Development Of Monitoring System Research Articles

Real-time Vertical Air Quality Monitoring System Development for Urban Scale

Real-time Vertical Air Quality Monitoring System Development for Urban Scale

Development of Safety Monitoring System Based on Visual Intelligence Technology

Development of Safety Monitoring System Based on Visual Intelligence Technology

Integrated health monitoring system for checking patients’ vitals

The increasing demand for efficient healthcare services has propelled the development of efficient health monitoring systems. This paper presents a comprehensive system designed to monitor the vital signs of patients in real-time, aimed at enhancing patient care and reducing the burden on healthcare providers. The system integrates various sensors to Data collected from these sensors are transmitted wirelessly to a central database, where it is processed and analyses using sophisticated algorithms. The results are accessible through a user-friendly interface for both healthcare professionals and patients. This health monitoring system offers several critical benefits. it provides real-time alerts for any abnormal vital signs, enabling prompt medical intervention. Secondly, the continuous monitoring allows for the early detection of potential health issues, which can significantly improve patient outcomes. The methodology involves is the analytical and systematic application of Arduino Nano Boards as microcontrollers, such as DS18B20, DHT11 and with WIFI Modula; which are driven- real time-based temperature frameworks for optimization and abstraction of accurate patient’s health data result. Results demonstrated the system's accuracy, reliability, and user satisfaction.

Development of monitoring system to prevent inflow of marine life into the nuclear power plant

Development of monitoring system to prevent inflow of marine life into the nuclear power plant

IOT Enabled Remote Monitoring Application For Telecom SMPS

This research paper presents a comprehensive study on the development of an IoT-enabled remote monitoring system for telecom SMPS (Switched-Mode Power Supply). The system aims to address the critical need for efficient power supply management in the telecom industry by utilizing a network of sensors to collect real-time data on key parameters such as voltage, current, temperature, and load. The collected data is securely transmitted to a central monitoring system for analysis and control.

Entropy-guided robust feature domain adaptation for electroencephalogram-based cross-dataset drowsiness recognition

To accurately identify driver drowsiness using Electroencephalogram (EEG), a significant amount of time is typically required to gather data from either the same subject or multiple subjects under the same experimental condition. The prolonged period required for data collection impedes the practical application of EEG. In this paper, we consider the challenging task of cross-dataset driver drowsiness recognition, where the EEG data collected by different devices are expected to be conveniently recognized by a classifier trained on an existing dataset. In order to solve the problem of distribution drift, we propose an Entropy-Guided Robust Feature (EGRF) adaptation framework. This framework utilizes the Entropy Minimization (EM) technique for domain alignment and an ensemble-like structure, consisting of multiple branch classifiers and a dropout layer, to enhance the robustness. The proposed method has been tested across two public datasets and achieves 2-class recognition accuracies of 77.4% and 85.9%. The innovative feature-robust block, combined with the EM loss, leads to substantial performance improvement, proving the effectiveness of our framework for cross-dataset driver drowsiness recognition. Our research demonstrates a promising direction for development of a calibration-free driver drowsiness monitoring system in the future.

Development of real-time energy monitoring system using IoT base

This paper aims to develop a real-time energy monitoring system based on smart metering to enhance energy efficiency in the single-phase residential sector. Based on the concept of low-cost IoT devices, this intelligent meter system is designed to monitor household energy consumption. It provides real-time information on a graphical Node-RED Dashboard, making it easy for households to track and manage their energy usage. The hardware of the power metering system included the Node MCU ESP8266, the PZEM-004T, and a cloud server built on the Raspberry Pi for storing electricity consumption data by using Node-RED to connect devices via an application programming interface system. This system can help analyze the electricity consumption behavior in the residential sector. It is a guideline for selecting the electricity rate between the TOD and TOU rates. The results found that in the electrical energy measurements of households 1 and 2, the mean deviations were 0.8758% and 0.5523%, respectively. The electricity cost-saving results when changing the electricity tariff from the TOD rate of households 1 and 2 to the TOU rate. It was found that electricity costs can be saved by 0.2807% and 1.0936%, respectively. The most critical variable is electricity consumption during peak periods. In the case study of household 1, if the electricity consumption during the peak period is less than 13%, it will be appropriate to select the Time of Use rate. In household 2, if there is electricity consumption during the peak period, less than 38% would be appropriate to choose the type of electricity user with a TOU rate.

