The hybrid CO_(2) thermal technique has achieved considerable success globally in extracting residual heavy oil from reserves following a long-term steam stimulation process.Using microscopic visualization experiments...The hybrid CO_(2) thermal technique has achieved considerable success globally in extracting residual heavy oil from reserves following a long-term steam stimulation process.Using microscopic visualization experiments and molecular dynamics(MD)simulations,this study investigates the microscopic enhanced oil recovery(EOR)mechanisms underlying residual oil removal using hybrid CO_(2) thermal systems.Based on the experimental models for the occurrence of heavy oil,this study evaluates the performance of hybrid CO_(2) thermal systems under various conditions using MD simulations.The results demonstrate that introducing CO_(2) molecules into heavy oil can effectively penetrate and decompose dense aggregates that are originally formed on hydrophobic surfaces.A stable miscible hybrid CO_(2) thermal system,with a high effective distribution ratio of CO_(2),proficiently reduces the interaction energies between heavy oil and rock surfaces,as well as within heavy oil.A visualization analysis of the interactions reveals that strong van der Waals(vdW)attractions occur between CO_(2) and heavy oil molecules,effectively promoting the decomposition and swelling of heavy oil.This unlocks the residual oil on the hydrophobic surfaces.Considering the impacts of temperature and CO_(2) concentration,an optimal gas-to-steam injection ratio(here,the CO_(2):steam ratio)ranging between 1:6 and 1:9 is recommended.This study examines the microscopic mechanisms underlying the hybrid CO_(2) thermal technique at a molecular scale,providing a significant theoretical guide for its expanded application in EOR.展开更多
In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper prese...In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.展开更多
Background As visual simultaneous localization and mapping(SLAM)is primarily based on the assumption of a static scene,the presence of dynamic objects in the frame causes problems such as a deterioration of system rob...Background As visual simultaneous localization and mapping(SLAM)is primarily based on the assumption of a static scene,the presence of dynamic objects in the frame causes problems such as a deterioration of system robustness and inaccurate position estimation.In this study,we propose a YGC-SLAM for indoor dynamic environments based on the ORB-SLAM2 framework combined with semantic and geometric constraints to improve the positioning accuracy and robustness of the system.Methods First,the recognition accuracy of YOLOv5 was improved by introducing the convolution block attention model and the improved EIOU loss function,whereby the prediction frame converges quickly for better detection.The improved YOLOv5 was then added to the tracking thread for dynamic target detection to eliminate dynamic points.Subsequently,multi-view geometric constraints were used for re-judging to further eliminate dynamic points while enabling more useful feature points to be retained and preventing the semantic approach from over-eliminating feature points,causing a failure of map building.The K-means clustering algorithm was used to accelerate this process and quickly calculate and determine the motion state of each cluster of pixel points.Finally,a strategy for drawing keyframes with de-redundancy was implemented to construct a clear 3D dense static point-cloud map.Results Through testing on TUM dataset and a real environment,the experimental results show that our algorithm reduces the absolute trajectory error by 98.22%and the relative trajectory error by 97.98%compared with the original ORBSLAM2,which is more accurate and has better real-time performance than similar algorithms,such as DynaSLAM and DS-SLAM.Conclusions The YGC-SLAM proposed in this study can effectively eliminate the adverse effects of dynamic objects,and the system can better complete positioning and map building tasks in complex environments.展开更多
Dynamic visual SLAM (Simultaneous Localization and Mapping) is an important research area, but existing methods struggle to balance real-time performance and accuracy in removing dynamic feature points, especially whe...Dynamic visual SLAM (Simultaneous Localization and Mapping) is an important research area, but existing methods struggle to balance real-time performance and accuracy in removing dynamic feature points, especially when semantic information is missing. This paper presents a novel dynamic SLAM system that uses optical flow tracking and epipolar geometry to identify dynamic feature points and applies a regional dynamic probability method to improve removal accuracy. We developed two innovative algorithms for precise pruning of dynamic regions: first, using optical flow and epipolar geometry to identify and prune dynamic areas while preserving static regions on stationary dynamic objects to optimize tracking performance;second, propagating dynamic probabilities across frames to mitigate the impact of semantic information loss in some frames. Experiments show that our system significantly reduces trajectory and pose errors in dynamic scenes, achieving dynamic feature point removal accuracy close to that of semantic segmentation methods, while maintaining high real-time performance. Our system performs exceptionally well in highly dynamic environments, especially where complex dynamic objects are present, demonstrating its advantage in handling dynamic scenarios. The experiments also show that while traditional methods may fail in tracking when semantic information is lost, our approach effectively reduces the misidentification of dynamic regions caused by such loss, thus improving system robustness and accuracy.展开更多
In dynamic scenes,the pose estimation and map consistency of visual simultaneous localisation and mapping(visual SLAM)are affected by intermittent changes in object motion states.An adaptive motion-state estimation an...In dynamic scenes,the pose estimation and map consistency of visual simultaneous localisation and mapping(visual SLAM)are affected by intermittent changes in object motion states.An adaptive motion-state estimation and feature-reuse mechanism is proposed which restores features once objects become stationary.Camera ego-motion is com-pensated via projection-based point-to-point red-green-blue-depth(RGB-D)Iterative Closest Point;the alignment residual yields a short-term jitter score.An Extended Kalman Filter fuses the centre-pixel trajectory and depth of the object,using depth innovation as strong evidence to suppress false triggers.Applied adaptive decision thresholds involve resolution,ego-motion intensity,jitter,and reference depth,and are combined with dual/single triggering and hysteresis to achieve robust switching.When an object is considered static,its feature points are reused.On the Bonn RGB-D Dynamic Dataset(BONN)and TUM RGB-D SLAM Dataset and Benchmark(TUM),the proposed method matches or exceeds baselines:In intermittent-motion-dominated BONN sequences Placing_non_box,it re-duces the root-mean-square of the absolute trajectory error(ATE-RMSE)by 27%relative to the baseline,remains comparable to Ellipsoid-SLAM on TUM,and consistently outperforms ORB-SLAM3 in dynamic scenes.The hysteresis counter reading on Placing_non_box2 shows that the proposed method can reduce the motion-state misclassification rate by nearly 40%.From the ablation experiment results,we confirm that adaptive thresholds yield the most significant optimisation effect.The approach improves robustness and map completeness in dynamic environments without degrading performance in low-dynamic settings.展开更多
