Real-time Data Acquisition Research Articles

Design and Implementation of SCADA Architecture Based on MATLAB App Designer for a Hybrid Power System

Hybrid renewable power systems (HRPS) are now considered reliable solutions for power generation under various conditions. A critical challenge for deploying HRPS involves the design and implementation of an effective supervisory control and data acquisition (SCADA) system, which is essential for real-time monitoring and control. The SCADA system plays a vital role in remote monitoring and control by enabling real-time data management, early fault detection, and timely troubleshooting. This study presents the design and implementation of a SCADA architecture based on a MATLAB App Designer for an HRPS consisting of wind turbines, photovoltaic panels, diesel generators, and batteries. The system utilizes Arduino Mega 2560 as remote terminal units (RTUs) to interface with actuators and measure critical system parameters such as voltage and current. Arduino boards can be controlled directly from MATLAB® via the MATLAB® Support Package for Arduino® Hardware. A laptop is the main terminal unit (MTU), communicating with the Arduino via the Firmata protocol. MATLAB App Designer is the central monitoring interface, providing real-time data acquisition, processing, and visualization. In addition, a relay module is used to control the operation of the diesel generator. The relay module acts as an intermediary between the control system and the diesel generator, allowing for automated control of the generator's operation. It receives commands from the SCADA system to either start or stop the generator based on the energy demands and the availability of renewable resources. By managing the diesel generator's activity, the relay module helps ensure that the HRPS maintains a balanced and efficient power supply, minimizing reliance on non-renewable sources when renewable energy is sufficient. Based on experimental results, the proposed SCADA system effectively monitors and controls HRPS under different conditions.

The real-time application and effectiveness assessment of an intelligent physical fitness testing system in physical training

An intelligent physical fitness testing system leverages advanced technologies to monitor and evaluate individuals’ fitness levels accurately. It integrates real-time data acquisition, and analysis, to support personalized physical training and health management. This study aims to evaluate the practical application and effectiveness of an intelligent system for real-time physical fitness testing in the context of physical training. Our suggested model employs portable sensing devices and we proposed a novel Northern Goshawk optimization-driven Gate Customized Long Short-Term Memory (NG-GC-LSTM) for enhancing accuracy in evaluating the individuals’ physical fitness levels. Data acquisition involves gathering bio-sensing data from 25 individuals during diverse physical training activities. The Min-Max Scaling algorithm is utilized to pre-process the obtained sensor data. We employed a Short-Time Fourier Transform (STFT) for extracting crucial features from the processed data. In our proposed framework, the NG optimization algorithm iteratively fine-tunes the GC-LSTM architecture for the accurate evaluation of an established intelligent physical fitness testing system. The recommended model is executed in Python software. During the result analysis phase, we assess the efficacy of our model’s performance across a variety of parameters. Additionally, we conduct comparative analyses with existing methodologies. The obtained outcomes demonstrate the efficacy and superiority of the suggested framework.

Rotor angle stability of a microgrid generator through polynomial approximation based on RFID data collection and deep learning

The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients. Redundant data, like as vibrations or noise signals, can cause delays in defect diagnosis and decrease diagnostic accuracy. This problem is addressed by integrating RFID technology. RFID technology could potentially be used with a hybrid CNN-LSTM model to develop a sophisticated fault diagnostic system. This entails identifying fault characteristics through the use of signal processing techniques and feature extraction methods, such as the Fourier transform and time-domain statistical features. In addition, we use Total Harmonic Distortion (THD) to reduce superfluous data. The suggested techniques significantly increase fault detection efficiency and precision, outperforming existing techniques with a 0.94 classification accuracy. An extensive case study on an IEEE 3-machine 9-bus system is used to illustrate its efficacy, showing observable improvements in fault detection speed and accuracy that make microgrid operations safer and more dependable.

Kalman Filter-Based Data Stabilization for an Automated Water Quality Monitoring System in Macrobrachium Rosenbergii Larvae Culture

