Visual Analytics Technologies Research Articles

User experience of english online classroom E-learning based on routing algorithm and data visualization analysis

With the development of the Internet, E-learning has become an important way of education. However, in online English classes, user experience problems still exist, such as network delay and slow video loading speed, which affect the learning effect and user engagement. Therefore, this paper hopes to improve E-learning user experience by studying routing algorithm and data visualization analysis technology. The routing algorithm is used to optimize the network connection. By analyzing the network topology and data transmission path, the optimal route is selected to reduce the network delay and bandwidth consumption. Data visualization technology is used to analyze and display the behavior and learning data of learners in the classroom in real time, so as to help learners better understand their learning situation. The experimental results show that the application of routing algorithm and data visualization analysis technology can significantly improve the user experience of online English classes. The stability of learners’ network connection in the classroom is improved, the video loading speed is accelerated, and the learning effect is also significantly improved. By optimizing network connectivity and providing real-time data analytics, it is possible to meet the individual needs of learners and improve their learning effectiveness and engagement.

Elderly tourism management: A bibliometric approach

Elderly tourism constitutes an important sector within the sustainable development of the tourism industry, attracting an increasing body of research focused on market development. This study aims to outline the progression of elderly tourism research within the past five years (2019–2023) and determine prospective research trajectories and opportunities in the subject. Employing CiteSpace visual analysis technology, this investigation constructs knowledge graphs of authors, institutions, and countries from 332 English-language academic articles from the Web of Science and culminates a keyword co-occurrence knowledge graph. Next, cluster and burst analyses revealed the prevailing trends and focal points in elderly tourism research. The results underscore that while many researchers have been drawn to elderly tourism, the collaborative relationships between these scholars remain tenuous, resulting in the relatively disparate study and the absence of a lead author group. China dominates the field, producing a far greater quantity of studies than other nations. Hence, this study encourages different countries, higher education institutions and disciplines to strengthen cooperation in the management and development of elderly tourism, especially paying attention to the importance of elderly tourism services, quality management and technology integration for the sustainable development of elderly tourism.

New traveling wave solutions, phase portrait and chaotic pattern for the stochastic modified Korteweg–de Vries equation

This article mainly studies the new traveling wave solutions of the stochastic modified Korteweg–de Vries equation with multiplicative noise. The traveling wave solutions in the form of hyperbolic function, trigonometric function, rational function and Jacobi elliptic function are obtained by transforming the modified Korteweg–de Vries equation into an ordinary differential equation through traveling wave transformation, and combining it with the complete discriminant system of polynomials. This article fully applies visual analysis technology and provides graphical representations of the obtained solutions in multiple dimensions such as phase maps, sensitivity maps, 3D maps and 2D maps, providing an intuitive and convenient channel for people to further understand the physical characteristics of the solutions for the stochastic modified Korteweg–de Vries equation. This article provides a rich graphical analysis of the solutions to the stochastic modified Korteweg–de Vries equation from multiple perspectives, and provides a fast and efficient solution method for the exact solution of the equation. The research method in this paper can also be used to study nonlinear partial differential equation in other fields.

Comparative Evaluation of the Accuracy of Numerical Methods on Reference Solutions

When organizing a mass practical solution of computational problems of gas dynamics with the help of mathematical modeling, information on the comparative accuracy of the numerical methods used is now increasingly in demand. As a rule, calculators need information not only for a particular combination of the defining gas-dynamic parameters of the problem (characteristic Mach, Reynolds numbers, etc.), but also for the variation of these parameters in certain ranges. This work presents numerical studies devoted to a comparative assessment of the accuracy of numerical methods for a number of problems with reference solutions. The calculation of these problems is carried out for ranges of characteristic numbers using various numerical methods. The results obtained are compared with the reference solution and make it possible to estimate the error for each of the numerical methods. Calculations are carried out using the construction of a generalized computational experiment. A generalized computational experiment is a computational technology that combines the solution of mathematical modeling problems, parallel technologies and visual analytics technologies. The results of the generalized computational experiment are multidimensional arrays, where the dimension of the arrays corresponds to the defining parameters. Analysis and visual representation of the obtained results provide information on the comparative accuracy of the numerical method for the selected class of problems.

