Real-time Solution Research Articles

GNSS Real-Time ZTD/PWV Retrieval Based on PPP with Broadcast Ephemerides

GNSS precise point positioning (PPP) plays an important role in retrieving atmospheric water vapor values and performing numerical weather prediction. However, traditional PPP relies on real-time orbits and clocks, which require continuous internet or satellite communication. Improved broadcast ephemerides of GNSSs offer new opportunities for PPP with broadcast ephemerides (BE-PPP) instead of using precise ephemeride products. Therefore, we investigated the feasibility of utilizing BE-PPP for retrieving zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data. We processed the GPS/Galileo observations from 80 IGS stations during a 30-day experiment to retrieve ZTD values using both real-time PPP (RT-PPP) and BE-PPP solutions. Then, we processed observations from 20 EUREF Permanent GNSS Network (EPN) stations to retrieve PWV data. The IGS final tropospheric products were used to validate the ZTD, and radiosonde (RDS) and ERA5 data were used to validate the PWV. The results show that the real-time ZTD from BE-PPP agrees well with that from RT-PPP: the standard deviation (STD) of the ZTD is 1.07 cm when using BE-PPP and 0.6 cm when using RT-PPP. Furthermore, the STD of the PWV is 1.69 mm when using BE-PPP, and 0.96 mm when using RT-PPP, compared to the ERA5-PWV. Compared to the RDS-PWV, the STD is 3.09 mm when using BE-PPP and 1.39 mm when using RT-PPP. In conclusion, the real-time ZTD/PWV products obtained using the BE-PPP solution are consistent with those of RT-PPP and meet the requirements of NWP, so this method can be used as an effective complement to RT-PPP to expand its application potential.

General heterogeneous real-time baseband backend system scheme applied to QTT based on RFSoC+GPU

This paper proposes a heterogeneous real-time baseband astronomical backend solution centered on RFSoC and combined GPU. The solution implemented the integration of the high-performance, completely configurable RF signal link to digitize, process, and transmit increasingly massive radio astronomy signals and provided a highly flexible and scalable scheme for the QTT backend. The RFSoC-based integration RF pre-processing front-end directly digitizes dual-polarization signals at the sampling rate of 2048 MHz and converts them to dual-16-channel data streams for parallel mixing and polyphase decimation filtering. The generated baseband data with 256 MHz center frequency and 100 MHz bandwidth is packaged and output to the GPU server through the 100GbE Interface. Complete processes of direct RF sampling, pre-processing, and high-speed transmission are implemented at the receiver. The GPU server executes real-time post-processing at the remote end. The dynamic configuration of two observation modes can be achieved with frequency resolutions of 3.051 and 0.763 kHz. The H2CO absorption line observations with the Nanshan 26-m radio telescope verified the system’s performance and the design’s rationality. This paper explores and confirms the RFSoC’s applicability on the backend. The flexible and efficient QTT backend scheme was planned based on the proposed real-time baseband pattern by expanding multi-RFSoC and GPU computing nodes.

MENSTRUAL CUP USAGE FOR SUSTAINABLE MENSTRUAL HYGIENE AMONG COLLEGE-GOING WOMEN IN SOUTH CHENNAI, TAMIL NADU

Menstrual health and hygiene play a pivotal role in the overall well-being of menstruating women. In the contemporary world where waste management is challenging, there is a growing concern for the disposal of sanitary napkins as well. The paper blends the theme of "Health and well-being" along with "Waste management and cleanliness" to arrive at one solution for the dual problem. The paper takes up a holistic approach to address challenges of menstrual health and waste management, thus promoting "Green menstruation". The core of the paper lies in carefully examining the issues faced by college-going women, in the South Chennai region, during sanitary napkins usage and tries to understand their perceptions on menstrual cups. In addition, environmental benefits revolving around the usage of menstrual cup are also explored. Quantitative method is used by the researchers to address the objectives of the paper. The findings obtained from the research method serves as a foundation for the suggestions and interventions proposed in the paper. Thus, the paper underscores sustainable and eco-friendly real-time solutions that improve the overall well-being of menstruating women while contributing to a cleaner environment. KEYWORDS: Menstruation, Menstrual Cups, Waste Management, Menstrual Health, Green Menstruation.

