Light detection and ranging(LiDAR)sensors play a vital role in acquiring 3D point cloud data and extracting valuable information about objects for tasks such as autonomous driving,robotics,and virtual reality(VR).Howe...Light detection and ranging(LiDAR)sensors play a vital role in acquiring 3D point cloud data and extracting valuable information about objects for tasks such as autonomous driving,robotics,and virtual reality(VR).However,the sparse and disordered nature of the 3D point cloud poses significant challenges to feature extraction.Overcoming limitations is critical for 3D point cloud processing.3D point cloud object detection is a very challenging and crucial task,in which point cloud processing and feature extraction methods play a crucial role and have a significant impact on subsequent object detection performance.In this overview of outstanding work in object detection from the 3D point cloud,we specifically focus on summarizing methods employed in 3D point cloud processing.We introduce the way point clouds are processed in classical 3D object detection algorithms,and their improvements to solve the problems existing in point cloud processing.Different voxelization methods and point cloud sampling strategies will influence the extracted features,thereby impacting the final detection performance.展开更多
In the Mexican Intertropical Convergence Zone, particle size distributions within 500 m of cloud boundaries at altitudes of 1000, 2500, and 4200 m, were compared against size distributions at the same levels but 1500 ...In the Mexican Intertropical Convergence Zone, particle size distributions within 500 m of cloud boundaries at altitudes of 1000, 2500, and 4200 m, were compared against size distributions at the same levels but 1500 m away from the clouds. The differences in the distributions near and far from the cloud are related to processes that may change particle properties inside the cloud. Chemical changes in the aerosols are deduced from the particles＇ refractive index, as derived from comparisons with the scattering coeflcient measured by a nephelometer. An analysis of ten cloud systems indicates that vertical transport of cloud base aerosol followed by entrainment/detrainment is the cloud processing signature most frequently observed in the comparisons （65%）. Changes in the chemical composition are observed in approximately 20% of the cases and another 20% of the cases showed removal by precipitation. About 5% of the comparisons showed clear evidence of changes by coalescence. The principal effect of these cloud-processed aerosols is observed in the increase of optical depth in the layer from 30 m to 4200 m in the near-cloud regions, in comparison with the atmosphere further from clouds.展开更多
As 3D acquisition technology develops and 3D sensors become increasingly affordable,large quantities of 3D point cloud data are emerging.How to effectively learn and extract the geometric features from these point clo...As 3D acquisition technology develops and 3D sensors become increasingly affordable,large quantities of 3D point cloud data are emerging.How to effectively learn and extract the geometric features from these point clouds has become an urgent problem to be solved.The point cloud geometric information is hidden in disordered,unstructured points,making point cloud analysis a very challenging problem.To address this problem,we propose a novel network framework,called Tree Graph Network(TGNet),which can sample,group,and aggregate local geometric features.Specifically,we construct a Tree Graph by explicit rules,which consists of curves extending in all directions in point cloud feature space,and then aggregate the features of the graph through a cross-attention mechanism.In this way,we incorporate more point cloud geometric structure information into the representation of local geometric features,which makes our network perform better.Our model performs well on several basic point clouds processing tasks such as classification,segmentation,and normal estimation,demonstrating the effectiveness and superiority of our network.Furthermore,we provide ablation experiments and visualizations to better understand our network.展开更多
Due to the increasing number of cloud applications,the amount of data in the cloud shows signs of growing faster than ever before.The nature of cloud computing requires cloud data processing systems that can handle hu...Due to the increasing number of cloud applications,the amount of data in the cloud shows signs of growing faster than ever before.The nature of cloud computing requires cloud data processing systems that can handle huge volumes of data and have high performance.However,most cloud storage systems currently adopt a hash-like approach to retrieving data that only supports simple keyword-based enquiries,but lacks various forms of information search.Therefore,a scalable and efficient indexing scheme is clearly required.In this paper,we present a skip list-based cloud index,called SLC-index,which is a novel,scalable skip list-based indexing for cloud data processing.The SLC-index offers a two-layered architecture for extending indexing scope and facilitating better throughput.Dynamic load-balancing for the SLC-index is achieved by online migration of index nodes between servers.Furthermore,it is a flexible system due to its dynamic addition and removal of servers.The SLC-index is efficient for both point and range queries.Experimental results show the efficiency of the SLC-index and its usefulness as an alternative approach for cloud-suitable data structures.展开更多
Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)...Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)products.Combined with image data,this technology can further enrich and extract spatial geographic information.However,practically,due to the limited operating range of airborne LiDAR and the large area of task,it would be necessary to perform registration and stitching process on point clouds of adjacent flight strips.By eliminating grow errors,the systematic errors in the data need to be effectively reduced.Thus,this paper conducts research on point cloud registration methods in urban building areas,aiming to improve the accuracy and processing efficiency of airborne LiDAR data.Meanwhile,an improved post-ICP(Iterative Closest Point)point cloud registration method was proposed in this study to determine the accurate registration and efficient stitching of point clouds,which capable to provide a potential technical support for applicants in related field.展开更多