Correction to:Neuroscience Bulletin https://doi.org/10.1007/s12264-025-01371-x In this article the affiliation"Department of Circuit Theory,Faculty of Electrical Engineering,Czech Technical University in Prague,M...Correction to:Neuroscience Bulletin https://doi.org/10.1007/s12264-025-01371-x In this article the affiliation"Department of Circuit Theory,Faculty of Electrical Engineering,Czech Technical University in Prague,Member of the Epilepsy Research Centre Prague-EpiReC Consortium,Prague,Czechia"should only be assigned to Radek Janca and Petr Jezdik.It is removed from the authors:Jiri Hammer,Michaela Kajsova,Adam Kalina,Petr Marusic,and Kamil Vlcek.展开更多
In order to study the spatial-temporal change and environmental management of regional karst LUCC (land use and land cover change) and its causative environmental effect-rocky desertification by integrating qualitativ...In order to study the spatial-temporal change and environmental management of regional karst LUCC (land use and land cover change) and its causative environmental effect-rocky desertification by integrating qualitative analysis and quantitative analysis, and relying on RS, GIS and GPS (3S) techniques, karst land rocky derification dynamic monitoring and visualization management information system (KLRD.DMVM.IS) is framed, which includes design aim and structure model, function design, database design and model system design. The model system design gives priority to dynamic monitoring, drive force diagnosis, comprehensive evaluation and decision support of karst rocky desertification. From the viewpoint of model type, mathematic expression and its meaning, the dynamic monitoring models are concretely devised to reflect the spatial and temporal changing features and the trend of karst LUCC and rocky desertification. Taking Du'an Yao Autonomic County of Guangxi as an example, the KLRD.DMVM.IS is systematically analyzed in the application of the process and trend of karst LUCC and rocky desertification in Du'an County, and it provides the technical support for the study on karst land rocky desertification.展开更多
Identifying the flow patterns is vital for understanding the complicated physical mechanisms in multiphase flows.For this purpose,electrical capacitance tomography(ECT) technique is considered as a promising visualiza...Identifying the flow patterns is vital for understanding the complicated physical mechanisms in multiphase flows.For this purpose,electrical capacitance tomography(ECT) technique is considered as a promising visualization method for the flow pattern identification,in which image reconstruction algorithms play an important role.In this paper,a generalized dynamic reconstruction model,which integrates ECT measurement information and physical evolution information of the objects of interest,was presented.A generalized objective functional that simultaneously considers the spatial constraints,temporal constraints and dynamic evolution information of the objects of interest was proposed.Numerical simulations and experiments were implemented to evaluate the feasibility and efficiency of the proposed algorithm.For the cases considered in this paper,the proposed algorithm can well reconstruct the flow patterns,and the quality of the reconstructed images is improved,which indicates that the proposed algorithm is competent to reconstruct the flow patterns in the visualization of multiphase flows.展开更多
The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient appro...The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient approach to such visualization is node-link diagrams,whereas for dense graphs with attached data,adjacency matrices might be the better choice.Because graphs can contain both properties,being globally sparse and locally dense,a combination of several visual metaphors as well as static and dynamic visualizations is beneficial.In this paper,a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described.As the novelty of this technique,insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views.Moreover,the importance of nodes and node groups can be detected,computed,and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes.As an additional feature set,an automatic identification of groups,clusters,and outliers is provided over time,and based on the visual outcome of the node-link and matrix visualizations,the repertoire of the supported layout and matrix reordering techniques is extended,and more interaction techniques are provided when considering the dynamics of the graph data.Finally,a small user experiment was conducted to investigate the usability of the proposed approach.The usefulness of the proposed tool is illustrated by applying it to a graph dataset,such as e co-authorships,co-citations,and a Comprehensible Perl Archive Network distribution.展开更多
A novel method combining visualization particle tracking with image-based dynamic light scattering was developed to achieve the in situ and real-time size measurement of nanobubbles(NBs).First,the in situ size distrib...A novel method combining visualization particle tracking with image-based dynamic light scattering was developed to achieve the in situ and real-time size measurement of nanobubbles(NBs).First,the in situ size distribution of NBs was visualized by dark-field microscopy.Then,real-time size during the preparation was measured using image-based dynamic light scattering,and the longitudinal size distribution of NBs in the sample cell was obtained in a steady state.Results show that this strategy can provide a detailed and accurate size of bubbles in the whole sample compared with the commercial ZetaSizer Nano equipment.Therefore,the developed method is a real-time and simple technology with excellent accuracy,providing new insights into the accurate measurement of the size distribution of NBs or nanoparticles in solution.展开更多