Real-Time Monitoring of Photovoltaic Panel Using Node-RED

This research aims to design and implement an Internet of Things (IoT)-based monitoring system for Photovoltaic (PV) panels using Node-RED. The system can monitor critical parameters such as voltage, current, power, and the electrical energy produced by the PV panels in real-time. The data obtained from the PV panels is sent to a Node-RED server and visualized in the form of indicators and graphs on a dashboard. Statistical analysis calculates the daily average power and total energy produced. The results show that the proposed system can enhance monitoring efficiency and significantly benefit PV system maintenance and management. Users can quickly identify and address issues that may arise, such as panel performance degradation or system disruptions. Energy analysis and maintenance planning can be carried out by collecting historical data. This research supports the broader renewable energy development and provides an effective real-time PV system monitoring solution.

Machine Learning Approaches for 3D Motion Synthesis and Musculoskeletal Dynamics Estimation: A Survey.

The inference of 3D motion and dynamics of the human musculoskeletal system has traditionally been solved using physics-based methods that exploit physical parameters to provide realistic simulations. Yet, such methods suffer from computational complexity and reduced stability, hindering their use in computer graphics applications that require real-time performance. With the recent explosion of data capture (mocap, video) machine learning (ML) has started to become popular as it is able to create surrogate models harnessing the huge amount of data stemming from various sources, minimizing computational time (instead of resource usage), and most importantly, approximate real-time solutions. The main purpose of this paper is to provide a review and classification of the most recent works regarding motion prediction, motion synthesis as well as musculoskeletal dynamics estimation problems using ML techniques, in order to offer sufficient insight into the state-of-the-art and draw new research directions. While the study of motion may appear distinct to musculoskeletal dynamics, these application domains provide jointly the link for more natural computer graphics character animation, since ML-based musculoskeletal dynamics estimation enables modeling of more long-term, temporally evolving, ergonomic effects, while offering automated and fast solutions. Overall, our review offers an in-depth presentation and classification of ML applications in human motion analysis, unlike previous survey articles focusing on specific aspects of motion prediction.

Navigating Wellness: Chatbot-Powered Solutions for Mental Health

Abstract: Our overall well-being depends heavily on our mental health, which has received more attention in recent years. At the heart of this platform lies a sophisticated chatbot system, meticulously crafted to provide empathetic and responsive interactions with users.this abstract introduces a pioneering mental health website designed to offer comprehensive assistance to individuals seeking to improve their mental well-being. This chatbot serves as a virtual companion, offering a safe space for individuals to express their thoughts, feelings, and concerns without fear of judgment or stigma. Crucially, this website goes beyond mere conversation; it offers real-time solutions to address mental health challenges head-on. Drawing upon evidencebased practices and therapeutic techniques, the platform provides users with actionable strategies to manage stress, anxiety, depression, and other common mental health issues.

Real-time analytical solution to energy management for hybrid electric vehicles using intelligent driving cycle recognition

For series hybrid electric vehicles, due to the limitation of engine operating characteristics, speed regulation is usually performed using discrete operating points. This leads to the problem of mixed integer programming when solving energy management problem. In this paper, an analytical method with strong real-time performance and small computational load is proposed. First, an intelligent cycle recognition system is designed. It can cluster driving cycles offline based on the probability distribution of the required power and efficiently recognize unknown driving cycles online. Then the analytical expression of power distribution is derived, and the selection scheme of engine operating point is given. Finally, the required power information obtained from the clustering is substituted into the analytical expression. The specific values of the corresponding analytical solutions for each type are computed offline. When facing unknown conditions, this method can match the power distribution scheme quickly once the driving cycle is recognized. The results of the simulation and hardware-in-loop experiments demonstrate that this method can effectively control the SOC trajectory and allocate power more optimally. Compared to the benchmark method, the proposed strategy improves the fuel economy by 5.52 % and 7.49 % in two cases, and the computational efficiency is significantly enhanced.

