This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional te...This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional techniques.The work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells,namely,eosinophils,neutrophils,monocytes,and lymphocytes,known for their relationship with human body damage,inflammatory regions,and organ illnesses,in particular,and with the health of the immune system and other hazards,such as cardiovascular disease or infections,more in general.The results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%,97%,and 89%,respectively,with regard to the training,verification,and testing of the corresponding datasets.展开更多
The mean-square exponential stability problem is investigated for a class of stochastic time-varying delay systems with Markovian jumping parameters. By decomposing the delay interval into multiple equidistant subinte...The mean-square exponential stability problem is investigated for a class of stochastic time-varying delay systems with Markovian jumping parameters. By decomposing the delay interval into multiple equidistant subintervals, a new delay-dependent and decay-rate-dependent criterion is presented based on constructing a novel Lyapunov functional and employing stochastic analysis technique. Besides, the decay rate has no conventional constraint and can be selected according to different practical conditions. Finally, two numerical examples are provided to show that the obtained result has less conservatism than some existing ones in the literature.展开更多
Photovoltaic power generating is one of the primary methods of utilizing solar energy resources,with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy....Photovoltaic power generating is one of the primary methods of utilizing solar energy resources,with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy.In order to provide reference strategies for pertinent researchers as well as potential implementation,this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches,statistical approaches and optimization techniques for solar power generation and forecasting.Deep learning-related methods,in particular,can theoretically handle arbitrary nonlinear transformations through proper model structural design,such as hidden layer topology optimization and objective function analysis to save information that can increase forecasting accuracy while filtering out irrelevant or less affected data for forecasting.The research’s results indicate that RBFNN-AG performed the best when applying the predetermined number of days,with an NRMSE value of 4.65%.RBFNN-AG performs better than sophisticated models like DenseNet(5.69%),SLFN-ELM(5.95%),and ANN-k-means-linear regression correction(6.11%).Additionally,scenario application and PV system investment techniques are provided to evaluate the current condition of new energy development and market trends both domestically and internationally.展开更多
3D pose transfer over unorganized point clouds is a challenging generation task,which transfers a source’s pose to a target shape and keeps the target’s identity.Recent deep models have learned deformations and used...3D pose transfer over unorganized point clouds is a challenging generation task,which transfers a source’s pose to a target shape and keeps the target’s identity.Recent deep models have learned deformations and used the target’s identity as a style to modulate the combined features of two shapes or the aligned vertices of the source shape.However,all operations in these models are point-wise and independent and ignore the geometric information on the surface and structure of the input shapes.This disadvantage severely limits the generation and generalization capabilities.In this study,we propose a geometry-aware method based on a novel transformer autoencoder to solve this problem.An efficient self-attention mechanism,that is,cross-covariance attention,was utilized across our framework to perceive the correlations between points at different distances.Specifically,the transformer encoder extracts the target shape’s local geometry details for identity attributes and the source shape’s global geometry structure for pose information.Our transformer decoder efficiently learns deformations and recovers identity properties by fusing and decoding the extracted features in a geometry attentional manner,which does not require corresponding information or modulation steps.The experiments demonstrated that the geometry-aware method achieved state-of-the-art performance in a 3D pose transfer task.The implementation code and data are available at https://github.com/SEULSH/Geometry-Aware-3D-Pose-Transfer-Using-Transfor mer-Autoencoder.展开更多
When obtaining three-dimensional(3D)face point cloud data based on structured light,factors related to the environment,occlusion,and illumination intensity lead to holes in the collected data,which affect subsequent r...When obtaining three-dimensional(3D)face point cloud data based on structured light,factors related to the environment,occlusion,and illumination intensity lead to holes in the collected data,which affect subsequent recognition.In this study,we propose a hole-filling method based on stereo-matching technology combined with a B-spline.The algorithm uses phase information acquired during raster projection to locate holes in the point cloud,simultaneously extracting boundary point cloud sets.By registering the face point cloud data using the stereo-matching algorithm and the data collected using the raster projection method,some supplementary information points can be obtained at the holes.The shape of the B-spline curve can then be roughly described by a few key points,and the control points are put into the hole area as key points for iterative calculation of surface reconstruction.Simulations using smooth ceramic cups and human face models showed that our model can accurately reproduce details and accurately restore complex shapes on the test surfaces.Simulation results indicated the robustness of the method,which is able to fill holes on complex areas such as the inner side of the nose without a prior model.This approach also effectively supplements the hole information,and the patched point cloud is closer to the original data.This method could be used across a wide range of applications requiring accurate facial recognition.展开更多
基金supported by National Natural Science Foundation of China(NSFC)(Nos.61806087,61902158).
