Data Visualization Module Research Articles

A Real-time Assessment System for Cognitive Performances of Armored Vehicle Crews Based upon the Multi-source Information Fusion

In the human-machine system of armored vehicles, the cognitive performance state of crews is crucial for the personnel security and combat efficiency. The purpose of this research was to establish a real-time assessment system for cognitive performances of armored vehicle crews, consisting of the data input module, data processing module, data visualization module, and scheduling module. Forty subjects were recruited to cooperate and execute the cross-platform strike task in a virtual simulation platform. The physiological data and operation behavior data was collected during the experiment process. To realize the accurate classification of different cognitive performance states, a multi-source information fusion algorithm was developed based on linear discriminant analysis (LDA) and D-S evidence theory, which included the information collection module, the feature extraction module, and the information fusion module. The results indicated that there existed a significant correlation between the extractive feature indicators (i.e., EOG, ECG, and task performance indicators) and the cognitive performance. The recognition accuracy and the data efficiency of the proposed assessment system were 91.25% and 96.69% respectively by using the complementarity of different evidences, which were higher than the others using partial information sources. This study can provide a reference for the comprehensive assessment of cognitive performance of human operators in military and industrial domains.

The Development of an Automated System for a Quality Evaluation of Engineering BIM Models: A Case Study

The growing adoption of Building Information Modeling (BIM) within the architectural, engineering, and construction (AEC) sector raises questions about the quality of BIM data deliverables for project owners. Therefore, assessment and evaluation of such BIM data against relevant documents such as the BIM Execution Plan (BEP), the Level of Definition (LOD)/Level of Information (LOI) matrix, and quality control customized checklists become critical, especially in large construction projects. This study primarily aims to create an automated system for assessing the quality of 3D BIM model data, utilizing a proposed project quality control checklist. The automated system consists of four key elements: a BIM-based model, a Data Extraction and Analysis Module, a Data Storage Module, and a Data Visualization Module. The Data Extraction and Analysis Module extracts relevant information and parameters from BIM models to evaluate their quality against predefined checklists. Then, it transfers the information and stores the results in a database. The database is connected to an engineering project collaboration tool, ProjectWise, to automatically update and store the data in the cloud. The database is then connected to an interactive data visualization platform, Power BI, to enable automatic visualization of the generated quality assessment results of the BIM models’ data. This system was applied to a Canadian infrastructure construction project by its BIM department during the preliminary and detailed design phases. It demonstrated an average quality score (AQS) of 87.6% for the BIM models and significantly reduced failing items by around 30%. This study concludes that the system offers a robust, practical solution for enhancing the quality control process in BIM model data management, thereby aiding engineers in timely model adjustments to meet project requirements.

Advancing Manufacturing Efficiency through Real-time Production Monitoring and Control Systems

The advantages of both real-time production monitoring and control systems in enhancing the advancement of manufacturing efficiency cannot be overlooked. Manufacturing industries need to meet customers’ demand while still maximizing their profits via avoiding emergency shutdown of plants during production. Nonetheless, quality products with high purity level should be supplied uninterruptedly without breakage of supply chain. In recent times, failure of many of manufacturing industries to deliver their products to end users have been cited and reported. This was due to inadequate scheduling which was not being monitored via real-time production and control systems. There is need to tackle this problem which calls for the adoption of real-time production monitoring and control systems. In this paper, the concepts behind the use of real-time production monitoring and control systems were discussed. Consideration was given to how real-time production monitoring can be integrated in manufacturing. The features of real-time control systems were discussed alongside with their industrial applications in various disciplines of Engineering. Data analysis and prognostics, data collection, visualization module and data storage were identified as the relevant sequential steps in real time production monitoring in advancing manufacturing efficiency. In conclusion, the significance and importance of real-time production monitoring and control systems in advancing manufacturing efficiency have been revealed and discussed in this paper.