VESBELT: An energy-efficient and low-latency aware task offloading in Maritime Internet-of-Things networks using ensemble neural networks

Due to increasing maritime activities, the number of Maritime Internet-of-things (MIoT) devices requiring real-time marine data processing is growing exponentially. To offload maritime tasks and address the limited computational capabilities of heterogeneous MIoT devices, edge and cloud computing networks are employed. However, these networks introduce several challenges, including increased energy consumption and service latency within the complex marine network environment. Current state-of-the-art solutions address these issues by focusing exclusively on real-time offloading data, neglecting the relationship with past offloading tasks. In this work, we develop an optimization framework, named VESBELT, for offloading tasks from Vessel users to nearby Edge Servers or the cloud server, aiming to reduce Energy consumption and service Latency Trade-off through a multi-objective linear programming problem. However, finding optimal solutions from this formulation is considered an NP-hard problem. To address this, we introduce VESBELT-ECNN, VESBELT-EANN, and VESBELT-ELSTM systems that leverage ensemble convolutional, artificial neural networks and Long-short-term memory, respectively, to achieve solutions in polynomial time. The developed ensemble models integrate multiple combinations of deep learning models and exploit the pre-trained models to provide real-time solutions with better prediction accuracy. The experimental findings, obtained using Python programming version 3.10.2, indicate that the proposed VESBELT-ECNN, VESBELT-EANN, and VESBELT-ELSTM systems outperform existing approaches in terms of user Quality of Experience (QoE) in the timeliness domain and energy savings.

A Proof of Concept Using BLE to Optimize Patients Turnaround Time (PTAT) in Health Care

In 2022, Malaysia's Ministry of Health (MOH) acknowledged that government hospitals continue to face challenges in delivering high-quality patient care. A significant issue is the absence of a Real-Time Patient Location Monitoring and Tracking Solution, which prevents doctors from efficiently tracking patients within medical facilities. This lack of real-time tracking contributes to a decline in service quality, creating an urgent need for optimization in patient monitoring and tracking solutions. Implementing an effective tracking system can significantly enhance patient care quality, improve resource utilization management, and boost staff productivity, ultimately saving costs, time, and resources for hospitals. One critical area for improvement is the Patient Turnaround Time (PTAT), which refers to the duration a patient spends being processed at a hospital before being discharged. PTAT is currently prolonged in many hospitals due to the complex processes involved and patients becoming untraceable while waiting for their turn. To address these challenges, we have developed an Internet of Things (IoT) solution utilizing Bluetooth Low Energy (BLE) technology to optimize PTAT. This solution enables hospital staff to track patient movements and localize them in real-time. By using wearable devices, the system measures the distance from patients to various Access Points (AP) spread throughout the hospital based on the dBm value. This paper presents comprehensive experimentation for the Proof of Concept (POC) and pilot testing of this BLE-based solution. The results demonstrate the accuracy of using the Received Signal Strength Indicator (RSSI) to optimize patient turnaround times in hospitals. By implementing this BLE solution, hospitals can reduce the time patients spend waiting for treatment, thereby improving overall patient satisfaction and hospital efficiency. The ability to monitor patient locations in real-time will ensure that patients receive timely care, reducing the likelihood of them wandering off and becoming untraceable. This technology represents a significant step forward in modernizing patient care and streamlining hospital operations in Malaysia.

Analysis of Real-time Processing Approaches for Large Data Volumes in Metering Infrastructure

Smart grid systems and communication technologies, such as Advanced Metering Infrastructure (AMI), have revolutionized utility service management and monitoring. AMI leverages smart meters equipped with advanced communication capabilities, facilitating bidirectional communication between utilities and consumers. The increasing deployment of smart meters and the adoption of sub-hourly data collection requirements by utility companies highlight significant data volume growth. Thus, there is a need for efficient real-time data processing solutions as existing approaches may not meet previously established Service-Level Agreements (SLAs) concerning performance, accuracy, and scalability metrics. This research aims to comprehensively review the latest publications relevant to distributed real-time data processing methods for smart grid applications and outline problems for further research. Specifically, the study delves into the effectiveness and application of reviewed approaches in managing the constant stream of data from smart meters and IoT devices within the smart grid context. By analysing existing methodologies and advancements, this study seeks to identify challenges and opportunities in real-time data processing for smart grid infrastructures, focusing on addressing the complexities of processing, managing, and storing large volumes of real-time data. The literature review revealed two primary applications of real-time data processing: optimization of data streaming performance and data analysis. The review encompasses various studies, each presenting distinct methodologies and technologies applied to address the challenges of processing large volumes of real- time data from smart meters and IoT devices. Future research should address the challenges and limitations discovered in this study.