Retracted: Mental Health Evaluation Based on Visual Analysis Technology.

[This retracts the article DOI: 10.1155/2022/6706698.].

(Retracted) Multidimensional graphic art design method based on visual analysis technology in intelligent environment

In the last few years, the artificial intelligence technology has provided unique methods for design analysis in the art field. With the development of China’s economy and cultural prosperity, graphic design has changed people’s lives greatly. Under the concept of modern scientific and technological creation, the variety, scale, and plasticity of graphic design art are constantly improving, and the diversification of design elements is becoming the development trend. We designed a graphic art element recognition model based on single shot multibox detector (SSD) method through deep learning of visual processing technology. This method can automatically identify various elements in multidimensional graphic art works, thus helping artists and learners analyze an art work better. In this method, we take the SSD structure as the main model backbone and use the improved attention mechanism module feature pyramid transformer to replace the original feature fusion module, inject long-distance dependency into the model, and improve the accuracy of object detection. In addition, we use the public dataset to make the relevant image target detection dataset. Different object detection evaluation metrics are used to evaluate the proposed methods, and several existing methods are selected for comparative experiments. Compared with YOLO V5 object detection model, our method improves 0.53%, 0.67%, 1.33%, and 1.28% on pixel accuracy, mean pixel accuracy, average recall, and mean intersection over union, respectively. The proposed algorithm has a great contribution to the performance improvement of object detection and the auxiliary analysis of multidimensional works of art.

SBGTool v2.0: An Empirical Study on a Similarity-Based Grouping Tool for Students’ Learning Outcomes

Visual learning analytics (VLA) tools and technologies enable the meaningful exchange of information between educational data and teachers. This allows teachers to create meaningful groups of students based on possible collaboration and productive discussions. VLA tools also allow a better understanding of students’ educational demands. Finding similar samples in huge educational datasets, however, involves the use of effective similarity measures that represent the teacher’s purpose. In this study, we conducted a user study and improved our web-based similarity-based grouping VLA tool, (SBGTool) to help teachers categorize students into groups based on their similar learning outcomes and activities. SBGTool v2.0 differs from SBGTool due to design changes made in response to teacher suggestions, the addition of sorting options to the dashboard table, the addition of a dropdown component to group the students into classrooms, and improvement in some visualizations. To counteract color blindness, we have also considered a number of color palettes. By applying SBGTool v2.0, teachers may compare the outcomes of individual students inside a classroom, determine which subjects are the most and least difficult over the period of a week or an academic year, identify the numbers of correct and incorrect responses for the most difficult and easiest subjects, categorize students into various groups based on their learning outcomes, discover the week with the most interactions for examining students’ engagement, and find the relationship between students’ activity and study success. We used 10,000 random samples from the EdNet dataset, a large-scale hierarchical educational dataset consisting of student–system interactions from multiple platforms at the university level, collected over a two-year period, to illustrate the tool’s efficacy. Finally, we provide the outcomes of the user study that evaluated the tool’s effectiveness. The results revealed that even with limited training, the participants were able to complete the required analysis tasks. Additionally, the participants’ feedback showed that the SBGTool v2.0 gained a good level of support for the given tasks, and it had the potential to assist teachers in enhancing collaborative learning in their classrooms.

Mental Health Evaluation Based on Visual Analysis Technology.

In order to solve the problems existing in the current mental health assessment system and how to better help individuals analyze their mental health, propose a mental health assessment method based on visual analysis technology; through the study of mental health assessment methods, use the SCL-90 symptom self-rating scale as the mental health assessment scale of this article. Combined with the conceptual design proposed in this article, realize a mental health assessment based on visual analysis technology. The system conducts visual analysis of personal current data and historical data and helps users understand their current mental health. Evaluate the system through user surveys and system quality, and the feasibility and practicability of this method are verified.

Visualization and Analysis of Mapping Knowledge Domain of Heterogeneous Traffic Flow.