Soil pollution in the European Union – An outlook

Soil pollution in the European Union (EU) is poorly quantified, because of several years of reduced attention and limited funding on soil research and monitoring. Moreover, issues such as different monitoring methodologies within Member States (MS), and quantification methods focused on pseudototal rather than the available fraction of pollutants, has been limiting our understanding of risk under soil pollution. Despite that, thanks to efforts from the scientific community, it was possible to achieve some progress, which is by far insufficient for the problem at hand. In the anticipation of future policy demands and towards a common strategy for tackling soil pollution in the EU, it seemed relevant to describe the limited knowledge and main uncertainties. Such analysis should provide evidence for the development of efficient policies, as well as updating current ones to better tackle the interdisciplinary of soil. This perspective provides our view on current knowledge on soil pollution at EU scale, by compiling the most updated assessments made at EU scale, identifying soil pollution drivers, impacts on health and the environment, and evaluating current state of knowledge. Allowing us to infer about current level of uncertainties and knowledge gaps and identify solutions through future research and policies. Our suggestions are in line with an increasing investment on research and innovation, but also more frequent updates of current legislation already tackling pollution and welcoming the new proposal for a Soil Monitoring Law as an important driver for soil knowledge development and implementation of a European monitoring system.

Paradigm Shift in Sports Science: The IOT-Enhanced Agility-Pad for Athletic Performance Evaluation

This paper explores the development and implementation of an agility monitoring system, aiming to provide real-time assessment of an athlete's agility. The need for such technology to facilitate instant and remote agility assessment has become increasingly apparent in the field of sports science. Unfortunately, progress in this area has been relatively slow until the emergence of IoT (Internet of Things) technology, which presents a promising avenue for athlete agility monitoring through telemetering. The core of this innovative system consists of an agility pad and a real-time monitoring module, with communication facilitated by LabVIEW software. Our approach involves rigorous testing, including unit testing of the agility pad, which serves as an MQTT publisher, the server, which functions as an MQTT broker, and the monitoring module, acting as an MQTT subscriber. In addition to these technical aspects, this article showcases various outcomes of our research. These include the designs of two activity diagrams, a comprehensive conceptual framework for agility monitoring, the database structure, communication designs for the agility pad, a wiring schematic, a 3D model of the agility pad, and the corresponding wiring configuration. We have successfully constructed two sets of agility pads, each integrated with an MCU system (ESP8266) and a dedicated database architecture designed for agile monitoring. One significant achievement of our work is the identification of the optimal delay interval required on the agility pad to prevent data loss on the MQTT broker. Furthermore, we have effectively developed a mechanism for detecting and addressing abnormal data during each testing phase, which contributes to the robustness and reliability of the system. This paper not only highlights the innovative technology behind the IOT-Infused Agility-Pad but also underscores its potential to revolutionize the assessment of athletic performance, offering coaches and athletes real-time insights into agility progress.

Development of 16N monitoring system for real-time detection of small leakage in reactor coolant systems

This paper introduces a16N monitoring system designed for the real-time detection of minor coolant leaks in nuclear reactors. Conventional coolant leak detection methods in nuclear power plants rely on observing changes in sump tank water levels, as guided by Nuclear Regulatory Commission standards. However, these methods are slow and often inaccurate, particularly for detecting small leakages of less than 0.5 gallons per minute (gpm). Such delays pose risks of corrosion from boric acid and potential safety hazards due to mechanical or thermal fatigue at critical junctions. To address these challenges, we have developed a Reactor Coolant System (RCS) leakage detection system that can swiftly identify small coolant leaks. This system employs beta-ray emissions from 16N, a nuclide predominantly produced during reactor operation and accounting for 90.4% of the total radioactivity in the APR1400 RCS coolant. High-energy beta-rays emitted by 16N are ideal for monitoring due to their significant energy levels, which distinguish them from other nuclides. This system is specifically designed for installation within the annulus zone of the reactor containment, where it captures air near reactor pipes to measure 16N beta-rays. Given the short half-life of 16N (7.13 s), direct measurement is challenging; hence, we use an indirect method to determine calibration factors between dose rate with count rate and estimate radioactivity concentrations based on simulation data and experimental measurements with a surrogate 90Sr/Y source. This system can enhance the safety protocols of nuclear plants by enabling early detection of leaks. Our findings demonstrate that this system can detect leaks as small as 0.01 gpm within seconds, significantly improving response times compared to conventional methods.

Current Challenges in Monitoring Low Contaminant Levels of Per- and Polyfluoroalkyl Substances in Water Matrices in the Field.