Artificial Neural Network Models for Solution Concentration Measurement during Cooling Crystallization of Ceritinib

The development of a quantitative in-line UV spectroscopic method for monitoring of solute concentration during the crystallization process of the active pharmaceutical ingredient (API), ceritinib is described. The method is based on artificial neural networks (ANN). A seeded cooling crystallization process of ceritinib from tetrahydrofuran was studied as a model system. The model was constructed from collected ATR-UV spectra and temperature records within the metastable zone. The collected spectra were preprocessed with the first derivative using the Savitzky-Golay filter. ANN models with different architectures were created and the optimal architecture was chosen based on the root mean square error of prediction (RMSEP) criterion. In addition, ANN models were compared with the models obtained by the linear partial least squares regression (PLSR). Due to the nonlinear relationship in the data set, ANN models predict the solution concentration with higher accuracy compared to linear models. The developed models were successfully used in real-time solution concentration measurement during ceritinib crystallization along with a supersaturation control module developed in-house.

Research on improved gangue target detection algorithm based on Yolov8s.

An improved algorithm based on Yolov8s is proposed to address the slower speed, higher number of parameters, and larger computational cost of deep learning in coal gangue target detection. A lightweight network, Fasternet, is used as the backbone to increase the speed of object detection and reduce the model complexity. By replacing Slimneck with the C2F part in the HEAD module, the aim is to reduce model complexity and improve detection accuracy. The detection accuracy is effectively improved by replacing the Detect layer with Detect-DyHead. The introduction of DIoU loss function instead of CIoU loss function and the combination of BAM block attention mechanism makes the model pay more attention to critical features, which further improves the detection performance. The results show that the improved model compresses the storage size of the model by 28%, reduces the number of parameters by 28.8%, reduces the computational effort by 34.8%, and improves the detection accuracy by 2.5% compared to the original model. The Yolov8s-change model provides a fast, real-time and efficient detection solution for gangue sorting. This provides a strong support for the intelligent sorting of coal gangue.

Smart Nutrient Management in Hydroponics: IoT-Driven Optimization for Enhanced Crop Yield and Resource Efficiency

Hydroponics is a potential option to farming in dirt because it gives you more control over how much water and nutrients are used. However, managing nutrients optimally in hydroponic systems is still a difficult task. This research suggests a smart nutrition management system that uses Internet of Things (IoT) technologies to get the most out of resources and increase food growth. The framework has sensors built in that can check external factors like pH, electrical conductivity, and nutrient amounts in the hydroponic solution in real time. These sensors send constant streams of data, which lets the exact amounts of nutrients be changed to meet the needs of different plants at different times of growth. Machine learning techniques are used to look at monitor data and guess what the best food mixes will be. The system changes with the times by using past data and weather models. This keeps resources from going to waste and increases food yield. The IoT system also allows tracking and control from afar, which makes upkeep and strategic actions easier. Automated alerts let farmers know when conditions aren't as good as they should be, so they can make changes quickly to avoid crop damage or nutrition shortages. Experiments are done in a controlled growing setting to see how well the planned system works. Compared to standard ways, the results show big gains in food growth and resource economy. It can also be scaled up or down, and it can be changed to work with different types of crops and environments.

Research on a Path Tracking Control Strategy for Autonomous Vehicles Based on State Parameter Identification

With the rapid development of autonomous driving technology, estimating and controlling key vehicle state parameters under complex road conditions have become critical challenges. This study combines Unscented Kalman Filtering (UKF) and Sliding Mode Control (SMC) methods to propose an integrated control model for achieving more efficient control. First, a three-degrees-of-freedom vehicle dynamics model based on the Dugoff tire model is constructed to accurately estimate key vehicle state parameters. Next, UKF is used to estimate road friction coefficients and key vehicle state parameters, and its performance is compared with Extended Kalman Filtering (EKF) under various conditions. The results show the superiority of UKF in identifying road friction coefficients. Based on SMC theory, a sliding surface is designed, and the functional relationship between state variables and control variables is derived to establish the corresponding control model. Joint simulations using Carsim and Simulink under different conditions validate the real-time performance and effectiveness of the designed UKF-SMC integrated control strategy in the presence of external disturbances and system uncertainties. Simulation results indicate that this strategy effectively enhances the overall performance and safety of autonomous vehicles, providing an accurate real-time solution capable of handling complex and variable road conditions. The proposed UKF-SMC integrated control strategy not only proves its theoretical superiority but also demonstrates promising practical applications in simulation experiments. This study provides reliable technical support for the development of autonomous driving technology under complex road conditions.