Manual monitoring and management of water quality parameters in Macrobrachium Rosenbergii (freshwater prawn) larval culture are labourintensive, time-consuming, and susceptible to human error. This research aims to develop and evaluate an automated water quality monitoring system for freshwater prawn larval culture, with a specific emphasis on data stabilization. An ESP32 microcontroller is implemented to allow remote communication using an internet connection. The system continuously monitors critical parameters using sensors such as temperature, pH, turbidity, and Total Dissolved Solids (TDS), facilitating remote real-time data acquisition. The Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol is employed for seamless communication between the microcontroller and the monitoring dashboard. A Kalman filter is integrated into the system, enabling real-time sensor noise reduction and dynamic adaptation to changes. The filtered data are displayed remotely using a Node-RED dashboard with graphical representations. The implementation of the ESP32 microcontroller with the MQTT protocol and Node-RED has proven to be a robust platform for seamless data communication and presentation. Integration of the Kalman Filter significantly mitigates fluctuations in sensor readings. This is further proven by comparisons of the filtered data with known-good sensors, which have shown minimal to acceptable error percentages between 1% - 8.5%, where the error can mainly be attributed to environmental factors. Overall, this study has established a framework that can contribute to improving aquaculture practices and promoting environmental sustainability infreshwater prawn culture operations. The validated system provides valuable insights that assist farmers in facilitating a more efficient prawn culture operation.

Development of an internet-of-things-based controlled-source ultrasonic audio frequency electromagnetic receiver

Abstract. Electromagnetic exploration, characterized by its low cost, wide applicability, and high operational efficiency, finds extensive applications in fields such as oil and gas exploration, mineral prospecting, and engineering geology. Traditional controlled-source electromagnetic detection methods are typically confined to operating frequencies below 250 kHz, resulting in insufficient detection accuracy for applications such as shallow- and intermediate-depth exploration, thereby constraining their performance in high-resolution imaging. To address these challenges, we propose a controlled-source ultrasonic audio frequency electromagnetic receive system based on the internet of things (IoT). We investigate cascaded digital filtering and sampling techniques to extend the receiver's sampling rate range, thereby elevating the operating frequency of controlled-source electromagnetic acquisition from the conventional maximum of 250 kHz to 1 MHz. The receiver achieves a sampling rate of up to 2.5 MHz, comprising three magnetic field measurement channels and two electric field measurement channels. The instrument is compact, lightweight, and capable of real-time data storage locally and real-time data transmission to an upper computer. Additionally, IoT technology is introduced, leading to the design of a cloud-based real-time remote control and data acquisition scheme. Experimental results demonstrate the stability of the instrument, meeting the requirements of field exploration.

Methods and equipment for analysis and diagnosis of marine engines during navigation

ABSTRACT Expert diagnostic systems are required for the development of intelligent marine engines and other ship systems. Measurement and analysis of marine engine parameters are fundamental in ensuring the efficient operation of these key propulsion systems. This paper introduces a novel measurement device and method capable of real-time monitoring of cylinder pressure and emissions in marine engines. The experimental procedure involved measuring critical engine parameters and emissions, such as NOx, CO2, and particulate matter, under various operating conditions. The device provided accurate real-time data acquisition, enabling detailed analysis of combustion processes and emission profiles under various operating conditions. Furthermore, a numerical model was developed, like digital twins, which utilized the measured data to simulate engine performance, predict potential failures, and optimize operational parameters to enhance efficiency and reduce environmental impact. The two most important parameters: the indicated cylinder pressure and NOx emissions were simulated and validated. Measured NOx was measured 889 ppm at 90% and simulated results was 985 ppm, which is 10% difference. Specific fuel consumption has less than 1% difference between measured and simulated data. Novelty of this work is the methodology used in the testing and calibration processes. Current actual parameters of engine operation are monitored, based on which a comparative analysis is made in real time. Previous diagnostic control systems did not provide diagnostics, comparative analysis and optimization in real time. The integration with digital twin allowed potential wear and tear predictions, fuel consumption optimization, and emission reduction strategies, showcasing the potential of this technology to improve marine engine diagnostics and maintenance. The device and its virtual counterpart worked well together to provide a complete understanding of the engine’s response. This facilitates proactive maintenance strategies and contributes to eco-efficiency in the maritime sector.

A framework for data-driven decision making in advanced manufacturing systems: Development and implementation

Integration of sophisticated technologies such as Internet of Things, cyber physical systems and big data analytics have revolutionized the advanced manufacturing systems (AMS). However, implementation of data-driven decision making in AMS still remains challenging due to data heterogeneity, real-time processing demands, and integration complexities. This paper overcomes this challenge by presenting a novel framework for adoption of DDDM in AMS to enhance its decision-making capabilities. This framework consists of six stages: manufacturing stage, sensing stage, data stage, knowledge stage, decision stage, and application stage. The proposed framework leverages big data analytics to extract actionable insights from diverse datasets, integrates CPS to create a seamless interaction between physical and digital systems, and employs IoT technologies for real-time data acquisition and monitoring. The framework is validated through a comprehensive case study involving a CNC milling machine dataset, demonstrating significant improvements in operational efficiency, decision accuracy, and response time. The case study involves detailed data collection steps, preprocessing, and analysis, showcasing the framework’s practical implementation and effectiveness. The results show that the proposed framework addresses existing challenges and provides a scalable solution for DDDM implementation in AMS.

