Automatic Continuous Monitoring Research Articles

Application of FBG Sensing Technology for Real-Time Monitoring in High-Stress Tunnel Environments

In the process of tunnel construction, problems such as high-stress rockburst, large deformation of soft rock, water inrush and mud gushing, secondary cracking of linings, blasting interference, man-made damage, and mechanical damage are often encountered. These pose a great challenge to the installation of monitoring equipment and line protection. In order to solve these problems, the 2# inclined shaft of Muzhailing Tunnel in the Gansu Province of China, which exists under high stress, water bearing, and bias conditions, was taken as the research object in this paper. By assembling a string, drilling grouting and sealing, and introducing multiple modes of protection, new fiber grating sensor group installation and line protection methods were proposed. The automatic continuous monitoring of the deep deformation of surrounding rock and the automatic continuous monitoring of steel arch stress were realized. The field monitoring results showed that: (1) the fiber grating displacement sensor group could be used to verify the authenticity of the surface displacement results monitored by the total station; (2) the NPR anchor cable coupling support effectively limited the large deformation of soft rock and the expansion of surrounding rock in a loose circle, and the range of the loose circle was stable at about 1 m; and (3) the main influence range of blasting was at a depth of 0~5 m in surrounding rock, and about 25 m away from the working face. In addition, to secure weak links in the steel arch due to the hardening phenomenon, a locking tube was set at the arch foot. In the support design, the fatigue life of the steel was found to be useful as the selection index for the steel arch frame to ensure the stability of the surrounding rock and the long-term safety of the tunnel. The present research adopted a robust method and integrates a variety of sensor technologies to provide a multifaceted view of the stresses and deformations encountered during the tunneling process, and the effective application of the above results could have certain research and reference value for the design and monitoring of high stress, water-bearing, and surrounding rock supports in tunnels.

Identifying sensitive precursors of ozone formation using photochemical indicators and transition values comprised by continuous automatic monitoring data

The O3 photochemical indicator is a tool used to identify the sensitive precursors of O3 formation. Establishing a reliable photochemical indicator and its transition value composed of continuous automatic monitoring of chemical components can not only analyze sensitive precursors of O3 formation in real-time but also analyze the time and space distribution characteristics of sensitive precursors of O3 formation at a relatively lower cost. Among the photochemical indicators that are often mentioned at present, the ratio of ozone to nitrogen oxides (O3/NOx, O3/NOZ, and O3/NOY) should have the greatest potential for continuous monitoring. This study used the Community Multiscale Air Quality Modeling System to evaluate whether these three indicators are applicable to environmental conditions in Taiwan. Research results show that the indicator O3/NOZ was less suitable for Taiwan, while the indicator O3/NOY (transition value = 5.3) and the indicator O3/NOx (transition value = 29.3) could more accurately evaluate the ozone sensitivity of Taiwan's overall grid, and had no obvious bias in judging the ozone sensitivity of rural and urban grids. This study also found that it is not appropriate to directly use the photochemical indicator transition values from the literature. Usually, because the value is too high or too low, there is an obvious bias in judging the ozone sensitivity of rural and urban grids. This result also demonstrates the need to consider local meteorological and emission conditions when estimating transition values of photochemical indicators. In addition, when using the photochemical indicator O3/NOY, attention should be paid to the calculation of NOY species composition. The more complete the NOY composition, the better its ability to correctly judge ozone sensitivity.

Nurse productivity: using evidence to enhance nurses' use of time.

The UK is experiencing a nursing shortage, making it challenging to maintain the staffing levels required to deliver effective patient care. One way of enhancing the care delivered by the existing workforce could be to optimise nurse productivity; however, previous efforts to do this have been largely ineffective, due in part to a focus on the processes of care delivery rather than the nursing activities within these processes. In this article, the author explores the concept of nurse productivity and suggests that enhancing productivity requires the identification of nursing activities and consideration of how these may be undertaken in a more time-efficient manner - or removed altogether. The author discusses two such activities: intentional (hourly) rounding, and fixed-time manual vital signs for patients on general wards. The author also considers the potential of using automatic continuous remote monitoring on general hospital wards to free up nurses' time for other care activities.