The realization and application of the data analysis platform of netizen behavior based on Hive

With advances in mobile technology and mobile Internet applications, smart mobile devices, such as smartphones and tablets, have become increasingly popular, and the number of Internet users worldwide continues to grow. In the Internet era, the amount of data is growing exponentially and companies must be able to harness the value of the vast amount of data. Data platforms must integrate massive amounts of data collection, storage, computation and analysis to meet these opportunities and challenges. In this study, the log data of Internet users browsing websites are analyzed and the technologies used in the platform are briefly described. Finally, a draft platform for analyzing offline Internet user behavior data is proposed, taking into account the current common needs of different industries, while incorporating some innovations. Three modules are designed and implemented: data collection, data warehouse and data visualization. The user's data is mainly collected by the data collection module. The data warehouse is mainly responsible for cleaning, modeling and analyzing the data. As part of the data visualization module, the result data from the ADS layer is used as a template to create tables in MySQL, export the results to MySQL periodically using the Sqoop tool, and visualize the data using the data visualization tool. With Flume, Kafka and Sqoop, HDFS is used as the data storage framework, Hive is used as the storage tool, and Spark is used as the Hive computation engine to build the platform in a large context to analyze Internet user behavior.

MicroEXPERT: Microbiome profiling platform with cross-study metagenome-wide association analysis functionality.

The framework of the MicroEXPERT platform. Our Platform was composed of five modules. Data management module: Users upload raw data and metadata to the system using a guided workflow. Data processing module: Uploaded data is processed to generate taxonomical distribution and functional composition results. Metagenome-wide association studies module (MWAS): Various methods, including biomarker analysis, PCA, co-occurrence networks, and sample classification, are employed using metadata. Data search module: Users can query nucleotide sequences to retrieve information in the MicroEXPERT database. Data visualization module: Visualization tools are used to illustrate the metagenome analysis results.

Development and design of an intelligent monitoring system for cold chain meat freshness

Cold chain meat has become the main force of meat consumption in China due to its unique taste and rich nutrition. However, there are serious regulatory blind spots throughout the cold chain process, making it difficult to monitor the freshness quality and shelf life of cold chain meat in real time. Therefore, in this study, the dominant spoilage microorganism prediction model for cold chain meat was parsed to predict the logarithmic value of the dominant spoilage microorganism in real time based on temperature information, which was compared with the freshness quality threshold to derive real-time quality information. This paper proposes to develop the system with ASP.NET three-tier architecture as the development framework and .NET Framework 4.6.2 as the development environment. Combining MySQL 5.7.31 application database, Vue 2.6 and Apache ECharts front-end technology development a intelligent monitoring system for cold chain meat freshness. The system has five core application modules: multi-role login module, big data visualization and analysis module, product information management module, product freshness quality and shelf life real-time monitoring and warning module, and system setting module, to monitor the freshness quality and shelf life of cold chain meat products at all stages in real time. The development of this system can realize the real-time supervision of the quality of cold chain meat products in the whole industrial chain of the processing, storage, transportation and sales process, timely detect product abnormalities, provide technical support for food enterprises, and provide food safety assurance for consumers.

SMART PUBLIC OPINION MONITORING AND ANALYSIS SYSTEM FOR ETHNIC REGIONS: A MULTI-MODAL APPROACH USING MACHINE LEARNING

In ethnic regions, public opinion plays a vital role in shaping social stability and harmony. However, monitoring and analyzing public opinion in these regions can be challenging due to the complex linguistic and cultural factors involved. To address this issue, this study proposes a Smart Public Opinion Monitoring and Analysis System for Ethnic Regions, which utilizes a multi-modal approach for data acquisition and machine learning techniques for analysis. The system collects public opinion data from multiple modalities, including text, speech, image, and video, and uses intelligent speech processing and text translation to analyze non-textual data. The sentiment analysis module evaluates the polarity of public opinion, while the topic modeling module identifies key topics and keywords of public interest. The system also includes an early warning and risk detection module that uses machine learning algorithms to detect potential risks and generate alerts in real-time. Finally, the data visualization module presents the analysis results in an intuitive and user-friendly manner. The proposed system has been tested on a dataset of public opinion data from ethnic regions, and the results demonstrate its effectiveness in monitoring and analyzing public opinion in different modalities. The system can help government agencies and relevant stakeholders to respond quickly to potential risks and maintain social stability in ethnic regions.

