Real-time Data Visualization Research Articles

Validation of a computational model for atmospheric pollution emanating from an industrial chimney using a solar based instrumentation system

This study examines the atmospheric dispersion of pollutants from industrial chimneys, highlighting the importance of assessments both before and after installation to safeguard public health and the environment. A Vantage Pro2 Plus weather station, powered by a solar panel, was used to continuously collect accurate meteorological data, which informed a Gaussian model for pollutant dispersion. The results revealed that the predominant wind direction is towards the east, with pollution mainly affecting areas between the southeast and northeast. Seasonal variations showed broader dispersion during winter that exceeds 4500 meters of large at a distance of 10 kilometers from the source during the day and higher pollutant concentrations at night that can reach 5.10-6 g/s, compared to a maximum of 10-6 g/s during other times. The study demonstrates that lower temperatures result in higher concentrations over smaller areas, whereas higher temperatures cause wider dispersion with lower concentrations. These findings support decision-making for optimal chimney placement, ongoing monitoring, and emergency planning. The integration of solar-powered weather stations and real-time data visualization significantly improves the efficiency of environmental monitoring and management.

Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification

Epidermally mounted sensors using triaxial accelerometers have been previously used to monitor physiological processes with the implementation of machine learning (ML) algorithm interfaces. The findings from these previous studies have established a strong foundation for the analysis of high-resolution, intricate signals, typically through frequency domain conversion. In this study we integrate a wireless mechano-acoustic sensor with a multi-modal deep learning system for the real-time analysis of signals emitted by the laryngeal prominence area of the thyroid cartilage at frequency ranges up to 1 kHz. This interface provides real-time data visualization and communication with the ML server, creating a system that assesses severity of chronic obstructive pulmonary disease and analyzes the user’s speech patterns.

Automated reporting and statistical analysis of test case results in continuous integration: A custom dashboard approach

CI has been one of the cornerstones of modern software development, where teams integrate code changes frequently and have automated testing to receive fast feedback. Running a large amount of test data, however, is overwhelming and can be fatiguing in extracting meaningful insights from traditional reporting methods like email notifications. The paper presents a bespoke dashboard, designed to support the extension of automation in test case result reporting and analysis within a CI environment by real-time data visualization, interactivity in filtering and sorting, and statistical insights that would make trends and anomalies easier for teams to detect. Implemented in a large software project, this solution brought immense improvement in decision-making, team collaboration, and efficiency, since the dashboard reduced time spent on manual report analyses and generations. The flexibility and ability to integrate with a number of CI tools make it a powerful solution for addressing traditional reporting shortcomings and let development teams focus on areas needing improvement, ultimately enhancing software quality.

System for the Acquisition and Monitoring of Energy Consumption in the Context of Industry 4.0

This paper examines methodologies for monitoring energy consumption and operational parameters of an industrial assembly line, with a particular emphasis on the utilization of virtual tools, augmented reality, and real-time data visualization. The research examines the integration of an interface and the implementation of a data acquisition system, employing augmented reality to facilitate interaction with the collected data. The proposed system employs the Node-RED interface to facilitate the establishment of connections between constituent elements. The data is stored in a database, which provides support for decision-making in the analysis of energy consumption and operational parameters of the industrial assembly line. The results demonstrate that the solution is effective in enhancing energy resource management, identifying inefficiencies, and optimizing the performance of industrial equipment. The findings indicate that the implementation of such a system can markedly enhance the industrial process.

Design and Implementation of a Virtual Experimental Teaching System for Deep Energy Exploitation Based on Digital Twin Technology

The exploration and development of deep oil and gas resources are becoming the primary focus in the fossil energy sector, thereby increasing the demand for highly skilled engineers. Colleges and universities play a crucial role in cultivating talent in petroleum engineering. However, the current traditional teaching systems, particularly in experimental practices, face significant challenges, such as low efficiency, limited environments, and a disconnect between theoretical knowledge and practical application. To address these issues and enhance learners’ practical abilities and comprehension, we introduced digital twin technology into the experimental teaching of deep energy exploitation. This paper analyzes innovative pedagogical approaches, with a special emphasis on the real-time visualization of hydraulic fracturing. Supported by the National Key Laboratory of Chengdu University of Technology, our research team developed multiple digital twin platforms for both indoor and onsite hydraulic fracturing. These platforms utilize advanced algorithms and models, enabling real-time data acquisition and visualization analysis. Pilot teaching results demonstrate that the virtual experimental system based on digital twin technology encourages active learner engagement, improves their understanding of digitalization in engineering, and enhances their professional skills in deep oil and gas exploration. The digital twin-based visualization system is a valuable tool for experimental teaching in deep energy exploitation, and its application could serve as a model for other engineering disciplines.