To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-sca...To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.展开更多
High-resolution numerical simulation data of a rainstorm triggering debris flow in Sichuan Province of China simulated by the Weather Research and Forecasting （WRF） Model were used to study the dominant cloud microp...High-resolution numerical simulation data of a rainstorm triggering debris flow in Sichuan Province of China simulated by the Weather Research and Forecasting （WRF） Model were used to study the dominant cloud microphysical processes of the torrential rainfall.The results showed that：（1） In the strong precipitation period,particle sizes of all hydrometeors increased,and mean-mass diameters of graupel increased the most significantly,as compared with those in the weak precipitation period; （2） The terminal velocity of raindrops was the strongest among all hydrometeors,followed by graupel＇s,which was much smaller than that of raindrops.Differences between various hydrometeors＇ terminal velocities in the strong precipitation period were larger than those in the weak precipitation period,which favored relative motion,collection interaction and transformation between the particles.Absolute terminal velocity values of raindrops and graupel were significantly greater than those of air upward velocity,and the stronger the precipitation was,the greater the differences between them were; （3） The orders of magnitudes of the various hydrometeors＇ sources and sinks in the strong precipitation period were larger than those in the weak precipitation period,causing a difference in the intensity of precipitation.Water vapor,cloud water,raindrops,graupel and their exchange processes played a major role in the production of the torrential rainfall,and there were two main processes via which raindrops were generated：abundant water vapor condensed into cloud water and,on the one hand,accretion of cloud water by rain water formed rain water,while on the other hand,accretion of cloud water by graupel formed graupel,and then the melting of graupel formed rain water.展开更多
High-resolution data of a torrential rainfall event in Sichuan, China, simulated by the WRF model, were used to analyze the cloud microphysical differences with precipitation intensity. Sixhourly accumulated rainfall ...High-resolution data of a torrential rainfall event in Sichuan, China, simulated by the WRF model, were used to analyze the cloud microphysical differences with precipitation intensity. Sixhourly accumulated rainfall was classified into five bins based on rainfall intensity, and the cloud microphysical characteristics and processes in different bins were studied. The results show that：（1） Hydrometeor content differed distinctly among different bins. Mixing ratios of cloud water, rain water, and graupel enhanced significantly and monotonously with increasing rainfall intensity. With increasing precipitation intensity, the monotonous increase in cloud water number concentration was significant. Meanwhile, number concentrations of rain water and graupel increased at first and then decreased or increased slowly in larger rainfall bins.（2） With precipitation intensity increasing, cloud microphysical conversion processes closely related to the production of rainwater, directly（accretion of cloud water by rain（QCLcr） and melting of graupel（QMLgr）） or indirectly（water vapor condensation and accretion of cloud water by graupel）, increased significantly.（3） As the two main sources of rainwater, QCLcrincreased monotonously with increasing precipitation intensity, while QMLgr increased slowly, even tending to cease increasing in larger rainfall bins.展开更多
Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years;however,...Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years;however, most of them have difficulties in handling multi-instance sub-processes. To address this challenge, we first introduce a multi-instance business process model(MBPM) to support the modeling of processes with multiple sub-process instantiations. Formal semantics of MBPMs are precisely defined by using multi-instance Petri nets(MPNs)that are an extension of Petri nets with distinguishable tokens.Then, a novel process discovery technique is developed to support the discovery of MBPMs from event logs with sub-process multi-instantiation information. In addition, we propose to measure the quality of the discovered MBPMs against the input event logs by transforming an MBPM to a classical Petri net such that existing quality metrics, e.g., fitness and precision, can be used.The proposed discovery approach is properly implemented as plugins in the Pro M toolkit. Based on a cloud resource management case study, we compare our approach with the state-of-theart process discovery techniques. The results demonstrate that our approach outperforms existing approaches to discover process models with multi-instance sub-processes.展开更多
The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it...The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.展开更多
The characteristics of summertime raindrop size distribution(DSD) and associated relations in the semi-arid region over the Inner Mongolian Plateau(IMP) were investigated,utilizing five-year continuous observations by...The characteristics of summertime raindrop size distribution(DSD) and associated relations in the semi-arid region over the Inner Mongolian Plateau(IMP) were investigated,utilizing five-year continuous observations by a PARSIVEL2disdrometer in East Ujimqin County(EUC),China.It is found that only 7.94% of the 15 664 one-min precipitation samples meet classification criteria as convective rain(CR),but its contribution to the total rainfall amount is 63.87%.Notably,40.72% of the rainfall comes from large-sized raindrops(D> 3 mm),despite the fact that large-sized raindrops account for only 1.73% of the CR total number concentration.Further results show that the mean value of mass-weighted mean diameters(Dm) is larger(2.43 mm) and generalized intercepts(lgN_(W)) is lower(3.19) in CR,aligning