Biofilm-i: A Platform for Predicting Biofilm Inhibitors Using Quantitative Structure-Relationship (QSAR) Based Regression Models to Curb Antibiotic Resistance.

Antibiotic drug resistance has emerged as a major public health threat globally. One of the leading causes of drug resistance is the colonization of microorganisms in biofilm mode. Hence, there is an urgent need to design novel and highly effective biofilm inhibitors that can work either synergistically with antibiotics or individually. Therefore, we have developed a recursive regression-based platform “Biofilm-i” employing a quantitative structure–activity relationship approach for making generalized predictions, along with group and species-specific predictions of biofilm inhibition efficiency of chemical(s). The platform encompasses eight predictors, three analysis tools, and data visualization modules. The experimentally validated biofilm inhibitors for model development were retrieved from the “aBiofilm” resource and processed using a 10-fold cross-validation approach using the support vector machine and andom forest machine learning techniques. The data was further sub-divided into training/testing and independent validation sets. From training/testing data sets the Pearson’s correlation coefficient of overall chemicals, Gram-positive bacteria, Gram-negative bacteria, fungus, Pseudomonas aeruginosa, Staphylococcus aureus, Candida albicans, and Escherichia coli was 0.60, 0.77, 0.62, 0.77, 0.73, 0.83, 0.70, and 0.71 respectively via Support Vector Machine. Further, all the QSAR models performed equally well on independent validation data sets. Additionally, we also checked the performance of the random forest machine learning technique for the above datasets. The integrated analysis tools can convert the chemical structure into different formats, search for a similar chemical in the aBiofilm database and design the analogs. Moreover, the data visualization modules check the distribution of experimentally validated biofilm inhibitors according to their common scaffolds. The Biofilm-i platform would be of immense help to researchers engaged in designing highly efficacious biofilm inhibitors for tackling the menace of antibiotic drug resistance.

Online monitoring of the axial thermal displacement of machine tools using a laser mouse sensor

We present a low-cost online system for monitoring the axial thermal displacement of machine tools. The proposed monitoring system includes an embedded optical sensor derived from a laser mouse; an image acquisition microcontroller; speckle patterns; and an edge computer that hosts software, including an image display module, a displacement calculation module, an image enhancement module, and a data visualization module. The proposed sensing system can measure the displacement in two orthogonal directions simultaneously by employing digital image correlation; thus, the proposed system is a two-dimensional displacement sensor. The sensing system is benefited from image enhancement techniques and customized optimal speckle patterns printed using a standard low-cost monochrome laser printer. Experimental results indicate that the proposed displacement sensing system has an accuracy and a precision of <5 μm in both orthogonal directions. The two-dimensional displacement sensing system was used for the online monitoring of the thermal deformation of a feed drive system for machine tools, and the performance was assessed experimentally. The complete procedure for measuring the displacement due to thermal deformation was conducted automatically and can be completed in 2 min using a program coded in a computer numerical control controller.

BIM Digital Shadow Technology and Risk Assessment Method of the Deep Foundation Pit’s Behavior for Zibo Light Rail

With the shortage of land resources, there has been a trend toward increasingly deep foundation pit engineering in urban areas. It is extremely important to reflect on the behavior and safety of deep foundation pits and conduct risk assessments in time. A nonhomologous and multi-indicator deep foundation pit risk assessment model was studied for nine types of nonhomologous on-site data monitored in the deep foundation pit. Based on the BIM (Building Information Modeling) technology, a family of monitored points was created that reflects the information on site from a deep foundation pit. The data visualization module was redeveloped by using Visual Studio 2019 to map the on-site data monitored to the components in the BIM model, which can visualize the data monitored in the BIM model of deep foundation pits. On this basis, the assessment of the safety level of deep foundation pits was realized in combination with a risk assessment model. Through the instance of the deep foundation pit of Zibo light rail, the analysis shows that the new visualization and risk assessment method can help construction workers locate dangerous units and formulate corresponding prevention and control measures better and faster.

