Development Of Monitoring System Research Articles

Intelligent process monitoring of smart polymer composites using large area graphene coated fabric sensor.

Herein, we report the development of an online process monitoring system for vacuum-assisted resin transfer molding (VARTM) process using large area graphene coated in-situ fabric sensor. Besides imparting excellent mechanical properties to the final composites, these sensors provide critical information during the composite processing including detecting defects and evaluating processing parameters. The obtained information can be used to create a digital passport of the manufacturing phase to develop a cost-effective production technique and fabricate high-quality composites. The fabric sensor was produced using a scalable dip-coating process by coating 1-, 3- or 5-layers of thermally reduced graphene oxide (rGO) onto glass fabric surface according to the number of dips of the fabrics into GO solution. The electrical resistances from all electrode pairs were simultaneously and continuously recorded during distinct stages of the VARTM process to determine the relative conductance. During the vacuum cycle, the range of relative conductance increased with the number of coated rGO layers, with the 5-layer rGO-coated sensor showing the highest conductance range of 16.9 %. Additionally, it was observed that the 5-layer coated sensor showed a consistent decrease in conductance during the infusion phase due to the fluid flow pressure dominating the resin electrical conductivity.

Live Data Monitoring in Industry

Abstract: In contemporary industrial environments, real-time monitoring of critical parameters is vital for ensuring safety, efficiency, and compliance with regulations. This thesis details the development and implementation of an advanced live data monitoring system designed to track temperature, humidity, fire presence, and oxygen levels. Key components of the system include a DHT sensor for temperature and humidity measurement, a fire sensor for fire detection, an oxygen sensor for monitoring atmospheric oxygen, and a buzzer for immediate auditory fire alerts. Central to the system is the ESP8266 WiFi module, which facilitates the wireless transmission of sensor data to ThingSpeak, a cloud-based data collection and visualization platform. This setup allows for continuous, remote monitoring of environmental conditions, providing critical insights and alerts to industrial operators. The project delves into the hardware configuration, sensor calibration, data acquisition processes, and wireless communication protocols essential for effective real-time monitoring. It also addresses the challenges of integrating multiple sensors and ensuring reliable data transmission in an industrial setting. The results demonstrate the system's capability to provide accurate, timely data, enhancing safety protocols and operational efficiency. By utilizing IoT technology, the proposed system offers a scalable solution for industrial environments aiming to implement advanced monitoring and alert systems. This work contributes to the field of industrial automation by illustrating a practical application of IoT in improving monitoring and safety measures, highlighting both the technical achievements and practical implications of the implemented system.

Real-time Water Level Monitoring System in Oriental Mindoro Using Neural Networks

Oriental Mindoro faced an environmental problem that was caused by the massive rains from the highlands of the city. The lowlands cannot prepare for the upcoming floods with manual prediction and based on instinct. A water monitoring level was setup in three different rivers that end the streams in the city of Oriental Mindoro. This study focuses on the development of a real-time water level monitoring system to help with disaster preparedness in Oriental Mindoro. The dataset collected was from the provincial government, which shows the diversity of data that can be correlated to predict if a flash flood will occur. The dataset was tested using different algorithms such as Gradient Boosted Tree, SVM, Decision Tree, and Neural Networks. The prediction of the Neural Network model’s accuracy is 98.61%, which is 0.6 percentage points higher than the Gradient Boosted Tree and more than the rest of the algorithms.

E-BLOOD BANK MONITORING

Abstract— The E-Blood Bank Monitoring system represents an innovative approach to modernizing the management and distribution of blood and blood products. Traditional blood bank operations face significant challenges, including inefficient inventory management, poor traceability, limited accessibility to real-time data, and difficulties in maintaining a responsive donor base. This research explores the development and implementation of an E-Blood Bank Monitoring system that leverages advanced technologies such as the Internet of Things (IoT), blockchain, and web/mobile applications to address these issues. The primary objective of the E-Blood Bank Monitoring system is to enhance the efficiency, transparency, and reliability of blood bank operations by integrating modern technologies. Specifically, the system aims to improve inventory management through real-time tracking of blood units, ensure end-to-end traceability using blockchain technology, and provide stakeholders with real-time access to data on blood availability. Additionally, it seeks to optimize donor management to maintain an active donor base, ensure the quality and safety of blood through IoT-monitored storage conditions, and facilitate quick responses to urgent blood needs. Index Term- Frontend development : HTML ,CSS,JavaScript , Backend development React , Database management PHP / MySQL.

