高精度、高分辨率的地形地貌数据是活动构造定量研究的基础。摄影测量方法的出现和快速发展为获取高精度地形地貌数据提供了一种经济有效的技术手段。相比于传统的测量方法,摄影测量方法可在大范围内同时进行,不受地面通视条件的限制,...高精度、高分辨率的地形地貌数据是活动构造定量研究的基础。摄影测量方法的出现和快速发展为获取高精度地形地貌数据提供了一种经济有效的技术手段。相比于传统的测量方法,摄影测量方法可在大范围内同时进行,不受地面通视条件的限制,且测量成本相对较低。尤其近年来,随着计算机视觉理论及高效的自动特征匹配算法的发展,一种名为"Structure from Motion"(SfM)的三维重建技术被引入摄影测量方法中,极大地提高了摄影测量的自动化程度。文中介绍了摄影测量方法的基本原理及发展历程,并综述了摄影测量方法在活动构造研究中的应用,最后通过SfM摄影测量方法在活动构造研究中的1个具体应用实例,展示了摄影测量方法在活动构造定量研究中的巨大应用潜力。展开更多
针对轮廓复杂多变、细节信息丰富等多因素,导致变电建筑重建效果不佳的问题,提出基于轮廓拼接的变电建筑数字三维重建算法。基于改进Snake模型提取变电建筑目标轮廓,为后续的三维重建提供关键轮廓信息;基于获取的二维轮廓信息,利用运动...针对轮廓复杂多变、细节信息丰富等多因素,导致变电建筑重建效果不佳的问题,提出基于轮廓拼接的变电建筑数字三维重建算法。基于改进Snake模型提取变电建筑目标轮廓,为后续的三维重建提供关键轮廓信息;基于获取的二维轮廓信息,利用运动恢复结构(Structure from Motion,SfM)完成变电建筑的三维轮廓重建;通过Jaccard距离和最近点迭代(ICP)算法将多个轮廓碎片精确拼接为完整的变电建筑三维轮廓,并使用附加三维线约束的网格优化算法对所构建三维轮廓实行优化,完成最终的变电建筑数字三维重建。实验结果表明:所提方法在变电建筑轮廓及三维重建中展现出高精度、细节保留良好且整体连贯性佳的优势。展开更多
In daily life,human need various senses to obtain information about their surroundings,and touch is one of the five major human sensing signals.Similarly,it is extremely important for robots to be endowed with tactile...In daily life,human need various senses to obtain information about their surroundings,and touch is one of the five major human sensing signals.Similarly,it is extremely important for robots to be endowed with tactile sensing ability.In recent years,vision-based tactile sensing technology has been the research hotspot and frontier in the field of tactile perception.Compared to conventional tactile sensing technologies,vision-based tactile sensing technologies are capable of obtaining highquality and high-resolution tactile information at a lower cost,while not being limited by the size and shape of sensors.Several previous articles have reviewed the sensing mechanism and electrical components of vision-based sensors,greatly promoting the innovation of tactile sensing.Different from existing reviews,this article concentrates on the underlying tracking method which converts real-time images into deformation information,including contact,sliding and friction.We will show the history and development of both model-based and model-free tracking methods,among which model-based approaches rely on schematic mechanical theories,and model-free approaches mainly involve machine learning algorithms.Comparing the efficiency and accuracy of existing deformation tracking methods,future research directions of vision-based tactile sensors for smart manipulations and robots are also discussed.展开更多
Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational...Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.展开更多
Sparse subspace clustering(SSC),a seminal clustering method,has demonstrated remarkable performance by effectively solving the data sparsity problem.However,it is not without its limitations.Key among these is the dif...Sparse subspace clustering(SSC),a seminal clustering method,has demonstrated remarkable performance by effectively solving the data sparsity problem.However,it is not without its limitations.Key among these is the difficulty of incremental learning with the original SSC,accompanied by a computationally demanding recalculation process that constrains its scalability to large datasets.Moreover,the conventional SSC framework considers dictionary construction,affinity matrix learning and clustering as separate stages,potentially leading to suboptimal dictionaries and affinity matrices for clustering.To address these challenges,we present a novel clustering approach,called SSCNet,which leverages differentiable programming.Specifically,we redefine and generalize the optimization procedure of the linearized alternating direction method of multipliers(ADMM),framing it as a multi-block deep neural network,where each block corresponds to a linearized ADMM iteration step.This reformulation is used to address the SSC problem.We then use a shallow spectral embedding network as an unambiguous and differentiable module to approximate the eigenvalue decomposition.Finally,we incorporate a self-supervised structure to mitigate the non-differentiability inherent in k-means to achieve the final clustering results.In essence,we assign unique objectives to different modules and jointly optimize all module parameters using stochastic gradient descent.Due to the high efficiency of the optimization process,SSCNet can be easily applied to large-scale datasets.Experimental evaluations on several benchmarks confirm that our method outperforms traditional state-of-the-art approaches.展开更多