Construction of E-Commerce Economic Management Platform Based on Data Visualization Technology

In the age of information explosion, users are exposed to overwhelmingly visual information every day. The raw unprocessed data are not attractive enough to the human brain. Thankfully, humans are extremely sensitive to graphic image perception and can understand the meaning behind numbers from graphic images. For this reason, data visualization is becoming the best way to communicate with people. In the context of big data, how to display complex data efficiently, what kind of drawing technology to use, and how to improve user interaction with data are urgent problems to be solved by data visualization technology. On the other hand, researchers’ understanding of data visualization is not consistent; there is no clear standard; and it is relatively confusing. Researchers engaged in data visualization and executives engaged in related data visualization industries have different interpretations of it, and the core theoretical basis and research methods are still in the exploration stage. Firstly, I systematically introduce the design principles of data visualization, software development related technologies, data visualization research methods, basic charts, and characteristics of data visualization. Secondly, based on the software development process, I analyzed the key issues of data visualization for the e-commerce system, gave the objectives of the design plan, and designed the whole architecture and development framework of the project. In particular, I designed the class diagram and timing diagram of the control layer, service layer, and data access layer for the visualization of user purchase preferences in the data visualization and analysis module as an example. Then, with the structure and characteristics of the data in the visualization studied, the business logic layer and the front-end visualization layer are implemented, and a good user interaction function is provided for the users. The data visualization is loaded asynchronously and on demand to improve the performance of data visualization and user interaction experience. Finally, the system data is presented with the analysis of the user population’s region, the analysis of user preferences, and the analysis of the product price sales relationship, and the product category tree diagram and the results of the data visualization run are given, providing a comprehensive platform for shopping, data analysis, and data visualization for the next generation e-commerce system.

Data Mining Method under Model-Driven Architecture (MDA)

With the development of university information technology, how to mine and visually analyze the data of the existing separated information system will become an important research topic. The current university information system is a combination of some proprietary business systems characterized by poor data separation and storage and data analysis power. In addition, the data mining methods based on cloud computing will make customers gradually lose the ability to control the data. Because of the above problems, this paper proposes a university data mining method based on the MDA idea by constructing a data analysis and visualization framework, including multidimensional data modeling, data extraction, and data display based on visualization technology. The framework makes full use of the design idea of MDA and models multidimensional data, data extraction, and data display, respectively. The multidimensional data model module, data extraction module, and data visualization module provide efficient solutions for data analysis and visualization in universities.

CancerMIRNome: an interactive analysis and visualization database for miRNome profiles of human cancer.

MicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising diagnostic and prognostic biomarkers for human cancer. In particular, circulating miRNAs that are secreted into circulation exist in remarkably stable forms, and have enormous potential to be leveraged as non-invasive biomarkers for early cancer detection. Novel and user-friendly tools are desperately needed to facilitate data mining of the vast amount of miRNA expression data from The Cancer Genome Atlas (TCGA) and large-scale circulating miRNA profiling studies. To fill this void, we developed CancerMIRNome, a comprehensive database for the interactive analysis and visualization of miRNA expression profiles based on 10 554 samples from 33 TCGA projects and 28 633 samples from 40 public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and machine learning algorithms have been packaged in CancerMIRNome, allowing for the pan-cancer analysis of a miRNA of interest across multiple cancer types and the comprehensive analysis of miRNome profiles to identify dysregulated miRNAs and develop diagnostic or prognostic signatures. The data analysis and visualization modules will greatly facilitate the exploit of the valuable resources and promote translational application of miRNA biomarkers in cancer. The CancerMIRNome database is publicly available at http://bioinfo.jialab-ucr.org/CancerMIRNome.

