Data Visualization System Research Articles

Less gets more attention: A novel human-centered MR remote collaboration assembly method with information recommendation and visual enhancement

Mixed reality remote collaboration assembly is a type of computer-supported collaborative assembly work that uses mixed reality technology to enable spatial information and collaboration status sharing among geographically distributed collaborators, including remote experts and local users. However, due to the abundance of mixed virtual and real-world information in the MR space and the limitations imposed by narrow field-of-view augmented reality (AR) glasses, users face challenges in effectively focusing on relevant and valuable visual information. Our research aims to enhance users' visual attention to critical guidance information in MR collaborative assembly tasks, thereby improving the clear expression of instructions and facilitating the transmission of collaborative intention. We developed the Information Recommendation and Visual Enhancement System (IRVES) through an assembly process information hierarchy division mechanism, a content-based information recommendation system, and a gesture interaction-based information visual enhancement method. IRVES can leverage the guidance expertise and preferences of remote experts to recommend information to filter out irrelevant information and present the key information that the remote expert conveys to the local user in an intuitive way through visual enhancement. We conducted a user study experiment of a collaborative assembly task of a small engine in a laboratory environment. The experimental results indicate that IRVES outperforms traditional MR remote collaborative assembly methods (VG3DV) in terms of time performance, operational errors, cognitive performance and user experience. Our research contributes a human-centered information visualization approach for remote experts and local users, providing a novel method and idea for designing visual information interfaces in MR remote collaboration assembly tasks.

Wind turbine real-time data analysis and monitoring and warning system based on Storm

Abstract This paper introduces a real-time data analysis and monitoring and warning system for wind turbines based on Storm platform. Firstly, the framework and core components of Storm technology are introduced in detail, as well as the related Kafka technology. At the same time, the method of data forecasting is introduced. In state prediction algorithm based on naive Bayes, the basic idea of naive Bayes algorithm is introduced in detail, and the pseudo-code setting meter of the algorithm is given. In addition, the wind turbine condition warning algorithm is designed and implemented. In the chapter of real-time data analysis and early warning system design and implementation for wind turbine, the database is designed, including the design of MySQL database and HBase database, the design and reality of real-time data analysis and visualization system, and the design and reality of wind turbine fault early warning system. Finally, in the chapter of system test and analysis, the system environment and test data set are described, and the prediction algorithm based on naive Bayes is tested and the system is tested.

Visualizing sustainable rainwater harvesting: A case study of Karbala Province

Abstract The management of rainwater collection in a practical way is a fundamental need for the management of water resources in a manner that is sustainable. The goal of this research is to determine whether or not remote sensing technology is effective in providing data on precipitation for the purpose of locating rainwater collection tank locations in the province of Karbala. Rainfall patterns fluctuate considerably. Remote sensing may not capture variability enough to estimate the rainfall period and location. Sustainable rainfall harvesting requires accurate rainfall timing and distribution. This information is applied in the modeling of hydrological processes, the management of disasters, and environmental research. Following the completion of a geographical study, it has been established that the city of Karbala may be divided into two basic sections. Through the use of estimation, it is possible to more easily identify the region that is ideal for the location of rainwater-harvesting reservoirs and lakes. On the contrary, it is crucial to keep in mind that a location that was chosen based on average rainfall over a period of two years could not be suitable for other time periods. This is an idea that should be kept in mind several times. To put this into perspective, when choosing a location, it is vital to take into consideration the severity of the rainfall as well as the geographical location of the area. Particularly in locations such as Karbala, the implementation of data visualization systems into water management practices has the potential to improve both the efficiency and sustainability of water management methods. The findings of this study show the significance of implementing precise site selection techniques to enhance rainwater collection systems and encourage activities that are environmentally responsible for water management.