Antimony selenide(Sb2Se3) films are widely used in phase change memory and solar cells due to their stable switching effect and excellent photovoltaic properties. These properties of the films are affected by the film...Antimony selenide(Sb2Se3) films are widely used in phase change memory and solar cells due to their stable switching effect and excellent photovoltaic properties. These properties of the films are affected by the film thickness. A method combining the advantages of Levenberg–Marquardt method and spectral fitting method(LM–SFM) is presented to study the dependence of refractive index(RI), absorption coefficient, optical band gap, Wemple–Di Domenico parameters, dielectric constant and optical electronegativity of the Sb2Se3films on their thickness. The results show that the RI and absorption coefficient of the Sb2Se3films increase with the increase of film thickness, while the optical band gap decreases with the increase of film thickness. Finally, the reasons why the optical and electrical properties of the film change with its thickness are explained by x-ray diffractometer(XRD), energy dispersive x-ray spectrometer(EDS), Mott–Davis state density model and Raman microstructure analysis.展开更多
In the recent years spam became as a big problem of Internet and electronic communication. There developed a lot of techniques to fight them. In this paper the overview of existing e-mail spam filtering methods is giv...In the recent years spam became as a big problem of Internet and electronic communication. There developed a lot of techniques to fight them. In this paper the overview of existing e-mail spam filtering methods is given. The classification, evaluation, and comparison of traditional and learning-based methods are provided. Some personal anti-spam products are tested and compared. The statement for new approach in spam filtering technique is considered.展开更多
Multiobjective combinatorial optimization(MOCO)problems have a wide range of applications in the real world.Recently,learning-based methods have achieved good results in solving MOCO problems.However,most of these met...Multiobjective combinatorial optimization(MOCO)problems have a wide range of applications in the real world.Recently,learning-based methods have achieved good results in solving MOCO problems.However,most of these methods use attention mechanisms and their variants,which have room for further improvement in the speed of solving MOCO problems.In this paper,following the idea of decomposition strategy and neural combinatorial optimization,a novel fast-solving model for MOCO based on retention is proposed.A brand new calculation of retention is proposed,causal masking and exponential decay are deprecated in retention,so that our model could better solve MOCO problems.During model training,a parallel computation of retention is applied,allowing for fast parallel training.When using the model to solve MOCO problems,a recurrent computation of retention is applied,enabling quicker problem-solving.In order to make our model more practical and flexible,a preference-based retention decoder is proposed,which allows generating approximate Pareto solutions for any trade-off preferences directly.An industry-standard deep reinforcement learning algorithm is used to train RM-MOCO.Experimental results show that,while ensuring the quality of problem solving,the proposed method significantly outperforms some other methods in terms of the speed of solving MOCO problems.展开更多
