●AIM:To determine the teaching effects of a real-time three dimensional(3D)visualization system in the operating room for early-stage phacoemulsification training.●METHODS:A total of 10 ophthalmology residents of th...●AIM:To determine the teaching effects of a real-time three dimensional(3D)visualization system in the operating room for early-stage phacoemulsification training.●METHODS:A total of 10 ophthalmology residents of the first-year postgraduate were included.All the residents were novices to cataract surgery.Real-time cataract surgical observations were performed using a custom-built 3D visualization system.The training lasted 4wk(32h)in all.A modified International Council of Ophthalmology’s Ophthalmology Surgical Competency Assessment Rubric(ICO-OSCAR)containing 4 specific steps of cataract surgery was applied.The self-assessment(self)and expert-assessment(expert)were performed through the microsurgical attempts in the wet lab for each participant.●RESULTS:Compared with pre-training assessments(self 3.2±0.8,expert 2.5±0.6),the overall mean scores of posttraining(self 5.2±0.4,expert 4.7±0.6)were significantly improved after real-time observation training of 3D visualization system(P<0.05).Scores of 4 surgical items were significantly improved both self and expert assessment after training(P<0.05).●CONCLUSION:The 3D observation training provides novice ophthalmic residents with a better understanding of intraocular microsurgical techniques.It is a useful tool to improve teaching efficiency of surgical education.展开更多
BACKGROUND When exposed to high-altitude environments,the cardiovascular system undergoes various changes,the performance and mechanisms of which remain controversial.AIM To summarize the latest research advancements ...BACKGROUND When exposed to high-altitude environments,the cardiovascular system undergoes various changes,the performance and mechanisms of which remain controversial.AIM To summarize the latest research advancements and hot research points in the cardiovascular system at high altitude by conducting a bibliometric and visualization analysis.METHODS The literature was systematically retrieved and filtered using the Web of Science Core Collection of Science Citation Index Expanded.A visualization analysis of the identified publications was conducted employing CiteSpace and VOSviewer.RESULTS A total of 1674 publications were included in the study,with an observed annual increase in the number of publications spanning from 1990 to 2022.The United States of America emerged as the predominant contributor,while Universidad Peruana Cayetano Heredia stood out as the institution with the highest publication output.Notably,Jean-Paul Richalet demonstrated the highest productivity among researchers focusing on the cardiovascular system at high altitude.Furthermore,Peter Bärtsch emerged as the author with the highest number of cited articles.Keyword analysis identified hypoxia,exercise,acclimatization,acute and chronic mountain sickness,pulmonary hypertension,metabolism,and echocardiography as the primary research hot research points and emerging directions in the study of the cardiovascular system at high altitude.CONCLUSION Over the past 32 years,research on the cardiovascular system in high-altitude regions has been steadily increasing.Future research in this field may focus on areas such as hypoxia adaptation,metabolism,and cardiopulmonary exercise.Strengthening interdisciplinary and multi-team collaborations will facilitate further exploration of the pathophysiological mechanisms underlying cardiovascular changes in high-altitude environments and provide a theoretical basis for standardized disease diagnosis and treatment.展开更多
Background A medical content-based image retrieval(CBIR)system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image.CBIR is widely used in evidence-based di...Background A medical content-based image retrieval(CBIR)system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image.CBIR is widely used in evidence-based diagnosis,teaching,and research.Although the retrieval accuracy has largely improved,there has been limited development toward visualizing important image features that indicate the similarity of retrieved images.Despite the prevalence of 3D volumetric data in medical imaging such as computed tomography(CT),current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images.Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information,including the size,shape,and spatial relations of multiple structures.This process is time-consuming and reliant on users'experience.Methods In this study,we proposed an importance-aware 3D volume visualization method.The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process.We then integrated the proposed visualization into a CBIR system,thereby complementing the 2D cross-sectional views for relevance feedback and further analyses.Results Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography(PETCT)images of a non-small cell lung cancer dataset.展开更多
Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for rese...Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.展开更多
The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor l...The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.展开更多
Digital technology has driven the innovation of architectural design methods and tools,applying digital techniques to allow greater possibilities for more innovative and scientific design of public building spaces.Thi...Digital technology has driven the innovation of architectural design methods and tools,applying digital techniques to allow greater possibilities for more innovative and scientific design of public building spaces.This article first analyzes the characteristics of digital visualization and its advantages in the design of urban public building spaces,including aspects such as visualizing three-dimensional expression,rational analysis of building space,Virtual Reality Experience,and integration of design and construction processes.Subsequently,by introducing digital design methods such as parametric design,algorithmic generation,nonlinear design,and artificial intelligence-assisted design,it explores the methods and implementation approaches of digital visualization in the design of public building spaces.The aim is to offer insights and references for the deeper integration of digital technology into architectural design practices.展开更多
Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however...Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.展开更多
