Development Of Monitoring System Research Articles

Personal Protective Equipment Detection: A Deep-Learning-Based Sustainable Approach

Personal protective equipment (PPE) can increase the safety of the worker for sure by reducing the probability and severity of injury or fatal incidents at construction, chemical, and hazardous sites. PPE is widely required to offer a satisfiable safety level not only for protection against the accidents at the aforementioned sites but also for chemical hazards. However, for several reasons or negligence, workers may not commit to and comply with the regulations of wearing the equipment, occasionally. Since manual monitoring is laborious and erroneous, the situation demands the development of intelligent monitoring systems to offer the automated real-time and accurate detection of PPE compliance. As a solution, in this study, Deep Learning and Computer Vision are investigated to offer near real-time and accurate PPE detection. The four colored hardhats, vest, safety glass (CHVG) dataset was utilized to train and evaluate the performance of the proposed model. It is noteworthy that the solution can detect eight variate classes of the PPE, namely red, blue, white, yellow helmets, head, person, vest, and glass. A two-stage detector based on the Fast-Region-based Convolutional Neural Network (RCNN) was trained on 1699 annotated images. The proposed model accomplished an acceptable mean average precision (mAP) of 96% in contrast to the state-of-the-art studies in literature. The proposed study is a potential contribution towards the avoidance and prevention of fatal/non-fatal industrial incidents by means of PPE detection in real-time.

Development of a Smart Air Quality Monitoring System Using Wireless Sensors

With the fast-growing urbanization and increased industrialization around cities in Nigeria, the quality of the air we breathe has become a paramount concern. There is a need for an accurate, real-time air quality monitoring system to monitor and evaluate the air quality we inhale. This article presents the development of a Smart Air Quality Monitoring System (SAQMS) utilizing wireless sensor technology. The SAQMS was designed to provide a comprehensive and intelligent solution for monitoring air quality index, including but not limited to particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon monoxide (CO), temperature and humidity. Leveraging wireless sensor nodes and advanced IOT, the system offers real-time data collection and analysis, enabling immediate response to environmental changes. Key features of the SAQMS include a network of wireless sensors (MQ 135 and DHT11) strategically deployed across targeted areas, data transmission through secure communication protocols, cloud-based data storage and processing, and a user-friendly interface for data visualization and interpretation. As we confront the challenges of air pollution and its impact on human well-being, the development of the SAQMS presents a significant step towards informed decision-making and a more sustainable future as the experiment shows our developed system can detect air quality in parts per million (ppm), with other environmental factors such as temperature and relative humidity.

Hydrophobicity and molecular mass-based separation method for autoantibody discovery from mammalian total cellular proteins.

Serum autoantibody profiles are unique to individuals and reflect the level and history of autoimmunity and tumor immunity. The identification of autoantibody biomarkers is critical for the development of immune monitoring systems for immune-related disorders. Here, we present a practical method for large-scale autoantibody discovery using total cellular proteins from cultured mammalian cells. We found that nucleic acid-free and fully denatured water-soluble total cellular proteins from mammalian cells were superior, allowing precise separation by reversed-phase HPLC after preparing a large set of homogeneous total cellular proteins. After separating the proteins based on hydrophobicity, the fractionated samples were subjected to molecular mass analysis using conventional SDS-PAGE. The resulting two-dimensional gel electrophoresis was successfully employed for immune blotting and LC-MS/MS analysis. All procedures, including TRIzol-based total cellular protein extraction, solubilization of denatured proteins, reversed-phase HPLC separation, and SDS-PAGE, were highly reproducible and easily scalable. We propose this novel two-dimensional gel electrophoresis system as an alternative proteomics-based methodology suitable for large-scale autoantibody discovery.

Cyclodextrin Host-Guest Recognition in Glucose-Monitoring Sensors.

