Conventional Monitoring Methods Research Articles

Space-sky-surface integrated monitoring system for overburden migration regularity in shallow-buried high-intensity mining

High-intensity coal mining activities in China’s western mining area are damaging fragile ecological environments. The key to coordinating ecological protection and coal mining is to understand the law of overlying strata migration in high-intensity mining. However, it is difficult for conventional monitoring methods to efficiently monitor overburden movement in high-intensity mining areas in real time. In this paper, a space-sky-surface (3S) integrated system is proposed to conquer this challenge. This monitoring system consists of three parts: a space monitoring component (a combination of a global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR)), a sky observation component (an unmanned aerial vehicle (UAV) equipped with a measuring camera (MC)), and a surface exploration component (ground-penetrating radar (GPR) and high-density electricity method (HDEM)). This system was successfully applied to the 12,401 longwall face in the Shangwan coal mine (the maximum mining height is 8.8 m). The deformation cloud map of the entire mining area was obtained through InSAR monitoring technology accompanying the 3D laser scanning to improve the accuracy in the local region of 12,401 longwall face. The distribution characteristics and dynamic evolution of surface fractures were obtained using UAV and MC (visible light camera + infrared camera). Using the HDEM and GPR technologies, the fracture development was well detected. Based on the strata movement theory, combined with the monitoring results, a three-dimensional model of overburden rock fracture after 12,401 longwall face mining was constructed. Monitoring results and the fracture model provide a basis for controlling air leakage in the goaf and remediation of surface fracture.

Commentary: integrating environmental DNA into applied ecological practice

Environmental DNA (eDNA) methods and technologies are increasingly being explored and applied in research studies and environmental monitoring. Although eDNA has some limitations, there is the potential for major advantages to using this method within a regulatory context for characterizing and assessing species, populations, communities, and whole ecosystems. This commentary explores some of the challenges and solutions to acceptance of eDNA methods by regulators and regulated communities as an option for environmental monitoring. Increased collaborative eDNA forums and eDNA studies among researchers, regulators, applied ecologists, and regulated communities would help delineate the benefits, limitations, and appropriate applications of eDNA methods. This could facilitate development of best practices and industry standards which could, in turn, lead to acceptance by regulators and regulated communities. Finally, opportunities for applying eDNA to complement or even improve conventional monitoring methods are illustrated using three examples of regulatory environmental monitoring studies for protecting species at risk.

Projection range of eDNA analysis in marshes: a suggestion from the Siberian salamander (Salamandrella keyserlingii) inhabiting the Kushiro marsh, Japan.

BackgroundFreshwater ecosystems are rapidly declining. The Siberian salamander (Salamandrella keyserlingii) which inhabits the Kushiro marsh in Hokkaido, Japan has lost some habitat due to human activity. There are many challenges associated with conventional monitoring methods, including cost, the need for specialist personnel, environmental impact, and ability to detect the presence of this species; thus, we investigated the feasibility of using environmental DNA (eDNA) analysis to detect its presence and identify its breeding grounds.MethodsWe performed tank experiments to confirm eDNA emission from egg sacs, larvae, and adult Siberian salamanders in the water. We also performed water sampling and visual observation of egg sacs in the Kushiro marsh during the end of the breeding season and the larval season.ResultsThe tank experiments found eDNA emission from all growth stages. It also implied concentrated emissions just after spawning and after hatching, and limited emissions during the incubation phase in egg sacs. We also detected eDNA in the field, likely reflecting the distribution of egg sacs or larvae. Combining this data with visual observations, it was determined that the eDNA results from the field were best explained by the number of egg sacs within 7–10 m of the sampling point.ConclusionsThe results of this investigation show that the breeding sites and habitats of marshland species can successfully be monitored using eDNA analysis. They also suggest that the eDNA results from the marshes may reflect the biomass that is in close range to the sampling point. These results support the increased use of eDNA analysis in marshes and provide knowledge that could improve the interpretation of future results.

Using Augmented Reality and Internet of Things for Control and Monitoring of Mechatronic Devices

At present, computer networks are no longer used to connect just personal computers. Smaller devices can connect to them even at the level of individual sensors and actuators. This trend is due to the development of modern microcontrollers and singleboard computers which can be easily connected to the global Internet. The result is a new paradigm—the Internet of Things (IoT) as an integral part of the Industry 4.0; without it, the vision of the fourth industrial revolution would not be possible. In the field of digital factories it is a natural successor of the machine-to-machine (M2M) communication. Presently, mechatronic systems in IoT networks are controlled and monitored via industrial HMI (human-machine interface) panels, console, web or mobile applications. Using these conventional control and monitoring methods of mechatronic systems within IoT networks, this method may be fully satisfactory for smaller rooms. Since the list of devices fits on one screen, we can monitor the status and control these devices almost immediately. However, in the case of several rooms or buildings, which is the case of digital factories, ordinary ways of interacting with mechatronic systems become cumbersome. In such case, there is the possibility to apply advanced digital technologies such as extended (computer-generated) reality. Using these technologies, digital (computer-generated) objects can be inserted into the real world. The aim of this article is to describe design and implementation of a new method for control and monitoring of mechatronic systems connected to the IoT network using a selected segment of extended reality to create an innovative form of HMI.