Image data visualization and communication system based on sensor network simulation and visual feature extraction

In the context of today's information explosion, how to extract useful information from a large amount of image data and convey it to users is a challenging problem. Therefore, this study aims to achieve effective communication of image data by constructing a sensor network simulation and utilizing visual feature extraction methods. The study constructed a sensor network simulation to simulate the working principle of a biosensor system, thereby achieving perception and understanding of image data, and automatically obtaining and processing information from input images. Research on the use of visual feature extraction methods to extract features that describe the content and structure of images, and apply them to the results obtained from sensor network simulation to accurately capture important information in image data. The research aims to convey the extracted feature information to users through visual means, and present the extracted feature information to users in an intuitive graphical form, in order to gain a more intuitive understanding of the meaning and features of image data, improve the ability to understand data and analyze results. By constructing a sensor network to simulate the perception process of image data and using visual feature extraction methods to extract key feature information, this article can effectively convey this information to users through visualization methods. This method provides a new solution for the field of image data visualization communication, which helps to improve the efficiency and accuracy of data communication.

Relationships between subjective experience, electroencephalogram, and heart rate variability during a series of cosmetic behavior.

Cosmetic behavior is an important daily activity, especially for women, because it increases visual attractiveness, self-confidence, and positive emotions. However, it is unknown whether a relationship exists between physiological measures and subjective experiences during the series of cosmetic behaviors. Electroencephalograms (EEG) and electrocardiograms (ECG) from thirty female participants who were asked to look in a mirror after applying skincare, as well as base, eye, cheek, and lip makeup were recorded. The price range of cosmetic products was also considered. Subjective evaluations of the skin surface, emotions, and self-confidence were equally measured after looking in the mirror at each step of the cosmetic behavior. Linear mixed models were fitted to examine whether the subjective experience could be explained by the variety of cosmetic products and/or physiological responses. The subjective evaluation was summarized into the following three factors using a factor analysis: self-confidence, hedonic perception, and negative emotion. Each theta-band (4-6 Hz) power, alpha-band (8-13 Hz) power of the EEG, and heart rate variability measures were subjected to a principal component analysis separately. The linear mixed models indicated that the variation in the self-confidence score and the negative emotion score was explained only by the steps of cosmetic behaviors, that is, self-confidence increased while negative emotions decreased as the steps of cosmetic behaviors proceeded. On the other hand, the hedonic perception score was explained by the interaction of the steps of cosmetic behaviors and price, indicating that positive tactile perception and positive emotion were higher when luxury cosmetic products were applied than when affordable products were applied. Furthermore, the model indicated that the hedonic perception score was positively associated with the alpha-band power over occipital sites whereas sympathetic nervous system activity was negatively associated with the alpha-band power over lateral central sites. These results suggest that positive perceptual and emotional experiences are associated with greater attention to somatosensory information than to visual information and sympathetic autonomic nervous system activities. The current results also emphasize the possibility of using physiological measurements as objective measures of cosmetic behavior.

APPLICATION OF OBJECT DETECTION TECHNOLOGY IN AORTOGRAPHY KEYPOINT TRACKING

HighlightsThe practical significance of the work lies in the fact that the presented aortography data visualization system is an effective tool for visually assisting surgeons during transcatheter aortic valve implantation interventions, supporting real-time operation mode. The proposed data preprocessing algorithm, which improves image quality with minimal performance costs, complements the system, allowing specialists to achieve the best result. AbstractAim. The aim of this study is to develop a visual assistance system for transcatheter aortic valve implantation procedures.Methods. To address the stated objective, our own dataset consisting of 35 videos of the intervention was used. The visualization system is based on the approach of detecting key points in aortography, utilizing “Object detection” technology with the application of YOLO family artificial neural networks. To achieve the best result, we proposed a method to enhance the quality of input data using convolutional neural networks, specifically the «Autoencoder» technology.Results. The results of the study revealed that the convolutional autoencoder model is capable of restoring the informativeness of noisy input images from 40 to 75%, thereby increasing the accuracy of object detection in images. The presented real-time tracking system for facilitating TAVI procedures achieves a final accuracy of 51.9% according to the Mean Average Precision (MAP) quality metric. The visual assistance system can recognize and track key points indicating the location of the aortic root, delivery system, and heart valve prosthesis during surgery. The practical significance of the work lies in the fact that the presented aortography data visualization system is an effective tool for visually assisting surgeons during interventions, supporting real-time operation mode.Conclusion. The proposed data preprocessing algorithm, which improves image quality with minimal performance costs, complements the visualization system, allowing specialists to achieve the best results.