Diabetes mellitus is a prevalent chronic health condition that has caused millions of deaths worldwide. Monitoring blood glucose levels is crucial in diabetes management, aiding in clinical decision making and reducing the incidence of hypoglycemic episodes, thereby decreasing morbidity and mortality rates. Despite advancements in glucose monitoring (GM), the development of noninvasive, rapid, accurate, sensitive, selective, and stable systems for continuous monitoring remains a challenge. Addressing these challenges is critical to improving the clinical utility of GM technologies in diabetes management. In this concept, cyclodextrins (CDs) can be instrumental in the development of GM systems due to their high supramolecular recognition capabilities based on the host-guest interaction. The introduction of CDs into GM systems not only impacts the sensitivity, selectivity, and detection limit of the monitoring process but also improves biocompatibility and stability. These findings motivated the current review to provide a comprehensive summary of CD-based blood glucose sensors and their chemistry of glucose detection, efficiency, and accuracy. We categorize CD-based sensors into four groups based on their modification strategies, including CD-modified boronic acid, CD-modified mediators, CD-modified nanoparticles, and CD-modified functionalized polymers. These findings shed light on the potential of CD-based sensors as a promising tool for continuous GM in diabetes mellitus management.

Air Quality Monitoring and Analysis for Sustainable Development of Solid Waste Dump Yards Using Smart Drones and Geospatial Technology

Air pollution has become a global health issue, affecting millions of people annually. It is essential to obtain accurate and up-to-date information on air quality levels to reduce the impact of air pollution on human health. Traditional air quality monitoring methods are limited by spatial coverage and the time required for sample collection and analysis. However, the Internet of Things (IoT), drone technology, and Geographic Information System (GIS) technology have enabled the development of real-time air quality monitoring systems that provide accurate and up-to-date information on air quality levels across large areas. The study found that gas concentration varied significantly at different heights, with the highest concentration at 3 m from the ground and the lowest at 9 m. The concentration of gases also varied by month, with the highest concentration of CO recorded in March at 45 ppm at 3 m, and the highest concentration of NH4 recorded in September at 66.7 ppm at 3 m. Kodungaiyur dump yard needs environmental monitoring due to the high levels of environmental pollution and health risks caused by unsegregated waste. In conclusion, developing real-time air quality monitoring systems using GIS technology is necessary to obtain accurate and up-to-date information on air quality levels. It is essential to monitor the Kodungaiyur dump yard and other, similar sites regularly to prevent the health risks associated with environmental pollution.

ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption

Recent advances in intelligent wearable devices have brought tremendous chances for the development of healthcare monitoring system. However, the data collected by various sensors in it are user-privacy-related information. Once the individuals’ privacy is subjected to attacks, it can potentially cause serious hazards. For this reason, a feasible solution built upon the compression-encryption architecture is proposed. In this scheme, we design an Adaptive Sparse Basis Compressive Sensing (ASB-CS) model by leveraging Singular Value Decomposition (SVD) manipulation, while performing a rigorous proof of its effectiveness. Additionally, incorporating the Parametric Deformed Exponential Rectified Linear Unit (PDE-ReLU) memristor, a new fractional-order Hopfield neural network model is introduced as a pseudo-random number generator for the proposed cryptosystem, which has demonstrated superior properties in many aspects, such as hyperchaotic dynamics and multistability. To be specific, a plain medical image is subjected to the ASB-CS model and bidirectional diffusion manipulation under the guidance of the key-controlled cipher flows to yield the corresponding cipher image without visual semantic features. Ultimately, the simulation results and analysis demonstrate that the proposed scheme is capable of withstanding multiple security attacks and possesses balanced performance in terms of compressibility and robustness.

Development of a Simplified Performance Monitoring System for Small and Medium Sized Ships

Development of a Simplified Performance Monitoring System for Small and Medium Sized Ships

Multiparametric Monitoring System of Mt. Melbourne Volcano (Victoria Land, Antarctica).

Volcano monitoring is the key approach in mitigating the risks associated with volcanic phenomena. Although Antarctic volcanoes are characterized by remoteness, the 2010 Eyjafjallajökull eruption and the 2022 Hunga eruption have reminded us that even the farthest and/or least-known volcanoes can pose significant hazards to large and distant communities. Hence, it is important to also develop monitoring systems in the Antarctic volcanoes, which involves installing and maintaining multiparametric instrument networks. These tasks are particularly challenging in polar regions as the instruments have to face the most extreme climate on the Earth, characterized by very low temperatures and strong winds. In this work, we describe the multiparametric monitoring system recently deployed on the Melbourne volcano (Victoria Land, Antarctica), consisting of seismic, geochemical and thermal sensors together with powering, transmission and acquisition systems. Particular strategies have been applied to make the monitoring stations efficient despite the extreme weather conditions. Fumarolic ice caves, located on the summit area of the Melbourne volcano, were chosen as installation sites as they are protected places where no storm can damage the instruments and temperatures are close to 0 °C all year round. In addition, the choice of instruments and their operating mode has also been driven by the necessity to reduce energy consumption. Indeed, one of the most complicated tasks in Antarctica is powering a remote instrument year-round. The technological solutions found to implement the monitoring system of the Melbourne volcano and described in this work can help create volcano monitoring infrastructures in other polar environments.