文摘This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional techniques.The work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells,namely,eosinophils,neutrophils,monocytes,and lymphocytes,known for their relationship with human body damage,inflammatory regions,and organ illnesses,in particular,and with the health of the immune system and other hazards,such as cardiovascular disease or infections,more in general.The results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%,97%,and 89%,respectively,with regard to the training,verification,and testing of the corresponding datasets.
基金supported by the Program for New Century Excellent Talents in University, the Graduate Innovation Program of Jiangsu Province (CX06B-051Z)the Scientific Research Foundation of Graduate School of Southeast University (YBJJ0929)
文摘The mean-square exponential stability problem is investigated for a class of stochastic time-varying delay systems with Markovian jumping parameters. By decomposing the delay interval into multiple equidistant subintervals, a new delay-dependent and decay-rate-dependent criterion is presented based on constructing a novel Lyapunov functional and employing stochastic analysis technique. Besides, the decay rate has no conventional constraint and can be selected according to different practical conditions. Finally, two numerical examples are provided to show that the obtained result has less conservatism than some existing ones in the literature.
基金supported by the National Natural Science Foundation of China(NSFC)(Nos.61902158,61806087).
文摘Photovoltaic power generating is one of the primary methods of utilizing solar energy resources,with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy.In order to provide reference strategies for pertinent researchers as well as potential implementation,this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches,statistical approaches and optimization techniques for solar power generation and forecasting.Deep learning-related methods,in particular,can theoretically handle arbitrary nonlinear transformations through proper model structural design,such as hidden layer topology optimization and objective function analysis to save information that can increase forecasting accuracy while filtering out irrelevant or less affected data for forecasting.The research’s results indicate that RBFNN-AG performed the best when applying the predetermined number of days,with an NRMSE value of 4.65%.RBFNN-AG performs better than sophisticated models like DenseNet(5.69%),SLFN-ELM(5.95%),and ANN-k-means-linear regression correction(6.11%).Additionally,scenario application and PV system investment techniques are provided to evaluate the current condition of new energy development and market trends both domestically and internationally.
基金supported by the Special Project on Basic Research of Frontier Leading Technology of Jiangsu Province,China(Grant No.BK20192004C).
文摘3D pose transfer over unorganized point clouds is a challenging generation task,which transfers a source’s pose to a target shape and keeps the target’s identity.Recent deep models have learned deformations and used the target’s identity as a style to modulate the combined features of two shapes or the aligned vertices of the source shape.However,all operations in these models are point-wise and independent and ignore the geometric information on the surface and structure of the input shapes.This disadvantage severely limits the generation and generalization capabilities.In this study,we propose a geometry-aware method based on a novel transformer autoencoder to solve this problem.An efficient self-attention mechanism,that is,cross-covariance attention,was utilized across our framework to perceive the correlations between points at different distances.Specifically,the transformer encoder extracts the target shape’s local geometry details for identity attributes and the source shape’s global geometry structure for pose information.Our transformer decoder efficiently learns deformations and recovers identity properties by fusing and decoding the extracted features in a geometry attentional manner,which does not require corresponding information or modulation steps.The experiments demonstrated that the geometry-aware method achieved state-of-the-art performance in a 3D pose transfer task.The implementation code and data are available at https://github.com/SEULSH/Geometry-Aware-3D-Pose-Transfer-Using-Transfor mer-Autoencoder.
基金supported by the National Natural Science Foundation of China(No.61405034)the Special Project on Basic Research of Frontier Leading Technology of Jiangsu Province,China(No.BK20192004C)+1 种基金the Shenzhen Science and Technology Innovation Committee(No.JCYJ20180306174455080)the Natural Science Foundation of Jiangsu Province,China(No.BK20181269)。
文摘When obtaining three-dimensional(3D)face point cloud data based on structured light,factors related to the environment,occlusion,and illumination intensity lead to holes in the collected data,which affect subsequent recognition.In this study,we propose a hole-filling method based on stereo-matching technology combined with a B-spline.The algorithm uses phase information acquired during raster projection to locate holes in the point cloud,simultaneously extracting boundary point cloud sets.By registering the face point cloud data using the stereo-matching algorithm and the data collected using the raster projection method,some supplementary information points can be obtained at the holes.The shape of the B-spline curve can then be roughly described by a few key points,and the control points are put into the hole area as key points for iterative calculation of surface reconstruction.Simulations using smooth ceramic cups and human face models showed that our model can accurately reproduce details and accurately restore complex shapes on the test surfaces.Simulation results indicated the robustness of the method,which is able to fill holes on complex areas such as the inner side of the nose without a prior model.This approach also effectively supplements the hole information,and the patched point cloud is closer to the original data.This method could be used across a wide range of applications requiring accurate facial recognition.