An improved three-dimensional （3-D） experimental visualization methodology is presented tor evaluating the fracture mechanisms of ferritic stainless steels by in-situ tensile testing with an environmental scanning e...An improved three-dimensional （3-D） experimental visualization methodology is presented tor evaluating the fracture mechanisms of ferritic stainless steels by in-situ tensile testing with an environmental scanning electron microscope （ESEM）. The samples were machined with a radial notched shape and a sloped surface. Both planar surface deformation and sloping surface deformation-induced microvoids were observed during dynamic tension experiments, where a greater amount of information could be obtained from the sloping surface. The results showed that microvoids formed at the grain boundaries of highly elongated large grains. The microvoids nucleated in the severely deformed regions grew nearly parallel to the tensile axis, predominantly along the grain boundaries. The microvoids nucleated at the interface of particles and the matrix did not propagate due to the high plasticity of the matrix. The large microvoids propagated and showed a zigzag shape along the grain boundaries,seemingly a consequence of the fracture of the slip bands caused by dislocation pile-ups. The final failure took place due to the reduction of the load-beating area.展开更多
Reaction dynamics in gases at operating temperatures at the atomic level are the basis of heterogeneous gas-solid catalyst reactions and are crucial to the catalyst function.Supported noble metal nanocatalysts such as...Reaction dynamics in gases at operating temperatures at the atomic level are the basis of heterogeneous gas-solid catalyst reactions and are crucial to the catalyst function.Supported noble metal nanocatalysts such as platinum are of interest in fuel cells and as diesel oxidation catalysts for pollution control,and practical ruthenium nanocatalysts are explored for ammonia synthesis.Graphite and graphitic carbons are of interest as supports for the nanocatalysts.Despite considerable literature on the catalytic processes on graphite and graphitic supports,reaction dynamics of the nanocatalysts on the supports in different reactive gas environments and operating temperatures at the single atom level are not well understood.Here we present real time in-situ observations and analyses of reaction dynamics of Pt in oxidation,and practical Ru nanocatalysts in ammonia synthesis,on graphite and related supports under controlled reaction environments using a novel in-situ environmental(scanning) transmission electron microscope with single atom resolution.By recording snapshots of the reaction dynamics,the behaviour of the catalysts is imaged.The images reveal single metal atoms,clusters of a few atoms on the graphitic supports and the support function.These all play key roles in the mobility,sintering and growth of the catalysts.The experimental findings provide new structural insights into atomic scale reaction dynamics,morphology and stability of the nanocatalysts.展开更多
Terrain Visualization is an important part of visualization systems of battlefield,and the visualization of dynamic terrain is also important for dynamic battle environment.In this paper,special attention has been pai...Terrain Visualization is an important part of visualization systems of battlefield,and the visualization of dynamic terrain is also important for dynamic battle environment.In this paper,special attention has been paid on real-time optimally adapting meshes (ROAM) algorithm,which is a candidate for dynamic terrain,and its mesh representation,mesh continuity algorithm and error metrics are discussed.The DEXTER-ROAM algorithm is discussed and analyzed.By revising the mesh representation of ROAM,a dynamic ROAM algorithm based on partial-regular grid is established.By introducing transition region,mesh discontinuity of dynamic partial-regular grid is resolved.Error metric blocks are removed for computation complexity and culling blocks are introduced to accelerate view frustum culling.The algorithm is implemented in a 3D rendering engine called OGRE.In the end,an example of dynamic crater is given to examine the dynamic ROAM algorithm.展开更多
A great number of visual simultaneous localization and mapping(VSLAM)systems need to assume static features in the environment.However,moving objects can vastly impair the performance of a VSLAM system which relies on...A great number of visual simultaneous localization and mapping(VSLAM)systems need to assume static features in the environment.However,moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption.To cope with this challenging topic,a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed.To reduce the influence of dynamic content,we incorporate the deep-learning-based object detection method in the visual odometry,then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system.Experiment with both on the TUM and KITTI benchmark dataset,as well as in a real-world environment,the results clarify that our method can significantly reduce the tracking error or drift,enhance the robustness,accuracy and stability of the VSLAM system in dynamic scenes.展开更多
This paper investigates the visual servoing robust stabilization of nonholonomic mobile robots.The calibration of visual parameters is not only complicated,but also needs great consumption of calculated time so that t...This paper investigates the visual servoing robust stabilization of nonholonomic mobile robots.The calibration of visual parameters is not only complicated,but also needs great consumption of calculated time so that the accurate calibration is impossible in some situations for high requirement of real timing.Hence,it is interesting and important to consider the design of stabilizing controllers for nonholonomic kinematic systems with uncalibrated visual parameters.A novel uncertain model of these nonholonomic kinematic systems is proposed.Based on this model,a stabilizing controller is discussed by using dynamic feedback and two-step techniques.The proposed robust controller makes the mobile robot image pose and the orientation converge to the desired configuration despite the lack of depth information and the lack of precise visual parameters.The stability of the closed loop system is rigorously proved.The simulation is given to show the effectiveness of the presented controllers.展开更多
Dynamic visual acuity test(DVAT)plays a key role in the assessment of vestibular function,the visual function of athletes,as well as various ocular diseases.As the visual pathways conducting dynamic and static signals...Dynamic visual acuity test(DVAT)plays a key role in the assessment of vestibular function,the visual function of athletes,as well as various ocular diseases.As the visual pathways conducting dynamic and static signals are different,DVATs may have potential advantages over the traditional visual acuity tests commonly used,such as static visual acuity,contrast sensitivity,and static perimetry.Here,we provide a review of commonly applied DVATs and their several uses in clinical ophthalmology.These data indicate that the DVAT has its unique clinical significance in the evaluation of several ocular disorders.展开更多