INVESTIGATION ANALYSIS ON E-HEALTHCARE MONITORING SYSTEM WITH IOT AND BIG DATA BY TSLR-CHSC MODEL

Internet of Things (IoT) technology helped the development of E-Health care Monitoring System from direct visit to virtual Monitoring (Telemedicine). Smart health care system in IoT environment monitored the basic health signs such as heartbeat rate, body temperature and blood pressure in real-time applications. IoT and big data is a prominent challenge in smart healthcare system. In this health monitoring system big data is employed to analyze the large volume of data and to determine the Normal and Abnormal patient condition. Numerous issues like accuracy, time and error have yet to be conveyed to generate a ductile system for health care monitoring. To address these issues, I proposed a method called Theil Sen Linear Regression and Canopy Hopkins Statistics Clustering (TSLR-CHSC) for IoT based Health monitoring system is proposed. This method splits into three sections such as Data collection, Feature Selection and Clustering. First, Cardiovascular disease dataset is acquired from sensors are collected. Second, appropriate features can be selected by using Theil Sen regression feature. Third, clustering is performed based on the cluster tendency by using canopy algorithms. Through this way I deployed an efficient E-Health Monitoring System with minimum time consumption. For evaluation, a cardiovascular disease dataset is obtained from various medical sensor devices are analyzed to identify the disease severity. Keyword: IoT, Big Data, Theil Sen Linear Regression, Canopy Hopkins Statistic Clustering, Health Care Monitoring.

Effects of Traffic Conditions on Driver Takeover Performance and Situational Awareness

This study aimed to investigate the impacts of oncoming traffic density, surrounding vehicle configuration, and driving lane on drivers' takeover performance and situational awareness in conditionally automated driving. We conducted a driving simulator experiment with 28 participants. Our results showed that high oncoming traffic led to shorter takeover response time and lower situational awareness. Drivers tended to brake more aggressively and had lower collision risks when the traffic was on the front side, and drivers were more likely to ignore traffic from behind and behind side when taking over. Taking over in the right lane on a two-way road led to lower collision risks and smoother driving compared to the left lane. Our results have valuable implications for the design and development of driver monitoring and support systems to improve the safety of conditionally automated vehicles.

Development of Real-time Monitoring System for Three-dimensional Printer Safety Booth

Development of Real-time Monitoring System for Three-dimensional Printer Safety Booth

Spatial, Temporal, and Vertical Variability of Ambient Environmental Conditions in Chinese Solar Greenhouses during Winter

The monitoring and control of environmental conditions are crucial as they influence crop quality and yield in Chinese solar greenhouses (CSGs). The objectives of this study were to assess the spatial, temporal, and vertical variability of major environmental parameters in CSGs during winter and to provide greenhouse climate/microclimate characteristics in order to facilitate the monitoring and control of greenhouse environmental conditions. A wireless sensor network (WSN) was deployed in two CSGs: one with crops and one without. Sensors were placed at different locations inside and outside the greenhouses, and the air temperature, humidity, CO2 concentration, light intensity, solar radiation, and wind conditions were measured and analyzed. Significant variability in the spatial, temporal, and vertical distribution of environmental factors was observed in both greenhouses. The average minimum and maximum temperatures and humidity inside the CSG with crops were 9.96 °C (4:00 h) and 24.5 °C (12:00 h), and 32.6% (12:00 h) and 92.1% (5:00 h), respectively. The temperature difference was 2.2 °C between layers in the CSG without crops and 1.4 °C between layers in the CSG with crops. The CO2 concentration in the different layers inside the CSG with crops was highest at night. The average maximum light intensity inside the CSG with crops was 32,660.19 lx, 36,618.12 lx, and 40,660.48 lx (12:00 h to 13:00 h) in the bottom, middle, and top layers, respectively. Sensor positioning in the greenhouse was evaluated by considering the sensors’ data variability. The findings of this study could aid in the development of a better monitoring and control system for CSG’s microclimate during winter. More research is needed on greenhouse microclimate control systems based on this variability analysis, which could improve crop quality and yield in greenhouses.