with a "continentallike" cluster,which is mainly influenced by the joint impact of in-cloud ice-based processes and the below-cloud environmental background.Also,the empirical relationships of shape-slope(μ-Λ),radar reflectivity-rain rate(Z-R),and rainfall kinetic energy(KE_(time)-Rand KE_(time)-Z) are localized.To quantitatively analyze the impact of DSD parameters on kinetic energy estimation,power-law KE_(time)-R and KE_(time)-Z relationships are derived based on the normalized gamma distribution.N_(W)takes precedence over μ in affecting variabilities of multiplicative coefficients,especially for KE_(time)-R relationship where the multiplicative coefficient is proportional to N_(W)^(-0.287).It should be noted that although the proportion of CR occurring throughout the summer is small,raindrops with lower N_(W) and larger Dmwill generate higher KE_(time),which will bring a higher potential risk of soil erosion in semi-arid regions over IMP.展开更多
This paper presents a novel dynamic A^*path finding algorithm and 3D lidar based local obstacle avoidance strategy for an autonomous vehicle.3D point cloud data is collected and analyzed in real time.Local obstacles a...This paper presents a novel dynamic A^*path finding algorithm and 3D lidar based local obstacle avoidance strategy for an autonomous vehicle.3D point cloud data is collected and analyzed in real time.Local obstacles are detected online and a 2D local obstacle grid map is constructed at 10 Hz/s.The A^*path finding algorithm is employed to generate a local path in this local obstacle grid map by considering both the target position and obstacles.The vehicle avoids obstacles under the guidance of the generated local path.Experiment results have shown the effectiveness of the obstacle avoidance navigation algorithm proposed.展开更多
In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe ope...In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines.Currently,assessment methods for fastener tightness include manual observation,acoustic wave detection,and image detection.There are limitations such as low accuracy and efficiency,easy interference and misjudgment,and a lack of accurate,stable,and fast detection methods.Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening,this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks.First,the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration.Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod.The point is projected onto the upper surface of the bolt to calculate the projection distance.Subsequently,the mapping relationship between the projection distance sequence and fastener tightness is established,and the influence of each parameter on the fastener tightness prediction is analyzed.Finally,by setting up a fastener detection scene in the track experimental base,collecting data,and completing the algorithm verification,the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm,which significantly improved the effect compared with other tightness detection methods,and realized an effective fastener tightness regression.展开更多
Sustainable forest management heavily relies on the accurate estimation of tree parameters.Among others,the diameter at breast height(DBH) is important for extracting the volume and mass of an individual tree.For syst...Sustainable forest management heavily relies on the accurate estimation of tree parameters.Among others,the diameter at breast height(DBH) is important for extracting the volume and mass of an individual tree.For systematically estimating the volume of entire plots,airborne laser scanning(ALS) data are used.The estimation model is frequently calibrated using manual DBH measurements or static terrestrial laser scans(STLS) of sample plots.Although reliable,this method is time-consuming,which greatly hampers its use.Here,a handheld mobile terrestrial laser scanning(HMTLS) was demonstrated to be a useful alternative technique to precisely and efficiently calculate DBH.Different data acquisition techniques were applied at a sample plot,then the resulting parameters were comparatively analysed.The calculated DBH values were comparable to the manual measurements for HMTLS,STLS,and ALS data sets.Given the comparability of the extracted parameters,with a reduced point density of HTMLS compared to STLS data,and the reasonable increase of performance,with a reduction of acquisition time with a factor of5 compared to conventional STLS techniques and a factor of3 compared to manual measurements,HMTLS is considered a useful alternative technique.展开更多
Acquisition and registration of terrestrial 3D laser scans is a fundamental task in mapping and modeling of cities in three dimensions. To automate this task marker-flee registration methods are required. Based on the...Acquisition and registration of terrestrial 3D laser scans is a fundamental task in mapping and modeling of cities in three dimensions. To automate this task marker-flee registration methods are required. Based on the existence of skyline features, this paper proposes a novel method. The skyline features are extracted from panoramic 3D scans and encoded as strings enabling the use of string matching for merging the scans. Initial results of the proposed method in the old city center of Bremen are presented.展开更多
After more than 30 years of scientific and social development, surveying and mapping technology by leaps and bounds, engineering surveying technology has undergone tremendous changes. In the process of protecting anci...After more than 30 years of scientific and social development, surveying and mapping technology by leaps and bounds, engineering surveying technology has undergone tremendous changes. In the process of protecting ancient buildings, it is necessary to obtain the precise dimensions of architectural details. In this study, the path of 3D laser scanning combined with BIM technology is explored. Taking the observation and protection of the ancestral hall of the Liu family as an example, this study aims to draw drawings that reflect the relevant information about the ancient buildings, the accurate three-dimensional model of ancient buildings is established with BIM technology, which provides new methods and ideas for the research and protection of ancient buildings. .展开更多