The Environmental Protection Agency (EPA) of the United States recently released the first-ever federal regulation on per- and polyfluoroalkyl substances (PFASs) for drinking water. While this represents an important landmark, it also brings about compliance challenges to the stakeholders in the drinking water industry as well as concerns to the general public. In this work, we address some of the most important challenges associated with measuring low concentrations of PFASs in drinking water in the field in real drinking water matrices. First, we review the "continuous monitoring for compliance" process laid out by the EPA and some of the associated hurdles. The process requires measuring, with some frequency, low concentrations (e.g., below 2 ppt or 2 ng/L) of targeted PFASs, in the presence of many other co-contaminants and in various conditions. Currently, this task can only (and it is expected to) be accomplished using specific protocols that rely on expensive, specialized, and laboratory-scale instrumentation, which adds time and increases cost. To potentially reduce the burden, portable, high-fidelity, low-cost, real-time PFAS sensors are desirable; however, the path to commercialization of some of the most promising technologies is confronted with many challenges, as well, and they are still at infant stages. Here, we provide insights related to those challenges based on results from ab initio and machine learning studies. These challenges are mainly due to the large amount and diversity of PFAS molecules and their multifunctional behaviors that depend strongly on the conditions of the media. The impetus of this work is to present relevant and timely insights to researchers and developers to accelerate the development of suitable PFAS monitoring systems. In addition, this work attempts to provide water system stakeholders, technicians, and even regulators guidelines to improve their strategies, which could ultimately translate in better services to the public.

Drone Technology for Precision Agriculture: Advancements and Optimization Strategies

This paper delves into the development and optimization of an intelligent agricultural monitoring system that makes use of drone technology to enhance agricultural productivity and sustainability. With the world's population on the rise and the scarcity of arable land, precision agriculture becomes essential in guaranteeing food security. By harnessing state-of-the-art sensors and imaging technologies, drone technology offers a new and innovative approach to agricultural monitoring. Efficiently collecting vital data on vegetation indices, soil moisture, and crop health. This study seeks to create a state-of-the-art drone-based monitoring system that uses advanced machine learning algorithms and data processing technologies to analyze agricultural data with exceptional precision. It explores the most efficient methods for operating drones, including flight planning and data collection protocols, with the aim of creating a comprehensive agricultural monitoring system. This platform provides the convenience of automating monitoring processes, leading to lower labor costs, improved resource allocation, and a beneficial impact on agricultural modernization. The article delves into the effects of the latest developments in drone technology and the subsequent decrease in costs on the agriculture industry worldwide. It highlights the ways in which these advancements are transforming conventional farming methods.

Development of Automobile Wheel Smart Alignment Monitoring System

This study addresses the pressing issue of ineffective traditional methods for monitoring wheel alignment in vehicles. The motivation stems from the limitations of manual inspection and the need for a more efficient and accurate solution. The aim is to develop a Smart Alignment Monitoring System that can provide real-time monitoring of alignment parameters. The objective is to enhance vehicle safety and performance by detecting misalignment issues early. The proposed solution involves the use of advanced sensor technologies integrated with vehicles' tire position. The results demonstrate the effectiveness of the Smart Alignment Monitoring System in reducing maintenance costs, improving fuel efficiency, and enhancing vehicle safety and performance. This study highlights the significance of adopting advanced technologies like Smart Alignment Monitoring Systems in the automotive industry to ensure safer and more efficient vehicles.

A qualitative exploration of the impact of the COVID-19 pandemic on gender-based violence against women living with HIV or tuberculosis in Timor Leste.

Violence against women or gender-based violence (GBV) is a significant public health issue facing women and girls in different settings. It is reported to have worsened globally during the COVID-19 pandemic. Despite the impact of the COVID-19 pandemic on increased violence against women in general, which has been reported in many settings globally, there is a paucity of evidence of its impact on violence against highly vulnerable women living with HIV or tuberculosis (TB). Using a qualitative design, this study aimed to explore the views and experiences of women living with HIV (n = 19) or TB (n = 23) in Timor Leste regarding the GBV they faced during the COVID-19 pandemic. They were recruited using the snowballing sampling technique. Data were collected using one-on-one, in-depth interviews and focus group discussions. The five steps of qualitative data analysis suggested in Ritchie and Spencer's analysis framework were employed to guide the analysis of the findings. Findings indicated that women in this study experienced intensified physical, verbal, sexual and psychological violence by their partners, spouses, in-laws, and parents or other family members during the COVID-19 pandemic. Several prominent risk factors that worsened violence against women during the pandemic were (i) HIV or TB-positive status, (ii) traditional gender roles or responsibilities and expectations, (iii) economic and financial difficulties reflected in the loss of jobs and incomes due to the pandemic, and (iv) individual factors such as jealousy and increased alcohol drinking developed during the lockdowns. The women's experience of GBV during the pandemic also led to various negative psychological impacts. The findings underscore the urgent need for multifaceted interventions to address GBV, which should encompass challenging traditional gender norms, addressing economic inequalities, and targeting individual-level risk factors. The findings also indicate the need for the development of robust monitoring and evaluation systems to assess the effectiveness of policies and interventions addressing GBV where the results can inform future improvement. The findings also indicate the need to include GBV in the protocol or guidelines for HIV and TB management. Future large-scale quantitative studies to capture the magnitude and specific drivers of GBV against women living with HIV and TB during the pandemic are recommended.