IoT Weather Station Optimized for Energy Efficiency

This work presents an economical IoT weather station with energy-efficient capabilities. It offers a multi-sensor platform, with balanced functionality and power optimization that ensures an extended- wireless range. The developed system features diverse sensors measuring temperature, humidity, pressure and light intensity. Employing power-saving techniques like sleep/wake cycles and controlled sampling rates makes the station economical and power-efficient. This setup allows distant observation via a web browser interface, addressing the challenge of low power and extended-range deployments of embedded systems. The adopted energy optimization strategy enabled autonomous long-range functioning, with real-time data collection and visualization. The recoded peak power consumption is only 2022 A for all the embedded operations.

Levitating Magnetic Insoles: A Novel Approach to Alleviating Plantar Fasciitis Through the Reduction and Redistribution of Plantar Pressures

Plantar fasciitis, a leading cause of foot pain, affects a significant portion of the population, with an estimated prevalence of 10% during an individual’s lifetime. This debilitating condition can result in substantial healthcare costs and decreased quality of life. The pain is attributed to the inflammation of the plantar fascia, which is crucial for supporting the foot's arch. Risk factors for plantar fasciitis include obesity, sedentary lifestyles, improper footwear, and biomechanical abnormalities. Current treatment options are primarily conservative, with varying levels of success. In response, an affordable, lightweight, and soft, magnetic insole was developed as a novel approach to alleviate plantar fasciitis by reducing and redistributing plantar pressures with neodymium magnets. The final prototype achieves a magnetic repulsion force of approximately 84.57 lbs. and incorporates sensor modules to monitor data. The device also facilitates real-time data visualization between Arduino and personal devices, providing valuable insights into plantar pressure and walking characteristics for various patients. Data analysis and structural optimization are further enhanced through the use of MATLAB and Excel, which generate heatmaps to visualize plantar pressure distribution. Future research should focus on increasing the magnetic intensity to improve pain relief effectiveness and incorporating personalized disease-specific adjustments to optimize the magnet placement within the sole. By addressing the prevalent issue of foot pain and offering a potential solution for plantar fasciitis, our levitating magnetic insoles demonstrate significant potential for enhancing the quality of life for a large portion of the population.

Data-driven visual model development and 3D visual analytics framework for underground mining

Data visualisation is broadly utilised to aid in presenting complex information, facilitating decision-making, and enhancing operational efficiency in the mining sector. However, due to the extensive utilisation of CAD files and the complexity and difficulty of converting 2D drawings into 3D models, it is challenging to realise (near) real-time 3D data visualisation in mining applications. The difficulties are including site drawing complexity, lack of standardisation, time-consuming cleaning, and imperative manual intervention. This paper addresses these challenges by focusing on an automatic, data-driven approach for 3D visual model development in mining, with an emphasis on lifetime support and sustainability. Therefore, we introduce standardised visual model data diagram and proposed conversion workflows to leverage digitised 2D drawings in automatic 3D visual model development. Moreover, to improve visualisation efficiency and facilitate visual analytics construction in mining, this paper also proposes a 3D tile index methodology for efficient visual model reconstruction and generic production data integration. To ensure lifetime support and sustainable development in visualisation, we propose two model expansion schemes: one based on dxf file-driven model development, and the other one is interaction-oriented 3D model design and development, which the two schemes share the same data exchange and conversion workflows. Furthermore, to facilitate data fusion among various datasets, we also provide insights into revealing spatial–temporal patterns among multimodality data by real-time visualisation. These advancements aim to propel the field of underground mining visualisation, offering significant insights into enhancing the efficiency and safety of mining operations.

IoT-enabled fire detection for sustainable agriculture: A real-time system using flask and embedded technologies

