Sensor Monitoring Technology Research Articles

Assessment of Tunnel Lining Stability through Integrated Monitoring of Fiber Bragg Grating Strain and Structural Deformation.

Tunnel excavation induces the stress redistribution of the surrounding rock. Structural cracks may develop in the secondary lining due to this stress redistribution and bias pressure, consequently affecting the overall construction safety of the tunnel. This paper aims to achieve real-time monitoring of the excavation stability of the lining structure by integrating two monitoring technologies: structural deformation monitoring and fiber grating strain monitoring. Additionally, it proposes a method to simultaneously measure the thermal strain and applied stress-strain of the structure. By analyzing the displacement and deformation of the lining structure, its stability can be preliminarily evaluated in the short term. To achieve long-term real-time monitoring and a more accurate assessment of the tunnel structure's stability, the paper introduces fiber Bragg grating (FBG) strain sensor monitoring technology. First, based on the geological stratigraphy information obtained from the exploration, a simulation model of the tunnel under different section bias angles is established. The displacement and stress concentration areas of the lining structure are then analyzed to optimize the sensor deployment array and provide a theoretical basis for the sensor arrangement. FBG strain sensors are installed on the surface of the structure to measure thermal strain and loading stress-strain, whereas FBG temperature sensors measure local temperature. The findings indicate that following tunnel excavation, the maximum daily strain differences at K107+043 and K107+240 were 126.87 µε and 209.38 µε, respectively. After a period of rock disturbance, the average daily strain differences due to applied stress-strain were 16.8 µε and 12.65 µε, respectively. The thermal strain was close to the daily strain difference. Therefore, after the rock disturbance subsided, the strain fluctuations in the lining structure were mainly caused by local temperature changes, and the surrounding rock tended to stabilize. This offers a viable method for evaluating structural stability post-tunnel excavation.

Application research of intelligent system based on BIM and sensors monitoring technology in construction management

Architectural engineering is a dynamic process, and it is difficult to achieve ideal results with fixed standard management. This article proposes research on intelligent building construction management methods based on BIM and information soft sensor monitoring technology, attempting to construct a construction building model and an information database that includes basic data and expanded data. Establish a BIM model, split it into various modules, and graft the real-time dynamic database detected by environmental monitoring sensors with the BIM model to simulate the entire construction process, conduct dynamic monitoring, and adjust emergency plans in real-time through intelligent internet to ensure the rationality of the entire construction stage of multiple blocks. Based on the expected construction process requirements, resources are scheduled and managed. Compared with traditional management models, information sensors and monitoring intelligent management systems are more efficient, effectively improving resource utilization, achieving low-carbon construction, and significantly accelerating project progress.

Application of Biological Methods and Remote Sensing in Marine Monitoring

Due to the serious side effects caused by poured nuclear sewage, marine monitoring ought to be paid much more attention than before. This article concludes two major general approaches, respectively biological methods and remote sensing. Biological methods contain specifically 4 different methods: ecological monitoring, biological monitoring, measurement of biological physiological and biochemical indicators, and determination of residual amounts of pollutants in the organism. Sensor monitoring technology based on marine organisms is a common technology, which desires marine life suitable for being sensor-equipped and well-developed tags to undergo successive monitoring. Biological detection methods are established by scientists in relevant fields to make up for the shortcomings of physical and chemical pollution detection. The remote sensing method, which includes a range of techniques such as satellite imagery, aerial photography, and sonar systems, offers a unique vantage point that allows us to observe and analyze large expanses of oceanic territory with unprecedented precision and efficiency. It can also be used to identify the sources of marine pollution, assess the impacts of marine pollution on marine ecosystems, monitor the effectiveness of pollution mitigation measures, and develop and implement policies to prevent marine pollution.

Study on the Technologies of Loss Reduction in Wheat Mechanization Harvesting: A Review

Wheat harvesting is one of the most important links in the whole wheat production process. In China, the wheat planting areas are wide, and the patterns are diversified. In addition, the problem of harvest losses caused by the numerous brands and low performance of domestic combine harvesters has always existed. Any losses during harvesting will result in less income for the farmers. Therefore, according to the actual situation of mechanized wheat harvesting and the losses occurring within different parts of the harvester, it is of great significance to select the appropriate loss reduction methods to effectively reduce wheat harvest losses. In accordance with the problems of loss during mechanized harvesting, this research first points out the main losses in the operation of a wheat combine harvester, then introduces sensor monitoring technology for grain harvesting loss and intelligent control technology for the combine harvester and analyzes their application to loss reduction in mechanized wheat harvesting. Finally, we put forward conclusions and suggestions on this loss reduction technology for wheat mechanization harvesting in order to provide a reference for reducing the losses and promoting the sustainable development of modern agriculture.