Enhanced prediction of reflected spectrum for FBG sensors with metallic coating embedded in CFRP composites: Unveiling the impact of process-induced residual stress and coating thickness

The occurrence of peak-split or distortion in the reflected light of fiber Bragg grating (FBG) sensors with metallic coatings embedded in composites is inevitable during the curing process, regardless of protection layers. In this study, we present a comprehensive methodology to numerically predict the reflected spectrum of metallic-coated FBG sensors, considering the process-induced residual stress in carbon fiber/epoxy composites. The finite element analysis was utilized to simulate the residual stress, which primarily arises from mechanical, thermal, and chemical cure mechanisms of the composites, including the thermosetting resin. Subsequently, the reflected spectra were calculated using the coupled mode theory. Contrary to common expectations, our findings indicate that the coating thickness has minimal influence on the reflected spectrum, while the residual stress and embedding position significantly impact it. By employing this proposed methodology, the number of experimental trials can be reduced, enabling the development of robust structural and state monitoring systems for composites using metallic-coated FBG sensors.

Development of a patient health monitoring system based on a service-oriented architecture using artificial intelligence

The object of the study is a patient health monitoring system that uses service-oriented architecture (SOA) and artificial intelligence (AI) to integrate and analyze medical data. Such a system integrates data from a variety of sources, including medical devices, health apps, electronic health records, and wearables and physiological performance recorders, providing a comprehensive approach to health monitoring. Thanks to SOA, the system is able to process large arrays of data in real time, providing the opportunity to quickly process and analyze them. This allows medical professionals to get a comprehensive picture of patients' health, taking into account both long-term trends and real-time indicators. One of the most challenging areas is ensuring effective integration and processing of disparate data from various medical devices and applications for accurate diagnosis and prognosis of diseases. It is also important to create a system that is easily scalable and can be adapted to the needs of different medical facilities and various monitoring systems. As a result of the research, it is concluded that the use of SOA allows creating flexible and scalable systems capable of integrating a wide range of medical devices and applications. The use of AI in these systems makes it possible to automatically detect deviations in health indicators, recognize pathologies in the early stages and predict the possible development of diseases. This is due to the fact that the proposed architecture has a number of features, in particular, the ability to collect, process and analyze large volumes of medical data in real time. Artificial intelligence algorithms provide high accuracy of diagnosis and forecasting thanks to the ability to quickly process complex data and find hidden patterns. Thanks to this, it is possible to obtain accurate and reliable indicators of the state of health of patients. Compared to similar known systems, it provides such advantages as increased efficiency of medical care, reduced risk of complications, early detection of diseases and a personalized approach to patient treatment, as well as the concentration of all data in one system.

Microbubble Contrast-Enhanced Transcutaneous Ultrasound Enables Real-Time Spinal Cord Perfusion Monitoring Following Posterior Cervical Decompression

Ultrasound imaging is inexpensive, portable, and widely available. The development of a real-time transcutaneous spinal cord perfusion monitoring system would allow more precise targeting of mean arterial pressure goals following acute spinal cord injury (SCI). There has been no prior demonstration of successful real-time cord perfusion monitoring in humans. Four adult patients who had undergone posterior cervical decompression and instrumentation at a single center were enrolled into this prospective feasibility study. All participants had undergone cervical laminectomies spanning ≥2 contiguous levels ≥2months prior to inclusion with no history of SCI. The first 2 underwent transcutaneous ultrasound without contrast and the second 2 underwent contrast-enhanced ultrasound (CEUS) with intravenously injected microbubble contrast. Using noncontrast ultrasound with or without Doppler (n= 2), the dura, spinal cord, and vertebral bodies were apparent however ultrasonography was insufficient to discern intramedullary perfusion or clear white-gray matter differentiation. With application of microbubble contrast (n= 2), it was possible to quantify differential spinal cord perfusion within and between cross-sectional regions of the cord. Further, it was possible to quantify spinal cord hemodynamic perfusion using CEUS by measuring peak signal intensity and the time to peak signal intensity after microbubble contrast injection. Time-intensity curves were generated and area under the curves were calculated as a marker of tissue perfusion. CEUS is a viable platform for monitoring real-time cord perfusion in patients who have undergone prior cervical laminectomies. Further development has the potential to change clinical management acute SCI by tailoring treatments to measured tissue perfusion parameters.