BACKGROUND Transarterial chemoembolization(TACE)combined with microwave ablation(MWA)is an effective treatment strategy for patients with advanced gastric cancer and liver metastasis.However,it may cause severe postop...BACKGROUND Transarterial chemoembolization(TACE)combined with microwave ablation(MWA)is an effective treatment strategy for patients with advanced gastric cancer and liver metastasis.However,it may cause severe postoperative pain and inflammatory responses.The paravertebral block(PVB)is a regional anesthetic technique that provides analgesia to the thoracic and abdominal regions.AIM To evaluate the effect of PVB on postoperative analgesia and inflammatory response in patients undergoing TACE combined with MWA for advanced gastric cancer and liver metastasis.METHODS Sixty patients were randomly divided into PVB and control groups.The PVB group received ultrasound-guided PVB with 0.375%ropivacaine preoperatively,whereas the control group received intravenous analgesia with sufentanil.The primary outcome was the visual analog scale(VAS)score for pain at 6 h,12 h,24 h,and 48 h after the procedure.Secondary outcomes were the dose of sufentanil used,incidence of adverse events,and levels of inflammatory markers(white blood cell count,neutrophil percentage,C-reactive protein,and procalcitonin)before and after the procedure.RESULTS The PVB group had significantly lower VAS scores at 6 h,12 h,24 h,and 48 h after the procedure compared with the control group(P<0.05).The PVB group also had a significantly lower consumption of sufentanil and a lower incidence of nausea,vomiting,and respiratory depression than did the control group(P<0.05).Compared with the control group,the PVB group had significantly lower levels of inflammatory markers 24 h and 48 h after the procedure(P<0.05).CONCLUSION PVB can effectively reduce postoperative pain and inflammatory responses and improve postoperative comfort and recovery in patients with advanced gastric cancer and liver metastasis treated with TACE combined with MWA.展开更多
The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research.The safety criteria for medical imaging are highly stringent,and models are required for an explanation...The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research.The safety criteria for medical imaging are highly stringent,and models are required for an explanation.However,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs.Thus,the interpretability of CNNs has come into the spotlight.Since medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning method.Unfortunately,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these models.In this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac MRI.Our enhanced GPU training system implemented interpretable global average pooling for graphics using deep learning.The deep learning tasks were simplified.Simplification included data management,neural network architecture,and training.Our system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment model.Our results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning architectures.This model is lightweight and more convenient to deploy on mobile devices than transfer learning models.展开更多
This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods.To reduce the volume of big data and minimize model training time(Tt)while ...This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods.To reduce the volume of big data and minimize model training time(Tt)while maintaining data quality.We contributed to meeting the challenges of big data visualization using the embedded method based“Select from model(SFM)”method by using“Random forest Importance algorithm(RFI)”and comparing it with the filter method by using“Select percentile(SP)”method based chi square“Chi2”tool for selecting the most important features,which are then fed into a classification process using the logistic regression(LR)algorithm and the k-nearest neighbor(KNN)algorithm.Thus,the classification accuracy(AC)performance of LRis also compared to theKNN approach in python on eight data sets to see which method produces the best rating when feature selection methods are applied.Consequently,the study concluded that the feature selection methods have a significant impact on the analysis and visualization of the data after removing the repetitive data and the data that do not affect the goal.After making several comparisons,the study suggests(SFMLR)using SFM based on RFI algorithm for feature selection,with LR algorithm for data classify.The proposal proved its efficacy by comparing its results with recent literature.展开更多
Based on the underground powerhouse of Shuangjiangkou hydropower station,Octree theory is adopted to define the indices of the microseismic(MS)spatial aggregation degree and the deviation values of MS count and energy...Based on the underground powerhouse of Shuangjiangkou hydropower station,Octree theory is adopted to define the indices of the microseismic(MS)spatial aggregation degree and the deviation values of MS count and energy.The relationship between the MS multiple parameters and surrounding rock mass instability is established from three aspects:time,space,and strength.Supplemented by the center frequency of the signal evolution characteristics,A fuzzy comprehensive evaluation model and the evolution trend of the MS event center frequency are constructed to quantitatively describe the early warning state of the surrounding rock mass instability.The results show that the multilevel tree structure and voxels generated based on the Octree theory fit relatively well with the set of MS points in threedimensional space.The fuzzy comprehensive evaluation model based on MS spatial aggregation and MS count and energy deviation values enables three-dimensional visualization of the potential damage area and damage extent of the surrounding rock mass.The warning time and potential damage zone quantified are highly consistent with the characteristics of MS precursors,with wide recognition and field investigation results,which fully validate the rationality and applicability of the proposed method.These findings can provide references for the early warning of surrounding rock mass instability in similar underground engineering.展开更多
This study proposes a detailed concept of how landscape changes can be transferred and communicated in a 3D environment using the storytelling approach. In 2018, Mocnik and Fairbairn argued that maps are good for repr...This study proposes a detailed concept of how landscape changes can be transferred and communicated in a 3D environment using the storytelling approach. In 2018, Mocnik and Fairbairn argued that maps are good for representing a geographic space but texts have a better benefit than maps for telling a story [1]. A framework is presented on how a landscape change can be retrieved out of textual descriptions. A prototype of a 3D model with a projection on top of it was developed. The case study tells the story of the landscape change in the city of Pristina. The key element of the visualization is a timeline. Several media like cadastral maps, orthophotos, texts, graphics, and background sound are used and combined in an animated light show and the visualization was evaluated within a user study.展开更多