Mapping knowledge domain (MKD) is an important application in bibliometrics, which is a method of visually presenting and explaining newly developed interdisciplinary scientific fields using data mining, information analysis, scientific measurement, and graphic rendering. This study combines applied mathematics, visual analysis technology, information science, and scientometrics to systematically analyze the development status, research distribution, and future trend of the heterogeneous traffic flow by using the MKD software tools VOSviewer and CiteSpace. Based on the MKD and Bibliometrics approaches, 4709 articles have been studied, which were published by Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) from 2004 to 2021 in the field of heterogeneous traffic flows. Firstly, this paper presents the annual numbers of articles, origin countries, main research organizations, and groups as well as the source journals on heterogeneous traffic flow studies. Then, cocitation analysis is used to divide heterogeneous traffic flow into three main research directions, which include “heterogeneous traffic flow model,” “traffic flow capacity analysis,” and “traffic flow stability analysis.” The keyword cooccurrence analysis is applied to identify five dominant clusters: “modeling and optimization methods,” “traffic flow characteristics analysis,” “driving behavior analysis,” “simulation experiment,” and “policies and barriers.” Finally, burst keywords were studied according to the publication date to present more clearly the change of research focus and direction over time.

Visual Data Analysis Technology Based on Data Center

Today, people are in an information explosion society, and visualization technology(VT) is an inevitable product of the development of the information society. With the emergence of multimedia products such as computers, networks, and communications, humans are paying more and more attention to data processing. Many countries in the world have already begun research in this area and have achieved remarkable results. VT is a core part of data analysis, also known as information processing and storage technology. It has a very extensive and important application in the field of data management. However, because the key information hidden in the data is often immersed in the massive data, it is necessary to filter the data information efficiently, and the visualization data analysis technology is a crucial part. This article adopts the experimental analysis method, which aims to provide a new method to solve the problems of traditional technology and the challenges that may arise in the future by further understanding the existing visual data analysis technology and development trend. According to the research results, the recognition rate of the optimized color visualization features under different classifiers is higher than that of the original emotional features. It can be seen that visual analysis technology is not limited to data sets with physical meaning, but can also be applied to abstract feature sets such as emotional features.

A bibliometric analysis on global eHealth.

BackgroundThe current coronavirus disease 2019 pandemic highlights the potential of eHealth. Drawing the knowledge map of eHealth research through data mining and visual analysis technology was helpful to systematically present the research status and future trends of global academic circles.MethodsBased on the web of Science Core Collection (SCIE/SSCI) database, using bibliometric theory and visual analysis technology, this work analyzed the global eHealth research publications from 2000 to 2021, and introduced the interdisciplinary characteristics, hot topics and future trends in this field.ResultsA total of 10188 authors, 891 journals, 3586 institutions, 98 countries using 12 languages had conducted eHealth research in the world. The United States, the Netherlands, Australia and the United Kingdom were the main forces and international cooperation. However, the international co-operation between Eastern and Western countries was still relatively few. The frontier of global eHealth research mainly focused on #0eHealth innovation, #1physical activity, #2generalised anxiety disorder, #3lightweight authentication protocol, #4 eHealth information, #5technology readiness, #6 ehealth literacy scale, #7family carer, #8citance analysis, #9 guiding patient. Clusters #3 lightweight authentication protocol and #9 guiding patient were the latest clusters, indicating the research trend and direction of eHealth in the future.ConclusionsCooperation network framework at the regional, national and global levels and the cooperation of multidisciplinary teams with complementary backgrounds and expertise were needed to realize the in-depth popularization and application of eHealth knowledge. Interdisciplinary international cooperation should be the trend of eHealth research in the future.

An Expressway Driving Stress Prediction Model Based on Vehicle, Road and Environment Features