Research on Multi-type and Multi-level Real-time Simulation and Optimization of New Power System

Abstract Aiming at the problem that the current real-time simulation algorithm is difficult to improve the performance and accuracy of multi-type and multi-level real-time simulation when dealing with new power systems, we propose a new multi-type and multi-level real-time simulation and optimization algorithm for power systems. Firstly, the efficiency and accuracy of the solution are improved by directly updating the conductance nodes (Gnodes) and current nodes (Inodes) on power system components, and the updating of Gnodes and Inodes is replaced by the prior calculation based on the inverse matrix (Ginv) of admittance matrix, thus further optimizing the simulation solution process and reducing the computational complexity. Then, the efficient numerical method, data structure optimization, and parallel computing technology are used to optimize the construction process of the inverse matrix (Ginv) of the admittance matrix to reduce the computational complexity and memory consumption of electromagnetic transient simulation. Finally, the idea of the sparse matrix is introduced to optimize the operation process of Gstruct and improve the calculation speed of the simulation solution. The research proves that the multi-type and multi-level real-time simulation solution and its optimization method of new power systems can be studied more systematically through the above technical route, which can improve the efficiency and accuracy of power system simulation solutions and provide strong support for practical application.

ADVANCEMENTS AND INNOVATIONS IN PM2.5 MONITORING: A COMPREHENSIVE REVIEW OF EMERGING TECHNOLOGIES

This comprehensive review examines the evolving landscape of PM2.5 monitoring, emphasizing its critical role in environmental chemistry, public health and electrical/electronic engineering. Traditional methods, including manual sampling, gravimetric analysis, and the Federal Reference Method (FRM), have long been relied upon for PM2.5 measurement but are hindered by limitations in spatial coverage, temporal resolution, and cost. In response, emerging technologies such as wireless sensor networks, low-cost sensor technologies, remote sensing techniques, and machine learning algorithms offer promising solutions to overcome these challenges. Through an analysis of case studies and applications in various environmental settings, including urban areas, industrial zones, and indoor environments, the review highlights the effectiveness of monitoring networks in enhancing spatial and temporal resolution, as well as the need for community engagement and real-time monitoring solutions. Furthermore, technological innovations such as sensor fusion, data analytics, and artificial intelligence hold great promise for improving the accuracy, reliability, and accessibility of PM2.5 monitoring data. Regulatory agencies and policymakers play a crucial role in advancing PM2.5 monitoring by harmonizing monitoring standards, strengthening quality assurance measures, and developing evidence-based regulations to mitigate air pollution and protect public health. In conclusion, international cooperation and collaboration are essential for addressing transboundary air pollution and global environmental challenges. Regional monitoring networks and international agreements provide frameworks for data sharing, standardization of monitoring practices, and collaborative research efforts. To this end, stakeholders can leverage PM2.5 monitoring by adopting new technologies, improving data quality, and supporting evidence-based actions to safeguard public health, the environment, and sustainability

Optimization of technological parameters of the methane tri-reforming process based on the phenomenon of conservatively perturbed equilibrium

course under the condition of the phenomenon of conservative perturbed equilibrium (CPE) are considered. It is shown that the onset of the CPE point - an extremum of the over-equilibrium concentration - occurs at a certain point in time. Therefore, one of the tasks of optimal control is to calculate the time of occurrence of this point and determine the effect of temperature and pressure on its position. The modeling of the TRM process is performed for an ideal displacement reactor. The DWSIM software product was used to model the TRM process. The kinetic equations were programmed in the form of scripts. The position of the over-equilibrium concentration point was also investigated depending on the initial composition of the mixture. According to the modeling results, it was concluded that the position of the CPE point for product H₂ is constant, and depends only on pressure and temperature and does not depend on the initial composition of the mixture. This allows us to conclude that the control can ensure that the product yield with the maximum concentration is achieved. The problem of optimizing the initial composition of the mixture for the methane tri-reforming process using CPE is formulated: the objective function and constraints are defined, and the solution method is chosen. The optimization problem is to maximize the target product of methane tri-reforming, H2. The solution to the problem is the composition of the mixture (concentrations of CH4, H2O, CO2). As for the constraints, the pressure and temperature are limited by the physicochemical properties of the process and the design features of the reactor, the elemental balance must be maintained, the key component at the input must have an equilibrium concentration at a given pressure and temperature, and the concentrations of other components must be deviated from their equilibrium values. A software implementation of the optimization problem based on the Optimization Toolbox Matlab was performed. The research results indicate an increase in the efficiency of the process (increase in the output of the target component) and the possibility of implementing optimal solutions in real time, which opens up prospects for implementation in existing control systems and development of decision-making systems.