A Smart Wristband Integrated with an IoT-Based Alarming System for Real-Time Sweat Alcohol Monitoring.

Breathalyzer is a common approach to measuring blood alcohol concentration (BAC) levels of individuals suspected of drunk driving. Nevertheless, this device is relatively high-cost, inconvenient for people with limited breathing capacity, and risky for COVID-19 exposure. Here, we designed and developed a smart wristband integrating a real-time noninvasive sweat alcohol metal oxide (MOX) gas sensor with a Drunk Mate, an Internet of Thing (IoT)-based alarming system. A MOX sensor acquired transdermal alcohol concentration (TAC) which was converted to BAC and sent via the IoT network to the Blynk application platform on a smartphone, triggering alarming messages on LINE Notify. A user would receive an immediate alarming message when his BAC level reached an illegal alcohol concentration limit (BAC 50 mg%; TAC 0.70 mg/mL). The sensor readings showed a high linear correlation with TAC (R2 = 0.9815; limit of detection = 0.045 mg/mL) in the range of 0.10–1.05 mg/mL alcohol concentration in artificial sweat, achieving an accuracy of 94.66%. The sensor readings of ethanol in water were not statistically significantly different (p > 0.05) from the measurements in artificial sweat and other sweat-related solutions, suggesting that the device responded specifically to ethanol and was not affected by other electrolytes in the artificial sweat. Moreover, the device could continuously monitor TAC levels simulated in real-time in an artificial sweat testing system. With the integration of an IoT-based alarming system, the smart wristband developed from a commercial gas sensor presented here offers a promising low-cost MOX gas sensor monitoring technology for noninvasive and real-time sweat alcohol measurement and monitoring.

Using Electrical Resistivity Tomography to Monitor the Evolution of Landslides’ Safety Factors under Rainfall: A Feasibility Study Based on Numerical Simulation

Although electrical resistivity tomography (ERT) may gather the internal resistivity information from a landslide area in a large-scale, low-cost, and non-invasive manner compared to point-based sensor monitoring technology, the indirect resistivity information obtained cannot directly evaluate the landslide’s current mechanical status, such as stress, strength, etc. Based on ERT monitoring data, a framework for quantitatively and directly evaluating the evolution of the factor of safety (FOS) of landslides during rainfall is proposed. The framework first inverts ERT observation data using the inexact Gauss–Newton method based on multiple constraints to obtain a more realistic resistivity distribution, then calculates the saturation distribution using Archie’s equation, and finally calculates the FOS of landslides using the finite element strength reduction method. Twelve sets of numerical experiments were designed and carried out based on the synthetic data of a theoretical model. The experimental results show that the proposed framework is valid and reliable under various arrays, apparent resistivity noise, and uncertainty in the water-electric correlation curve, with the Dipole-Dipole array outperforming the others in terms of accuracy, sensitivity, and anti-noise capability. The proposed framework is significant in improving ERT monitoring and early warning capabilities for rainfall-induced landslides.

Digital Health Privacy and Age: Quality and Safety Improvement in Long-Term-Care

Delivering basic and fundamental community-based, long-term care in the private home and the local community is less costly than providing it in an institution; however, it still faces impediments such as workforce challenges, rising costs, and concerns with the increasing prevalence of elder abuse. As the population ages and the number of older Americans managing multiple chronic conditions grows, it is essential to develop additional resources to provide sustainable community-based, long-term care. Sensor monitoring technologies provide a promising avenue for improving access to such care, with developers making major breakthroughs in the provision of long-term care at both senior residential facilities and directly in the home. We are seeing a growth in the use of sensors to reduce staffing, enable older adults to have a greater degree of self-management, and manage overall costs. The advantages of sensor monitoring technology are not, however, without their risks: the necessary trade-offs between innovation and privacy are heightened when applied to an older population where decreased cognitive function plays a larger role. While there is emerging scholarship on sensor privacy concerns in health care and for patient data sharing, there is limited literature that explores sensor privacy and consent practices for these devices by seniors in long-term care settings. This Article is dedicated to the forward-looking insights generated by Professor Eleanor Kinney to improve quality and safety in community-based, long-term care and investigates how the use of sensor technology can be optimally integrated with privacy protections to support this goal.