Data from a long term measurement of Micro Rain Radar （MRR） at a mountain site （Daegwallyeong, DG, one year period of 2005） and a coastal site （Haenam, HN, three years 2004-2006） in South Korea were analyzed to ...Data from a long term measurement of Micro Rain Radar （MRR） at a mountain site （Daegwallyeong, DG, one year period of 2005） and a coastal site （Haenam, HN, three years 2004-2006） in South Korea were analyzed to compare the MRR measured bright band characteristics of stratiform precipitation at the two sites. On average, the bright band was somewhat thicker and the sharpness （average gradient of reflectivity above and below the reflectivity peak） was slightly weaker at DG, compared to those values at HN. The peak reflectivity itself was twice as strong and the relative location of the peak reflectivity within the bright band was higher at HN than at DG. Importantly, the variability of these values was much larger at HN than at DG. The key parameter to cause these differences is suggested to be the difference of the snow particle densities at the two sites, which is related to the degree of riming. Therefore, it is speculated that the cloud microphysical processes at HN may have varied significantly from un-rimed snow growth, producing low density snow particles, to the riming of higher density particles, while snow particle growth at DG was more consistently affected by the riming process, and therefore high density snow particles. Forced uplifting of cloudy air over the mountain area around DG might have resulted in an orographic supercooling effect that led to the enhanced riming of supercooled cloud drops.展开更多
Representation of cloud microphysical processes is one of the key aspects of numerical models.An improved double-moment bulk cloud microphysics scheme(named IMY)was created based on the standard Milbrandt-Yau(MY)schem...Representation of cloud microphysical processes is one of the key aspects of numerical models.An improved double-moment bulk cloud microphysics scheme(named IMY)was created based on the standard Milbrandt-Yau(MY)scheme in the Weather Research and Forecasting(WRF)model for the East Asian monsoon region(EAMR).In the IMY scheme,the shape parameters of raindrops,snow particles,and cloud droplet size distributions are variables instead of fixed constants.Specifically,the shape parameters of raindrop and snow size distributions are diagnosed from their respective shape-slope relationships.The shape parameter for the cloud droplet size distribution depends on the total cloud droplet number concentration.In addition,a series of minor improvements involving detailed cloud processes have also been incorporated.The improved scheme was coupled into the WRF model and tested on two heavy rainfall cases over the EAMR.The IMY scheme is shown to reproduce the overall spatial distribution of rainfall and its temporal evolution,evidenced by comparing the modeled results with surface gauge observations.The simulations also successfully capture the cloud features by using satellite and ground-based radar observations as a reference.The IMY has yielded simulation results on the case studies that were comparable,and in ways superior to MY,indicating that the improved scheme shows promise.Although the simulations demonstrated a positive performance evaluation for the IMY scheme,continued experiments are required to further validate the scheme with different weather events.展开更多
The use of point clouds is becoming increasingly popular. We present a general framework for performing geometry filtering on point-based surface through applying the meshless local Petrol-Galelkin （MLPG） to obtain ...The use of point clouds is becoming increasingly popular. We present a general framework for performing geometry filtering on point-based surface through applying the meshless local Petrol-Galelkin （MLPG） to obtain the solution of a screened Poisson equation. The enhancement or smoothing of surfaces is controlled by a gradient scale parameter. Anisotropic filtering is supported by the adapted Riemannian metric. Contrary to the other approaches of partial differential equation for point-based surface, the proposed approach neither needs to construct local or global triangular meshes, nor needs global parameterization. It is only based on the local tangent space and local interpolated surfaces. Experiments demonstrate the efficiency of our approach.展开更多
The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing.This paper presents a novel framework named Point Cloud Transformer(PCT)for point cloud learning....The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing.This paper presents a novel framework named Point Cloud Transformer(PCT)for point cloud learning.PCT is based on Transformer,which achieves huge success in natural language processing and displays great potential in image processing.It is inherently permutation invariant for processing a sequence of points,making it well-suited for point cloud learning.To better capture local context within the point cloud,we enhance input embedding with the support of farthest point sampling and nearest neighbor search.Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification,part segmentation,semantic segmentation,and normal estimation tasks.展开更多
文摘Light detection and ranging(LiDAR)sensors play a vital role in acquiring 3D point cloud data and extracting valuable information about objects for tasks such as autonomous driving,robotics,and virtual reality(VR).However,the sparse and disordered nature of the 3D point cloud poses significant challenges to feature extraction.Overcoming limitations is critical for 3D point cloud processing.3D point cloud object detection is a very challenging and crucial task,in which point cloud processing and feature extraction methods play a crucial role and have a significant impact on subsequent object detection performance.In this overview of outstanding work in object detection from the 3D point cloud,we specifically focus on summarizing methods employed in 3D point cloud processing.We introduce the way point clouds are processed in classical 3D object detection algorithms,and their improvements to solve the problems existing in point cloud processing.Different voxelization methods and point cloud sampling strategies will influence the extracted features,thereby impacting the final detection performance.