In modern agriculture, the threat of fire poses significant economic and environmental risks. Additionally, traditional fire detection methods are inadequate and poorly integrated with advanced technologies. It is crucial to develop a more efficient and reliable fire detection system. Also, the lack of real-time and accurate monitoring in current systems compromises the resilience and sustainability of farming operations. This article details developing and implementing a real-time fire detection system tailored for smart agriculture. It integrates cutting-edge technologies, including the Internet of Things (IoT), embedded systems, and a Flask-based web application, fortified by cybersecurity measures such as login authentication and secure HTTP protocols. The system's primary aim is to monitor environmental conditions continuously in agricultural fields to detect signs of smoke or flame swiftly, facilitating preventive actions to safeguard crops and infrastructure. The architecture employs sensors distributed across fields to monitor conditions, a Raspberry Pi 3 B+ as the central processing unit for data acquisition and transmission, and a web interface developed using Flask, HTML, and CSS for secure and real-time visualization of detection data. Critical components like the MCP3208 analog-to-digital converter ensure the system's reliability and accuracy. Experimental results confirm the system's efficacy in early fire detection and real-time data visualization via the web browser. Enhanced security features, such as login authentication, protect sensitive information, while regular updates maintain data relevance. This study advances fire prevention and farm preservation efforts, offering a high-performance technological solution for proactive monitoring and quick response to fire risks, thereby supporting food security and sustainable agricultural practices.

An integrated cyberinfrastructure system for water quality resources in the Upper Mississippi River Basin

ABSTRACT The Upper Mississippi Information System (UMIS) is a cyberinfrastructure framework designed to support large-scale real-time water-quality data integration, analysis, and visualization for the Upper Mississippi River Basin (UMRB). UMIS is intended to directly address three of the Grand Challenges for Engineering including (1) understanding access to clean drinking water, (2) management of the nitrogen cycle, and (3) engineering the tools of scientific discovery. The UMIS is designed to provide significant immediate and long-term impacts including a central platform for data access, integration, discovery, and adoption of cyberinfrastructure tools and services. The UMIS demonstrates that public data aggregators and central repositories can provide important services to anyone interested in water-quality research or education. In addition, working across multiple scales (e.g., state, region, county, or watershed) allows researchers to understand broad and narrow effects of water-quality strategies. Exploration of data across these scales encourages the development of problem-based research questions that can eventually provide feedback to public policies.

The Importance of the Use of Unmanned Aerial Vehicles (UAVs) in the Oil and Gas Industry

The oil and gas industry, renowned for its extensive and intricate operations, confronts numerous challenges in maintaining and inspecting its infrastructure. Traditional inspection methodologies, often reliant on manual labor, are not only time-consuming and costly but also expose workers to hazardous conditions. These conventional methods necessitate the shutdown of operations, leading to considerable productivity losses and elevated costs. Over the past decade, the advent of unmanned aerial vehicles (UAVs) has introduced a transformative technology in this sector, providing a safer, more efficient, and cost-effective alternative to traditional inspection techniques. UAVs, equipped with advanced sensors and high-resolution cameras, facilitate detailed visual and thermal inspections of critical assets such as pipelines, flare stacks, and offshore platforms. These capabilities enable companies to perform routine inspections without halting operations, thereby minimizing downtime and operational disruptions. The implementation of UAV technology has notably enhanced safety by reducing the need for human intervention in perilous environments. For instance, the traditional inspection of flare stacks necessitates workers to ascend these structures, posing significant risks. UAVs obviate this requirement by delivering real-time visual data from secure distances. Moreover, the precision and accuracy inherent in UAV inspections contribute to the early detection of defects and potential issues, allowing for prompt maintenance and repairs, which further augment safety and operational efficiency. The environmental benefits of UAV technology are also noteworthy. By diminishing the reliance on heavy machinery and extensive transportation typically associated with inspections and maintenance, UAVs aid in reducing carbon emissions, thus aligning with the industry's sustainability objectives. In summary, the integration of UAVs into the oil and gas industry's inspection protocols represents a significant technological advancement. This integration aligns with the sector's goals of enhancing safety, efficiency, and environmental sustainability, marking a pivotal step forward in the industry's evolution.

Application of augmented reality in space exploration and astronomy

Introduction: augmented reality (AR) integrates signals captured from the real world with signals generated by computers, making them correspond in the construction of new coherent realities, which complement and coexist in the real world and the virtual world. Objective: characterize the impact of augmented reality on astronomy and space exploration issues. Method: a review of the bibliography was carried out using articles rescued from databases such as SciELO, Dialnet, Scopus, Researchgate, recovering a total of 16 reference articles from available literature related to the topic in question, included in the time frame. between 2019 and 2024. Results: AR constitutes powerful tools for teaching Astronomy, since they allow the observation of stars, constellations and the solar system, facilitating the explanation of celestial phenomena to the educator, generating behaviors of satisfaction, interest, autonomy and, above all, motivation towards The learning of topics related to the universe, maintains the real world that the user sees, complementing it with virtual information superimposed on the real one, thus favoring decision making, the formulation of predictions and solutions and even developing mathematical models. Conclusions: augmented reality constitutes a powerful tool for the acquisition of skills and knowledge, allowing real-time visualization of astronomical data, scientific education and dissemination, space simulations, but above all it allows us to understand and discover the immensity of the universe that surrounds us