The health Loss Distribution of Coal-fired Power Plants and the relationship to Air Pollution Charges

In this study, the regional health loss due to the air pollution of a coal-fired power plant is analyzed by using the atmospheric dispersion model. AERMOD was used to simulate the increment concentration map of point source emission, and the GIS geographic information system was used to displace the distribution of air pollution concentration and regional health loss in grid receptors. The data of the continuous automatic monitoring facilities and the source of pollution sources were analyzed. Moreover, the effects of emissions, seasons, population, and distance on the simulated concentration were evaluated. The various influencing factors of air pollution concentration were evaluated.the R2 value of the empty product prediction map in summer has a better correlation with the concentration from monitor station, between 0.38 and 0.83, and the sulphur dioxide correlation has the best R2 value of 0.83. AERMOD was used to combined with pollutant emissions, weather, and terrain data, to simulate the distribution of rising concentration values due to coal-fired power plant emissions within the research area, and the study used the simulated rising concentration values to calculate the health loss. [1] The study used health loss of 25,647,291,161 NTD/year and medical loss of 319,153,198 NTD/year.

APPLICATION OF DOMESTIC GAS ANALYZERS FOR MEASURING PM CONCENTRATION IN ATMOSPHERIC AIR

Environmental monitoring is one of the key elements to ensure the most effective security measures in various areas of life. The most famous technology in this field is the gas detection system within the framework of atmospheric air monitoring. It serves not only for an early warning program, but also for a constant check of the state of the atmosphere in certain areas. In the guidelines of the World Health Organization (WHO), updated in 2005, solid particles (dust or suspended substances) are among the main indicators of atmospheric air quality. According to the requirements of Directive 2008/50/EC of the European Parliament and the Council of 05.21.2008 on atmospheric air quality and cleaner air for Europe Ukraine should establish zones and agglomerations according to the degree of atmospheric air pollution throughout its territory, as well as the procedure for their review. In general, the goal set by the Directive is extremely difficult for Ukraine ‑ to create a European monitoring system with modern equipment, which is a reliable tool for the development and implementation of environmental policy, on the remains of the air pollution control network that was created back in Soviet times. Nowadays gas sensors can be divided into two categories: conductive metal oxides and electrochemical, with the latter gaining more recognition over the last decade as the future standard component in any gas sensor. The paper presents the complete concept of an electrochemical gas sensor, including the basic science and basic electronic engineering that go into the device. The main focus is on a specific type of amperometric sensors as the most promising electrochemical sensor on the Ukrainian market. That is why the scientific novelty of the article lies in the fact that for the first time, on the basis of analysis and theoretical generalizations, it is proposed to create a program for the production of devices specifically for continuous automatic monitoring of atmospheric air quality. The practical value of the article lies in the systematization of scientific ideas regarding the monitoring of PM, which should become the basis for initial measures aimed at reducing emissions, which will increase the efficiency of management decision-making and improve the efficiency of the DSMD at all levels.

The effect of various air pollution and participants' age on semen quality in southern Taiwan

ObjectiveThis study aimed to investigate the association between semen quality and air pollution in southern Taiwan. Materials and methodsIn this retrospective study, 4338 males aged 21–70 years were recruited between 2001 and 2018 from a reproductive medical center. Semen quality was assessed according to standardized methods outlined in the World Health Organization (WHO) Laboratory Manual 1999 and 2010, including total sperm count, progressive sperm motility (%), rapid progressive sperm motility (%), and sperm with normal morphology (%). All designated national air quality automatic continuous monitoring stations measured the levels of air pollution [particulate matter (PM10 and PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3)], and was documented by Environmental Protection Administration in Taiwan. We collected data on the levels of air pollution based on the participants' residential addresses. ResultsIn our study, we found that progressive and rapid progressive sperm motility significantly decreased annually (p < 0.05). In addition, increasing age influenced total sperm count, progressive sperm motility, rapid progressive sperm motility, and sperm with normal morphology (p < 0.05). Among different air pollution, we observed SO2 was associated with lower rapid progressive sperm motility and lower sperm with normal morphology (β = −0.103, p = 0.043; β = 0.118, p = 0.001, respectively). However, NO2 was associated with higher rapid progressive sperm motility and a high number of sperm with normal morphology (β = 0.129, p = 0.002; β = 0.127, p < 0.001, respectively). ConclusionsThe semen quality in southern Taiwan appears to have declined in recent years. The participant's age for semen analysis was most strongly associated with semen parameters, Moreover, a significant association between SO2 and NO2 levels and semen motility was observed, even after adjusting for multiple comparisons. Further study is required to analyze the dose-dependent effect of SO2 and NO2 on semen parameters.