Essentially, CesiumJS—which can be accessed through the link, http://cesiumjs.org, is an open-source JavaScript library for creating virtual globe environment in performance effective, high quality of rendering, prec...Essentially, CesiumJS—which can be accessed through the link, http://cesiumjs.org, is an open-source JavaScript library for creating virtual globe environment in performance effective, high quality of rendering, precision, and user friendly. It is a wonderful tool for 3D-themed visualizations of earth. CesiumJS has a number of data sources, but none of them supports vector tile format. This article explains how to visualize Mapbox vector tiles in a CesiumJS virtual globe environment. CartoDB/BigQuery hosts vector tiles, and a process for producing vector tiles from massive vector data using the BigQuery tiler of CartoDB has been provided.展开更多
The Corona Virus Disease 2019(COVID-19)effect has made telecommuting and remote learning the norm.The growing number of Internet-connected devices provides cyber attackers with more attack vectors.The development of m...The Corona Virus Disease 2019(COVID-19)effect has made telecommuting and remote learning the norm.The growing number of Internet-connected devices provides cyber attackers with more attack vectors.The development of malware by criminals also incorporates a number of sophisticated obfuscation techniques,making it difficult to classify and detect malware using conventional approaches.Therefore,this paper proposes a novel visualization-based malware classification system using transfer and ensemble learning(VMCTE).VMCTE has a strong anti-interference ability.Even if malware uses obfuscation,fuzzing,encryption,and other techniques to evade detection,it can be accurately classified into its corresponding malware family.Unlike traditional dynamic and static analysis techniques,VMCTE does not require either reverse engineering or the aid of domain expert knowledge.The proposed classification system combines three strong deep convolutional neural networks(ResNet50,MobilenetV1,and MobilenetV2)as feature extractors,lessens the dimension of the extracted features using principal component analysis,and employs a support vector machine to establish the classification model.The semantic representations of malware images can be extracted using various convolutional neural network(CNN)architectures,obtaining higher-quality features than traditional methods.Integrating fine-tuned and non-fine-tuned classification models based on transfer learning can greatly enhance the capacity to classify various families ofmalware.The experimental findings on the Malimg dataset demonstrate that VMCTE can attain 99.64%,99.64%,99.66%,and 99.64%accuracy,F1-score,precision,and recall,respectively.展开更多
With the development and innovation of digital information technology, digital visualization plays an increasingly important role in the design of urban public building spaces. This paper explores the application of d...With the development and innovation of digital information technology, digital visualization plays an increasingly important role in the design of urban public building spaces. This paper explores the application of digital visualization technology in the design of urban public building spaces and looks ahead to future trends. Firstly, it analyzes the challenges in the design of urban public building spaces, including extensive professional involvement, complex functional layout requirements, rational emergency evacuation routes, multidimensional analysis of architectural spatial environments, and appropriate selection of decorative materials. Next, it introduces the applications of digital visualization technology in showcasing visual design and expression, optimizing spatial functional layouts, enhancing the rationality of evacuation routes, analyzing dynamic environmental impacts and energy consumption, and improving the effectiveness of material selection in the design of urban public building spaces. Lastly, it discusses the prospects of extended reality (XR) technology, interactive design using data platforms, and AI technology in the design of public building spaces. It is hoped that this paper provides inspiration and reference for the deeper application of digital information technology in the field of architecture. .展开更多
In this paper,we propose a novel approach to visualizing global geographical information:a panoramic sphere in an immersive environment.The whole geographical surface can be observed through the rotating of heads as t...In this paper,we propose a novel approach to visualizing global geographical information:a panoramic sphere in an immersive environment.The whole geographical surface can be observed through the rotating of heads as the viewpoint of the panoramic sphere is inside the sphere.We compared three approaches to visualizing the earth for rendering the geographical information in a virtual reality environment.On the tasks of terrestrial and marine geographical information,we compare the visualization effects on a)a globe,b)a flat map and c)a panoramic sphere.Terrestrial geographical information tasks include the area comparison and direction determination.Marine geographical information tasks contain the visualization of sea surface temperature and sea surface currents.For terrestrial geographical information tasks,the experimental results show that the panoramic sphere outperforms the globe and the flat map,with a higher average accuracy and a shorter time.On marine geographical information task,the panoramic sphere visualization is also superior to the flat map and the globe in an immersive environment for the sea surface temperature data and the sea surface current fields.In all three visualization experiments,the panoramic sphere is most preferred by the participants,particularly for global,transcontinental and transoceanic needs.展开更多