The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology play...The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2, MG-SLAM incorporates a dynamic target detection process that enables the detection of both known and unknown moving objects. In this process, a separate semantic segmentation thread is required to segment dynamic target instances, and the Mask R-CNN algorithm is applied on the Graphics Processing Unit (GPU) to accelerate segmentation. To reduce computational cost, only key frames are segmented to identify known dynamic objects. Additionally, a multi-view geometry method is adopted to detect unknown moving objects. The results demonstrate that MG-SLAM achieves higher precision, with an improvement from 0.2730 m to 0.0135 m in precision. Moreover, the processing time required by MG-SLAM is significantly reduced compared to other dynamic scene SLAM algorithms, which illustrates its efficacy in locating objects in dynamic scenes.展开更多
The dynamic multichannel binocular visual image modeling is studied based on Internet of Things (IoT) Perception Layer, using mobile robot self-organizing network. By employing multigroup mobile robots with binocular ...The dynamic multichannel binocular visual image modeling is studied based on Internet of Things (IoT) Perception Layer, using mobile robot self-organizing network. By employing multigroup mobile robots with binocular visual system, the real visual images of the object will be obtained. Then through the mobile self-organizing network, a three-dimensional model is rebuilt by synthesizing the returned images. On this basis, we formalize a novel algorithm for multichannel binocular visual three-dimensional images based on fast three-dimensional modeling. Compared with the method based on single binocular visual system, the new algorithm can improve the Integrity and accuracy of the dynamic three-dimensional object modeling. The simulation results show that the new method can effectively accelerate the modeling speed, improve the similarity and not increase the data size.展开更多
With the utilization of underground space,backward erosion piping(BEP)has been observed in many underground structures(e.g.,shield tunnels)founded on sandy aquifers.However,due to invisibility,the geometry of the erod...With the utilization of underground space,backward erosion piping(BEP)has been observed in many underground structures(e.g.,shield tunnels)founded on sandy aquifers.However,due to invisibility,the geometry of the eroded pipe and its spatial evolution with time during the piping process was still not clear.In this study,we developed a Hele-Shaw cell to visualize the dynamic progression of BEP.With imaging process technology,we obtained a typical process of BEP(the erosion process can be divided into a piping progression phase and a piping stabilization phase),quantitatively characterized the formation of erosion pipes,and compared the patterns of erosion(e.g.,the erosion area A and the maximum erosion radius R)that spontaneously develop under different fluxes of water.The most interesting finding is that the sand grains in a thicker Hele-Shaw model are easier to dislodge,which is possibly due to the granular system in a thicker model having more degrees of freedom,reducing the stability of the sand grains.展开更多
Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dyn...Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dynamic sign language requires identifying keyframes that best represent the signs, and missing these keyframes reduces accuracy. Secondly, some methods do not focus enough on hand regions, which are small within the overall frame, leading to information loss. To address these challenges, we propose a novel Video Transformer Attention-based Network (VTAN) for dynamic sign language recognition. Our approach prioritizes informative frames and hand regions effectively. To tackle the first issue, we designed a keyframe extraction module enhanced by a convolutional autoencoder, which focuses on selecting information-rich frames and eliminating redundant ones from the video sequences. For the second issue, we developed a soft attention-based transformer module that emphasizes extracting features from hand regions, ensuring that the network pays more attention to hand information within sequences. This dual-focus approach improves effective dynamic sign language recognition by addressing the key challenges of identifying critical frames and emphasizing hand regions. Experimental results on two public benchmark datasets demonstrate the effectiveness of our network, outperforming most of the typical methods in sign language recognition tasks.展开更多
基金financially supported by the National Natural Science Foundation of China(No.U20B6003)the China Scholarship Council(No.202306440015)a project of the China Petroleum&Chemical Corporation(No.P22174)。
文摘The hybrid CO_(2) thermal technique has achieved considerable success globally in extracting residual heavy oil from reserves following a long-term steam stimulation process.Using microscopic visualization experiments and molecular dynamics(MD)simulations,this study investigates the microscopic enhanced oil recovery(EOR)mechanisms underlying residual oil removal using hybrid CO_(2) thermal systems.Based on the experimental models for the occurrence of heavy oil,this study evaluates the performance of hybrid CO_(2) thermal systems under various conditions using MD simulations.The results demonstrate that introducing CO_(2) molecules into heavy oil can effectively penetrate and decompose dense aggregates that are originally formed on hydrophobic surfaces.A stable miscible hybrid CO_(2) thermal system,with a high effective distribution ratio of CO_(2),proficiently reduces the interaction energies between heavy oil and rock surfaces,as well as within heavy oil.A visualization analysis of the interactions reveals that strong van der Waals(vdW)attractions occur between CO_(2) and heavy oil molecules,effectively promoting the decomposition and swelling of heavy oil.This unlocks the residual oil on the hydrophobic surfaces.Considering the impacts of temperature and CO_(2) concentration,an optimal gas-to-steam injection ratio(here,the CO_(2):steam ratio)ranging between 1:6 and 1:9 is recommended.This study examines the microscopic mechanisms underlying the hybrid CO_(2) thermal technique at a molecular scale,providing a significant theoretical guide for its expanded application in EOR.