Driving stress is the demand for reserved cognitive space after a driver perceives changes in vehicle, road and environmental factors during driving, which has been proven to affect driving behaviour, interfering with driving safety. Traditional stress prediction relies extensively on psychological data and is limited by the unpopularity of psychological data collection technology, which cannot be applied in daily life on a large scale. In recent years, advances in high-precision visual analysis technology represented by deep learning have laid the foundation for automated and large-scale visual environment analysis. This study proposes a framework for the quantitative analysis of highway driving stress based on multiple vehicle, road, and environmental factors. A dilated residual network model and other methods were used to extract visual environmental indexes. Combined with multisource data such as traffic volume and road design parameters, the LightGBM method was used to construct an expressway driving stress prediction model with high accuracy. The MAE, RMSE and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> values of the proposed model are 0.042, 0.004 and 0.881, respectively, demonstrating the usefulness for scaled and efficient assessment of expressway stress loads. The SHAP method was used to explore the relationship between different influencing factors and driving stress to quantify the mechanism of vehicle, road and environment influences on stress load, and to propose recommendations for highway design and planning from the perspective of reducing stress load. This study provides a new way of thinking to quantitatively investigate the link between multiple road traffic factors and driving stress, providing efficient and large-scale assessment of expressway driving stress, as well as proposing some suggestions for highway design and planning to enhance stress reduction.

Wireless Sensor Network-Based Mathematical Modeling for Communication Intelligent Monitoring System

Knowing the behavioral patterns of city residents is of great value in formulating and adjusting urban planning strategies, such as urban road planning, urban commercial development, and urban pedestrian flow control. Based on the high penetration rate of cell phones, it is possible to indirectly understand the behavior of city residents based on the call records of users. However, the behavioral patterns of large-scale users over a long period of time can present characteristics such as large dispersion, difficult to discover patterns, and difficult to explain behavioral patterns. In this paper, we design and implement a human behavior pattern analysis system based on massive mobile communication data based on serial data modeling method and visual analysis technology. For the problem that it is difficult to capture the behavioral patterns of residents in cities in call records, this paper constructs base station trajectories based on users’ cell phone call records and uses users’ long-time base station trajectories to mine users’ potential behavioral patterns. Since users with similar activity characteristics will exhibit similar base station trajectories, this paper focuses on the similarity between text sequences and base station trajectory sequences and combines the word embedding method in natural language processing to build a Cell2vec model to identify the semantics of base stations in cities. In order to obtain the group behavior patterns of users from the base station trajectories of group users, a user clustering method based on users’ regional mobile preferences is proposed, and the results are projected using the Stochastic Neighbor Embedding (t-SNE) algorithm to expose the clustering features of large-scale cell phone users in the low-dimensional space. To address the problem that user behavior patterns are difficult to interpret, a visual analysis model with group as well as regional semantics is designed for the spatial and temporal characteristics of user behavior. Among them, the clustering model uses the distance between scatter points to map the similarity between users, which helps analysts to explore the behavioral characteristics of group users.

Processing and visualization of the results of parametric numerical calculations

In modern problems of mathematical modeling in computational gas dynamics, it is increasingly necessary to implement parametric studies. In these cases, the key factors of the problem under consideration vary with the chosen step within the given ranges. Calculations of this kind can be effectively carried out by constructing a generalized computational experiment. A generalized computational experiment is a computational technology that combines the solution of mathematical modeling problems, parallel technologies, and visual analytics technologies. The results of a generalized computational experiment are multidimensional arrays, where the dimension of the arrays corresponds to the number of key factors. Processing and visual presentation of such arrays requires solving a number of separate tasks. The processing and visual presentation of the results are carried out for target functionals represented as a function of many variables. The report presents an examples of solving specific processing and visualization problems based on the implemented generalized computational experiment for 3D cone in supersonic flow.

A Survey on OD Data Visualization

<p indent=0mm>OD data is a type of data that provides origin and destination positions, and sparsely describes the movement trajectory of objects. Being the research hotspots in data visualization and geospatial analysis, visual analysis of OD data contributes to discovering group movement patterns, exploring spatial anomalies and hidden relationships, and in-depth analysis of various statistics attributes. It is started from the pair-point structure of OD data in geospatial space, and classical OD data visualization methods and principles are elaborated, such as OD matrix, OD map, OD flow, etc. Then from the perspective of spatial-temporal feature exploration, spatial and spatial-temporal joint dimensions, the visual analysis of OD data is performed, while its application scenarios and enhanced significance of feature expression, perception, extraction and presentation are researched. To solve the problem of visual clutter in large-scale OD data visualization, the visual simplification methods such as filtering, aggregation, and sampling are sorted out in the research methods of visual analysis of OD data. Finally, the applications and specific cases of OD data visual analysis technology are investigated in different domains, the challenging problems in the research of OD data visual analysis are summarized, and the trend of its future development is prospected.