In feature-based visual localization for small-scale scenes,local descriptors are used to estimate the camera pose of a query image.For large and ambiguous environments,learning-based hierarchical networks that employ...In feature-based visual localization for small-scale scenes,local descriptors are used to estimate the camera pose of a query image.For large and ambiguous environments,learning-based hierarchical networks that employ local as well as global descriptors to reduce the search space of database images into a smaller set of reference views have been introduced.However,since global descriptors are generated using visual features,reference images with some of these features may be erroneously selected.In order to address this limitation,this paper proposes two clustering methods based on how often features appear as well as their covisibility.For both approaches,the scene is represented by voxels whose size and number are computed according to the size of the scene and the number of available 3Dpoints.In the first approach,a voxel-based histogram representing highly reoccurring scene regions is generated from reference images.A meanshift is then employed to group the most highly reoccurring voxels into place clusters based on their spatial proximity.In the second approach,a graph representing the covisibility-based relationship of voxels is built.Local matching is performed within the reference image clusters,and a perspective-n-point is employed to estimate the camera pose.The experimental results showed that camera pose estimation using the proposed approaches was more accurate than that of previous methods.展开更多
Objective:This study aimed to have a good knowledge of heart failure complicated with malnutrition research status,hotspots,and forecast research trends using bibliometric analysis and visualization of studies of hear...Objective:This study aimed to have a good knowledge of heart failure complicated with malnutrition research status,hotspots,and forecast research trends using bibliometric analysis and visualization of studies of hear t failure complicated with malnutrition in the past 10 years.Methods:Ar ticles related to malnutrition-induced hear t failure were obtained from the Web of Science Core Collection(Wo SCC)series from January 2012 to January 2022.Two software(Java Cite Space 5.8R3 and Microsoft Excel 2019)were used to perform bibliometric and knowledge-map analysis,including analyzing the annual publication and trend of ar ticles,authors and co-cited authors,institutions/countries,co-cited journals,co-occurrence,clusters,and burst of keywords,co-cited references,and reference burst.Results:A total of 672 articles appeared in 180 scientific journals by 4249 authors from 1453 institutions in 71 countries.Over the past 10 years,the number of related studies released has shown an upward trend,and the range of increases has been larger over the past 5 years.Takatoshi Kasai released the most papers,while Anker S D had the most co-quotes.Simultaneously,active cooperation existed in complicated hear t failure with researchers on malnutrition.Circulation,Journal of The American College of Cardiology,and Clinical Nutrition were the top 3 co-cited journals.The USA,Japan,and China were the most productive countries,while Johns Hopkins University was the most active institution.In addition,the reference published by Ponikowski P had the most co-citations,relating to as a knowledge base.The latest hotspots of hear t failure complicated with malnutrition mainly included research on:(1)assessment of nutrition status,the impact/risk factor of nutrition,and the prevalence and outcome of the patients;(2)hear t failure complicated with malnutrition when complicated with other diseases,for example,chronic kidney disease and diabetes mellitus;(3)the nutrition of child patients with heart failure complicated with malnutrition;and(4)the treatment of heart failure complicated with malnutrition,including assist devices,organ transplantation,and drug therapy.While the frontier topics included research on:(1)the index of hear t failure complicated with malnutrition;(2)the effect of sarcopenia to hear t failure complicated with malnutrition patients and the use of geriatric nutritional risk index to assess the nutritional statue;and(3)find the nutritional risk index.Conclusions:This study conducted a full overview of the present research situation,hotspots,and frontiers by using bibliometric and visualization analysis methods in hear t failure complicated with malnutrition,which would provide a helpful reference for future research.展开更多
Three-dimensional(3D) scanning technology has undergone remarkable developments in recent years.Data acquired by 3D scanning have the form of 3D point clouds.The 3D scanned point clouds have data sizes that can be con...Three-dimensional(3D) scanning technology has undergone remarkable developments in recent years.Data acquired by 3D scanning have the form of 3D point clouds.The 3D scanned point clouds have data sizes that can be considered big data.They also contain measurement noise inherent in measurement data.These properties of 3D scanned point clouds make many traditional CG/visualization techniques difficult.This paper reviewed our recent achievements in developing varieties of high-quality visualizations suitable for the visual analysis of 3D scanned point clouds.We demonstrated the effectiveness of the method by applying the visualizations to various cultural heritage objects.The main visualization targets used in this paper are the floats in the Gion Festival in Kyoto(the float parade is on the UNESCO Intangible Cultural Heritage List) and Borobudur Temple in Indonesia(a UNESCO World Heritage Site).展开更多
Background With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system ...Background With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system load and resource consumption in the processing of these huge models, the huge 3D model lightweighting methods for online Web3D visualization are reviewed in this paper. Methods By observing the geometry redundancy introduced by man-made operations in the modeling procedure, several categories of light-weighting related work that aim at reducing the amount of data and resource consumption are elaborated for Web3D visualization. Results By comparing perspectives, the characteristics of each method are summarized, and among the reviewed methods, the geometric redundancy removal that achieves the lightweight goal by detecting and removing the repeated components is an appropriate method for current online Web3D visualization. Meanwhile, the learning algorithm, still in improvement period at present, is our expected future research topic. Conclusions Various aspects should be considered in an efficient lightweight method for online Web3D visualization, such as characteristics of original data, combination or extension of existing methods, scheduling strategy, cache man-agement, and rendering mechanism. Meanwhile, innovation methods, particularly the learning algorithm, are worth exploring.展开更多
基金Supported by research grants from the National Key Research and Development Program of China(No.2020YFE0204400)the National Natural Science Foundation of China(No.82271042+1 种基金No.52203191)the Zhejiang Province Key Research and Development Program(No.2023C03090).