基金the National Natural Science Foundation of China(No.62063006)to the Guangxi Natural Science Foundation under Grant(Nos.2023GXNSFAA026025,AA24010001)+3 种基金to the Innovation Fund of Chinese Universities Industry-University-Research(ID:2023RY018)to the Special Guangxi Industry and Information Technology Department,Textile and Pharmaceutical Division(ID:2021 No.231)to the Special Research Project of Hechi University(ID:2021GCC028)to the Key Laboratory of AI and Information Processing,Education Department of Guangxi Zhuang Autonomous Region(Hechi University),No.2024GXZDSY009。
文摘In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.
基金Supported by Jiangsu Key R&D Program(BE2021622)Jiangsu Postgraduate Practice and Innovation Program(SJCX23_0395).
文摘Background As visual simultaneous localization and mapping(SLAM)is primarily based on the assumption of a static scene,the presence of dynamic objects in the frame causes problems such as a deterioration of system robustness and inaccurate position estimation.In this study,we propose a YGC-SLAM for indoor dynamic environments based on the ORB-SLAM2 framework combined with semantic and geometric constraints to improve the positioning accuracy and robustness of the system.Methods First,the recognition accuracy of YOLOv5 was improved by introducing the convolution block attention model and the improved EIOU loss function,whereby the prediction frame converges quickly for better detection.The improved YOLOv5 was then added to the tracking thread for dynamic target detection to eliminate dynamic points.Subsequently,multi-view geometric constraints were used for re-judging to further eliminate dynamic points while enabling more useful feature points to be retained and preventing the semantic approach from over-eliminating feature points,causing a failure of map building.The K-means clustering algorithm was used to accelerate this process and quickly calculate and determine the motion state of each cluster of pixel points.Finally,a strategy for drawing keyframes with de-redundancy was implemented to construct a clear 3D dense static point-cloud map.Results Through testing on TUM dataset and a real environment,the experimental results show that our algorithm reduces the absolute trajectory error by 98.22%and the relative trajectory error by 97.98%compared with the original ORBSLAM2,which is more accurate and has better real-time performance than similar algorithms,such as DynaSLAM and DS-SLAM.Conclusions The YGC-SLAM proposed in this study can effectively eliminate the adverse effects of dynamic objects,and the system can better complete positioning and map building tasks in complex environments.
基金the National Natural Science Foundation of China(No.62063006)to the Guangxi Natural Science Foundation under Grant(Nos.2023GXNSFAA026025,AA24010001)+4 种基金to the Innovation Fund of Chinese Universities Industry-University-Research(ID:2023RY018)to the Special Guangxi Industry and Information Technology Department,Textile and Pharmaceutical Division(ID:2021 No.231)to the Special Research Project of Hechi University(ID:2021GCC028)to the Key Laboratory of AI and Information Processing,Education Department of Guangxi Zhuang Autonomous Region(Hechi University)No.2024GXZDSY009.
文摘Dynamic visual SLAM (Simultaneous Localization and Mapping) is an important research area, but existing methods struggle to balance real-time performance and accuracy in removing dynamic feature points, especially when semantic information is missing. This paper presents a novel dynamic SLAM system that uses optical flow tracking and epipolar geometry to identify dynamic feature points and applies a regional dynamic probability method to improve removal accuracy. We developed two innovative algorithms for precise pruning of dynamic regions: first, using optical flow and epipolar geometry to identify and prune dynamic areas while preserving static regions on stationary dynamic objects to optimize tracking performance;second, propagating dynamic probabilities across frames to mitigate the impact of semantic information loss in some frames. Experiments show that our system significantly reduces trajectory and pose errors in dynamic scenes, achieving dynamic feature point removal accuracy close to that of semantic segmentation methods, while maintaining high real-time performance. Our system performs exceptionally well in highly dynamic environments, especially where complex dynamic objects are present, demonstrating its advantage in handling dynamic scenarios. The experiments also show that while traditional methods may fail in tracking when semantic information is lost, our approach effectively reduces the misidentification of dynamic regions caused by such loss, thus improving system robustness and accuracy.