In large-scale social media,sentiment classification is a significant one for connecting gaps among social media contents as well as real-world actions,including public emotional status monitoring,political election p...In large-scale social media,sentiment classification is a significant one for connecting gaps among social media contents as well as real-world actions,including public emotional status monitoring,political election prediction,and so on.On the other hand,textual sentiment classification is well studied by various platforms,like Instagram,Twitter,etc.Sentiment classification has many advantages in various fields,like opinion polls,educa-tion,and e-commerce.Sentiment classification is an interesting and progressing research area due to its applications in several areas.The information is collected from vari-ous people about social,products,and social events by web in sentiment analysis.This review provides a detailed survey of 50 research papers presenting sentiment classifica-tion schemes such as active learning-based approach,aspect learning-based method,and machine learning-based approach.The analysis is presented based on the categorization of sentiment classification schemes,the dataset used,software tools utilized,published year,and the performance metrics.Finally,the issues of existing methods considering conventional sentiment classification strategies are elaborated to obtain improved contri-bution in devising significant sentiment classification strategies.Moreover,the probable future research directions in attaining efficient sentiment classification are provided.展开更多
文摘高精度、高分辨率的地形地貌数据是活动构造定量研究的基础。摄影测量方法的出现和快速发展为获取高精度地形地貌数据提供了一种经济有效的技术手段。相比于传统的测量方法,摄影测量方法可在大范围内同时进行,不受地面通视条件的限制,且测量成本相对较低。尤其近年来,随着计算机视觉理论及高效的自动特征匹配算法的发展,一种名为"Structure from Motion"(SfM)的三维重建技术被引入摄影测量方法中,极大地提高了摄影测量的自动化程度。文中介绍了摄影测量方法的基本原理及发展历程,并综述了摄影测量方法在活动构造研究中的应用,最后通过SfM摄影测量方法在活动构造研究中的1个具体应用实例,展示了摄影测量方法在活动构造定量研究中的巨大应用潜力。
文摘针对轮廓复杂多变、细节信息丰富等多因素,导致变电建筑重建效果不佳的问题,提出基于轮廓拼接的变电建筑数字三维重建算法。基于改进Snake模型提取变电建筑目标轮廓,为后续的三维重建提供关键轮廓信息;基于获取的二维轮廓信息,利用运动恢复结构(Structure from Motion,SfM)完成变电建筑的三维轮廓重建;通过Jaccard距离和最近点迭代(ICP)算法将多个轮廓碎片精确拼接为完整的变电建筑三维轮廓,并使用附加三维线约束的网格优化算法对所构建三维轮廓实行优化,完成最终的变电建筑数字三维重建。实验结果表明:所提方法在变电建筑轮廓及三维重建中展现出高精度、细节保留良好且整体连贯性佳的优势。
基金supported by the National Key Research and Development of China(Grant No.2022YFB3805700)the National Natural Science Foundation of China(Grant Nos.12122202 and 12372162)the Fundamental Research Funds for the Central Universities(Grant No.2024CX06021).
文摘In daily life,human need various senses to obtain information about their surroundings,and touch is one of the five major human sensing signals.Similarly,it is extremely important for robots to be endowed with tactile sensing ability.In recent years,vision-based tactile sensing technology has been the research hotspot and frontier in the field of tactile perception.Compared to conventional tactile sensing technologies,vision-based tactile sensing technologies are capable of obtaining highquality and high-resolution tactile information at a lower cost,while not being limited by the size and shape of sensors.Several previous articles have reviewed the sensing mechanism and electrical components of vision-based sensors,greatly promoting the innovation of tactile sensing.Different from existing reviews,this article concentrates on the underlying tracking method which converts real-time images into deformation information,including contact,sliding and friction.We will show the history and development of both model-based and model-free tracking methods,among which model-based approaches rely on schematic mechanical theories,and model-free approaches mainly involve machine learning algorithms.Comparing the efficiency and accuracy of existing deformation tracking methods,future research directions of vision-based tactile sensors for smart manipulations and robots are also discussed.
基金This work was jointly supported by the National Natural Science Foundation of China(Grant Nos.41975137,42175012,and 41475097)the National Key Research and Development Program(Grant No.2018YFF0300103).
文摘Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.
基金supported by the National Natural Science Foundation of China(No.62276004)the major key project of Pengcheng Laboratory,China(No.PCL2021A12)and Qualcomm.