文摘●AIM:To determine the teaching effects of a real-time three dimensional(3D)visualization system in the operating room for early-stage phacoemulsification training.●METHODS:A total of 10 ophthalmology residents of the first-year postgraduate were included.All the residents were novices to cataract surgery.Real-time cataract surgical observations were performed using a custom-built 3D visualization system.The training lasted 4wk(32h)in all.A modified International Council of Ophthalmology’s Ophthalmology Surgical Competency Assessment Rubric(ICO-OSCAR)containing 4 specific steps of cataract surgery was applied.The self-assessment(self)and expert-assessment(expert)were performed through the microsurgical attempts in the wet lab for each participant.●RESULTS:Compared with pre-training assessments(self 3.2±0.8,expert 2.5±0.6),the overall mean scores of posttraining(self 5.2±0.4,expert 4.7±0.6)were significantly improved after real-time observation training of 3D visualization system(P<0.05).Scores of 4 surgical items were significantly improved both self and expert assessment after training(P<0.05).●CONCLUSION:The 3D observation training provides novice ophthalmic residents with a better understanding of intraocular microsurgical techniques.It is a useful tool to improve teaching efficiency of surgical education.
基金Supported by Natural Science Foundation of Sichuan Province,No.2022NSFSC1295the 2021 Annal Project of the General Hospital of Western Theater Command,No.2021-XZYG-B31.
文摘BACKGROUND When exposed to high-altitude environments,the cardiovascular system undergoes various changes,the performance and mechanisms of which remain controversial.AIM To summarize the latest research advancements and hot research points in the cardiovascular system at high altitude by conducting a bibliometric and visualization analysis.METHODS The literature was systematically retrieved and filtered using the Web of Science Core Collection of Science Citation Index Expanded.A visualization analysis of the identified publications was conducted employing CiteSpace and VOSviewer.RESULTS A total of 1674 publications were included in the study,with an observed annual increase in the number of publications spanning from 1990 to 2022.The United States of America emerged as the predominant contributor,while Universidad Peruana Cayetano Heredia stood out as the institution with the highest publication output.Notably,Jean-Paul Richalet demonstrated the highest productivity among researchers focusing on the cardiovascular system at high altitude.Furthermore,Peter Bärtsch emerged as the author with the highest number of cited articles.Keyword analysis identified hypoxia,exercise,acclimatization,acute and chronic mountain sickness,pulmonary hypertension,metabolism,and echocardiography as the primary research hot research points and emerging directions in the study of the cardiovascular system at high altitude.CONCLUSION Over the past 32 years,research on the cardiovascular system in high-altitude regions has been steadily increasing.Future research in this field may focus on areas such as hypoxia adaptation,metabolism,and cardiopulmonary exercise.Strengthening interdisciplinary and multi-team collaborations will facilitate further exploration of the pathophysiological mechanisms underlying cardiovascular changes in high-altitude environments and provide a theoretical basis for standardized disease diagnosis and treatment.
文摘Background A medical content-based image retrieval(CBIR)system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image.CBIR is widely used in evidence-based diagnosis,teaching,and research.Although the retrieval accuracy has largely improved,there has been limited development toward visualizing important image features that indicate the similarity of retrieved images.Despite the prevalence of 3D volumetric data in medical imaging such as computed tomography(CT),current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images.Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information,including the size,shape,and spatial relations of multiple structures.This process is time-consuming and reliant on users'experience.Methods In this study,we proposed an importance-aware 3D volume visualization method.The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process.We then integrated the proposed visualization into a CBIR system,thereby complementing the 2D cross-sectional views for relevance feedback and further analyses.Results Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography(PETCT)images of a non-small cell lung cancer dataset.
文摘Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.
文摘The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.
文摘Digital technology has driven the innovation of architectural design methods and tools,applying digital techniques to allow greater possibilities for more innovative and scientific design of public building spaces.This article first analyzes the characteristics of digital visualization and its advantages in the design of urban public building spaces,including aspects such as visualizing three-dimensional expression,rational analysis of building space,Virtual Reality Experience,and integration of design and construction processes.Subsequently,by introducing digital design methods such as parametric design,algorithmic generation,nonlinear design,and artificial intelligence-assisted design,it explores the methods and implementation approaches of digital visualization in the design of public building spaces.The aim is to offer insights and references for the deeper integration of digital technology into architectural design practices.