文摘In dynamic scenes,the pose estimation and map consistency of visual simultaneous localisation and mapping(visual SLAM)are affected by intermittent changes in object motion states.An adaptive motion-state estimation and feature-reuse mechanism is proposed which restores features once objects become stationary.Camera ego-motion is com-pensated via projection-based point-to-point red-green-blue-depth(RGB-D)Iterative Closest Point;the alignment residual yields a short-term jitter score.An Extended Kalman Filter fuses the centre-pixel trajectory and depth of the object,using depth innovation as strong evidence to suppress false triggers.Applied adaptive decision thresholds involve resolution,ego-motion intensity,jitter,and reference depth,and are combined with dual/single triggering and hysteresis to achieve robust switching.When an object is considered static,its feature points are reused.On the Bonn RGB-D Dynamic Dataset(BONN)and TUM RGB-D SLAM Dataset and Benchmark(TUM),the proposed method matches or exceeds baselines:In intermittent-motion-dominated BONN sequences Placing_non_box,it re-duces the root-mean-square of the absolute trajectory error(ATE-RMSE)by 27%relative to the baseline,remains comparable to Ellipsoid-SLAM on TUM,and consistently outperforms ORB-SLAM3 in dynamic scenes.The hysteresis counter reading on Placing_non_box2 shows that the proposed method can reduce the motion-state misclassification rate by nearly 40%.From the ablation experiment results,we confirm that adaptive thresholds yield the most significant optimisation effect.The approach improves robustness and map completeness in dynamic environments without degrading performance in low-dynamic settings.
文摘Correction to:Neuroscience Bulletin https://doi.org/10.1007/s12264-025-01371-x In this article the affiliation"Department of Circuit Theory,Faculty of Electrical Engineering,Czech Technical University in Prague,Member of the Epilepsy Research Centre Prague-EpiReC Consortium,Prague,Czechia"should only be assigned to Radek Janca and Petr Jezdik.It is removed from the authors:Jiri Hammer,Michaela Kajsova,Adam Kalina,Petr Marusic,and Kamil Vlcek.
基金Under the auspices of the National Natural Science Foundation of China (No. 40161004, 40361002)Guangxi Natural Science Foundation (No. 023646, 0342001-2).
文摘In order to study the spatial-temporal change and environmental management of regional karst LUCC (land use and land cover change) and its causative environmental effect-rocky desertification by integrating qualitative analysis and quantitative analysis, and relying on RS, GIS and GPS (3S) techniques, karst land rocky derification dynamic monitoring and visualization management information system (KLRD.DMVM.IS) is framed, which includes design aim and structure model, function design, database design and model system design. The model system design gives priority to dynamic monitoring, drive force diagnosis, comprehensive evaluation and decision support of karst rocky desertification. From the viewpoint of model type, mathematic expression and its meaning, the dynamic monitoring models are concretely devised to reflect the spatial and temporal changing features and the trend of karst LUCC and rocky desertification. Taking Du'an Yao Autonomic County of Guangxi as an example, the KLRD.DMVM.IS is systematically analyzed in the application of the process and trend of karst LUCC and rocky desertification in Du'an County, and it provides the technical support for the study on karst land rocky desertification.
基金Supported by the National Natural Science Foundation of China (50736002,50806005,51006106)the Program for Changjiang Scholars and Innovative Research Team in University (IRT0952)
文摘Identifying the flow patterns is vital for understanding the complicated physical mechanisms in multiphase flows.For this purpose,electrical capacitance tomography(ECT) technique is considered as a promising visualization method for the flow pattern identification,in which image reconstruction algorithms play an important role.In this paper,a generalized dynamic reconstruction model,which integrates ECT measurement information and physical evolution information of the objects of interest,was presented.A generalized objective functional that simultaneously considers the spatial constraints,temporal constraints and dynamic evolution information of the objects of interest was proposed.Numerical simulations and experiments were implemented to evaluate the feasibility and efficiency of the proposed algorithm.For the cases considered in this paper,the proposed algorithm can well reconstruct the flow patterns,and the quality of the reconstructed images is improved,which indicates that the proposed algorithm is competent to reconstruct the flow patterns in the visualization of multiphase flows.
文摘The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient approach to such visualization is node-link diagrams,whereas for dense graphs with attached data,adjacency matrices might be the better choice.Because graphs can contain both properties,being globally sparse and locally dense,a combination of several visual metaphors as well as static and dynamic visualizations is beneficial.In this paper,a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described.As the novelty of this technique,insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views.Moreover,the importance of nodes and node groups can be detected,computed,and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes.As an additional feature set,an automatic identification of groups,clusters,and outliers is provided over time,and based on the visual outcome of the node-link and matrix visualizations,the repertoire of the supported layout and matrix reordering techniques is extended,and more interaction techniques are provided when considering the dynamics of the graph data.Finally,a small user experiment was conducted to investigate the usability of the proposed approach.The usefulness of the proposed tool is illustrated by applying it to a graph dataset,such as e co-authorships,co-citations,and a Comprehensible Perl Archive Network distribution.