Applicability of pre-trained CNNs in temperate deforestation detection

ABSTRACT Technological advancements have opened up new possibilities for accurately identifying deforested areas and developing effective data collection strategies. Our paper proposes a practical approach to improve deforestation monitoring using satellite images and Convolutional Neural Networks (CNNs) for image classification. To train the CNNs, we used labeled data primarily sourced from the Amazon basin, while images from the Romanian Carpathian mountains were reserved for testing. Building on this foundation, we evaluated various pre-trained CNN architectures, including AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, GoogLeNet, and Swin Transformer, comparing their performance with a typical CNN architecture. In addition, we refined the performance by testing four ensemble learning methods. We found a decrease of only 10% in accuracy for using the models on data from the temperate area. Our findings contribute to this interdisciplinary field by providing insights into the effectiveness of pre-trained CNNs in classifying satellite images from temperate regions, trained with knowledge from tropical deforestation data. This contribution may open ways for the development of a quasi real-time deforestation monitoring system and serve as a reference for future research in temperate areas.

The Electrostatic Induction Characteristics of SiC/SiC Particles in Aero-Engine Exhaust Gases: A Simulated Experiment and Analysis

This study investigates the electrostatic induction characteristics of silicon carbide-fiber-reinforced silicon carbide (SiC/SiC) particles within aero-engine exhaust gases using a dedicated J20 turbojet engine experimental platform. Our comprehensive experiments explored the electrostatic properties of SiC/SiC particles under varying engine operational states—specifically focusing on different thermal conditions, particle mass concentrations, particle sizes, and exhaust gas velocities compared to those of common engine exhaust constituents like carbon (C) and iron (Fe) particles. The results demonstrate that SiC/SiC particles consistently maintain a stable positive charge across varied temperatures, significantly diverging from the behaviors of carbon (C) and iron (Fe) particles. Additionally, our findings reveal that higher mass concentrations of SiC/SiC particles, smaller particle sizes within a certain range, and greater exhaust gas velocities of the aero-engine all lead to increased particle charge and more pronounced electrostatic induction characteristics. This study highlights the potential of electrostatic sensors for the early detection and diagnosis of failures in aero-engines, offering crucial insights into the development of more resilient real-time aero-engine health monitoring systems.

Meta-Embedded Clustering (MEC): A new method for improving clustering quality in unlabeled bird sound datasets

In recent years, ecoacoustics has offered an alternative to traditional biodiversity monitoring techniques with the development of passive acoustic monitoring (PAM) systems allowing, among others, to detect and identify species that are difficult to detect by human observers, automatically. PAM systems typically generate large audio datasets, but using these monitoring techniques to infer ecologically meaningful information remains challenging. In most cases, several thousand hours of recordings need to be manually labeled by experts limiting the operability of the systems. Based on recent developments of meta-learning algorithms and unsupervised learning techniques, we propose here Meta-Embedded Clustering (MEC), a new method with high potential for improving clustering quality in unlabeled bird sound datasets. MEC method is organized in two main steps, with: (a) fine-tuning of a pretrained convolutional neural network (CNN) backbone with different meta-learning algorithms using pseudo-labeled data, and (b) clustering of manually-labeled bird sounds in the latent space based on vector embeddings extracted from the fine-tuned CNN. The MEC method significantly enhanced average clustering performance from less than 1% to more than 80%, greatly outperforming the traditional approach of relying solely on CNN features extracted from a general neotropical audio database. However, this enhanced performance came with the cost of excluding a portion of the data categorized as noise. By improving the quality of clustering in unlabeled bird sound datasets, the MEC method should facilitate the work of ecoacousticians in managing acoustic units of bird song/call clustered according to their similarities, and in identifying potential clusters of species undetected using traditional approaches.

Dynamic surface river pollution identification by a hybrid multivariate-based anomaly detection algorithm

With the rapid development of monitoring systems, extensive amount of water quality high-resolution measurements are accumulated, which make it unrealistic to manually extract the water quality anomaly features from the huge river environment information. In this study, a hybrid anomaly detection framework is developed by the combination of prediction-based and classification-based data-driven methods to provide a scientific indication for river pollution identification. A Variational Mode Decomposition-Back Propagation Neural Network (VMD-BPNN) model is used to analyze the real-time water quality variation tendencies in the first stage. Additionally, a Support Vector Data Description (SVDD) algorithm is adopted to capture the multi-dimensional water quality anomaly characteristics in the second stage. The developed hybrid framework is then applied to the Kansas River in America, to verify its river pollution identification performance in comparison to different anomaly detection methods and in various anomaly-level scenarios. The developed hybrid framework can achieve a maximum Area Under the Curve (AUC) value of 0.932 under a two-dimensional anomaly detection pattern with the True Positive Rate (TPR) and False Positive Rate (FPR) values of 0.861 and 0.142, respectively. The results indicate that the developed hybrid framework can provide an effective river pollution identification performance with dynamically determined warning thresholds. Meanwhile, a vigorous anomaly detection pattern can improve the pollution identification performance by considering the cumulative interactions among the multi-dimensional water quality parameters.