基金supported by a Korea Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure,and Transport(Grant 22CTAP-C163951-02).
文摘Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.
文摘BACKGROUND Transarterial chemoembolization(TACE)combined with microwave ablation(MWA)is an effective treatment strategy for patients with advanced gastric cancer and liver metastasis.However,it may cause severe postoperative pain and inflammatory responses.The paravertebral block(PVB)is a regional anesthetic technique that provides analgesia to the thoracic and abdominal regions.AIM To evaluate the effect of PVB on postoperative analgesia and inflammatory response in patients undergoing TACE combined with MWA for advanced gastric cancer and liver metastasis.METHODS Sixty patients were randomly divided into PVB and control groups.The PVB group received ultrasound-guided PVB with 0.375%ropivacaine preoperatively,whereas the control group received intravenous analgesia with sufentanil.The primary outcome was the visual analog scale(VAS)score for pain at 6 h,12 h,24 h,and 48 h after the procedure.Secondary outcomes were the dose of sufentanil used,incidence of adverse events,and levels of inflammatory markers(white blood cell count,neutrophil percentage,C-reactive protein,and procalcitonin)before and after the procedure.RESULTS The PVB group had significantly lower VAS scores at 6 h,12 h,24 h,and 48 h after the procedure compared with the control group(P<0.05).The PVB group also had a significantly lower consumption of sufentanil and a lower incidence of nausea,vomiting,and respiratory depression than did the control group(P<0.05).Compared with the control group,the PVB group had significantly lower levels of inflammatory markers 24 h and 48 h after the procedure(P<0.05).CONCLUSION PVB can effectively reduce postoperative pain and inflammatory responses and improve postoperative comfort and recovery in patients with advanced gastric cancer and liver metastasis treated with TACE combined with MWA.
基金The National Natural Science Foundation of China (62176048)provided funding for this research.
文摘The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research.The safety criteria for medical imaging are highly stringent,and models are required for an explanation.However,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs.Thus,the interpretability of CNNs has come into the spotlight.Since medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning method.Unfortunately,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these models.In this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac MRI.Our enhanced GPU training system implemented interpretable global average pooling for graphics using deep learning.The deep learning tasks were simplified.Simplification included data management,neural network architecture,and training.Our system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment model.Our results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning architectures.This model is lightweight and more convenient to deploy on mobile devices than transfer learning models.
文摘This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods.To reduce the volume of big data and minimize model training time(Tt)while maintaining data quality.We contributed to meeting the challenges of big data visualization using the embedded method based“Select from model(SFM)”method by using“Random forest Importance algorithm(RFI)”and comparing it with the filter method by using“Select percentile(SP)”method based chi square“Chi2”tool for selecting the most important features,which are then fed into a classification process using the logistic regression(LR)algorithm and the k-nearest neighbor(KNN)algorithm.Thus,the classification accuracy(AC)performance of LRis also compared to theKNN approach in python on eight data sets to see which method produces the best rating when feature selection methods are applied.Consequently,the study concluded that the feature selection methods have a significant impact on the analysis and visualization of the data after removing the repetitive data and the data that do not affect the goal.After making several comparisons,the study suggests(SFMLR)using SFM based on RFI algorithm for feature selection,with LR algorithm for data classify.The proposal proved its efficacy by comparing its results with recent literature.
基金the Science Foundation for Distinguished Young Scholars of Sichuan Province(No.2020JDJQ0011)the National Natural Science Foundation of China(Nos.42177143,51809221,and 52274145)the State Key Laboratory for GeoMechanics and Deep Underground Engineering,China University of Mining&Technology(No.SKLGDUEK2013)。
文摘Based on the underground powerhouse of Shuangjiangkou hydropower station,Octree theory is adopted to define the indices of the microseismic(MS)spatial aggregation degree and the deviation values of MS count and energy.The relationship between the MS multiple parameters and surrounding rock mass instability is established from three aspects:time,space,and strength.Supplemented by the center frequency of the signal evolution characteristics,A fuzzy comprehensive evaluation model and the evolution trend of the MS event center frequency are constructed to quantitatively describe the early warning state of the surrounding rock mass instability.The results show that the multilevel tree structure and voxels generated based on the Octree theory fit relatively well with the set of MS points in threedimensional space.The fuzzy comprehensive evaluation model based on MS spatial aggregation and MS count and energy deviation values enables three-dimensional visualization of the potential damage area and damage extent of the surrounding rock mass.The warning time and potential damage zone quantified are highly consistent with the characteristics of MS precursors,with wide recognition and field investigation results,which fully validate the rationality and applicability of the proposed method.These findings can provide references for the early warning of surrounding rock mass instability in similar underground engineering.