基金The National Key Research and Development Program of China(No.2017YFA0104302)the National Natural Science Foundation of China(No.51832001,61821002,81971750).
文摘A novel method combining visualization particle tracking with image-based dynamic light scattering was developed to achieve the in situ and real-time size measurement of nanobubbles(NBs).First,the in situ size distribution of NBs was visualized by dark-field microscopy.Then,real-time size during the preparation was measured using image-based dynamic light scattering,and the longitudinal size distribution of NBs in the sample cell was obtained in a steady state.Results show that this strategy can provide a detailed and accurate size of bubbles in the whole sample compared with the commercial ZetaSizer Nano equipment.Therefore,the developed method is a real-time and simple technology with excellent accuracy,providing new insights into the accurate measurement of the size distribution of NBs or nanoparticles in solution.
文摘An improved three-dimensional （3-D） experimental visualization methodology is presented tor evaluating the fracture mechanisms of ferritic stainless steels by in-situ tensile testing with an environmental scanning electron microscope （ESEM）. The samples were machined with a radial notched shape and a sloped surface. Both planar surface deformation and sloping surface deformation-induced microvoids were observed during dynamic tension experiments, where a greater amount of information could be obtained from the sloping surface. The results showed that microvoids formed at the grain boundaries of highly elongated large grains. The microvoids nucleated in the severely deformed regions grew nearly parallel to the tensile axis, predominantly along the grain boundaries. The microvoids nucleated at the interface of particles and the matrix did not propagate due to the high plasticity of the matrix. The large microvoids propagated and showed a zigzag shape along the grain boundaries,seemingly a consequence of the fracture of the slip bands caused by dislocation pile-ups. The final failure took place due to the reduction of the load-beating area.
基金the Engineering and Physical Science Research Council(EPSRC),U.K.for the award of a research grant EP/J0118058/1 and postdoctoral research assistantships(PDRAs) to M.R.W.and R.W.M.from the grant。
文摘Reaction dynamics in gases at operating temperatures at the atomic level are the basis of heterogeneous gas-solid catalyst reactions and are crucial to the catalyst function.Supported noble metal nanocatalysts such as platinum are of interest in fuel cells and as diesel oxidation catalysts for pollution control,and practical ruthenium nanocatalysts are explored for ammonia synthesis.Graphite and graphitic carbons are of interest as supports for the nanocatalysts.Despite considerable literature on the catalytic processes on graphite and graphitic supports,reaction dynamics of the nanocatalysts on the supports in different reactive gas environments and operating temperatures at the single atom level are not well understood.Here we present real time in-situ observations and analyses of reaction dynamics of Pt in oxidation,and practical Ru nanocatalysts in ammonia synthesis,on graphite and related supports under controlled reaction environments using a novel in-situ environmental(scanning) transmission electron microscope with single atom resolution.By recording snapshots of the reaction dynamics,the behaviour of the catalysts is imaged.The images reveal single metal atoms,clusters of a few atoms on the graphitic supports and the support function.These all play key roles in the mobility,sintering and growth of the catalysts.The experimental findings provide new structural insights into atomic scale reaction dynamics,morphology and stability of the nanocatalysts.
文摘Terrain Visualization is an important part of visualization systems of battlefield,and the visualization of dynamic terrain is also important for dynamic battle environment.In this paper,special attention has been paid on real-time optimally adapting meshes (ROAM) algorithm,which is a candidate for dynamic terrain,and its mesh representation,mesh continuity algorithm and error metrics are discussed.The DEXTER-ROAM algorithm is discussed and analyzed.By revising the mesh representation of ROAM,a dynamic ROAM algorithm based on partial-regular grid is established.By introducing transition region,mesh discontinuity of dynamic partial-regular grid is resolved.Error metric blocks are removed for computation complexity and culling blocks are introduced to accelerate view frustum culling.The algorithm is implemented in a 3D rendering engine called OGRE.In the end,an example of dynamic crater is given to examine the dynamic ROAM algorithm.
基金the National Natural Science Foundation of China(No.61671470).
文摘A great number of visual simultaneous localization and mapping(VSLAM)systems need to assume static features in the environment.However,moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption.To cope with this challenging topic,a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed.To reduce the influence of dynamic content,we incorporate the deep-learning-based object detection method in the visual odometry,then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system.Experiment with both on the TUM and KITTI benchmark dataset,as well as in a real-world environment,the results clarify that our method can significantly reduce the tracking error or drift,enhance the robustness,accuracy and stability of the VSLAM system in dynamic scenes.