Wayside wheelset lateral motion detection and vehicle hunting instability evaluation

Wheel profile wear, or bogie suspension failure will result in hunting instability of the vehicle system which will lead to dramatic changes in the lateral motion of the wheelset. The occurrence of hunting instability will worsen the ride performance of the vehicle or even cause the danger of wheelset derailment. Thus, the in-time and effective detection of wheelset lateral motion is very important for the vehicle hunting stability evaluation. By installing a certain amount of displacement sensors on the rail side along the track and detecting the distance between the wheel and rail when the wheelset passing by, the lateral displacements of the wheelset in measuring points can be obtained. The detection method of wheelset lateral motion by the wayside is studied, which includes the measuring principle, structure design, sensor installation, data acquisition and data analysis, and the application of the method to on-site detection for typical instability cases are demonstrated. At the same time, innovate on the existing on-board hunting instability monitoring method, proposed an evaluation method of hunting instability for wayside lateral movement monitoring system. The results show that: installing 20 eddy current sensors on both sides of the straight track to measure the lateral displacement of the wheelset can meet the requirement of detecting the vehicle hunting instability. At the same time, analyzing the measurement data and fitting the maximum value of the signal collected by each sensor can obtain the lateral trajectory of the wheelset. The wheelset lateral displacement can be used to monitor the occurrence of vehicle hunting instability if periodic motion is detected. The amplitude of the motion can determine the hunting instability level, and the frequency can be used to describe the wheel wear level and calculate the wheel/rail contact equivalent conicity. This method is able to provide a reliable data support for the vehicle operation safety. The proposed monitoring method and monitoring index are also of reference value to the development of other wayside hunting instability monitoring systems.

Assessing the physiological effect of non-driving-related task performance and task modality in conditionally automated driving systems: A systematic review and meta-analysis

In conditionally automated driving, the driver is free to disengage from controlling the vehicle, but they are expected to resume driving in response to certain situations or events that the system is not equipped to respond to. As the level of vehicle automation increases, drivers often engage in non-driving-related tasks (NDRTs), defined as any secondary task unrelated to the primary task of driving. This engagement can have a detrimental effect on the driver’s situation awareness and attentional resources. NDRTs with resource demands that overlap with the driving task, such as visual or manual tasks, may be particularly deleterious. Therefore, monitoring the driver’s state is an important safety feature for conditionally automated vehicles, and physiological measures constitute a promising means of doing this. The present systematic review and meta-analysis synthesises findings from 32 studies concerning the effect of NDRTs on drivers’ physiological responses, in addition to the effect of NDRTs with a visual or a manual modality. Evidence was found that NDRT engagement led to higher physiological arousal, indicated by increased heart rate, electrodermal activity and a decrease in heart rate variability. There was mixed evidence for an effect of both visual and manual NDRT modalities on all physiological measures. Understanding the relationship between task performance and arousal during automated driving is of critical importance to the development of driver monitoring systems and improving the safety of this technology.

On-line detecting soluble sugar, total acids, and bacterial concentration during kombucha fermentation based on the visible/near infrared combined meta-heuristic algorithm

On-line monitoring of key growth indicators is essential to regulate the kombucha fermentation process and improve utilization of summer-autumn tea. This study developed an on-line visible/near-infrared spectroscopy (V-NIR) detection system and method to monitor the concentration of soluble sugars, total acids, and bacteria during kombucha fermentation. The partial least squares method (PLS) was coupled with the sparrow search algorithm (SSA), whale optimization algorithm (WOA), and African vulture optimization algorithm (AVOA) to select characteristic variables, extract high-frequency variables, and establish calibration models. In general, the model performance of the high-frequency variable subset was better than that of the selected variable subset. The correlation coefficients of the optimal model of the six indexes in the two stages were 0.8692, 0.8513, 0.9392, 0.9746, 0.9570, and 0.9830, respectively. The developed monitoring system and modelling strategy can be applied to the real-time online determination of kombucha fermentation and further applied to the food industry.