Development of Remote Condition Monitoring System for Panauti Hydropower Station

Abstract Condition monitoring is the process of predicting faults in machine through the measurement and analysis of pre-identified parameters. This paper aims to present the features and process of development of a real time condition monitoring system installed at Panauti Hydropower station. In total, seven different sensors including Pressure, Temperature, Guide Vane Position and Tri-axial Vibration sensor were installed at the power plant. The locations of the pressure sensors are at the inlet of the turbine and the outlet of the draft tube. Tri-axial vibration sensor was installed at the turbine shaft to capture the turbine vibrations at different operating conditions. The temperature sensors provide the temperature data from the bearings and housing of a generator. The sensors were connected to the NI-DAQ (data acquisition system) hardware including current measuring module and sound and vibration module integrated with the LabVIEW program. The data acquisition system is capable of recording the pressure pulsations at the outlet of draft tube. The monitoring system processes the data from sensors and uploads the relevant information to the Kathmandu University’s central server. A remote monitoring program in LabVIEW has been developed which uses an Application Programming Interface (API) to retrieve the information from the central server in the real time, enabling the monitoring of the power plant from any location.

Density of soil composite composition in a changing magnetic field

The advancement of technical means for determining soil density in precision (controlled) agriculture necessitates the enhancement of non-destructive flow interaction methods. There is also a need to improve methods for assessing soil density, as existing deviations from optimal values adversely affect the yield of agricultural crops. This research aims to establish the density of the composites in the granulometric composition of soil by determining the self-induction voltage in a changing magnetic field. The research methods are adapted to determine the relationships of Larmor precession under variable inductive current for the density of each composite in the granulometric soil composition. Experimental investigations were conducted by measuring the self-induction voltage of a solenoid acting as a sensor for each of the composites located within the core. The processing of experimental results was carried out according to the principles of mathematical statistics and probability theory, using approximations in the Excel and Statistica software packages. Based on the results of the experimental investigations, models of the relationships between the density ρ (1.0-1.5 g/cm3) of soil composites and the self-induction voltage eL=184-192 mV, with a generator voltage of 5 V and determination coefficients R2=0.95-0.99, have been established. These models enable the assessment of soil density with high accuracy, thereby facilitating the optimisation of agronomic processes. It has been determined that these models can be used with a high degree of reliability as calibration characteristics for the design of technical means for flow-based non-destructive measurement of the density of the main soil types in Ukraine. Furthermore, it has been identified that future research should focus on a more in-depth investigation of the relationships between magnetic permeability and the agrophysical characteristics of soil within the locally defined inductive field of the sensor (solenoid). An applied aspect of the obtained results is the further development of adaptive machinery and monitoring systems for soil conditions, aimed at achieving optimal cultivation indicators, as well as their utilisation by research institutions and instrument-making enterprises

Assessment of different optimal sensor placement methods for dynamic monitoring of civil structures and infrastructures

The optimal sensor placement is a critical aspect in the structural health monitoring of structures, as the number and layout of sensors directly impact the quality and usefulness of collected data. When developing a monitoring system, it is of paramount importance to use the minimum number of sensors to gain the maximum amount and quality of information. However, in the case of a limited number of sensors, their location on the structure becomes crucial. This study aims to assess the potential of several optimal sensor placement methods for the development of cost-efficient and well-dimensioned dynamic structural health monitoring systems to be implemented in a wide range of civil engineering structures and infrastructures. Indeed, the construction stock consists of a great variety of civil engineering structures and infrastructures that significantly differ from each other in terms of typology, historical period, construction techniques, materials, etc. Some of the most widely used optimal sensor placement methods are selected and applied in real case studies, and the obtained sensor configurations are discussed and compared. The results illustrated in this paper will contribute to defining good practice in the optimal sensor placement procedure for typical civil engineering structures and infrastructures.