Three-dimensional turning force sensor based on a decagonal ring structure: crosstalk mitigation design and performance optimization

Abstract Real-time data acquisition using high-precision three-dimensional turning force sensors is crucial for intelligent manufacturing. However, existing sensors face challenges such as low integration levels and poor crosstalk resistance, rendering them inadequate for high-precision measurements. Theoretical analysis has demonstrated that a fully centered decagonal ring structure can effectively mitigate the effects of eccentric loading. This study focuses on designing a fully centered turning force sensor with minimal crosstalk. To achieve a balance between sensitivity and stiffness, multi-objective optimization was conducted using GWO-BP and TOP algorithms, resulting in a twofold increase in the sensitivity of the decagonal ring. The sensor design incorporates metallic strain gauges, instrumentation amplifiers, and peripheral circuits into the ring arms of the decagonal structure. Experimental results show that the sensor’s amplified sensitivities in the Fc, Ff, and Fp directions are 11.78 mV N−1, 10.31 mV N−1, and 1.78 mV N−1, respectively. The first three natural frequencies in an unconstrained state are 2518.5 Hz, 2537.5 Hz, and 4256.4 Hz. The sensor’s Fc-Fy crosstalk ranges from 0.18% to 0.88%, with noise limits for the Fc, Ff, and Fp directions of 0.035 N, 0.03 N, and 0.23 N, respectively. Cutting experiments have demonstrated that under varying spindle speeds and cutting depths, the sensor effectively detects surges in cutting force caused by rapid tool retraction, as well as other common faults.

Use of Probes and Sensors in Agriculture—Current Trends and Future Prospects on Intelligent Monitoring of Soil Moisture and Nutrients

Soil monitoring is essential for promoting sustainability in agriculture, as it helps prevent degradation and optimize the use of natural resources. The introduction of innovative technologies, such as low-cost sensors and intelligent systems, enables the acquisition of real-time data on soil health, increasing productivity and product quality while reducing waste and environmental impact. This study examines various agricultural monitoring technologies, focusing on soil moisture sensors and nutrient detection, along with examples of IoT-based systems. The main characteristics of these technologies are analyzed, providing an overview of their effectiveness and the key differences among various tools for optimizing agricultural management. The aim of the review is to support an informed choice of the most appropriate sensors and technologies, thus contributing to the promotion of sustainable agricultural practices.

Advancing Sustainable Mobility: A Data Acquisition System for Light Vehicles and Active Mobility

Active mobility and light vehicles, such as e-bikes, are gaining increasing attention as sustainable transportation alternatives to internal combustion solutions. In this context, collecting comprehensive data on environmental conditions, vehicle performance, and user interaction is crucial for improving system efficiency and user experience. This paper presents a data acquisition system designed to collect data from multiple sensor platforms. The architecture is optimized to maintain low power consumption and operate within limited computational resources, making it suitable for real-time data acquisition on light vehicles. To achieve this, a data acquisition module was developed using a single-board computer integrated with a custom shield, which also captures data related to the assistance of an e-bike motor through a wireless interface. The paper provides an in-depth discussion of the architecture and software development, along with a detailed overview of the sensors used. A demonstrator was created to verify the system architecture idea and prove the potentialities of the system overall. The demonstrator has been qualified by professional and semi-professional riders in the framework of the Giro-E, a cyclist event which took place in May 2024, on the same roads of the Giro d’Italia. Finally, some preliminary analyses on the data acquired are provided to show the performance of the system, particularly in reconstructing the user behavior, the environmental parameters, and the type of road.

Otomatisasi Perhitungan OEE pada Mesin Filling ILAPAK 50g Menggunakan PLC dan HMI