Data Management System for a Semiautonomous Shuttle Car for Underground Room and Pillar Coal Mines

In recent years, autonomous solutions in the multidisciplinary field of mining engineering have been an extremely popular applied research topic. This is a result of the increasing demands of society on mineral resources along with the accelerating exploitation of the currently economically viable resources, which lead the mining sector to turn to deeper, more-difficult-to-mine orebodies. An appropriate data management system comprises a crucial aspect of the designing and the engineering of a system that involves autonomous or semiautonomous vehicles. The vast volume of data collected from onboard sensors, as well as from a potential IoT network dispersed around a smart mine, necessitates the development of a reliable data management strategy. Ideally, this strategy will allow for fast and asynchronous access to the data for real-time processing and decision-making purposes as well as for visualization through a corresponding human–machine interface. The proposed system has been developed for autonomous navigation of a coalmine shuttle car and has been implemented on a 1/6th scale shuttle car in a mock mine. It comprises three separate nodes, namely, a data collection node, a data management node, and a data processing and visualization node. This approach was dictated by the large amount of collected data and the need to ensure uninterrupted and fast data management and flow. The implementation of an SQL database server allows for asynchronous, real-time, and reliable data management, including data storage and retrieval. On the other hand, this approach introduces latencies between the data management node and the other two nodes. In general, these latencies include sensor latencies, network latencies, and processing latencies. However, the data processing and visualization module is able to retrieve and process the latest data and make a decision about the next optimal movement of the shuttle car prototype in less than 900 ms. This allows the prototype to navigate efficiently around the pillars without interruptions.

JWES: a new pipeline for whole genome/exome sequence data processing, management, and gene-variant discovery, annotation, prediction, and genotyping.

Whole genome and exome sequencing (WGS/WES) are the most popular next‐generation sequencing (NGS) methodologies and are at present often used to detect rare and common genetic variants of clinical significance. We emphasize that automated sequence data processing, management, and visualization should be an indispensable component of modern WGS and WES data analysis for sequence assembly, variant detection (SNPs, SVs), imputation, and resolution of haplotypes. In this manuscript, we present a newly developed findable, accessible, interoperable, and reusable (FAIR) bioinformatics‐genomics pipeline Java based Whole Genome/Exome Sequence Data Processing Pipeline (JWES) for efficient variant discovery and interpretation, and big data modeling and visualization. JWES is a cross‐platform, user‐friendly, product line application, that entails three modules: (a) data processing, (b) storage, and (c) visualization. The data processing module performs a series of different tasks for variant calling, the data storage module efficiently manages high‐volume gene‐variant data, and the data visualization module supports variant data interpretation with Circos graphs. The performance of JWES was tested and validated in‐house with different experiments, using Microsoft Windows, macOS Big Sur, and UNIX operating systems. JWES is an open‐source and freely available pipeline, allowing scientists to take full advantage of all the computing resources available, without requiring much computer science knowledge. We have successfully applied JWES for processing, management, and gene‐variant discovery, annotation, prediction, and genotyping of WGS and WES data to analyze variable complex disorders. In summary, we report the performance of JWES with some reproducible case studies, using open access and in‐house generated, high‐quality datasets.

Aneulysis – A system for the visual analysis of aneurysm data

We present Aneulysis, a system to improve risk assessment and treatment planning of cerebral aneurysms. Aneurysm growth, rupture and treatment success depend on the interplay of vascular morphology and hemodynamics. Blood flow simulations can obtain the patient-specific hemodynamics. However, analyzing the time-dependent, multi-attribute data is time-consuming and error-prone. Aneulysis supports the visual analysis of morphological and hemodynamic aneurysm information by providing different data processing and visualization modules that form an application. In this paper, we integrated a web-based module to analyze aneurysm data, which was designed and evaluated together with domain experts who confirmed its usefulness and clinical relevance. The web-based module supports the delivery of exploration results to people who do not have direct access to the entire database, such as experts at different locations, students or patients. Thus, data sets including findings from previous exploration sessions can be exploited without installing the complete non-web-based application locally.