文摘This study proposes a detailed concept of how landscape changes can be transferred and communicated in a 3D environment using the storytelling approach. In 2018, Mocnik and Fairbairn argued that maps are good for representing a geographic space but texts have a better benefit than maps for telling a story [1]. A framework is presented on how a landscape change can be retrieved out of textual descriptions. A prototype of a 3D model with a projection on top of it was developed. The case study tells the story of the landscape change in the city of Pristina. The key element of the visualization is a timeline. Several media like cadastral maps, orthophotos, texts, graphics, and background sound are used and combined in an animated light show and the visualization was evaluated within a user study.
文摘Essentially, CesiumJS—which can be accessed through the link, http://cesiumjs.org, is an open-source JavaScript library for creating virtual globe environment in performance effective, high quality of rendering, precision, and user friendly. It is a wonderful tool for 3D-themed visualizations of earth. CesiumJS has a number of data sources, but none of them supports vector tile format. This article explains how to visualize Mapbox vector tiles in a CesiumJS virtual globe environment. CartoDB/BigQuery hosts vector tiles, and a process for producing vector tiles from massive vector data using the BigQuery tiler of CartoDB has been provided.
基金This work is supported,in part,by the National Natural Science Foundation of China Grant No.62102190 and 62272236in part,by the Natural Science Foundation of Jiangsu Province under Grant No.BK20201136 and BK20191401.
文摘The Corona Virus Disease 2019(COVID-19)effect has made telecommuting and remote learning the norm.The growing number of Internet-connected devices provides cyber attackers with more attack vectors.The development of malware by criminals also incorporates a number of sophisticated obfuscation techniques,making it difficult to classify and detect malware using conventional approaches.Therefore,this paper proposes a novel visualization-based malware classification system using transfer and ensemble learning(VMCTE).VMCTE has a strong anti-interference ability.Even if malware uses obfuscation,fuzzing,encryption,and other techniques to evade detection,it can be accurately classified into its corresponding malware family.Unlike traditional dynamic and static analysis techniques,VMCTE does not require either reverse engineering or the aid of domain expert knowledge.The proposed classification system combines three strong deep convolutional neural networks(ResNet50,MobilenetV1,and MobilenetV2)as feature extractors,lessens the dimension of the extracted features using principal component analysis,and employs a support vector machine to establish the classification model.The semantic representations of malware images can be extracted using various convolutional neural network(CNN)architectures,obtaining higher-quality features than traditional methods.Integrating fine-tuned and non-fine-tuned classification models based on transfer learning can greatly enhance the capacity to classify various families ofmalware.The experimental findings on the Malimg dataset demonstrate that VMCTE can attain 99.64%,99.64%,99.66%,and 99.64%accuracy,F1-score,precision,and recall,respectively.
文摘With the development and innovation of digital information technology, digital visualization plays an increasingly important role in the design of urban public building spaces. This paper explores the application of digital visualization technology in the design of urban public building spaces and looks ahead to future trends. Firstly, it analyzes the challenges in the design of urban public building spaces, including extensive professional involvement, complex functional layout requirements, rational emergency evacuation routes, multidimensional analysis of architectural spatial environments, and appropriate selection of decorative materials. Next, it introduces the applications of digital visualization technology in showcasing visual design and expression, optimizing spatial functional layouts, enhancing the rationality of evacuation routes, analyzing dynamic environmental impacts and energy consumption, and improving the effectiveness of material selection in the design of urban public building spaces. Lastly, it discusses the prospects of extended reality (XR) technology, interactive design using data platforms, and AI technology in the design of public building spaces. It is hoped that this paper provides inspiration and reference for the deeper application of digital information technology in the field of architecture. .
基金This research was funded by the Science and Technology Innovation Project for Laoshan Laboratory(No.LSKJ202204303)the National Natural Science Foundation of China(No.42030406)+1 种基金the Fundamental Research Funds for the Central Universities(No.202261006)the ESANRSCC Scientific Cooperation Project on Earth Observation Science and Applications:Dragon 5(No.58393).
文摘In this paper,we propose a novel approach to visualizing global geographical information:a panoramic sphere in an immersive environment.The whole geographical surface can be observed through the rotating of heads as the viewpoint of the panoramic sphere is inside the sphere.We compared three approaches to visualizing the earth for rendering the geographical information in a virtual reality environment.On the tasks of terrestrial and marine geographical information,we compare the visualization effects on a)a globe,b)a flat map and c)a panoramic sphere.Terrestrial geographical information tasks include the area comparison and direction determination.Marine geographical information tasks contain the visualization of sea surface temperature and sea surface currents.For terrestrial geographical information tasks,the experimental results show that the panoramic sphere outperforms the globe and the flat map,with a higher average accuracy and a shorter time.On marine geographical information task,the panoramic sphere visualization is also superior to the flat map and the globe in an immersive environment for the sea surface temperature data and the sea surface current fields.In all three visualization experiments,the panoramic sphere is most preferred by the participants,particularly for global,transcontinental and transoceanic needs.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2018R1D1A1B07049932).