This study addressed the limitations in calculating Overall Equipment Effectiveness (OEE) manually, which frequently led to inaccuracies and inefficiencies in industrial processes. The purpose of this research was to design an automated OEE calculation system for the ILAPAK 50g filling machine to improve data accuracy and reduce dependency on manual input. A Design Science Research (DSR) methodology was employed, involving stages of system analysis, design, implementation, and evaluation. The system utilized key components, including an Omron CP2E PLC, proximity sensors, relays, and a Human-Machine Interface (HMI) for real-time data acquisition. The findings indicated that the automated system significantly improved data accuracy, achieving reliable, continuous OEE calculations across different machine shifts. The system successfully replaced manual logbook methods, reducing human estimation errors, and displayed OEE data locally on the HMI, making it accessible for operational decisions. Furthermore, the implemented automation reduced overall downtime, enhanced machine availability, and minimized errors associated with manual record-keeping. This research has practical implications for industries aiming to optimize production efficiency. The automated OEE system demonstrated potential for scalability to other machines in manufacturing settings, highlighting its adaptability and utility in modern industrial environments. Future research could explore integration with cloud-based databases to enhance data storage and accessibility, providing a foundation for advanced predictive maintenance and performance monitoring across multiple machines.

Advancements and Challenges in Antenna Design and Rectifying Circuits for Radio Frequency Energy Harvesting.

The proliferation of smart devices increases the demand for energy-efficient, battery-free technologies essential for sustaining IoT devices in Industry 4.0 and 5G networks, which require zero maintenance and sustainable operation. Integrating radio frequency (RF) energy harvesting with IoT and 5G technologies enables real-time data acquisition, reduces maintenance costs, and enhances productivity, supporting a carbon-free future. This survey reviews the challenges and advancements in RF energy harvesting, focusing on far-field wireless power transfer and powering low-energy devices. It examines miniaturization, circular polarization, fabrication challenges, and efficiency using the metamaterial-inspired antenna, concentrating on improving diode nonlinearity design. This study analyzes key components such as rectifiers, impedance matching networks, and antennas, and evaluates their applications in biomedical and IoT devices. The review concludes with future directions to increase bandwidth, improve power conversion efficiency, and optimize RF energy harvesting system designs.

Making decisions on the basis of data science in managing social and economic systems

Aim. To identify the prospects and possible areas of data science use in the decision-making process in the management of social and economic systems.Objectives. To analyze existing approaches and promising directions of data science use; to analyze methods that allow to obtain real-time data for decision-making, to identify gaps and shortcomings; to briefly formulate conclusions relevant for practitioners and policy makers in the process of decision-making based on data science.Methodology. The authors analyzed the scientific literature, applied methods of logical analysis and interpretation of data.Results. In the course of the research, it was found that data science makes a significant contribution to the global transformation of society, allowing solving urgent problems of socioeconomic development. Methods that facilitate the acquisition of real-time data increase the effectiveness of decisions in the management of social and economic systems. These methods were analyzed, their advantages and disadvantages were identified, and examples of their use were presented. The key data requirements were defined: confidentiality, ethics, and security. A range of new questions for future research in the context of the topic under consideration was proposed.Conclusions. The results obtained contribute to the theoretical development of new approaches to the application of data science, as well as the practical use of best practices in cases of decision-making in the management of social and economic systems. These results can form the basis for the design and implementation of solutions by policy makers and practitioners.

A Portable Electronic Nose Coupled with Deep Learning for Enhanced Detection and Differentiation of Local Thai Craft Spirits

In this study, our primary focus is the biomimetic design and rigorous evaluation of an economically viable and portable ‘e-nose’ system, tailored for the precise detection of a broad range of volatile organic compounds (VOCs) in local Thai craft spirits. This e-nose system is innovatively equipped with cost-efficient metal oxide gas sensors and a temperature/humidity sensor, ensuring comprehensive and accurate sensing. A custom-designed real-time data acquisition system is integrated, featuring gas flow control, humidity filters, dual sensing/reference chambers, an analog-to-digital converter, and seamless data integration with a laptop. Deep learning, utilizing a multilayer perceptron (MLP), is employed to achieve highly effective classification of local Thai craft spirits, demonstrated by a perfect classification accuracy of 100% in experimental studies. This work underscores the significant potential of biomimetic principles in advancing cost-effective, portable, and analytically precise e-nose systems, offering valuable insights into future applications of advanced gas sensor technology in food, biomedical, and environmental monitoring and safety.