文摘In the Mexican Intertropical Convergence Zone, particle size distributions within 500 m of cloud boundaries at altitudes of 1000, 2500, and 4200 m, were compared against size distributions at the same levels but 1500 m away from the clouds. The differences in the distributions near and far from the cloud are related to processes that may change particle properties inside the cloud. Chemical changes in the aerosols are deduced from the particles＇ refractive index, as derived from comparisons with the scattering coeflcient measured by a nephelometer. An analysis of ten cloud systems indicates that vertical transport of cloud base aerosol followed by entrainment/detrainment is the cloud processing signature most frequently observed in the comparisons （65%）. Changes in the chemical composition are observed in approximately 20% of the cases and another 20% of the cases showed removal by precipitation. About 5% of the comparisons showed clear evidence of changes by coalescence. The principal effect of these cloud-processed aerosols is observed in the increase of optical depth in the layer from 30 m to 4200 m in the near-cloud regions, in comparison with the atmosphere further from clouds.
基金supported by the National Natural Science Foundation of China (Grant Nos.91948203,52075532).
文摘As 3D acquisition technology develops and 3D sensors become increasingly affordable,large quantities of 3D point cloud data are emerging.How to effectively learn and extract the geometric features from these point clouds has become an urgent problem to be solved.The point cloud geometric information is hidden in disordered,unstructured points,making point cloud analysis a very challenging problem.To address this problem,we propose a novel network framework,called Tree Graph Network(TGNet),which can sample,group,and aggregate local geometric features.Specifically,we construct a Tree Graph by explicit rules,which consists of curves extending in all directions in point cloud feature space,and then aggregate the features of the graph through a cross-attention mechanism.In this way,we incorporate more point cloud geometric structure information into the representation of local geometric features,which makes our network perform better.Our model performs well on several basic point clouds processing tasks such as classification,segmentation,and normal estimation,demonstrating the effectiveness and superiority of our network.Furthermore,we provide ablation experiments and visualizations to better understand our network.
基金Projects(61363021,61540061,61663047)supported by the National Natural Science Foundation of ChinaProject(2017SE206)supported by the Open Foundation of Key Laboratory in Software Engineering of Yunnan Province,China
文摘Due to the increasing number of cloud applications,the amount of data in the cloud shows signs of growing faster than ever before.The nature of cloud computing requires cloud data processing systems that can handle huge volumes of data and have high performance.However,most cloud storage systems currently adopt a hash-like approach to retrieving data that only supports simple keyword-based enquiries,but lacks various forms of information search.Therefore,a scalable and efficient indexing scheme is clearly required.In this paper,we present a skip list-based cloud index,called SLC-index,which is a novel,scalable skip list-based indexing for cloud data processing.The SLC-index offers a two-layered architecture for extending indexing scope and facilitating better throughput.Dynamic load-balancing for the SLC-index is achieved by online migration of index nodes between servers.Furthermore,it is a flexible system due to its dynamic addition and removal of servers.The SLC-index is efficient for both point and range queries.Experimental results show the efficiency of the SLC-index and its usefulness as an alternative approach for cloud-suitable data structures.
基金Guangxi Key Laboratory of Spatial Information and Geomatics(21-238-21-12)Guangxi Young and Middle-aged Teachers’Research Fundamental Ability Enhancement Project(2023KY1196).
文摘Airborne LiDAR(Light Detection and Ranging)is an evolving high-tech active remote sensing technology that has the capability to acquire large-area topographic data and can quickly generate DEM(Digital Elevation Model)products.Combined with image data,this technology can further enrich and extract spatial geographic information.However,practically,due to the limited operating range of airborne LiDAR and the large area of task,it would be necessary to perform registration and stitching process on point clouds of adjacent flight strips.By eliminating grow errors,the systematic errors in the data need to be effectively reduced.Thus,this paper conducts research on point cloud registration methods in urban building areas,aiming to improve the accuracy and processing efficiency of airborne LiDAR data.Meanwhile,an improved post-ICP(Iterative Closest Point)point cloud registration method was proposed in this study to determine the accurate registration and efficient stitching of point clouds,which capable to provide a potential technical support for applicants in related field.