Real-Time Detection of Face Mask Usage Using Convolutional Neural Networks

The widespread adoption of face masks has been a crucial strategy in mitigating the spread of infectious diseases, particularly in communal settings. However, ensuring compliance with mask-wearing directives remains a significant challenge due to inconsistencies in usage and the difficulty in monitoring adherence in real time. This paper addresses these challenges by leveraging advanced deep learning techniques within computer vision to develop a real-time mask detection system. We have designed a sophisticated convolutional neural network (CNN) model, trained on a diverse and comprehensive dataset that includes various environmental conditions and mask-wearing behaviors. Our model demonstrates a high degree of accuracy in detecting proper mask usage, thereby significantly enhancing the ability of organizations and public health authorities to enforce mask-wearing rules effectively. The key contributions of this research include the development of a robust real-time monitoring system that can be integrated into existing surveillance infrastructures to improve public health safety measures during ongoing and future health crises. Furthermore, this study lays the groundwork for future advancements in automated compliance monitoring systems, extending their applicability to other areas of public health and safety.

Cytogenetic monitoring of spontaneous mutagenesis

Due to the challenging environmental situation in Ukraine, there is a need for cytogenetic monitoring, establishing cause-and-effect relationships, and assessing the toxic-mutagenic activity of environmental components. The results of such monitoring can serve as a basis for developing rehabilitation measures aimed at improving the environmental condition. Cytogenetic bioassay methods allow the evaluation of ecological and genetic risks to biota, considering the overall impact of pollutants, predicting changes in ecosystems, and making timely management decisions to improve environmental quality and preserve the nation’s gene pool. The article provides cytogenetic monitoring of apical meristems of the roots of Raphanus sativus subsp. radicula (Pers.) DC. and Allium cepa L. seedlings., grown on soils of residential areas, and the effect of ions of some elements on the cytological parameters of Pisum sativum L. was studied. It was noted that the spectrum of mitosis abnormalities and chromosome aberrations was expanded due to lagging chromosomes and micronuclei. Residential areas with a consistently high level of spontaneous cytogenetic disorders require the development of a local monitoring system to identify genetic hazards. The research on the proliferative activity of apical meristem cells confirms the hypothesis of aluminum toxicity to plants. Cytotoxic effects are assessed at the micro- and macroscopic levels. Macroscopically, reduced root growth of bioindicator plants was observed, which may result from several possible mechanisms: cell death, inhibition of division, cell elongation, or nutrient absorption. It was found that the duration of the prophase is directly influenced by the mobile forms of Zn and Pb, while Cu has a reverse effect. According to the results of multiple regression analysis, the most significant influence on these processes is exerted by the mobile forms of Zn, Cu, and Pb in the surface layers of the soils of the studied residential areas. Under the influence of AlCl3, the proportion of cells in the anaphase stage increases; CdCl2 affects the prophase stage; Na2SeO3 affects the meta- and anaphase stages; X-ray irradiation affects the telophase stage. Regarding the ability to induce the frequency of aberrant anaphases, a ranking can be constructed: Na2SeO3 (3.75·10–6 M) > AlCl3 (3.86·10–5 M) > CdCl2 (8.44·10–5 M). A radiation dose of 9.03·10–3 C/kg leads to the frequency of chromosomal aberrations, similar to the effects of AlCl3 (3.86·10–4 M) and Na2SeO3 (8.34·10–6 M). High concentrations of aluminum are associated with an increase in anaphases with fragments and two to three bridges.

Towards the Development of an In-Process Quality Monitoring System for Polyethylene Recyclates by Pyrolysis Gas Chromatography Ion Mobility Spectrometry

Over the past few decades, the topic of recycling has become increasingly prominent in the field of sustainable materials and circular economy. One significant challenge is the physical separation of different types of plastics to obtain recyclates of one plastic type as pure as possible with comparable quality and properties to those of virgin material. Given the substantial effort involved in such separation, small amounts of contamination from other plastics may be tolerated. However, these contaminations must be monitored to ensure high-level recyclate quality. In recent years, compact, low-cost ion mobility spectrometers (IMS) with high analytical performance have been developed, and have thus become widely used in a variety of sensing applications. Due to their high sensitivity, IMS are particularly suited for detecting lowest concentration levels of various compounds, as required for the detection of impurities in recyclate quality monitoring. When coupled to a miniature gas chromatograph (GC), GC-IMS reach even higher separation power while being still compact. To bring recyclate samples to the gas phase, pyrolysis (Py) is used in this work. A first feasibility study was conducted to assess the potential of such a pyrolysis–gas chromatography-ion mobility spectrometer (Py-GC-IMS) with the objective of detecting contaminations of polyethylene terephthalate (PET) in polyethylene (PE) recyclates. The study clearly demonstrates the ability to identify PET-related fingerprints while suppressing the PE background matrix by design so that Py-GC-IMS seems a promising approach for in-process monitoring PET contaminations in PE recyclates.