Survey of lightweighting methods of huge 3D models for online Web3D visualization

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.

Intelligent Deep Convolutional Neural Network Based Object DetectionModel for Visually Challenged People

Artificial Intelligence(AI)and Computer Vision(CV)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged people.Object detection includes a variety of challenges,for example,handlingmultiple class images,images that get augmented when captured by a camera and so on.The test images include all these variants as well.These detection models alert them about their surroundings when they want to walk independently.This study compares four CNN-based pre-trainedmodels:ResidualNetwork(ResNet-50),Inception v3,DenseConvolutional Network(DenseNet-121),and SqueezeNet,predominantly used in image recognition applications.Based on the analysis performed on these test images,the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and speed.To further improve the performance of the Inception v3 model,the thermal exchange optimization(TEO)algorithm is applied to tune the hyperparameters(number of epochs,batch size,and learning rate)showing the novelty of the work.Better accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer,hyperparameter optimizer,and factorization approach.Additionally,Inception V3 can handle images of different sizes.This makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things(IoT)-based devices.

Visual Modflow在和田县农业经济新区地下水资源评价中的应用

为全面了解地下水系统受各种激励因素影响后的水动力场和地下水量变化规律,以及为当地地下水资源开发与利用提供科学的指导意见,本次在和田县农业经济新区地下水资源评价中采用可视化标准软件Visnal Modflow实现地下水资源的模拟与预报,通过构建三维非稳定流数值模型,使用迭代方法求解,并根据设计开采量对地下水水位变动做出预测,再基于建模预测结果,结合经济新区地下水资源量确定合理的地下水开采量。

DATA MODEL AND VISUALIZATION OF 3D CITY LANDSCAPE BASED ON INTEGRATED DATABASES

Data acquisition and modeling are the two important, difficult and costful aspects in a Cybercity project. 2D-GIS is mature and can manage a lot of spatial data. Thus 3D-GIS should make the best of data and technology of 2D-GIS. Construction of a useful synthetic environment requires integration of multiple types of information like DEM, texture images and 3D representation of objects such as buildings. In this paper, the method for 3D city landscape data model and visualization based on integrated databases is presented. Since the data volume of raster are very huge, special strategies(for example, pyramid gridded method) must be adopted in order to manage raster data efficiently. Three different methods of data acquisition, the proper data structure and a simple modeling method are presented as well. At last, a pilot project of Shanghai Cybercity is illustrated.

3D visualization model and key techniques for digital mine

Digital mine is the inevitable outcome of the information processing, and is also a complicated system engineering. Firstly, for the 3D visualization application of the digital mine, the ground and underground integrative visualization framework model was proposed based on the mine entity database. So, the visualization problem was availably resolved, as well as the professional analytical ability was improved. Secondly, aiming at the irregularities, non-uniformity, dynamics of mine entities, mix modeling method based on the entity character was put forward, in which 3D expression of mine entities was realized. Lastly, the 3D visualization project for a copper mine was experimentally studied. Satisfactory results were acquired, and the rationality of visualization model and feasibility of 3D modeling were validated.

Modeling Tracer Flow Characteristics in Different Types of Pores: Visualization and Mathematical Modeling

Structure of porous media and fluid distribution in rocks can significantly affect the transport characteristics during the process of microscale tracer flow.To clarify the effect of micro heterogeneity on aqueous tracer transport,this paper demonstrates microscopic experiments at pore level and proposes an improved mathematical model for tracer transport.The visualization results show a faster tracer movement into movable water than it into bound water,and quicker occupancy in flowing pores than in storage pores caused by the difference of tracer velocity.Moreover,the proposed mathematical model includes the effects of bound water and flowing porosity by applying interstitial flow velocity expression.The new model also distinguishes flowing and storage pores,accounting for different tracer transport mechanisms(dispersion,diffusion and adsorption)in different types of pores.The resulting analytical solution better matches with tracer production data than the standard model.The residual sum of squares(RSS)from the new model is 0.0005,which is 100 times smaller than the RSS from the standard model.The sensitivity analysis indicates that the dispersion coefficient and flowing porosity shows a negative correlation with the tracer breakthrough time and the increasing slope,whereas the superficial velocity and bound water saturation show a positive correlation.

Physical modeling and visualization of soil liquefaction under high confining stress

The mechanisms of seismically-induced liquefaction of granular soils under high confining stresses are still not fully understood. Evaluation of these mechanisms is generally based on extrapolation of observed behavior at shallow depths.Three centrifuge model tests were conducted at RPI's experimental facility to investigate the effects of confining stresses on the dynamic response ora deep horizontal deposit of saturated sand. Liquefaction was observed at high confining stresses in each of the tests. A system identification procedure was used to estimate the associated shear strain and stress time histories.These histories revealed a response marked by shear strength degradation and dilative patterns. The recorded accelerations and pore pressures were employed to generate visual animations of the models. These visualizations revealed a liquefaction front traveling downward and leading to large shear strains and isolation of upper soil layers.