文摘To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.
基金supported by the Key Research Program of the Chinese Academy of Sciences (Grant No. KZZD-EW-05-01)the National Basic Research Program of China (973 Program) (Grant No. 2014CB441402)
文摘High-resolution numerical simulation data of a rainstorm triggering debris flow in Sichuan Province of China simulated by the Weather Research and Forecasting （WRF） Model were used to study the dominant cloud microphysical processes of the torrential rainfall.The results showed that：（1） In the strong precipitation period,particle sizes of all hydrometeors increased,and mean-mass diameters of graupel increased the most significantly,as compared with those in the weak precipitation period; （2） The terminal velocity of raindrops was the strongest among all hydrometeors,followed by graupel＇s,which was much smaller than that of raindrops.Differences between various hydrometeors＇ terminal velocities in the strong precipitation period were larger than those in the weak precipitation period,which favored relative motion,collection interaction and transformation between the particles.Absolute terminal velocity values of raindrops and graupel were significantly greater than those of air upward velocity,and the stronger the precipitation was,the greater the differences between them were; （3） The orders of magnitudes of the various hydrometeors＇ sources and sinks in the strong precipitation period were larger than those in the weak precipitation period,causing a difference in the intensity of precipitation.Water vapor,cloud water,raindrops,graupel and their exchange processes played a major role in the production of the torrential rainfall,and there were two main processes via which raindrops were generated：abundant water vapor condensed into cloud water and,on the one hand,accretion of cloud water by rain water formed rain water,while on the other hand,accretion of cloud water by graupel formed graupel,and then the melting of graupel formed rain water.
基金supported by the Key Research Program of the Chinese Academy of Sciences[grant number KZZD-e W-05-01]the National Basic Research Program of China[973 Program,grant number 2014CB441402]
文摘High-resolution data of a torrential rainfall event in Sichuan, China, simulated by the WRF model, were used to analyze the cloud microphysical differences with precipitation intensity. Sixhourly accumulated rainfall was classified into five bins based on rainfall intensity, and the cloud microphysical characteristics and processes in different bins were studied. The results show that：（1） Hydrometeor content differed distinctly among different bins. Mixing ratios of cloud water, rain water, and graupel enhanced significantly and monotonously with increasing rainfall intensity. With increasing precipitation intensity, the monotonous increase in cloud water number concentration was significant. Meanwhile, number concentrations of rain water and graupel increased at first and then decreased or increased slowly in larger rainfall bins.（2） With precipitation intensity increasing, cloud microphysical conversion processes closely related to the production of rainwater, directly（accretion of cloud water by rain（QCLcr） and melting of graupel（QMLgr）） or indirectly（water vapor condensation and accretion of cloud water by graupel）, increased significantly.（3） As the two main sources of rainwater, QCLcrincreased monotonously with increasing precipitation intensity, while QMLgr increased slowly, even tending to cease increasing in larger rainfall bins.
基金supported by the National Natural Science Foundation of China(61902222)the Taishan Scholars Program of Shandong Province(tsqn201909109)+1 种基金the Natural Science Excellent Youth Foundation of Shandong Province(ZR2021YQ45)the Youth Innovation Science and Technology Team Foundation of Shandong Higher School(2021KJ031)。
文摘Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years;however, most of them have difficulties in handling multi-instance sub-processes. To address this challenge, we first introduce a multi-instance business process model(MBPM) to support the modeling of processes with multiple sub-process instantiations. Formal semantics of MBPMs are precisely defined by using multi-instance Petri nets(MPNs)that are an extension of Petri nets with distinguishable tokens.Then, a novel process discovery technique is developed to support the discovery of MBPMs from event logs with sub-process multi-instantiation information. In addition, we propose to measure the quality of the discovered MBPMs against the input event logs by transforming an MBPM to a classical Petri net such that existing quality metrics, e.g., fitness and precision, can be used.The proposed discovery approach is properly implemented as plugins in the Pro M toolkit. Based on a cloud resource management case study, we compare our approach with the state-of-theart process discovery techniques. The results demonstrate that our approach outperforms existing approaches to discover process models with multi-instance sub-processes.
基金funded by the Ministry of Science and Technology,Taiwan,grant number(MOST 111-2221-E167-025-MY2).
文摘The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.
基金supported by the National Natural Science Foundation of China(Grant Nos.42325503,42075063,42075066,and 42021004)the Hubei Provincial Natural Science Foundation and the Meteorological Innovation and Development Project of China(Grant No.2023AFD096)the Beijige Foundation of NJIAS(Grant No.BJG202304).