Hadoop-Based Painting Resource Storage and Retrieval Platform Construction and Testing

This paper adopts Hadoop to build and test the storage and retrieval platform for painting resources. This paper adopts Hadoop as the platform and MapReduce as the computing framework and uses Hadoop Distributed Filesystem (HDFS) distributed file system to store massive log data, which solves the storage problem of massive data. According to the business requirements of the system, this paper designs the system according to the process of web text mining, mainly divided into log data preprocessing module, log data storage module, log data analysis module, and log data visualization module. The core part of the system is the log data analysis module. The analysis of search keywords ranking, Uniform Resource Locator (URL), and user click relationship, URL ranking, and other dimensions are realized through data statistical analysis, and Canopy coarse clustering is performed first according to search keywords, and then K-means clustering is used for the results after Canopy clustering, and the calculation of cosine similarity is adopted to realize the grouping of users and build user portrait. The Hadoop development environment is installed and deployed, and functional and performance tests are conducted on the contents implemented in this system. The constructed private cloud platform for remote sensing image data can realize online retrieval of remote sensing image metadata and fast download of remote sensing image data and solve the problems in storage, data sharing, and management of remote sensing image data to a certain extent.

On-line linear guideway monitoring using laser mouse sensor

A low-cost, on-line linear guideway straightness monitoring system for machine tools is presented. The monitoring system which includes an embedded optical sensor from the optical mouse, an image display module, a displacement calculation module, an image enhancement module and a data visualization module are integrated into an edge computer. This proposed sensing system can measure the displacement in two orthogonal directions simultaneously, i.e. a two-dimensional displacement sensor based on digital image correlation, DIC. With image enhancement technique and a custom-made speckle patterns printed using a standard low-cost monochrome laser printer, results show that this two-dimensional displacement sensing system has a displacement resolution less than 5 µm in both orthogonal directions. The measuring range is 1mm for static measurement but is effectively infinite while moving at a low speed less than 0.05m/s. However, the accumulation error increases with the measuring range. A novel application of utilizing this displacement sensing system upon online monitoring the linear guideway healthy status, i.e. linear guideway straightness, of machine tools is presented. The performance and the associated advantages and disadvantages of this proposed sensor as well as the on-line monitoring system is assessed experimentally.

NeedFull - a Tweet Analysis Platform to Study Human Needs During the COVID-19 Pandemic in New York State.

Governments and municipalities need to understand their citizens’ psychological needs in critical times and dangerous situations. COVID-19 brings lots of challenges to deal with. We propose NeedFull, an interactive and scalable tweet analysis platform, to help governments and municipalities to understand residents’ real psychological needs during those periods. The platform mainly consists of four parts: data collection module, data storage module, data analysis module and data visualization module. The four parts interact with each other and provide users with a thorough human needs analysis based on their queries. We employed the proposed platform to investigate the reaction of people in New York State to the ongoing worldwide COVID-19 pandemic.

An Audification and Visualization System (AVS) of an Autonomous Vehicle for Blind and Deaf People Based on Deep Learning

When blind and deaf people are passengers in fully autonomous vehicles, an intuitive and accurate visualization screen should be provided for the deaf, and an audification system with speech-to-text (STT) and text-to-speech (TTS) functions should be provided for the blind. However, these systems cannot know the fault self-diagnosis information and the instrument cluster information that indicates the current state of the vehicle when driving. This paper proposes an audification and visualization system (AVS) of an autonomous vehicle for blind and deaf people based on deep learning to solve this problem. The AVS consists of three modules. The data collection and management module (DCMM) stores and manages the data collected from the vehicle. The audification conversion module (ACM) has a speech-to-text submodule (STS) that recognizes a user’s speech and converts it to text data, and a text-to-wave submodule (TWS) that converts text data to voice. The data visualization module (DVM) visualizes the collected sensor data, fault self-diagnosis data, etc., and places the visualized data according to the size of the vehicle’s display. The experiment shows that the time taken to adjust visualization graphic components in on-board diagnostics (OBD) was approximately 2.5 times faster than the time taken in a cloud server. In addition, the overall computational time of the AVS system was approximately 2 ms faster than the existing instrument cluster. Therefore, because the AVS proposed in this paper can enable blind and deaf people to select only what they want to hear and see, it reduces the overload of transmission and greatly increases the safety of the vehicle. If the AVS is introduced in a real vehicle, it can prevent accidents for disabled and other passengers in advance.