Deep Learning-Based Composite Fault Diagnosis

As the core component of many large machinery, rotating equipment occupies a high proportion in industrial manufacturing. Intelligent fault diagnosis for them is becoming the core focus of Industry 4.0 and its evolution. To address the problem of poor fault classification accuracy of rotating equipment, this paper proposes a new solution to deploy a composite fault diagnosis model at the front end to achieve low power consumption, continuous automatic identification and monitoring of equipment faults. We creatively combined the idea of speech recognition to solve the problem that traditional feature extraction methods are insufficient in handling composite fault diagnosis when dealing with fault waveform files, and used Fbank speech feature extraction idea to extract fault features, thus simplifying the complex fault diagnosis problem into an image classification problem. The generated composite fault Fbank feature data set is significantly different in visual observation. Therefore, we further designed the LeNet-F network based on the LeNet network for composite fault diagnosis. The model was validated on the K210 development board to achieve an accuracy of 97.41% for hybrid fault classification at a model size of 2.4MB. This makes it possible to apply the deep learning method to the front end to directly complete the composite fault diagnosis.

Automated systems for monitoring microclimate parameters and electricity metering of an experimental building

The Arctic is a strategically important region for Russia. In the process of searching for new solutions for the rapid construction of energy-efficient and safe buildings in the Arctic, it is necessary to develop systems for monitoring and maintaining indoor microclimate, ensuring optimal use of resources and implementing building management systems. Continuous automatic monitoring will help to increase comfort and safety in the room, as well as reduce energy consumption. The possibility of remote data transmission will allow you to quickly respond to emergencies and take actions to prevent the development of accidents. The article presents the results of the development and testing of a software and hardware complex based on a model object on the territory of the Murmansk State Technical University. The development and research of systems for automated monitoring of microclimate parameters and electricity metering of a model object in Murmansk has been carried out. The results of the study of the parameters of the deviation of temperature indicators are presented. The parameters of the deviation of the indicators of the humidity control system from the sensors are determined. A comparative analysis of the total electricity consumption of the model object for the main consumers of the log part and the frame part was carried out.

Development of a system for automated monitoring of microclimate parameters and an automated system for monitoring and accounting for electricity of a model facility

The Arctic is a strategically important region for Russia. In the process of searching for new solutions for the rapid construction of energy-efficient and safe buildings in the Arctic, it is necessary to develop systems for monitoring and maintaining indoor microclimate, ensuring optimal use of resources and implementing building management systems. Continuous automatic monitoring will help to increase comfort and safety in the room, as well as to reduce energy costs. The possibility of remote data transmission will allow you quickly respond to emergency situations and take actions to prevent the development of accidents. Employees and students of the Department of Automation and Computer Engineering of the Murmansk State Technical University have carried out the development and testing of a software and hardware complex based on a model object on the University territory. At the first stage, a group of researchers has developed a system for automated monitoring of microclimate parameters and an automated system for electric energy metering. The paper presents a detailed description of the functionality of the developed hardware and software modules; it describes the concept of an automated building management system, which is currently the next stage in the development of the system.

Validation of a small cough detector.