文摘The characteristics of summertime raindrop size distribution(DSD) and associated relations in the semi-arid region over the Inner Mongolian Plateau(IMP) were investigated,utilizing five-year continuous observations by a PARSIVEL2disdrometer in East Ujimqin County(EUC),China.It is found that only 7.94% of the 15 664 one-min precipitation samples meet classification criteria as convective rain(CR),but its contribution to the total rainfall amount is 63.87%.Notably,40.72% of the rainfall comes from large-sized raindrops(D> 3 mm),despite the fact that large-sized raindrops account for only 1.73% of the CR total number concentration.Further results show that the mean value of mass-weighted mean diameters(Dm) is larger(2.43 mm) and generalized intercepts(lgN_(W)) is lower(3.19) in CR,aligning with a "continentallike" cluster,which is mainly influenced by the joint impact of in-cloud ice-based processes and the below-cloud environmental background.Also,the empirical relationships of shape-slope(μ-Λ),radar reflectivity-rain rate(Z-R),and rainfall kinetic energy(KE_(time)-Rand KE_(time)-Z) are localized.To quantitatively analyze the impact of DSD parameters on kinetic energy estimation,power-law KE_(time)-R and KE_(time)-Z relationships are derived based on the normalized gamma distribution.N_(W)takes precedence over μ in affecting variabilities of multiplicative coefficients,especially for KE_(time)-R relationship where the multiplicative coefficient is proportional to N_(W)^(-0.287).It should be noted that although the proportion of CR occurring throughout the summer is small,raindrops with lower N_(W) and larger Dmwill generate higher KE_(time),which will bring a higher potential risk of soil erosion in semi-arid regions over IMP.
基金the National Natural Science Foundation of China(No.51577112,51575328)Science and Technology Commission of Shanghai Municipality Project(No.16511108600).
文摘This paper presents a novel dynamic A^*path finding algorithm and 3D lidar based local obstacle avoidance strategy for an autonomous vehicle.3D point cloud data is collected and analyzed in real time.Local obstacles are detected online and a 2D local obstacle grid map is constructed at 10 Hz/s.The A^*path finding algorithm is employed to generate a local path in this local obstacle grid map by considering both the target position and obstacles.The vehicle avoids obstacles under the guidance of the generated local path.Experiment results have shown the effectiveness of the obstacle avoidance navigation algorithm proposed.
基金Supported by Fundamental Research Funds for the Central Universities of China(Grant No.2023JBMC014).
文摘In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines.Currently,assessment methods for fastener tightness include manual observation,acoustic wave detection,and image detection.There are limitations such as low accuracy and efficiency,easy interference and misjudgment,and a lack of accurate,stable,and fast detection methods.Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening,this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks.First,the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration.Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod.The point is projected onto the upper surface of the bolt to calculate the projection distance.Subsequently,the mapping relationship between the projection distance sequence and fastener tightness is established,and the influence of each parameter on the fastener tightness prediction is analyzed.Finally,by setting up a fastener detection scene in the track experimental base,collecting data,and completing the algorithm verification,the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm,which significantly improved the effect compared with other tightness detection methods,and realized an effective fastener tightness regression.
基金funded by University College GhentGhent University。
文摘Sustainable forest management heavily relies on the accurate estimation of tree parameters.Among others,the diameter at breast height(DBH) is important for extracting the volume and mass of an individual tree.For systematically estimating the volume of entire plots,airborne laser scanning(ALS) data are used.The estimation model is frequently calibrated using manual DBH measurements or static terrestrial laser scans(STLS) of sample plots.Although reliable,this method is time-consuming,which greatly hampers its use.Here,a handheld mobile terrestrial laser scanning(HMTLS) was demonstrated to be a useful alternative technique to precisely and efficiently calculate DBH.Different data acquisition techniques were applied at a sample plot,then the resulting parameters were comparatively analysed.The calculated DBH values were comparable to the manual measurements for HMTLS,STLS,and ALS data sets.Given the comparability of the extracted parameters,with a reduced point density of HTMLS compared to STLS data,and the reasonable increase of performance,with a reduction of acquisition time with a factor of5 compared to conventional STLS techniques and a factor of3 compared to manual measurements,HMTLS is considered a useful alternative technique.
文摘Acquisition and registration of terrestrial 3D laser scans is a fundamental task in mapping and modeling of cities in three dimensions. To automate this task marker-flee registration methods are required. Based on the existence of skyline features, this paper proposes a novel method. The skyline features are extracted from panoramic 3D scans and encoded as strings enabling the use of string matching for merging the scans. Initial results of the proposed method in the old city center of Bremen are presented.