Experimental Study on Overburden Deformation Evolution under Mining Effect Based on Fiber Bragg Grating Sensing Technology

In order to study the regularity of overburden deformation and mining pressure changes in the stope. Take the geological conditions of Hanglaiwan coal mine in Yushen mining area as the background, a 3000 mm×1340 mm×200 mm physical similar material model was established in laboratory. Eight fiber Bragg grating sensors were embedded in the model, used to monitor the movement and deformation of the overburden key stratum and the change of mining abutment pressure in real time and then to determine the influencing factors and strain transfer coefficients of the fiber grating through theoretical analysis and calibration experiments. The results show the following. (1) The changes in value, position, and shape of the strain can reflect the dynamic evolution process of bending deformation, breaking, and rotation of the key strata. When the key layer breaks for the first time, the strain curve shows a peak shape; when the key layer breaks periodically, the strain curve of the fiber grating sensor showed a plateau shape for a period of time. (2) The strain curve has a good corresponding relationship with the change in supporting pressure. When the strain curve of the fiber Bragg grating sensor was at the trough stage, the abutment pressure was at the peak stage, with the increase of sensor buried height, and the supporting pressure and the strain value had a linear relationship within the range of the advanced abutment pressure. The conclusion shows that the fiber Bragg grating sensor monitoring technology has good effect on the internal strain and key supporting pressure monitoring of the overburden in the model test; it provides new monitoring methods and means for the model tests.

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Anomaly detection has attracted considerable attention from the research community in the past few years due to the advancement of sensor monitoring technologies, low-cost solutions, and high impact in diverse application domains. Sensors generate a huge amount of data while monitoring the physical spaces and objects. These huge collected data streams can be analyzed to identify unhealthy behaviors. It may reduce functional risks, avoid unseen problems, and prevent downtime of the systems. Many research methodologies have been designed and developed to determine such anomalous behaviors in security and risk analysis domains. In this paper, we present the results of a systematic literature review about anomaly detection techniques except for these dominant research areas. We focus on the studies published from 2000 to 2018 in the application areas of intelligent inhabitant environments, transportation systems, health care systems, smart objects, and industrial systems. We have identified a number of research gaps related to the data collection, the analysis of imbalanced large datasets, limitations of statistical methods to process the huge sensory data, and few research articles in abnormal behavior prediction in real scenarios. Based on our analysis, researchers and practitioners can acquaint themselves with the existing approaches, use them to solve real problems, and/or further contribute to developing novel techniques for anomaly detection, prediction, and analysis.

Power to the People: Disaster Resilience support with advance energy storage systems

The occurrence of both natural and man-made disasters is inescapable, but the consequences for the lives of vulnerable communities can be mitigated. United Nation (UN) and organizations such as the Federal Emergency Management Agency (FEMA) recommend investing proactive rather than re-active actions in dealing with disasters. It is commonly understood that “$1 spent on risk reduction saves between $5 and $10 in economic losses from disasters.” Currently Central Asian countries including Kazakhstan have weak proactive support programs and disaster mitigation plans. Most of the support activities are post disaster. Proactive actions, monitoring and prediction analysis of the potential disasters in Kazakhstan and other Central Asian countries require a multidisciplinary cooperative approach with applications of remote sensing sensor monitoring technologies. These require a permanent energy supply in various harsh environmental conditions. What advance energy storage systems are available and can be used for a Disaster Resilience programs? How can these energy storage systems be integrated into remote sensing sensor monitoring technologies? Our group of researchers is developing the multidisciplinary Disaster Resilience Institute (www.drinu.org) and Laboratory to Monitor Engineering Constructions (LaMEC, www.lamec.org) to work on proactive monitoring actions related to engineering facilities that are interconnected to surface and underground water resources such as bridges, dams, and reservoirs. We are looking for the cooperation in these areas and are convinced that success is presaged on a multidisciplinary approach.