The assessment of cough frequency in clinical practice relies predominantly on the patient's history. Currently, objective evaluation of cough is feasible with bulky equipment during a brief time (i.e. hours up to 1 day). Thus, monitoring of cough has been rarely performed outside clinical studies. We developed a small wearable cough detector (SIVA-P3) that uses deep neural networks for the automatic counting of coughs. This study examined the performance of the SIVA-P3 in an outpatient setting. We recorded cough epochs with SIVA-P3 over eight consecutive days in patients suffering from chronic cough. During the first 24 h, the detector was validated against cough events counted by trained human listeners. The wearing comfort and the device usage were assessed using a questionnaire. In total, 27 participants (mean±sd age 50±14 years) with either chronic unexplained cough (n=12), COPD (n=4), asthma (n=5) or interstitial lung disease (n=6) were studied. During the daytime, the sensitivity of SIVA-P3 cough detection was 88.5±2.49% and the specificity was 99.97±0.01%. During the night-time, the sensitivity was 84.15±5.04% and the specificity was 99.97±0.02%. The wearing comfort and usage of the device was rated as very high by most participants. SIVA-P3 enables automatic continuous cough monitoring in an outpatient setting for objective assessment of cough over days and weeks. It shows comparable sensitivity or higher sensitivity than other devices with fully automatic cough counting. Thanks to its wearing comfort and the high performance for cough detection, it has the potential for being used in routine clinical practice.

Development of a System for Continuous Automatic Monitoring of the Cable Rope Condition

Development of a System for Continuous Automatic Monitoring of the Cable Rope Condition

Разработка системы непрерывного автоматического контроля запыленности и интенсивности пылеотложения как подсистемы многофункциональной системы безопасности угольной шахты

In connection with the growth of coal production and the load on mining enterprises, as well as the modernization of the main mining industries, the urgent task is to improve the level of industrial and occupational safety, which is impossible without improving the system for monitoring safety parameters in the automated mode. The control of dust and explosion safety of the mine workings in accordance with the safety requirements is conducted with special devices or other continuous equipment, in accordance with paragraph 22 of the Federal rules and regulations in the field of industrial safety «Safety rules in the coal mines». Within the framework of the article, the development of a new device for monitoring dust content and dust deposition is considered — a system for continuous automatic monitoring of dust content and intensity of dust deposition. Investigations of the regularities of the process of dust deposition of hovering coal dust were carried out depending on various factors affecting the distribution of dust along the network of the mine workings, namely, the grade composition of coal, a set of technological parameters and the parameters of atmospheric indicators. Performed complex of the laboratory and mine research of the innovation system confirmed the correctness of the developed physical model for assessing the state of dust and explosion safety in the coal mining. The system of continuous automatic monitoring of the dust content and the intensity of dust deposition meets the modern requirements of the Federal rules and regulations in the field of industrial safety in terms of automatic continuous measurement of dust concentration in the mine atmosphere, and (or) dust deposition, telemetry, remote signaling when the threshold values of dust deposition concentrations are exceeded, and in case of failure of the dust sensor, as well as local light and (or) sound alarms, automatic transmission of data on exceeding the threshold values of dust concentrations in the mine atmosphere, and (or) dust deposits in real time via communication channels to the coal mining organization.

Consumer Smartwatches As a Portable PSG: LSTM Based Neural Networks for a Sleep-Related Physiological Parameters Estimation.

Recently, mobile and wearable devices have become an increasingly integral part of our lives. They provide a possibility of detailed health monitoring using noninvasive and user-friendly techniques. However, lack of continuous monitoring, the need of specific sensors, and the limitations in memory and power consumption are only some of the potential drawbacks of such devices. In the current paper a system based on a deep recurrent neural network is developed for an automatic continuous monitoring of sleep-related physiological parameters by means of a wearable biosignal monitoring systems. Smartwatches based algorithm for non-invasive monitoring of sleep stages, respiratory events (including sleep apnea and hypopnea), snore and blood oxygen saturation is developed. Our experimental results demonstrate that proposed model constitutes a noninvasive and inexpensive screening system for sleep-related physiological parameters and pathological states. The model has shown a 77 % accuracy in sleep stages prediction, more than 80 % accuracy in epoch-by-epoch respiratory events classification, above 60 % accuracy in snore events classification and above 70 % accuracy in blood oxygen saturation (SpO2) level classification (for a two class problem with a SpO2 threshold of 95 %).