Implementation of the Internet of Things for Monitoring and Protecting Hydrogen Production in Dry Cell HHO Generators

In the last few decades, researchers have been conducting various studies in search of alternative energy sources to reduce dependence on fossil fuels and anticipate future energy scarcity. One such alternative energy is hydrogen, which can be obtained through various methods, including electrolysis of water. Hydrogen possesses the characteristics of being flammable, odorless, colorless, and tasteless, necessitating the development of a rigorous monitoring and protection system to mitigate potential hazards. In this study, a monitoring and protection system was designed for the operation of an electrolyzer using a Dry Cell type HHO Generator. The system employs the concept of the Internet of Things, where data is connected through the internet network. The data received includes hydrogen concentration readings, electrolyzer operation automation, electrolyzer operating status, and hydrogen gas leak detection in the system. The research results obtained showed that the higher the concentration of electrolyte and the voltage supplied to the electrolysis process, the faster the production of hydrogen gas is accelerated, accompanied by an increase in temperature. If a leakage occurs in a system, the occurrence of the fire triangle will be faster when the concentration of electrolyte and the voltage supplied are greater.

Design and Development of Web-Based Information System Order Monitoring System

PT. Sanityasa Anjaya Daniswara is a technology company operating as a software house in Solo. Efficient monitoring of project progress is essential for improving efficiency. Thus, a monitoring system that allocates programmers, details work, and summarizes progress is urgently required. This study applies the RAD (Rapid Application Development) method to design a monitoring system that meets the company's needs. The result is a development progress monitoring system that provides an overview of work status, ensuring credibility of information, and enhancing productivity and success in software development projects

Studies on the use of genetic algorithms to improve radial basis neural networks to monitor capacitive voltage transformers online

The metering accuracy and stability of the power grid transformer devices are of great concern to all parties of the power grid, power plants and large users, as well as the relevant government departments, with the further deepening of the electric power system reform and the gradual establishment of the electric power spot trading market. The outcomes demonstrate the viability and efficacy of the employed diagnosis method. The completed research can serve as a reference point for China’s development of a capacitive voltage transformer line monitoring system.

Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology

Noncompliance with safety rules is a major cause of unsatisfactory performance in construction safety worldwide. Although some research efforts have focused on using computer vision (CV) methods for safety rule inspection, these methods are still in their early stages and cannot be effectively applied on construction job sites. Therefore, it is necessary to present a feasible prototype and conduct a detailed analysis of safety rules to ensure compliance at the job site. This study aims to extend the validation of safety rule analysis through four case scenarios. The proposed structured classification of safety rules includes categorizing them based on project phases and work stages. The construction phase-related rules are divided into four groups: (1) before work, (2) with intervals, (3) during work, and (4) after work. To validate the proposed framework, this research developed prototypes for each group’s scenarios using deep learning algorithms, a storage database to record compliance with safety rules, and an Android application for edge computing, which is required in the “before work” and “after work” groups. The findings of this study could contribute to the development of a compact CV-based safety monitoring system to enhance the current safety management process in the construction industry.

Triboelectric-electromagnetic hybrid generator based self-powered flexible wireless sensing for food monitoring

With the growing demand for food around the world, real-time monitoring is essential to ensure the quality and safety of food. However, traditional monitoring techniques necessitate an external power source, and untidy wiring systems make monitoring difficult. Here, we report a self-powered flexible wireless sensing system that is powered by a triboelectric-electromagnetic hybrid generator (TEHG) that obtains energy from the airflow when the refrigeration fan is running. The TEHG makes up for the shortcomings of the EMG and the TENG alone, and can still reach a saturation voltage above 8 V when working at a low frequency of 90 r/min, and the slowdown of charging speed only occurs in the high-voltage charging stage. At 600 r/min, the charging characteristics of the EMG alone and the compound generator were compared, and the saturation voltage of the compound charge increased by 33.3% compared with the EMG alone. Through the power management module, the TEHG is proven to power a commercial 500 mAh lithium battery, and it takes 25000 s from the initial state to the maximum voltage. The flexible wireless sensor (FWS) can be continuously powered by the lithium battery and transmit real-time data to a cloud platform, demonstrating the capabilities of the Internet of Things. In the long run, the TEHG-based FWS (TE-FS) will be a revolutionary step in the development of food quality monitoring systems.