Digital occupancy assessment for lighting evaluation: a pilot study to prepare for real-time research results

The relationship between lighting and human well-being is pivotal in architecture and environmental design. Adequate lighting enhances visual appeal and significantly influences occupant comfort, productivity, and satisfaction. This pilot study aimed to integrate digital occupancy assessment methods to understand indoor lighting conditions' impact on occupant well-being. Hypotheses explored the potential link between lighting conditions and occupant well-being, as assessed through physiological indicators and the influence of interior attributes on occupants' perceptions and physiological responses. The identified main components were satisfaction with artificial and daylighting, ambiance, and stimulation. The study explored the relationship between lighting conditions, physiological responses, and subjective assessments, revealing potential influences on occupant perceptions. A real-time data visualization could be valuable for data-intensive projects, enhancing comprehension and decision-making for facility management. While limited by the small sample size, trends, and moderate correlations were observed, indicating the need for larger, more diverse samples in future research.

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.

Data-Driven Quality Improvement for Sustainability in Automotive Packaging

This study addresses sustainability challenges in automotive packaging by introducing a novel data-driven approach to audit and improve packaging quality. Traditional manual auditing processes are both time-consuming and prone to errors. To counter these inefficiencies, we developed an automated system utilizing Microsoft Power Apps and SQL databases to streamline data collection and processing. The automated tools facilitate real-time data capture and visualization through Microsoft Power BI, enabling precise tracking and management of returnable and expendable packaging materials. Our results show that our approach offers a significant enhancement in the accuracy and speed of data analysis, leading to more informed decision-making. Our methodology supports sustainability by reducing waste and reliance on expendable packaging and improving the economic and operational efficiency of automotive packaging systems.

Smart IV Bag and Health Monitoring System for Hospitals and Remote Places Using IOT

The health monitoring system utilizing an ESP32 controller and Arduino Nano presents an innovative approach to enhance patient care through continuous real-time monitoring of vital health parameters. This system integrates various sensors, including ECG, DHT11, HX711, and an R305 fingerprint sensor, to collect comprehensive physiological data and ensure secure access. The ESP32 microcontroller processes this data, providing immediate local alerts via a buzzer and LED indicators, while the Arduino Nano manages the GSM module to send remote notifications to healthcare professionals. Furthermore, the system employs IoT capabilities to transmit data to cloud platforms, facilitating remote monitoring and analysis. By automating the monitoring process, this project reduces the workload on healthcare staff and ensures timely medical interventions, thereby improving patient outcomes. The system's ability to provide real-time data visualization, secure access control, and remote monitoring aligns with the growing need for connected and intelligent healthcare solutions. This comprehensive health monitoring system not only enhances patient safety and care efficiency but also supports the advancement of modern healthcare infrastructures through robust data management and communication technologies.

A near real-time interactive dashboard for monitoring and anticipating demands in emergency care in the Île-de-France region (France)

Abstract Objective To allow health professionals to monitor and anticipate demands for emergency care in the Île-de-France region of France. Materials and Methods Data from emergency departments and emergency medical services are automatically processed on a daily basis and visualized through an interactive online dashboard. Forecasting methods are used to provide 7 days predictions. Results The dashboard displays data at regional and departmental levels or for 5 different age categories. It features summary statistics, historical values, predictions, comparisons to previous years, and monitoring of common reasons for care and outcomes. Discussion A large number of health professionals have already requested access to the dashboard (n = 606). Although the quality of data transmitted may vary slightly, the dashboard has already helped improve health situational awareness and anticipation. Conclusions The high access demand to the dashboard demonstrates the operational usefulness of real-time visualization of multisource data coupled with advanced analytics.