Sulfur and Nitrogen Oxides in the Atmosphere of Lake Baikal: Sources, Automatic Monitoring, and Environmental Risks

This paper analyzes the results of the automatic (in situ) recording of the regional transport of pollutants from the large regional coal-fired thermal power plants in the atmospheric boundary layer above the southern basin of Lake Baikal. Due to high stacks (about 200 m), emissions from large thermal power plants rise to the altitudes of several hundreds of meters and spread over long distances from their source by tens and hundreds of kilometers. The continuous automatic monitoring of the atmosphere in the southern basin of Lake Baikal on top of the coastal hill (200 m above the lake) revealed the transport of a large number of sulfur oxides and nitrogen oxides in the form of high-altitude plumes from thermal power plants of the large cities located 70 to 100 km to the northwest of the lake (Irkutsk and Angarsk). The consequence of such transport is the increased acidity of precipitation in the southern basin of Lake Baikal and the additional influx of biogenic nitrogen compounds to the lake ecosystem. The spatial scale and possible risks of such regional transport of air pollution for the lake ecosystem require further closer study.

Environmental monitoring through Vehicular Ad Hoc Network: A productive application for smart cities

SummaryIndustrial growth, technological development, and increased human activities for comfort and luxurious life increase different environmental parameters like temperature, humidity, carbon monoxide, methane, and smoke. The value of these parameters are increasing steadily which has adverse effects on human life and human health and is also the cause of various disease like cancers and lung disease. It is now essential to monitor the environmental parameters of a region frequently. For the automatic continuous monitoring of environmental parameters of any location, this paper proposes to use vehicular ad hoc network (VANET), which is one of the promising subclasses of mobile ad hoc network (MANET). Automatic monitoring of environmental parameters of any location includes measurement of environmental parameters of a location, transmission of environmental parameters from the measured location to the observation center, and analysis of the parameters at the observation center. Two aspects are discussed for the transmission of parameters from the location of measurement to the observation center: first by vehicular communication and second by vehicular cloud. The analysis of the environmental parameters at the observation center is done using probabilistic neural network (PNN). Apart from this, different generic network parameters like end‐to‐end delay, number of hops, and network gaps for the proposed work are computed for the transmission of different environmental parameters and is compared with different VANET routing protocols like geographic source routing (GSR), anchor‐based street and traffic‐aware routing (A‐STAR), and peripheral node‐based geographic distance routing (P‐GEDIR). Vehicular motion is created using Simulation of Urban MObility (SUMO), and the network parameter values, like end‐to‐end delay and number of hops, are obtained using OMNET++. A testbed experiment is conducted for the classification of sensor data which is adoptable in practice.

MyWear: A Novel Smart Garment for Automatic Continuous Vital Monitoring

Wearables are getting large acceptance in the continuous monitoring of health status and physiological data. Medical devices and their connectivity through Internet along with the electronics health record (EHR) and AI analytics making smart healthcare possible. Internet-of-Medical-Things (IoMT)-end devices like wearables and implantables are key for smart healthcare. Smart garment is a specific wearable which can be used for smart healthcare. This paper presents the design and development of a smart garment called MyWear that continuously monitors and collects physiological data. It can analyze muscle activity, stress levels, and heart rate variations and send all the data to the cloud. Through abnormal variations in vitals, it can also predict the risk of heart failure and with the in-built alert system, it can notify the associated medical officials if necessary. We also propose a deep neural network model that classifies heartbeat data into abnormalities with 96.9% accuracy and 97.3% precision.

Accuracy analysis of dam deformation monitoring and correction of refraction with robotic total station.

Robotic total stations have been widely used in continuous automatic monitoring of dam deformations. In this regard, monitoring accuracy is an important factor affecting deformation analysis. First the displacements calculation methods for dam deformation monitoring with total stations are presented, and the corresponding mean square error formulas are derived. Then for errors caused by atmospheric refraction, two correction methods are described. Simulations were conducted to compare the displacement accuracy calculated through different methods. It indicated that the difference between polar coordinate method and forward intersection is less than 0.5mm within around 400m’ monitoring range, and in such cases, the polar coordinate method is preferred, as only one total station is required. Refraction correction tests with observations from two dams demonstrated that both correction methods could effectively enhance the monitoring accuracy. For observation correction, correction through the closest reference point achieves better correction results.