基金supported by the National Natural Science Foundation of China(No.60874002)the Key Program of Scientific Innovation of Shanghai Education Committee(No.09zz158)the Shanghai Key Discipline(No.S30501)
文摘This paper investigates the visual servoing robust stabilization of nonholonomic mobile robots.The calibration of visual parameters is not only complicated,but also needs great consumption of calculated time so that the accurate calibration is impossible in some situations for high requirement of real timing.Hence,it is interesting and important to consider the design of stabilizing controllers for nonholonomic kinematic systems with uncalibrated visual parameters.A novel uncertain model of these nonholonomic kinematic systems is proposed.Based on this model,a stabilizing controller is discussed by using dynamic feedback and two-step techniques.The proposed robust controller makes the mobile robot image pose and the orientation converge to the desired configuration despite the lack of depth information and the lack of precise visual parameters.The stability of the closed loop system is rigorously proved.The simulation is given to show the effectiveness of the presented controllers.
基金Supported by Chinese Capital’s Funds for Health Improvement and Research(No.CFH2018-2-4093)National Science and Technology Major Project(No.2018ZX10101-004)。
文摘Dynamic visual acuity test(DVAT)plays a key role in the assessment of vestibular function,the visual function of athletes,as well as various ocular diseases.As the visual pathways conducting dynamic and static signals are different,DVATs may have potential advantages over the traditional visual acuity tests commonly used,such as static visual acuity,contrast sensitivity,and static perimetry.Here,we provide a review of commonly applied DVATs and their several uses in clinical ophthalmology.These data indicate that the DVAT has its unique clinical significance in the evaluation of several ocular disorders.
基金funded by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(grant number 22KJD440001)Changzhou Science&Technology Program(grant number CJ20220232).
文摘The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2, MG-SLAM incorporates a dynamic target detection process that enables the detection of both known and unknown moving objects. In this process, a separate semantic segmentation thread is required to segment dynamic target instances, and the Mask R-CNN algorithm is applied on the Graphics Processing Unit (GPU) to accelerate segmentation. To reduce computational cost, only key frames are segmented to identify known dynamic objects. Additionally, a multi-view geometry method is adopted to detect unknown moving objects. The results demonstrate that MG-SLAM achieves higher precision, with an improvement from 0.2730 m to 0.0135 m in precision. Moreover, the processing time required by MG-SLAM is significantly reduced compared to other dynamic scene SLAM algorithms, which illustrates its efficacy in locating objects in dynamic scenes.
基金supported by HiTech Researchand Development Program of China under Grant No.2007AA10Z235
文摘The dynamic multichannel binocular visual image modeling is studied based on Internet of Things (IoT) Perception Layer, using mobile robot self-organizing network. By employing multigroup mobile robots with binocular visual system, the real visual images of the object will be obtained. Then through the mobile self-organizing network, a three-dimensional model is rebuilt by synthesizing the returned images. On this basis, we formalize a novel algorithm for multichannel binocular visual three-dimensional images based on fast three-dimensional modeling. Compared with the method based on single binocular visual system, the new algorithm can improve the Integrity and accuracy of the dynamic three-dimensional object modeling. The simulation results show that the new method can effectively accelerate the modeling speed, improve the similarity and not increase the data size.
基金the National Engineering Laboratory for Digital Construction and Evaluation Technology of Urban Rail Transit(No.2021GY01)the National Natural Science Foundation of China(No.41630641)。
文摘With the utilization of underground space,backward erosion piping(BEP)has been observed in many underground structures(e.g.,shield tunnels)founded on sandy aquifers.However,due to invisibility,the geometry of the eroded pipe and its spatial evolution with time during the piping process was still not clear.In this study,we developed a Hele-Shaw cell to visualize the dynamic progression of BEP.With imaging process technology,we obtained a typical process of BEP(the erosion process can be divided into a piping progression phase and a piping stabilization phase),quantitatively characterized the formation of erosion pipes,and compared the patterns of erosion(e.g.,the erosion area A and the maximum erosion radius R)that spontaneously develop under different fluxes of water.The most interesting finding is that the sand grains in a thicker Hele-Shaw model are easier to dislodge,which is possibly due to the granular system in a thicker model having more degrees of freedom,reducing the stability of the sand grains.
基金supported by the National Natural Science Foundation of China under Grant Nos.62076117 and 62166026the Jiangxi Provincial Key Laboratory of Virtual Reality under Grant No.2024SSY03151.
文摘Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dynamic sign language requires identifying keyframes that best represent the signs, and missing these keyframes reduces accuracy. Secondly, some methods do not focus enough on hand regions, which are small within the overall frame, leading to information loss. To address these challenges, we propose a novel Video Transformer Attention-based Network (VTAN) for dynamic sign language recognition. Our approach prioritizes informative frames and hand regions effectively. To tackle the first issue, we designed a keyframe extraction module enhanced by a convolutional autoencoder, which focuses on selecting information-rich frames and eliminating redundant ones from the video sequences. For the second issue, we developed a soft attention-based transformer module that emphasizes extracting features from hand regions, ensuring that the network pays more attention to hand information within sequences. This dual-focus approach improves effective dynamic sign language recognition by addressing the key challenges of identifying critical frames and emphasizing hand regions. Experimental results on two public benchmark datasets demonstrate the effectiveness of our network, outperforming most of the typical methods in sign language recognition tasks.