REAL-TIME DATA VISUALIZATION FOR IOT NETWORK SYSTEMS: CHALLENGES AND STRATEGIES FOR PERFORMANCE OPTIMIZATION

Real-time data visualization has become an essential tool for decision-making systems in various industries, including finance, healthcare, IoT, and manufacturing. Real-time data visualization enables organizations to monitor and analyze data as it is generated, providing real-time insights into critical business operations. However, real-time data visualization poses several challenges, including performance, data quality, and visualization complexity. This paper will explore the importance of real-time data visualization in IoT network systems, and the challenges associated with it. Specifically, the paper will discuss the challenges of real-time data visualization and ideas to increase performance. The paper will also provide a comprehensive analysis of the impact of real-time data visualization on IoT network and decision-making systems, highlighting its benefits and potential drawbacks. The paper will begin by discussing the importance of real-time data visualization in IoT network systems, highlighting its role in providing timely insights into critical operations. It will then delve into the challenges associated with real-time data visualization, including data quality, visualization complexity, and performance. The paper will provide a detailed analysis of each challenge, outlining the potential impact on real-time data visualization systems and deci-sion-making processes. The paper will also explore ideas to increase performance in real-time data visualization, including implementing high-performance computing infrastructure, op-timizing data processing and analysis, using caching techniques, using visualization techniques optimized for performance, implementing data compression, and using real-time analytics. The paper will provide a comprehensive analysis of each idea, outlining its potential impact on real-time data visualization systems' performance and overall effective-ness. Finally, the paper will conclude by highlighting the importance of real-time data visualization in IoT network systems and the need to address the challenges associated with it. The paper will also provide recommendations about how to implement real-time data visualization systems, outlining key considerations and best practices to ensure successful implementation and optimal performance.

Multidimensional visualization analysis based on large-scale GNSS data

Abstract With the deployment of global navigation systems such as GPS, GLONASS, GALILEO, and BeiDou, and the construction of the global observation network, global navigation satellite system (GNSS) data have greatly expanded. Traditional relational databases and file systems can no longer efficiently store and process GNSS data. In addition, multidimensional, in-depth visual analysis methods for such data are lacking. In this paper, we provide a solution and implement a GNSS data visualization system based on MapV and cesium running on the Hadoop platform, which can effectively solve the problems of the lack of storage and computing resources for these massive multisource heterogeneous data. The proposed system provides multidimensional presentations to show how the space and ground segments of the entire system are organized by analyzing GNSS data.

An Effective and Affordable Internet of Things (IoT) Scale System to Measure Crop Water Use

Scales are widely used in many agricultural applications, ranging from weighing crops at harvest to determine crop yields to regularly weighing animals to determine growth rate. In agricultural research applications, there is a long history of measuring crop water use (evapotranspiration [ET]) using a particular type of scale called weighing lysimeters. Typically, weighing lysimeters require very accurate data logging systems that tend to be expensive. Recent developments in open-source technologies, such as micro-controllers and Internet of Things (IoT) platforms, have created opportunities for developing effective and affordable ways to monitor crop water use and transmit the data to the Internet in near real-time. Therefore, this study aimed to create an affordable Internet of Things (IoT) scale system to measure crop ET. A scale system to monitor crop ET was developed using an Arduino-compatible microcontroller with cell phone communication, electronic load cells, an Inter-Integrated Circuit (I2C) multiplexer, and analog-to-digital converters (ADCs). The system was powered by a LiPo battery, charged by a small (6 W) solar panel. The IoT scale system was programmed to collect data from the load cells at regular time intervals and send the data to the ThingSpeak IoT platform. The system performed successfully during indoor and outdoor experiments conducted in 2023 at the Clemson University Edisto Research and Education Center, Blackville, SC. Calibrations relating the measured output of the scale load cells to changes in mass resulted in excellent linear relationships during the indoor (r2 = 1.0) and outdoor experiments (r2 = 0.9994). The results of the outdoor experiments showed that the IoT scale system could accurately measure changes in lysimeter mass during several months (Feb to Jun) without failure in data collection or transmission. The changes in lysimeter mass measured during that period reflected the same trend as concurrent soil moisture data measured at a nearby weather station. The changes in lysimeter mass measured with the IoT scale system during the outdoor experiment were accurate enough to derive daily and hourly crop ET and even detect what appeared to be dew formation during the morning hours. The IoT scale system can be built using open-source, off-the-shelf electronic components which can be purchased online and easily replaced or substituted. The system can also be developed at a fraction of the cost of data logging, communication, and visualization systems typically used for lysimeter and scale applications.