Vehicle Detection Based on Visual Saliency and Deep Sparse Convolution Hierarchical Model

Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

Geometrical Modeling by NURBS Surface and RCS Computing by Visualization for Complex Targets

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A Visualizing Software Developing Environment Based Object Model

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Visualized Analysis Model for Hadoop Business Data

With the social development, we are stepping into an information technology world. In such a world, our life is getting more and more diversified and rich because of e-business. E-business not only provides us convenience but also large amounts of business data. However, how shall we better store, manage and use these business data has become a major field being studied by e-business. With the rapid growth of data volume, the relational database system cannot meet the requirements of the current status. In this paper, focusing on the visualized analysis model of Hadoop business data, it analyzed the business data in terms of the visualized platform, database and analysis model etc. Depending on the analysis, offline-data analysis and data visualization for Hive database will be greatly improved, so that references and suggestions can be provided for the visualized analysis model of Hadoop business data.

Modelling Design of Color Graphics Books Using Visual Vocabulary Based on Children’s Color Language Preferences

Color language has important meaning for children’s picture books.Understanding children’s preferences in terms of color language will be helpful when designers model computerized picture books in order to adapt to children’s visual senses and effectively stimulate children’s interest in reading.In this study,we aimed to further explore the general characteristics of children’s preference for colors,color depth and color matching forms in picture books.For the study,256 children between the ages of three and six were selected and divided into four groups.According to the implementation needs of the children’s color preference test,these childrenwere asked to complete a series of tasks so that their choices in the tasks could subsequently be analyzed.The tasks included making the following choices:selecting their preferred colors from nine options(red,orange,yellow,green,cyan,blue,purple,black and white);selecting their preferred color brightness fromthree different color shades;and indicating their preferences in relation to complementary colors,matching colors and pure colors.The experimental results showed that children aged between three and six mainly favor red,blue and yellow,and colors with a high degree of brightness.As children grow older,boys tend to prefer blue,and girls tend to prefer pink,with red remaining the favorite color of both genders.As for complementary colors,matching colors and solid colors,three-to six-year-old children prefer solid colors.In conclusion,it seems that color language preferences in children between the ages of three and six are very similar,which could be of use when designing children’s picture books to match their visual vocabulary and psychological cognition.

Hierarchical Merging &Generalization Method of Three-Dimension City Model Group Based on the Theory of Spatial Visual Cognition

In order to simplify the three-dimensional building group model, this paper proposes a clustering generalization method based on visual cognitive theory. The method uses road elements to roughly divide scenes, and then uses spatial cognitive elements such as direction, area, height and their topological constraints to classify them precisely, so as to make them conform to the urban morphological characteristics. Delaunay triangulation network and boundary tracking synthesis algorithm are used to merge and summarize the models, and the models are stored hierarchically. The proposed algorithm should be verified experimentally with a typical urban complex model. The experimental results show that the efficiency of the method used in this paper is at least 20% higher than that of previous one, and with the growth of test data, the higher efficiency is improved. The classification results conform to human cognitive habits, and the generalization levels of different models can be relatively unified by adaptive control of each threshold in the clustering generalization process.

A ROBUST ADAPTIVE VIDEO ENCODER BASED ON HUMAN VISUAL MODEL

A Robust Adaptive Video Encoder (RAVE) based on human visual model is proposed. The encoder combines the best features of Fine Granularity Scalable (FGS) coding, framedropping coding, video redundancy coding, and human visual model. According to packet loss and available bandwidth of the network, the encoder adjust the output bit rate by jointly adapting quantization step-size instructed by human visual model, rate shaping, and periodically inserting key frame. The proposed encoder is implemented based on MPEG-4 encoder and is compared with the case of a conventional FGS algorithm. It is shown that RAVE is a very efficient robust video encoder that provides improved visual quality for the receiver and consumes equal or less network resource. Results are confirmed by subjective tests and simulation tests.