Predict Future Transient Fire Heat Release Rates Based on Fire Imagery and Deep Learning

The fire heat release rate (HRR) is a crucial parameter for describing the combustion process and its thermal effects. In recent years, some studies have employed fire scene images and deep learning algorithms to predict real-time fire HRR, which has led to the advancement of HRR prediction in terms of both lightweightness and real-time monitoring. Nevertheless, the development of an early-stage monitoring system for fires and the ability to predict future HRR based on current moment data represents a crucial foundation for evaluating the scale of indoor fires and enhancing the capacity to prevent and control such incidents. This paper proposes a deep learning model based on continuous fire scene images (containing both flame and smoke features) and their time-series information to predict the future transient fire HRR. The model (Att-BiLSTM) comprises three bi-directional long- and short-term memory (Bi-LSTM) layers and one attention layer. The model employs a bidirectional feature extraction approach, followed by the introduction of an attention mechanism to highlight the image features that have a critical impact on the prediction results. In this paper, a large-scale dataset is constructed by collecting 27,231 fire scene images with instantaneous HRR annotations from 40 different fire trials from the NIST database. The experimental results demonstrate that Att-BiLSTM is capable of effectively utilizing fire scene image features and temporal information to accurately predict future transient HRR, including those in high-brightness fire environments and complex fire source situations. The research presented in this paper offers novel insights and methodologies for fire monitoring and emergency response.

Characteristics and Influencing Factors of Spatiotemporal Distribution of Rural Houses Construction Development in Mountainous Villages of China (1980–2019): A Case Study of Qingyuan Town

Rural house is a fundamental component of rural settlements, and understanding its construction and development characteristics is crucial for rural land use and development planning. This paper focuses on the spatiotemporal characteristics and influencing factors of Rural Houses Construction Development (RHCD) from 1980 to 2019 with a case study of Qingyuan Town in China. Based on the literature review and filed research, a set of evaluation indicators for RHCD was established. The article calculates RHCD indicators from temporal and spatial dimensions, uses the location entropy method to demonstrate the spatial distribution of indicators, and classifies the RHCD type of 14 villages in Qingyuan Town using clustering algorithms. It also analyzes the influencing factors of spatiotemporal distribution. The results show that the RHCD in Qingyuan Town exhibits typical characteristics of mountainous areas and aligns with the development trends of rural society in China. Population growth, geographical location, and economic development are the primary driving factors for the quantity indicator (Qi), while economic growth, construction technology, industrial development, and policy adjustments are the key factors influencing the form indicator (Fi). In future policy-making, greater emphasis should be placed on optimizing development strategies, improving data and monitoring systems, and integrating administrative strength with actual development needs.

Injury and illness surveillance monitoring in team sports: a framework for all

BackgroundSport-related injuries and illnesses can negatively impact athlete welfare at all standards of participation in team sports. Injury and illness surveillance (IIS), and the development of monitoring systems, initiates the sequence of injury and illness prevention. Operational IIS monitoring systems help to appraise epidemiological estimates of injury and illness incidence and burden in various athlete populations. However, the methodological underpinnings of various monitoring systems are not harmonized or widely documented, with the presence of efficient and successful programmes rarely showcased at non-elite levels. The aim is to provide a framework that guides the development of IIS, which will enhance overall surveillance, to indirectly inform injury prevention strategies.MethodsThe process involved all members of the research group initially discussing the research gaps, scope of the project, and the aims of the article. Unique experiences were shared, and specific and global challenges and barriers to IIS at all standards of team sport participation were identified. A tiered system of data collection with corresponding content were produced, with experiences and guidance provided throughout the article.ResultsThe literature has been reviewed and using first-hand experience in conducting IIS programmes in complex and diverse sport settings, the authors have identified key enablers and barriers for best practise as time, technological and human resources, reporter/practitioner training, and medical expertise. Areas of greatest importance regarding the conducting of IIS have been outlined, providing guidance and recommendations across all levels of team sport participation. These areas include definitions, data context, collection procedures, handling, security, ethics, storage, dissemination, quality, compliance, and analysis. Given the barriers to IIS, 3-tiered levels of data collection and content have been proposed. The levels indicate data collection variables, with a focus on sufficiency and achievability, aiming to support the successful conducting of IIS in team sports across all standards of participation. Future opportunities in IIS have been discussed, with several predictive measures and analytical techniques expanded upon.ConclusionsThe framework provides universal guidance for implementing IIS monitoring systems, facilitating athletes, coaches, parents/guardians, governing bodies and practitioners to implement IIS processes, identify challenges, complete analysis, and interpret outcomes at all standards of participation.