文摘Sparse subspace clustering(SSC),a seminal clustering method,has demonstrated remarkable performance by effectively solving the data sparsity problem.However,it is not without its limitations.Key among these is the difficulty of incremental learning with the original SSC,accompanied by a computationally demanding recalculation process that constrains its scalability to large datasets.Moreover,the conventional SSC framework considers dictionary construction,affinity matrix learning and clustering as separate stages,potentially leading to suboptimal dictionaries and affinity matrices for clustering.To address these challenges,we present a novel clustering approach,called SSCNet,which leverages differentiable programming.Specifically,we redefine and generalize the optimization procedure of the linearized alternating direction method of multipliers(ADMM),framing it as a multi-block deep neural network,where each block corresponds to a linearized ADMM iteration step.This reformulation is used to address the SSC problem.We then use a shallow spectral embedding network as an unambiguous and differentiable module to approximate the eigenvalue decomposition.Finally,we incorporate a self-supervised structure to mitigate the non-differentiability inherent in k-means to achieve the final clustering results.In essence,we assign unique objectives to different modules and jointly optimize all module parameters using stochastic gradient descent.Due to the high efficiency of the optimization process,SSCNet can be easily applied to large-scale datasets.Experimental evaluations on several benchmarks confirm that our method outperforms traditional state-of-the-art approaches.
基金supported by the National Natural Science Foundation of China (Grant Nos. 62075109, 62135011, 62075107, and 61935006)K. C. Wong Magna Fund in Ningbo University。
文摘Antimony selenide(Sb2Se3) films are widely used in phase change memory and solar cells due to their stable switching effect and excellent photovoltaic properties. These properties of the films are affected by the film thickness. A method combining the advantages of Levenberg–Marquardt method and spectral fitting method(LM–SFM) is presented to study the dependence of refractive index(RI), absorption coefficient, optical band gap, Wemple–Di Domenico parameters, dielectric constant and optical electronegativity of the Sb2Se3films on their thickness. The results show that the RI and absorption coefficient of the Sb2Se3films increase with the increase of film thickness, while the optical band gap decreases with the increase of film thickness. Finally, the reasons why the optical and electrical properties of the film change with its thickness are explained by x-ray diffractometer(XRD), energy dispersive x-ray spectrometer(EDS), Mott–Davis state density model and Raman microstructure analysis.
文摘In the recent years spam became as a big problem of Internet and electronic communication. There developed a lot of techniques to fight them. In this paper the overview of existing e-mail spam filtering methods is given. The classification, evaluation, and comparison of traditional and learning-based methods are provided. Some personal anti-spam products are tested and compared. The statement for new approach in spam filtering technique is considered.
基金supported by the National Natural Science Foundation of China(No.62102002).
文摘Multiobjective combinatorial optimization(MOCO)problems have a wide range of applications in the real world.Recently,learning-based methods have achieved good results in solving MOCO problems.However,most of these methods use attention mechanisms and their variants,which have room for further improvement in the speed of solving MOCO problems.In this paper,following the idea of decomposition strategy and neural combinatorial optimization,a novel fast-solving model for MOCO based on retention is proposed.A brand new calculation of retention is proposed,causal masking and exponential decay are deprecated in retention,so that our model could better solve MOCO problems.During model training,a parallel computation of retention is applied,allowing for fast parallel training.When using the model to solve MOCO problems,a recurrent computation of retention is applied,enabling quicker problem-solving.In order to make our model more practical and flexible,a preference-based retention decoder is proposed,which allows generating approximate Pareto solutions for any trade-off preferences directly.An industry-standard deep reinforcement learning algorithm is used to train RM-MOCO.Experimental results show that,while ensuring the quality of problem solving,the proposed method significantly outperforms some other methods in terms of the speed of solving MOCO problems.
文摘In large-scale social media,sentiment classification is a significant one for connecting gaps among social media contents as well as real-world actions,including public emotional status monitoring,political election prediction,and so on.On the other hand,textual sentiment classification is well studied by various platforms,like Instagram,Twitter,etc.Sentiment classification has many advantages in various fields,like opinion polls,educa-tion,and e-commerce.Sentiment classification is an interesting and progressing research area due to its applications in several areas.The information is collected from vari-ous people about social,products,and social events by web in sentiment analysis.This review provides a detailed survey of 50 research papers presenting sentiment classifica-tion schemes such as active learning-based approach,aspect learning-based method,and machine learning-based approach.The analysis is presented based on the categorization of sentiment classification schemes,the dataset used,software tools utilized,published year,and the performance metrics.Finally,the issues of existing methods considering conventional sentiment classification strategies are elaborated to obtain improved contri-bution in devising significant sentiment classification strategies.Moreover,the probable future research directions in attaining efficient sentiment classification are provided.