文摘In feature-based visual localization for small-scale scenes,local descriptors are used to estimate the camera pose of a query image.For large and ambiguous environments,learning-based hierarchical networks that employ local as well as global descriptors to reduce the search space of database images into a smaller set of reference views have been introduced.However,since global descriptors are generated using visual features,reference images with some of these features may be erroneously selected.In order to address this limitation,this paper proposes two clustering methods based on how often features appear as well as their covisibility.For both approaches,the scene is represented by voxels whose size and number are computed according to the size of the scene and the number of available 3Dpoints.In the first approach,a voxel-based histogram representing highly reoccurring scene regions is generated from reference images.A meanshift is then employed to group the most highly reoccurring voxels into place clusters based on their spatial proximity.In the second approach,a graph representing the covisibility-based relationship of voxels is built.Local matching is performed within the reference image clusters,and a perspective-n-point is employed to estimate the camera pose.The experimental results showed that camera pose estimation using the proposed approaches was more accurate than that of previous methods.
文摘Objective:This study aimed to have a good knowledge of heart failure complicated with malnutrition research status,hotspots,and forecast research trends using bibliometric analysis and visualization of studies of hear t failure complicated with malnutrition in the past 10 years.Methods:Ar ticles related to malnutrition-induced hear t failure were obtained from the Web of Science Core Collection(Wo SCC)series from January 2012 to January 2022.Two software(Java Cite Space 5.8R3 and Microsoft Excel 2019)were used to perform bibliometric and knowledge-map analysis,including analyzing the annual publication and trend of ar ticles,authors and co-cited authors,institutions/countries,co-cited journals,co-occurrence,clusters,and burst of keywords,co-cited references,and reference burst.Results:A total of 672 articles appeared in 180 scientific journals by 4249 authors from 1453 institutions in 71 countries.Over the past 10 years,the number of related studies released has shown an upward trend,and the range of increases has been larger over the past 5 years.Takatoshi Kasai released the most papers,while Anker S D had the most co-quotes.Simultaneously,active cooperation existed in complicated hear t failure with researchers on malnutrition.Circulation,Journal of The American College of Cardiology,and Clinical Nutrition were the top 3 co-cited journals.The USA,Japan,and China were the most productive countries,while Johns Hopkins University was the most active institution.In addition,the reference published by Ponikowski P had the most co-citations,relating to as a knowledge base.The latest hotspots of hear t failure complicated with malnutrition mainly included research on:(1)assessment of nutrition status,the impact/risk factor of nutrition,and the prevalence and outcome of the patients;(2)hear t failure complicated with malnutrition when complicated with other diseases,for example,chronic kidney disease and diabetes mellitus;(3)the nutrition of child patients with heart failure complicated with malnutrition;and(4)the treatment of heart failure complicated with malnutrition,including assist devices,organ transplantation,and drug therapy.While the frontier topics included research on:(1)the index of hear t failure complicated with malnutrition;(2)the effect of sarcopenia to hear t failure complicated with malnutrition patients and the use of geriatric nutritional risk index to assess the nutritional statue;and(3)find the nutritional risk index.Conclusions:This study conducted a full overview of the present research situation,hotspots,and frontiers by using bibliometric and visualization analysis methods in hear t failure complicated with malnutrition,which would provide a helpful reference for future research.
文摘Three-dimensional(3D) scanning technology has undergone remarkable developments in recent years.Data acquired by 3D scanning have the form of 3D point clouds.The 3D scanned point clouds have data sizes that can be considered big data.They also contain measurement noise inherent in measurement data.These properties of 3D scanned point clouds make many traditional CG/visualization techniques difficult.This paper reviewed our recent achievements in developing varieties of high-quality visualizations suitable for the visual analysis of 3D scanned point clouds.We demonstrated the effectiveness of the method by applying the visualizations to various cultural heritage objects.The main visualization targets used in this paper are the floats in the Gion Festival in Kyoto(the float parade is on the UNESCO Intangible Cultural Heritage List) and Borobudur Temple in Indonesia(a UNESCO World Heritage Site).
文摘Background With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system load and resource consumption in the processing of these huge models, the huge 3D model lightweighting methods for online Web3D visualization are reviewed in this paper. Methods By observing the geometry redundancy introduced by man-made operations in the modeling procedure, several categories of light-weighting related work that aim at reducing the amount of data and resource consumption are elaborated for Web3D visualization. Results By comparing perspectives, the characteristics of each method are summarized, and among the reviewed methods, the geometric redundancy removal that achieves the lightweight goal by detecting and removing the repeated components is an appropriate method for current online Web3D visualization. Meanwhile, the learning algorithm, still in improvement period at present, is our expected future research topic. Conclusions Various aspects should be considered in an efficient lightweight method for online Web3D visualization, such as characteristics of original data, combination or extension of existing methods, scheduling strategy, cache man-agement, and rendering mechanism. Meanwhile, innovation methods, particularly the learning algorithm, are worth exploring.