Low-Cost Sensor Deployment on a Public Minibus in Fukushima Prefecture.

This study analyzed radiation dose data to observe the annual decline in ambient radiation doses and assess the factors contributing to fluctuations in reconstructed areas of the Fukushima prefecture. Utilizing a novel mobile monitoring system installed on a community minibus, the study employed a cost-effective sensor, namely, Pocket Geiger which was integrated with a microcontroller and telecommunication system for data transfer, access, visualization, and accumulation. The study area included the region between Okuma and Tomioka towns. The ambient dose rate recorded along the minibus route was depicted on a map, averaged within a 1 × 1 km mesh created with the Quantum Geographic Information System. To ensure accuracy, the shielding factor of the minibus material is determined to adjust the dose readings. A significant decrease (p < 0.001) in the radiation dose ranges from 2022 to 2023 was observed. The land use classification by the Advanced Land Observation Satellite revealed an ecological half-life ranging from 2.41 years to 1 year, suggesting a rapid radiation decay across all land types. This underscores the close connection between radiation attenuation and environmental factors, as well as decontamination efforts across diverse land categories.

Bridging the clinical-research gap: Harnessing an electronic data capture, integration, and visualization platform to systematically assess prospective patient-reported outcomes in mitochondrial medicine

PurposeOptimizing individualized clinical care in heterogeneous rare disorders, such as primary mitochondrial disease (PMD), will require gaining more comprehensive and objective understanding of the patient experience by longitudinally tracking quantifiable patient-specific outcomes and integrating subjective data with clinical data to monitor disease progression and targeted therapeutic effects. MethodsElectronic surveys of patient (and caregiver) reported outcome (PRO) measures were administered in REDCap within clinical domains commonly impaired in patients with PMD in the context of their ongoing routine care, including quality of life, fatigue, and functional performance. Descriptive statistics, group comparisons, and inter-measure correlations were used to evaluate system feasibility, utility of PRO results, and consistency across outcome measure domains. Real-time tracking and visualization of longitudinal individual-level and cohort-level data were facilitated by a customized data integration and visualization system, MMFP-Tableau. ResultsAn efficient PRO electronic capture and analysis system was successfully implemented within a clinically and genetically heterogeneous rare disease clinical population spanning all ages. Preliminary data analyses demonstrated the flexibility of this approach for a range of PROs, as well as the value of selected PRO scales to objectively capture qualitative functional impairment in four key clinical domains. High inter-measure reliability and correlation were observed. Between-group analyses revealed that adults with PMD reported significantly worse quality of life and greater fatigue than did affected children, while PMD patients with nuclear gene disorders reported lower functioning relative to those with an mtDNA gene disorder in several clinical domains. ConclusionIncorporation of routine electronic data collection, integration, visualization, and analysis of relevant PROs for rare disease patients seen in the clinical setting was demonstrated to be feasible, providing prospective and quantitative data on key clinical domains relevant to the patient experience. Further work is needed to validate specific PROs in diverse PMD patients and cohorts, and to formally evaluate the clinical impact and utility of harnessing integrated data systems to objectively track and integrate quantifiable PROs in the context of rare disease patient clinical care.