An efficient deep learning-based workflow for real-time CO2 plume visualization in saline aquifer using distributed pressure and temperature measurements

Underground carbon dioxide (CO2) sequestration is widely accepted as a proven and established technology to respond to global warming from greenhouse gas emissions. It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficacy. We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site.Our proposed workflow integrates field measurements, including distributed pressure and temperature at wells to visualize spatial and temporal migration of the CO2 plume in the subsurface. The deep learning framework has two key concepts that enhance the efficiency of the training and robustness of the CO2 plume prediction: first, instead of using multiple saturation images as output of the deep-learning model, a single Time of Flight (TOF) image are used to represent CO2 plume propagation; second, we use variational autoencoder-decoder (VAE) for high dimensional image data compression considering uncertainties in the predicted images. Time-of-Flight (TOF) is the travel time of a neural particle from the injection point, which is calculated along streamline based on the velocity field by considering reservoir heterogeneity and driving forces. The latent variables of VAE are estimated through a deep neural network model with inputs of distributed pressure and temperature measurements. Subsequently, the decoder part of the VAE expands the estimated latent variables to the original dimension of the TOF images. We demonstrate the efficacy of the proposed method by comparison with the more traditional pareto-based multi-objective Genetic Algorithm (MOGA) for the assimilation of the field measurements and forecasting of the CO2 plume migration.The power and utility of our proposed workflow are demonstrated by application to the Illinois Basin-Decatur Project (IBDP). The IBDP is a large scale 3-year CO2 storage test and is part of the Midwest Regional Carbon Sequestration Partnership (MRCSP). The field measurements include bottom-hole pressure and distributed temperature sensing (DTS) data at the injection well, and the distributed pressure measurements at several locations along the monitoring well. The dynamic model is a compositional model with 1.7 million cells. First, we identify the influential parameters using sensitivity analysis based on the pressure and temperature responses. Next, the sensitive parameters are sampled from a pre-specified range and a comprehensive training dataset is generated, including the pressure and temperature measurements and TOF images based on flow simulation results from a variety of geological model realizations. The trained neural network model can identify geological model realizations that capture the spatial and temporal migration of pressure and CO2 saturation based on Euclidean distance within the VAE latent space. For comparison with traditional history matching workflow, the selected sensitive parameters are also tuned using MOGA with the pressure and temperature measurements. A reasonable agreement is obtained for the predicted CO2 plume migration between two workflows. The proposed deep learning workflow shows significant speed-up compared to the traditional multi-objective optimization-based history matching workflow (5 h for training and seconds for prediction; compared to several days/weeks for the MOGA).The novelty of this work is the application of an efficient deep learning-based workflow to a large-scale CO2 storage test site for assimilating field measurements and predicting CO2 plume propagation in a near real-time and comparison with traditional history matching approach.

Enhancing Performance Metrics: A Google Looker Studio Approach to Key Performance Indicator (KPI) Management System for Homecorp Offshore Drafting Team

The Homecorp Offshore Drafting Key Performance Indicator (KPI) Management System is a comprehensive process that aims to improve the management and monitoring of key performance indicators within the drafting department. This system utilizes Google Looker Studio, which provides a user-friendly real-time data analysis and visualization platform. It enables managers to make well-informed decisions and enhance departmental performance. The process begins with collecting daily task data, logged manually or through automated Google Apps Scripts into a Google Sheets database. This data includes task details such as type, status, and completion time. A KPI-pointing system, developed through focus group discussions, also assigns point values to tasks based on their complexity and time requirements. Integrating Google Looker Studio with Google Sheets allows real-time synchronization and web-based monitoring of key performance indicators (KPIs). The dashboard offers detailed insights such as task counts, average KPIs, and monthly performance graphs, providing a comprehensive performance evaluation. The implementation of this system has been well-received by the team, with survey results indicating improvements in tracking performance metrics, ease of use, and enhanced collaboration. While these outcomes are positive, there is still a recognized need for further refinement, particularly in incorporating qualitative data and improving IT support. The KPI Management System has significantly enhanced performance monitoring and management for the drafting team. To keep the system up-to-date and effective, ongoing feedback and iterative improvements must be maintained. Potential future enhancements could include extending the system's use company-wide to foster a culture of continuous improvement and accountability.

Advanced Tactical Helmet for Military Use

Abstract: Advanced features on a tactical helmet designed for military use. Soldiers in today's military operations must have improved situational awareness, strong communication skills, and superior protection when fighting. A new generation of tactical helmets with enhanced features is required because the standard helmets now in use do not adequately fulfil these criteria. To improve army performance and survivability The helmet has several extra features in addition to its extensive ESP32 CAM for real-time data visualization, communication systems for seamless inter-squad communication, and improved ballistic protection materials. Among these characteristics include the incorporation of a helmet camera's First-Person-View (FPV) live video feed into a base station, along with the base station's face detection capabilities for enhanced identification. SolidWorks was used in the design process to produce a unique 3D-printed helmet, while ESP32 and ESP8266 were used to power a custom mainboard that effectively controlled the helmet's functions. this creative solution aims to significantly improve military operations, increase survivability, and ensure the safety of soldiers on the battlefield.