文摘After more than 30 years of scientific and social development, surveying and mapping technology by leaps and bounds, engineering surveying technology has undergone tremendous changes. In the process of protecting ancient buildings, it is necessary to obtain the precise dimensions of architectural details. In this study, the path of 3D laser scanning combined with BIM technology is explored. Taking the observation and protection of the ancestral hall of the Liu family as an example, this study aims to draw drawings that reflect the relevant information about the ancient buildings, the accurate three-dimensional model of ancient buildings is established with BIM technology, which provides new methods and ideas for the research and protection of ancient buildings. .
基金funded by the Korean Meteorological Administration Research and Development Program under Grant CATER 2006-2307.
文摘Data from a long term measurement of Micro Rain Radar （MRR） at a mountain site （Daegwallyeong, DG, one year period of 2005） and a coastal site （Haenam, HN, three years 2004-2006） in South Korea were analyzed to compare the MRR measured bright band characteristics of stratiform precipitation at the two sites. On average, the bright band was somewhat thicker and the sharpness （average gradient of reflectivity above and below the reflectivity peak） was slightly weaker at DG, compared to those values at HN. The peak reflectivity itself was twice as strong and the relative location of the peak reflectivity within the bright band was higher at HN than at DG. Importantly, the variability of these values was much larger at HN than at DG. The key parameter to cause these differences is suggested to be the difference of the snow particle densities at the two sites, which is related to the degree of riming. Therefore, it is speculated that the cloud microphysical processes at HN may have varied significantly from un-rimed snow growth, producing low density snow particles, to the riming of higher density particles, while snow particle growth at DG was more consistently affected by the riming process, and therefore high density snow particles. Forced uplifting of cloudy air over the mountain area around DG might have resulted in an orographic supercooling effect that led to the enhanced riming of supercooled cloud drops.
基金the National Natural Science Foundation of China(Grant No.42075083)National Key Research and Development Program of China(Grant No.2019YFC1510400)+1 种基金Guangdong Major Project of Basic and Applied Basic Research(Grant No.2020B0301030004)the Second Tibetan Plateau Scientific Expe-dition and Research(STEP)program(2019QZKK010402)。
文摘Representation of cloud microphysical processes is one of the key aspects of numerical models.An improved double-moment bulk cloud microphysics scheme(named IMY)was created based on the standard Milbrandt-Yau(MY)scheme in the Weather Research and Forecasting(WRF)model for the East Asian monsoon region(EAMR).In the IMY scheme,the shape parameters of raindrops,snow particles,and cloud droplet size distributions are variables instead of fixed constants.Specifically,the shape parameters of raindrop and snow size distributions are diagnosed from their respective shape-slope relationships.The shape parameter for the cloud droplet size distribution depends on the total cloud droplet number concentration.In addition,a series of minor improvements involving detailed cloud processes have also been incorporated.The improved scheme was coupled into the WRF model and tested on two heavy rainfall cases over the EAMR.The IMY scheme is shown to reproduce the overall spatial distribution of rainfall and its temporal evolution,evidenced by comparing the modeled results with surface gauge observations.The simulations also successfully capture the cloud features by using satellite and ground-based radar observations as a reference.The IMY has yielded simulation results on the case studies that were comparable,and in ways superior to MY,indicating that the improved scheme shows promise.Although the simulations demonstrated a positive performance evaluation for the IMY scheme,continued experiments are required to further validate the scheme with different weather events.
基金This work is partly supported by the National Natural Science Foundation of China under Grant Nos. 61772097 and U1401252, and Scientific and Technological Research Program of Chongqing Municipal Education Commission of China under Grant No. KJ1400429.
文摘The use of point clouds is becoming increasingly popular. We present a general framework for performing geometry filtering on point-based surface through applying the meshless local Petrol-Galelkin （MLPG） to obtain the solution of a screened Poisson equation. The enhancement or smoothing of surfaces is controlled by a gradient scale parameter. Anisotropic filtering is supported by the adapted Riemannian metric. Contrary to the other approaches of partial differential equation for point-based surface, the proposed approach neither needs to construct local or global triangular meshes, nor needs global parameterization. It is only based on the local tangent space and local interpolated surfaces. Experiments demonstrate the efficiency of our approach.
基金supported by the National Natural Science Foundation of China(Project Number 61521002)the Joint NSFC–DFG Research Program(Project Number 61761136018).
文摘The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing.This paper presents a novel framework named Point Cloud Transformer(PCT)for point cloud learning.PCT is based on Transformer,which achieves huge success in natural language processing and displays great potential in image processing.It is inherently permutation invariant for processing a sequence of points,making it well-suited for point cloud learning.To better capture local context within the point cloud,we enhance input embedding with the support of farthest point sampling and nearest neighbor search.Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification,part segmentation,semantic segmentation,and normal estimation tasks.