Catastrophe in Rio Grande do Sul/Brazil in 2024: sensing for survey on the radiological sources of submerged equipment and their possible environmental damage

The increasing occurrence of environmental catastrophes has heightened global concern regarding the management of radiological sources. This study addresses the challenges related to the detection and assessment of radiological damage in the state of Rio Grande do Sul, Brazil. Given the growing use of radiological materials for industrial, medical, and energy production purposes, the risk of environmental contamination from accidental or intentional releases has intensified. Our research focuses on the implementation of advanced sensing technologies capable of detecting low-level radiological emissions over large areas. The study highlights the need for precise, real-time data acquisition methods to mitigate the effects of radiological exposure and protect both ecosystems and human populations from potential harm. Additionally, we explore the role of early warning systems in reducing the response time during environmental disasters, particularly in regions vulnerable to radiological threats. Field experiments conducted in Rio Grande do Sul demonstrate the effectiveness of our proposed detection methodologies in identifying radiological contamination and assessing the extent of environmental damage. The findings underscore the importance of continuous monitoring and development of more sophisticated detection techniques in areas prone to radiological hazards. This work contributes to improving regional preparedness for radiological incidents, safeguarding public health, and preserving environmental integrity.

Open Source based Smart Meter Real-time Data Acquisition and Management using MQTT Protocol

Open Source based Smart Meter Real-time Data Acquisition and Management using MQTT Protocol

Analysis of User Interfaces for Ground Control Stations of Unmanned Aerial Vehicles

Introduction. In the modern world, software (SW) is updated daily, particularly for ground control stations (GCS) of unmanned aerial vehicles (UAVs). These systems ' user interfaces (UI) ensure operator interaction with the drone, flight control, mission planning, and realtime data acquisition. These interfaces must be functional, convenient, and intuitive, allowing operators to perform their tasks effectively. Examining global experience allows for an evaluation of existing systems and the identification of areas for improvement. Important aspects include creating intuitive UIs to prevent information overload, ensuring situational awareness, adapting to extreme conditions, and integrating with other systems. The use of virtual and augmented reality technologies, as well as artificial intelligence, can enhance the functionality and convenience of GCS. Such analysis will help in creating safe, efficient, and reliable systems for UAV control. The purpose of the paper is to investigate and conduct a comprehensive analysis of existing user interfaces of software for UAV ground control stations, focusing on their functional capabilities and ease of use. Methods. The following methodological tools were used: concepts of intellectualization of information technologies, the theory of intelligent control, the methodology of building autonomous systems, decision-making theory, and artificial intelligence theory. Results. A review of global experience in user interface development was conducted. The UIs of each of the studied software were analyzed, and their functional capabilities were assessed, identifying their strengths and weaknesses. Comparative tables of interface products were compiled based on their functional capabilities and UI usability levels. Generalized recommendations were prepared for creating a unified interface that combines the best features of existing solutions and addresses their shortcomings. Conclusions. The analysis of GCS UI design for UAVs showed that all systems have strengths and weaknesses. The UI of the Mission Planner software has the most extensive capabilities, but it also requires improvement. Future development should add roles for military pilots and operators, develop a more intuitive and user-friendly interface that meets user needs, and simplify SW settings. For working in extreme conditions, the interface needs to be optimized. Enhancements in data visualization will help make information clear and easy to understand, which is critically important in fast-paced and dangerous situations. Keywords: user interface, ground control station, UAV, virtual reality, augmented reality.

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.

The impact of latent policy synergy between cities on air pollution: An empirical analysis from China's 12,764 air policies

Policy synergy plays a crucial role in managing regional air pollution. However, the current process of formulating collaborative policies often focuses too heavily on achieving optimal emission reduction outcomes, while neglecting the execution costs at the local level. This not only leads to a lack of coordination between existing governance measures and new policies but may also result in inefficient policy implementation and the wastage of administrative resources. To address this issue, we have conducted a thorough analysis of the latent interconnections in local governance and explored the possibility of formulating collaborative policies based on latent policy synergies. Utilizing a dataset of 12,764 policy documents from China spanning from 2000 to 2023, we employed unsupervised learning techniques and panel regression models to investigate the impacts of latent policy synergies among local governments. Our findings reveal that local governments initially relied on rigid command-and-control strategies to address air pollution, which were later enhanced with economic incentives like emissions trading and subsidies, thus broadening the scope of policy tools and improving compliance flexibility. Subsequently, as comprehensive monitoring networks were established, enabling real-time data acquisition and more accurate pollution assessments, the focus of policy efforts shifted towards a more integrated approach to regional air pollution management. This similar evolution facilitated latent policy synergy among local governments, leading to significant reductions in air pollution emissions. Notably, regions such as the Central Plains and coastal cities, characterized by their dense industrial activity and high energy consumption, have shown remarkable potential for collaborative efforts, achieving greater pollution control efficiencies. This study provides a valuable reference for the coordinated governance of air pollution policies.

Retraction Note: Optical imaging technology based on embedded processors in real-time data acquisition system for motion training

Retraction Note: Optical imaging technology based on embedded processors in real-time data acquisition system for motion training