Neighbourhood identity: a methodological perspective from Nantes-France

PurposeThis research aims to develop a method for defining the identity of multilayered neighbourhoods by taking a case study in Nantes/France. It utilizes the urban identity concept to achieve this goal, which is defined by physical and identificatory relation to the neighbourhood.Design/methodology/approachThe methodology includes historical periodical analysis, housing form and architectural stylistic definition, visualization and geographic information system (GIS) mapping. The research conducts spatial analysis to reveal the physical component of the urban identity of the neighbourhood and interviews (No = 50) with dwellers for the identificatory relation, asking about neighbourhood tangible/non-tangible elements. All these data are mapped through GIS.FindingsThe study found that the physical component is defined by three urban layers (identified as industrial, reconstruction and development, and post-industrial) and eleven housing typologies. As for the identificatory relation, the authors found that the interviewees mostly identified with their neighbourhood, whereas a minority did not. The most important form of identification with the neighbourhood was its atmosphere, and as reasons were given, the neighbourhood's positively evaluated quality, good location and social values.Originality/valueIt proposes the definition of the physical component through urban layers and housing typologies. The identificatory relation also considers the identification of the residents with the neighbourhood's tangible/non-tangible urban elements.

Analisis Bibliometrik Pada Kecurangan Laporan Keuangan: Publikasi Tiga Dekade Terakhir

Purpose of the research is to determine the growth of research publications regarding fraud in financial reporting by examining bibliometric analysis from the Scopus database from 1993 to 2022. This research method uses a bibliometric analysis approach. The application system for data visualization uses VOSviewer and Microsoft Excel to perform data frequency analysis. The results of this research indicate that the issue of financial statement fraud is a topic that continues to attract the attention of researchers. Authors contribute to the literature by analyzing publications and authors by year, country, journal, author, and organization. This research can help increase the understanding of other researchers to plan future research to detect and prevent financial statement fraud for the sustainability of an organization. The author tries to conduct a comprehensive review of research on financial statement fraud conducted over three decades. This study makes a significant contribution and provides new insights into financial statement fraud.

Design and Implementation of Data Management and Visualisation Module in Financial Digital Management

Enterprise financial data is the key indicator of enterprise development, which provides the important basis for management to analyse and make decisions. Therefore, the provision of reliable and effective information services to enterprises through visualisation technology has become an urgent problem to be solved in the construction of enterprise informatisation. At present, the common data statistics and visualisation tools in the market are difficult to meet the needs of specialised financial enterprises for data analysis. Additionally, the current financial management system has several issues, including an abundance of data and lack of observation suitability. Aiming at the deficiency of data management function in the system, this paper studies the improvement design of data management and visualisation module in financial digital management. First, [Formula: see text]-means clustering algorithm and C4.5 decision tree algorithm are selected to improve the financial data management system. Then, through the existing hierarchical data visualisation scheme, the node link method, space filling method and Sankey chart are proposed to display the changes of financial data. Finally, the data management and visualisation module and the corresponding algorithm flow are designed. The experiment indicates a contour coefficient of 0.53 for the performance evaluation model based on the [Formula: see text]-means algorithm, indicating a satisfactory clustering result. The employee violation prediction model, based on the C4.5 decision tree algorithm, exhibits a high prediction accuracy of 92.35% for the training dataset, demonstrating its effectiveness in predicting employee violations. The data rendering accuracy of the visualised tool is 98.46%, significantly surpassing that of traditional visualisation tools. At the same time, its visual effect and operation are better than traditional tools. Compared with the traditional data visualisation system, this research method improves the efficiency of enterprise financial data management, converts complicated financial data into graphics that are easier for people to understand, realises visualisation, and effectively reduces the management cost of financial operations.

VisCARS: Knowledge Graph-based Context-Aware Recommender System for Time-Series Data Visualization and Monitoring Dashboards.

Data visualization recommendation aims to assist the user in creating visualizations from a given dataset. The process of creating appropriate visualizations requires expert knowledge of the available data model as well as the dashboard application that is used. To relieve the user from requiring this knowledge and from the manual process of creating numerous visualizations or dashboards, we present a context-aware visualization recommender system (VisCARS) for monitoring applications that automatically recommends a personalized dashboard to the user, based on the system they are monitoring and the task they are trying to achieve. Through a knowledge graph-based approach, expert knowledge about the data and the application is included as contextual features to improve the recommendation process. A dashboard ontology is presented that describes key components in a dashboard ecosystem in order to semantically annotate all the knowledge in the graph. The recommender system leverages knowledge graph embedding and comparison techniques in combination with a context-aware collaborative filtering approach to derive recommendations based on the context, i.e., the state of the monitored system, and the end-user preferences. The proposed methodology is implemented and integrated in a dynamic dashboard solution. The resulting recommender system is evaluated on a smart healthcare use-case through a quantitative performance and scalability analysis as well as a qualitative user study. The results highlight the performance of the proposed solution compared to the state-of-the-art and its potential for time-critical monitoring applications.