Halochromic Polyaniline as smart detector packaging for fresh food: a review

Abstract Early spoilage is a challenge that is often encountered in fresh food. One prevention that can be done is by using packaging technology. There has been an increasing interest in using smart detector packaging for fresh products. Smart detector has high sensitivity and is responsive to visual changes when exposed to pH, temperature, water activity, or composition. The advantage of using a smart detector is that it can detect and give information on which products are safe to consume and which are not. Overall, the concept of a smart detector is not much different from the function of smart packaging. Smart packaging that is sensitive to pH is called halochromic properties. The mechanism of halochromic depends on the compound or the indicator used. Polyaniline (PANI) is one of the candidates for halochromic materials, which have high stability and sensitivity to pH changes. PANI also has a high sensitivity to volatile compounds such as ammonia, indicated by a change in colour from green to blue. This review will overview the use of PANI as a smart detector halochromic packaging and its potential application in the food industry. It has been found that the application of PANI has opportunities to advance the development of modern smart packaging and better food quality monitoring systems.

Development of Water Quality Control and Monitoring System for Hydroponic Plants with a Cloud-Based System

Development of Water Quality Control and Monitoring System for Hydroponic Plants with a Cloud-Based System

IoT Based Transformer Monitoring System using ESP-32

Abstract: The integration of Internet of Things (IoT) technology with power transformer monitoring systems presents a significant advancement in electrical grid management. This paper explores the development of an IoT-based transformer monitoring system utilizing the ESP-32 microcontroller. The primary objective is to enhance the reliability and efficiency of transformers by providing real-time data on their operational status and health. The system leverages the ESP-32's capabilities in wireless communication and data processing to monitor critical parameters such as temperature, load current, voltage, and oil levels. Data is transmitted to a centralized cloud platform for analysis and visualization, enabling predictive maintenance and rapid response to potential failures. The proposed system promises to reduce downtime and maintenance costs while ensuring the longevity and optimal performance of transformers. This research highlights the design, implementation, and testing phases of the monitoring system, demonstrating its effectiveness through various case studies and field trials.

Monitoring the Use of Electrical Energy and Water in Homes Based on IoT

Increased awareness of energy efficiency and sustainable resource management has encouraged the development of intelligent electrical energy and water usage monitoring systems. In this context, the Internet of Things (IoT) has become a promising solution for connecting household devices to the internet, enabling users to monitor and control electricity and water consumption in real-time. This research aims to design and implement an IoT-based monitoring system for electrical energy and water usage in homes. This system uses a powerful Arduino ESP32 microcontroller and can connect to the internet via WiFi. This microcontroller acts as a control center that collects data from sensors installed in the house. Based on these problems, this research was carried out, with the aim of creating a prototype to record electrical power loads and display the amount of electricity consumption in rupiah levels to monitor the use of electrical power loads and convert them into rupiah. To conduct this research, the author used observational research methodology or field study and literature study. In this research, it is necessary to pay attention to understanding how to think and how to carry out the results of thinking according to scientific steps.

Insights from the development of an innovative air quality monitoring system

Insights from the development of an innovative air quality monitoring system

Weather, Air Pollution, Rainfall Monitoring with ZigBee System

based air and rainfall detection system. The design and development of a ZigBee-based monitoring system is a key component of this paper. The proposed monitoring system is in need of the hour in metro cities of dense population countries like India. The carbon emission from industries and a large number of vehicles in metro cities in India demand low-cost, low-power, real-time distributed monitoring systems. We have chosen Pune city in Maharashtra, India for the implementation of the monitoring system due to the frequent changes in weather and rainfall conditions observed in 2019. Our main objective is to provide real-time monitoring of air quality at distributed locations by using ZigBee-based distributed real-time air quality monitoring. The extra efforts that we have done here is to observe and monitor Pollution levels after and before rainfall in Pune City, India. The highlight of this paper is the establishment of the interrelation of air pollution and rainfall and the comparisons of environmental monitoring Keywords: - ZigBee,AQI, rainfall monitoring, , air quality, weather monitoring, Pune India weather, GSM, GPRS, environment monitoring, Wireless sensor networks, MQ135.