied to establish an L^(2)norm error estimate on nonuniform time meshes.An adaptive time-stepping strategy according to the dynamical feature of the system is presented to capture the multi-scale behaviors and to improve the computational performance.展开更多
基金funded by the National Science and Technology Council,Taiwan(Grant No.NSTC 112-2121-M-039-001)by China Medical University(Grant No.CMU112-MF-79).
文摘Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.
基金supported by National Natural Science Foundation of China[grant numbers 61573233]Natural Science Foundation of Guangdong,China[grant numbers 2021A1515010661]+1 种基金Special projects in key fields of colleges and universities in Guangdong Province[grant numbers 2020ZDZX2005]Innovation Team Project of University in Guangdong Province[grant numbers 2015KCXTD018].
文摘A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes of variable sample morphological characteristics,low contrast and complex background texture.Firstly,by analyzing the spectral component distribution and spatial contour feature of the image,a salient feature model is established in spatial-frequency domain.Then,the salient object detection method based on Gaussian band-pass filter and the design criterion of adaptive convolution kernel are proposed to extract the salient contour feature of the target in spatial and frequency domain.Finally,the selection and growth rules of seed points are improved by integrating the gray level and contour features of the target,and the target is segmented by seeded region growing.Experiments have been performed on Berkeley Segmentation Data Set,as well as sample images of online detection,to verify the effectiveness of the algorithm.The experimental results show that the Jaccard Similarity Coefficient of the segmentation is more than 90%,which indicates that the proposed algorithm can availably extract the target feature information,suppress the background texture and resist noise interference.Besides,the Hausdorff Distance of the segmentation is less than 10,which infers that the proposed algorithm obtains a high evaluation on the target contour preservation.The experimental results also show that the proposed algorithm significantly improves the operation efficiency while obtaining comparable segmentation performance over other algorithms.
文摘Geographic visualization is essential for explaining and describing spatiotemporal geographical processes in flow fields.However,due to multi-scale structures and irregular spatial distribution of vortices in complex geographic flow fields,existing two-dimensional visualization methods are susceptible to the effects of data accuracy and sampling resolution,resulting in incomplete and inaccurate vortex information.To address this,we propose an adaptive Line Integral Convolution(LIC)based geographic flow field visualization method by means of rotation distance.Our novel framework of rotation distance and its quantification allows for the effective identification and extraction of vortex features in flow fields effectively.We then improve the LIC algorithm using rotation distance by constructing high-frequency noise from it as input to the convolution,with the integration step size adjusted.This approach allows us to effectively distinguish between vortex and non-vortex fields and adaptively represent the details of vortex features in complex geographic flow fields.Our experimental results show that the proposed method leads to more accurate and effective visualization of the geographic flow fields.
文摘由于行人交互的复杂性和周围环境的多变性,行人轨迹预测仍是一项具有挑战性的任务.然而,基于图结构的方法建模行人之间的交互时,存在着网络感受野小、成对行人间的相互交互对称、固定的图结构不能适应场景变化的问题,导致预测轨迹与真实轨迹偏差较大.为了解决这些问题,本文提出一种基于全局自适应有向图的行人轨迹预测方法(pedestrian trajectory prediction method based on Global Adaptive Directed Graph,GADG).设计全局特征更新(Global Feature Updating,GFU)和全局特征选择(Global Feature Selection,GFS)分别提升空间域和时间域的网络感受范围,以获取全局交互特征.构建有向特征图,定义行人间的不对称交互,提高网络建模的方向性.建立自适应图模型,灵活调整行人间的交互关系,减少冗余连接,增强图模型的自适应能力.在ETH和UCY数据集上的实验结果表明,与最优值相比,平均位移误差降低14%,最终位移误差降低3%.
文摘在自适应光学（AO）系统中，成像是不可或缺的一部分。AO仿真系统中的探测器和哈特曼-夏克波前传感器的成像过程一般用二维的离散卷积来计算，而通常它的数值算法用快速傅立叶变换（FFT）实现。但是随着矩阵维数的增加，卷积的运算量会急剧增大，成为制约整个AO仿真效率的一个瓶颈。利用图形处理器（GPU）的强大计算能力，可以使成像系统运行速度大幅提高。在NVIDIA Tesla C2050 GPU上，针对不同分辨率的图像，获得了相对于串行程序5-24倍的加速比。
基金The authors would like to thank Dr.Bingquan Ji for his help on numerical computations.H.-L.Liao is supported by the National Natural Science Foundation of China(Grant 12071216)J.Wang is supported by the Hunan Provincial Innovation Foundation for Postgraduate(Grant XDCX2020B078).
文摘In this work,we revisit the adaptive L1 time-stepping scheme for solving the time-fractional Allen-Cahn equation in the Caputo’s form.The L1 implicit scheme is shown to preserve a variational energy dissipation law on arbitrary nonuniform time meshes by using the recent discrete analysis tools,i.e.,the discrete orthogonal convolution kernels and discrete complementary convolution kernels.Then the discrete embedding techniques and the fractional Gronwall inequality are applied to establish an L^(2)norm error estimate on nonuniform time meshes.An adaptive time-stepping strategy according to the dynamical feature of the system is presented to capture the multi-scale behaviors and to improve the computational performance.