Mechanism analysis and improved model for stick-slip friction behavior considering stress distribution variation of interface

Studying the evolution of interface contact state, revealing the “black box” behavior in interface friction and establishing a more accurate friction model are of great significance to improve the prediction accuracy of mechanical system performance. Based on the principle of total reflection, a visual analysis technology of interface contact behavior is proposed. Considering the dynamic variation of stress distribution in interface contact, we analyze the nonlinear characteristics of contact parameters in different stages of stick-slip process using the above-mentioned experimental technology. Then, we find that the tangential stiffness of the interface is not a fixed value during the stick-slip process and the stress distribution variation is one of the important factors affecting the tangential stiffness of interface. Based on the previous experimental results, we present an improved stick-slip friction model, considering the change of tangential stiffness and friction coefficient caused by the stress distribution variation. This improved model can characterize the variation characteristics of contact parameters in different stages of stick-slip process, whose simulation results are in good agreement with the experimental data. This research may be valuable for improving the prediction accuracy of mechanical system performance.

Visualization Analysis Technology based on 2.5D FEM for Molding Phenomenon in Twin Screw Extruder

Visualization Analysis Technology based on 2.5D FEM for Molding Phenomenon in Twin Screw Extruder

The Research Hotspots and Visualization Analysis Technology of Health Big Data

By understanding the international research hotspots in this field, we hope to provide reference information for health big data researchers to grasp the research hotspots and frontiers. By searching the relevant literature in the field of international health big data research in the Web of Science database, using the method of bibliometrics to draw a visual map. Results A total of 3850 articles were retrieved. Since 2013, the number of health big data research papers has shown a rapid growth. Harvard Medical School, University of Michigan and Stanford University are the research institutions with the largest number of papers in this field. The United States is the most active in the field of big data research, with 1506 papers, far higher than other countries, followed by China, the United Kingdom and South Korea. International health big data research hotspots include research on Intelligent wearable devices, design and development of health big data monitoring system, and application of health big data in precision medicine. The research reveals the characteristics and hotspots of international health big data research field, and provides some references for the development direction of this field in China.

Sports Injury Rehabilitation Intervention Algorithm Based on Visual Analysis Technology

Sports injuries of high-level athletes restrict the improvement of sports performance. Under this premise, an efficient and accurate sports injury assessment method is needed to detect potential sports injuries and conduct injury prevention training. Therefore, this paper proposes a novel sports injury prediction algorithm based on visual analysis technology. The proposed algorithm first takes the time-frequency of sensed data as the convolutional neural network (CNN) input. The one-dimensional time series collected by the sensor is converted into two-dimensional images using the Gram angle domain algorithm. The one-dimensional sensed data provides a new perspective and provides a basis for better use of convolutional neural networks and computer vision technology. Second, combining the residual network’s structure and advantages and hole convolution and multihole convolution kernel residual module is proposed. It improves the model’s ability to extract features at different scales while effectively controlling the parameter scale. Based on these modules, a single-sensor-based athlete action recognition algorithm is proposed. Several comparative experiments have been conducted on a public data set containing only acceleration sensors to verify the proposed algorithm’s effectiveness.

A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN+

With the development of city size and vehicle interconnection, visual analysis technology is playing a very important role in the course of city calculation and city perception. A Reasonable visual model can effectively present the feature of city. In order to solve the problem of traditional density algorithm that cluster the large scale data slowly and cannot find cluster centers to adapt taxi track data. The DBSCAN+ (density-based spatial clustering of applications with noise plus) algorithm that can split data and extract maximum density clusters under the large scale data was proposed in the paper. The passenger points should be cleaned from the original point of the passenger trajectory data firstly, and then the massive passenger points are sliced and clustered cyclically. In the clustering process, the cluster centers can be extracted based on maximum density, and finally the clustering results are visualized according to the results. The experimental results show that compared with other popular methods, the proposed method has significant advantages in clustering speed, precision and visualization for large-scale city passenger hotspots. Moreover, it provides important decisions for further urban planning and promotes the traffic efficiency.