The Impact of Sleep on Neuropsychological Performance in Cognitively Intact Older Adults Using a Novel In-Home Sensor-Based Sleep Assessment Approach

The relationship between recent episodes of poor sleep and cognitive testing performance in healthy cognitively intact older adults is not well understood. In this exploratory study we examined the impact of recent sleep disturbance, sleep duration, and sleep variability on cognitive performance in 63 cognitively intact older adults using a novel unobtrusive in-home sensor-based sleep assessment methodology. Specifically, we examined the impact of sleep the night prior, the week prior, and the month prior to a neuropsychological evaluation on cognitive performance. Results showed that mildly disturbed sleep the week prior and month prior to cognitive testing was associated with reduced working memory on cognitive evaluation. One night of mild sleep disturbance was not associated with decreased cognitive performance the next day. Sleep duration was unrelated to cognition. In-home, unobtrusive, sensor monitoring technologies provide a novel method for objective, long-term, and continuous assessment of sleep behavior and other everyday activities that might contribute to decreased or variable cognitive performance in healthy older adults.

Research on Application of Sensor Monitoring Technology Based on the IOT in the Campus GIS Pipeline System

Application of the IOT (Internet of Things) technology has promoted the “digital campus” developing into “smart campus”. This paper applies the sensor monitoring technology based on the IOT to the existing GIS underground pipeline management system, to achieve across species and a full range of real-time monitoring of the pipeline. Use sensors for collecting data, and RS485 communication bus and GPRS network for data transmission, and batch estimation data fusion method to optimize the monitoring data. It has practical significance to realize intelligent management of campus underground pipelines.

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Introduction: Respiratory failure (RF) following extubation is common after critical illness. Rapid shallow breathing patterns are ineffective and lead to increased work and RF. However, its conversion to a metric (rapid-shallow breathing index) failed to predict weaning failure. We used a continuous, piezoelectric, under-the-mattress sensor plate (EarlySense Inc) to record chest wall motion-generated complex waveforms in order to identify respiratory patterns characteristic or predictive of RF. Methods: Study design and patient population: Prospective, observational study recording waveforms 24/7 with a continuous, non-invasive piezoelectric sensor monitoring technology while the patients were intubated/mechanically ventilated with PSV and up to 24Hr after extubation. Adult critically ill surgical patients (37; or their surrogates) were consented for the study. Analysis: Two patient cohorts were studied: those who successfully extubated and those who failed (end-points of failure: reintubation, tracheostomy or death). The periods (6.5–24 hours) prior to extubation and (9–24 hours) following extubation were studied. The recordings were analyzed by an algorithm for abnormal patterns that correlate with respiratory outcomes and confirmed by visual examination of the raw signals. Results: Healthy respiratory pattern (normal frequency, minimally variable amplitude) is prevalent for those successfully extubated. Three forms of non-reassuring respiratory patterns were seen with those who failed extubation: 1. Generalized dys-organized respirations; 2.Frequent occurrences [every 30–120 sec] of a single high amplitude gasping breath; 3. Multiple periods of apnea >30sec. Positive patients had a cumulative duration of non-reassuring pattern at least twice as long as their periods with normal respiratory pattern during the entire recording period. After extubation, non-reassuring respiratory patterns will identify those patients likely to require ventilatory support (90% specificity, 50% sensitivity, PPV:60%, NPV:86%). Non-reassuring respiratory patterns prior to extubation show 91% specificity, 44% sensitivity, PPV:78%, NPV:71%). Conclusions: A pattern of unstable respirations (apparent in inconsistent chest wall motion amplitude) has been found to precede and correlate with respiratory failure. For ventilated patients this non-reassuring pattern (seen in PSV mode) may indicate that a patient is not ready for extubation. In the case of recently extubated patients, the pattern may indicate a need for interventions to prevent re-intubation. These conclusions will be tested in a prospective study to further validate their utility for clinical decision making.

Sensor monitoring of a newly designed foundation pit supporting structure

A new type of pit supporting structure, which was tested and verified using the sensor monitoring technology, was presented. The new supporting structure is assembled by prefabricated steel structural units. The adjacent steel structural units are jointed with fasteners, and each steel structural unit has a certain radian and is welded by two steel tubes and one piece of steel disc. In order to test and verify the reliability of the new supporting structure, the field tests are designed. The main monitoring programs include the hoop stress of supporting structure, lateral earth pressure, and soil deformation. The monitoring data of the field tests show that the new supporting structure is convenient, reliable and safe.