Short-Term Seismic Precursor Anomalies of Hydrogen Concentration in Luojishan Hot Spring Bubbling Gas, Eastern Tibetan Plateau

The gas compositions (He, H2, CO2, CH4, Ar and N2) and isotope ratios (3He/4He and δ13C) were yearly investigated from April 2010 to April 2019 at the Luojishan spring located in the proximity of the Zemuhe Fault, eastern Tibetan Plateau. The continuous automatic monitoring of hydrogen concentrations in Luojishan hot spring bubbling gas for the purpose of earthquake prediction requires the discrimination of seismic precursor anomalies. Helium isotope ratios (3He/4He) in the bubbling gas of hot springs varied from 0.05 to 0.18 Ra (Ra = 3He/4He = 1.39 × 10−6 in the air), with a maximum mantle-derived He up to 2.2% of the total He measured in the Luojishan hot spring (assuming R/Ra = 8.0 for mantle). This suggests that Zemuhe Fault might act as a conduit for crustal-derived fluid. N2 concentrations in the majority of the hot spring was ≥80 vol%, and δ13CCO2 values varied from −13.2 to −9.3‰ (vs.PDB). Hydrogen concentration time series display a complex temporal pattern reflecting a wide range of different physical processes. There were short-term (5–60 h) seismic precursor anomalies of hydrogen concentration before natural earthquake. The anthropogenically-induced earthquakes provoke only post-earthquake responses. The concentration of hydrogen in bubbling gas of the Luojishan hot spring is sensitive to increase of stress in the Xianshuihe-Xiaojiang fault system. Monitoring the hydrogen concentrations with automatic gas stations may be promising tool for unraveling earthquake mechanisms and for predicting earthquakes.

NEW TECHNOLOGIES FOR THE STUDY AND MONITORING OF LANDSLIDES AFFECTING CRITICAL TRANSPORT INFRASTRUCTURE

Landslide monitoring is becoming increasingly important for mitigating and eliminating risks to critical infrastructure (roads and highways, railways, waterways, energy transmission networks, etc.).This work aims to present the technique of electrical resistivity tomography (ERT) for the analysis and monitoring of landslides in conjunction with radar satellite data, InSAR (Interferometry for Synthetic Aperture Radar).The ERT and InSAR methods are complemented by the methods of automatic continuous monitoring during the execution and in the exploration of critical structures and infrastructures by introducing the real-time warning component of the events in progress. By applying these modern techniques, it is possible to reduce the geotechnical risk in the design phase, it is possible to reduce the construction and operation costs, but also to avoid losses due to landslides that can short-circuit critical transport infrastructures.

Applying a deployment strategy and data analysis model for water quality continuous monitoring and management

In Taiwan, where residential and industrial areas are in close proximity, finding ways to effectively continuous monitor and manage water quality is an essential issue. This study established a total solution for an Internet of things water quality monitoring network that integrates domestic miniaturized water quality monitoring sensors for real-time transport data of pH, temperature, conductivity, chemical oxygen demand, and copper ions. The data will be used to establish an analysis model based on continuous monitoring of the nation’s background concentration. We designed an automatic continuous monitoring and early warning analysis module for automatic analysis of environmental and instrumental anomalies for decision makers, a “pollution source analysis module” utilizing static and dynamic cross-environment data to swiftly trace upstream pollution sources, and a “pollution hotspot analysis module” to evaluate the impact area of pollutants, and immediate response measures to achieve early warning and swift evaluation for the prevention of water pollution. To do this, we installed 100 domestic miniaturized water monitoring devices in Taoyuan City for testing the solution. We found that the establishment of an Internet of things environment analysis and response model integrated with cross-environment analysis can be applied in water quality monitoring and management to assure improved environmental quality.