Predefined-time robust adaptive gradient neural network for solving linear time-varying equations and its applications

Adaptive gradient neural networks (AGNN) have been extensively employed as effective models for addressing time-varying problems. However, the existing AGNN are convergent in infinite time and only considered in noiseless environment. Consequently, the AGNN with a certain convergence time and noise tolerance is urgently required. Motivated by the advantages of predefined-time convergence, a novel predefined-time robust adaptive gradient neural network (PRAGNN) is developed for solving linear time-varying equations. It can be concluded that PRAGNN can efficiently obtain real-time solutions of linear time-varying equations within predefined time, independent of initial conditions, by properly selecting parameters. In the process of solving linear time-varying equations, PRAGNN can avoid matrix inversion, which reduces the computing cost compared with zeroing neural networks (ZNN). Furthermore, PRAGNN can completely resist both bounded vanishing noise and bounded non-vanishing noise. Numerical experiments demonstrate the effectiveness of PRAGNN. Finally, PRAGNN has been successfully utilized in 2-D dynamic positioning based on the angle of arrival (AOA) positioning algorithm, as well as in alternating currents (AC) computing.

Cloud-edge-end-based aircraft assembly production quality monitoring system framework and applications

In recent years, the increasing complexity of quality control in aircraft assembly has necessitated the development of innovative and integrated solutions. This paper introduces a novel framework based on cloud-edge-end architecture to enhance quality monitoring systems in aircraft assembly production. The framework consists of five parts: cloud, edge, end, network, and the trained model. By leveraging the advantages of cloud computing, edge computing, and terminal devices, this framework aims to provide a comprehensive, real-time, and efficient quality monitoring solution. Key technologies within the cloud-edge-end framework are proposed, including data collection, storage, and transmission, deep learning methods based on Channel Attention Convolutional Neural Network (CACNN), and feedback mechanisms from the cloud layer to the lower layers. These innovations enable effective information sharing and utilization at various stages of the aircraft assembly process, providing operational managers with timely and accurate insights into assembly quality. A prototype system application has been implemented at an aircraft assembly site, facilitating process data monitoring and assembly status analysis. Experimental validation of the proposed CACNN algorithm has demonstrated its capability to monitor hole diameters online. Various case studies confirm the framework's effectiveness, showcasing substantial improvements in monitoring accuracy and operational efficiency.

Hybrid Learning Integrated Remote Laboratory: A Pedagogical Strategy for Future Practicum Learning

The hybrid learning integrated remote laboratory, an innovation in educational technology, is a practical and real-time solution that allows students to access and interact with laboratory equipment remotely via the internet network. This research aims to analyze the implementation and effectiveness of this model in an embedded systems practicum course. The study, conducted using a quasi-experimental method, involved 35 students in the experimental group and 36 in the control group. The learning model in the experimental group was implemented with a differentiated approach, allowing students to participate in face-to-face learning in the laboratory or attend online. The technology in this research was built with an interactive user interface in the form of an e-learning integrated remote laboratory application, providing online students with access to learning materials, discussion forums, assignments, chat, video conference, and an online microcontroller coding editor. The use of the online microcontroller coding editor empowers students to create programs and control physical equipment in the laboratory, such as Arduino modules, several sensors, and output devices, remotely and in real-time. Descriptive analysis and t-tests were used to analyze students’ comprehension of the embedded systems course. The test results showed a difference in the average academic achievement of students, with the learning outcomes of the experimental group students being higher than those of the control group. This model, therefore, demonstrates its impact on optimal and practical learning in the embedded system course.