Analysis of prospective demand for hydrogen in the road transportation sector: Evidence from 14 countries

This paper precisely estimates the looming demand for hydrogen within the transportation sector, spanning 14 pivotal G20 nations from 2022 to 2050, anchored in the global vehicle hydrogen market penetration rate. Employing a robust techno-economic modeling methodology and leveraging Geographic Information System (GIS) for enriched visualization, it unveils distinct environmental dividends across three thoughtfully devised scenarios: Business-as-Usual, Moderate, and Aggressive. Findings herald India and China as pinnacles of hydrogen demand, with South Korea and Japan tailing closely. The study forecasts the energy consumption for solar hydrogen production in G20 countries to oscillate between 3.02 and 2.89 million GWh in 2022, while production costs are anticipated to range from $8.47/kg to $10.01/kg. In an assertive scenario, the paper illuminates a pathway towards achieving a significant CO2 reduction, unmasking environmental savings of up to a staggering 48%, equivalent to 1.5 million mtCO2, by 2050.

Selecting a Monitoring Technology for a Control System of Distributed Oil Production Facilities

The article proposes the structure of a SCADA system for monitoring and control of oil production facilities that are distributed over a large area. The main emphasis is on the selection of technology that will enable effective monitoring of the equipment of each oil well. Factors such as reliability, ease of use, availability of protection against third-party interference, as well as availability and accessibility of an open-source software code were taken into account. After reviewing the most common software platforms, a system based on Prometheus and Grafana was selected. It is a combination of the Prometheus time series database server and the Grafana information visualization and analysis system. The important factors that determined the choice of this platform were the availability of the open source code and a large library of ready-made templates for displaying the well parameters in real time. An example of the created visualization window of the dynamometer card of the well, built on the basis of the experimentally recorded data, is presented.

Designing Data Visualization System Based on Language-Oriented Approach

The data visualization method based on a language-oriented approach is proposed. An analysis of data visualization tools and their customizability for subject areas based on user needs has been carried out. It is noted that these tools require highly qualified users to customize the data visualization format (users must have programming skills). It is proposed to customize visualization tools to the needs of users and the specifics of the user's tasks being solved by creating domain-specific languages (DSL). A system architecture based on the use of multifaceted ontology is described. The ontology includes descriptions of languages and domains, as well as rules for generating new languages and transforming constructed models. Languages are designed to describe different classes of diagrams. This system includes tools for automatic new DSL generation via mapping domain ontology onto the base language metamodel according to user-specified rules. Different types of diagrams have been classified and the main components of each type diagrams have been identified, which provides the basis for creating an ontology of data visualization languages. A base language is proposed for creating diagrams. The language customizability for specific domains is demonstrated. An example of the created data visualization models is shown.

A ROOT-based General Online Data Visualization System

A ROOT-based General Online Data Visualization System

상수관망 관리를 위한 3D 공간정보 시스템 및 모델 프로토타입 개발

Recently, water supply problems and the advent of the 4th Industrial Revolution, smart water supply network management using digital twin technology is becoming needs. In this study, to develop a prototype of the model and system 3D visualization information system for water supply network management based on own technology without restrictions on licenses. Based on the data from the ME(2017), an inventory DB according to the Level classification was established, and for 3D visualization based on 2D information systems, 9-steps of implementation process and verification were conducted. Comparing and analyzing the values of latitude and longitude of polygons, it was confirmed that the reliability and accuracy of the two-dimensional information system implemented in this study were secured. Therefore, 3D visualization was performed using this, and the process of designing data linkage plans within the program and implementing 3D visualization was established. The results of this study are considered insignificant in the construction of a network management space server with high economic benefits and high reliability and accuracy of GIS.