Experimental investigation on migration and retention mechanisms of elastic gel particles(EGPs)in pore-throats using multidimensional visualized models

Knowledge of migration and retention mechanisms of elastic gel particles(EGPs)in pore-throats is essential for the effective application of EGPs as a smart sweep improvement and profile control agent for enhanced oil recovery(EOR).The matching coefficient(defined as the ratio of particle size to pore-throat size)is used to investigate its influence on migration,retention and profile control performance of EGPs.A 1-D continuous pore-throat visualization model(PTVM),a 2-D heterogeneous PTVM and a 3-D heterogeneous core model were constructed and used to investigate pore-scale migration,retention and controlling mechanism of migration and retention characteristics on EGPs profile control.The results of the 1-D continuous PTVM indicated that while the matching coefficient was in the optimal range(i.e.,0.20-0.32),the EGPs could not only smoothly migrate to the deeper pore-throats,but also form stable retention in the pores to resist the erosion of injected water,which was conducive to the effective indepth profile control.The results of the 2-D heterogeneous PTVM verified that the sweep efficiency in low-permeability regions could be significantly improved by in-depth migration and stable retention of EGPs in the pore-throats with an optimal matching coefficient(0.29),which was much better than that in cases with a smaller matching coefficient(0.17)or an excessive matching coefficient(0.39).Moreover,the NMR displacement experiments of 3-D heterogeneous cores were carried out to simulate the EGPs profile control in actual reservoir porous media.Saturation images and T2 spectrum curves of crude oil showed that EOR in the low-permeability layer was highest(56.1%)using EGPs profile control with an optimal matching coefficient,attributing to the in-depth migration and stable retention of EGPs.

Classification and Visualization of Surrounding Rock Mass Stability Based on Multi-Dimensional Cloud Model

The classification of the stability of surrounding rock is an uncertain system with multiple indices.The Multidimensional Cloud Model provides an advanced solution through the use of an improved model of One-dimensional Cloud Model.Setting each index as a one-dimensional attribute,the Multi-dimensional Cloud Model can set the digital characteristics of each index according to the cloud theory.The Multi-dimensional cloud generator can calculate the certainty of each grade,and then determine the stability levels of the surrounding rock according to the principle of maximum certainty.Using this model to 5 coal mine roadway surrounding rock examples and comparing the results with those of One-dimensional and Two-dimensional Cloud Models,we find that the Multi-dimensional Cloud Model can provide a more accurate solution.Since the classification results of the Multidimensional Cloud Model are difficult to be presented intuitively and visually,we reduce the Multi-dimensional Cloud Model to One-dimensional and Two-dimensional Cloud Models in order to visualize the results achieved by the Multi-dimensional Cloud Model.This approach provides a more accurate and intuitive method for the classification of the surrounding rock stability,and it can also be applied to other types of classification problems.

A Visual Analysis and Prediction System for Infectious Diseases Based on Improved SIR Model

To effectively track the impact of population migration between regions on the spread of infectious diseases, this paper proposes a visualized analysis and prediction system of infectious diseases based on the improved SIR model. The research contents including: using the multi graph link interaction mode, visualizing the space-time distribution and development trend of infectious diseases;The LightGBM model is used to track the changes of infection rate and recovery rate, and the Mi/Mo SIR model is constructed according to the initial data of different populations;Mi/Mo SIR model is used to predict infectious diseases in combination with visual panel, providing users with tools to analyze and explain the space-time characteristics and potential laws of infectious diseases. The study found that the closure of cities and the restriction of personnel mobility were necessary and effective, and the system provided an important basis for the prediction and early warning of infectious diseases.

NPIPVis:A visualization system involving NBA visual analysis and integrated learning model prediction

Background Data-driven event analysis has gradually become the backbone of modern competitive sports analysis. Competitive sports data analysis tasks increasingly use computer vision and machine-learning models for intelligent data analysis. Existing sports visualization systems focus on the player–team data visualization, which is not intuitive enough for team season win–loss data and game time-series data visualization and neglects the prediction of all-star players. Methods This study used an interactive visualization system designed with parallel aggregated ordered hypergraph dynamic hypergraphs, Calliope visualization data story technology,and i Storyline narrative visualization technology to visualize the regular statistics and game time data of players and teams. NPIPVis includes dynamic hypergraphs of a team’s wins and losses and game plot narrative visualization components. In addition, an integrated learning-based all-star player prediction model, SRR-voting, which starts from the existing minority and majority samples, was proposed using the synthetic minority oversampling technique and Random Under Sampler methods to generate and eliminate samples of a certain size to balance the number of allstar and average players in the datasets. Next, a random forest algorithm was introduced to extract and construct the features of players and combined with the voting integrated model to predict the all-star players, using GridSearch CV, to optimize the hyperparameters of each model in integrated learning and then combined with five-fold cross-validation to improve the generalization ability of the model. Finally, the SHapley Additive ex Planations(SHAP) model was introduced to enhance the interpretability of the model. Results The experimental results of comparing the SRR-voting model with six common models show that the accuracy, F1-score, and recall metrics are significantly improved, which verifies the effectiveness and practicality of the SRR-voting model. Conclusions This study combines data visualization and machine learning to design a National Basketball Association data visualization system to help the general audience visualize game data and predict all-star players;this can also be extended to other sports events or related fields.