Social media: a valuable tool to inform shark conservation in Greece

Sharks in the Mediterranean Sea are facing an elevated risk of extinction; several species are considered endangered and some have been reduced to such low population numbers that they are hard to detect through conventional monitoring methods. The recent emergence of new technologies, such as social media, makes it easier to collect and transmit information that may contribute to the conservation of endangered species. From 2017 – 2019 we carried out a project in Greece that searched social media (i.e. Facebook, Twitter and YouTube) postings with the aim of collecting data on the occurrence and basic biological parameters of sharks in the country and their interactions with fisheries. We recorded 116 social media postings referring to sharks in Greece, of which, 100 were identified to the lowest taxonomic level; sixty four percent of these postings referred to threatened sharks, while the majority of them referred to species that had not been evaluated for the Greek Red Data Book. Sharks occurred throughout the country, were often involved in negative fishery interactions and were rarely reported to have been released back to the sea. Endangered sharks were often misidentified as commercially valuable species. Our study highlights the importance of social media as a valuable tool in collecting baseline information, while identifying and/or focusing on important conservation issues about sharks in Greece.

Vehicle-Assisted Techniques for Health Monitoring of Bridges.

Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle’s speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges.

Intelligent based novel embedded system based IoT enabled air pollution monitoring system

The rapid growth of industry and transport within this contemporary progress, there was sufficient consideration given to air quality monitoring; but conventional air quality monitoring methods are inefficient to produce adequate spatial and temporal resolutions of the air quality information by cost-effective also the period time clarifications. During the paper, we propose a distinct methodology to achieve the air quality monitoring system, using this fog computing-based Internet of Things (IoT). In this paper proposed an embedded system, where sensors collect the air quality information within period time and send it over the fog nodes. Every fog node may be an extraordinarily virtualized program hosted at a committed computing node implemented with a connection interface. Data gathered by Microprocessor based IoT sensing things do not seem to be causing on into the cloud server to the process. Preferably, they do send through the adjacent fog node to get quick, including high-rise rate service. Though, fog node will refine non-actionable data (e.g., regular device measurement) also forward them to the Cloud for lengthy run storage and batch analytics. The Cloud may be a convenient location to run world analytics at information gathered from commonly shared devices over sustained periods (months, years). General purpose processor (microprocessor) and IoT cloud platforms were involved in developing this whole infrastructure and model for analysis. Empirical outcomes reveal that this advanced method is responsible for sensing air quality, which serves to expose the modification patterns regarding air quality through a certain level.

Discussion on deformation monitoring method of asphalt concrete core wall of high earth-rock dam

The deformation of asphalt concrete core wall of earth rock dam is an important monitoring item of safety management in the construction and operation period of the project. This paper summarizes the conventional monitoring methods and principles of the deformation of asphalt concrete core wall, analyzes the application scope and existing problems of the conventional monitoring methods, and introduces a new method and principle of the deformation monitoring of asphalt concrete core wall of high earth rock dam, which effectively solves the problems The problems of conventional monitoring methods are solved, and the three-dimensional deformation monitoring of asphalt concrete core wall is realized.

EDNA‐based monitoring: Advancement in management and conservation of critically endangered killifish species

AbstractEcosystems are currently changing at unprecedented rates due to anthropogenic influences. Application of appropriate management regimes and mitigation measures requires knowledge of ecological community composition and monitoring of any changes that occur. Environmental DNA‐based monitoring is becoming increasingly common and offers substantial potential as a noninvasive method associated with highly repeatable and reliable results. In this study, we monitored river systems in Western Greece that have been strongly impacted by anthropogenic activities and the spread of an alien invasive fish species, the Eastern mosquitofish (Gambusia holbrooki). This invasive species has been credited as the major cause for the drastic decline of two endemic killifish species (Valencia letourneuxi and Valencia robertae). Here, we investigated the efficacy of an environmental DNA (eDNA‐based) method of detection for all three species, as an alternative to conventional monitoring methods. Initially, a mesocosm experiment provided material for the design and validation of the sampling protocol. This was followed by two sampling periods in the field conducted in autumn 2017 and 2018, comparing the novel eDNA assays with the conventional surveying methods in six and 20 systems, respectively. eDNA detection consistently outperformed the traditional monitoring methods for both V. letourneuxi and V. robertae and was comparable for the invasive G. holbrooki. This supports the now increasing body of literature, highlighting the benefits of species‐specific, targeted eDNA assays for the assessment of threatened and/or invasive species, one which can be utilized by conservation organizations and government bodies alike. However, we note that care should always be taken when designing such tools and strict validation steps should be adhered to, particularly with respect to minimizing the probability of false positives and negatives.

Drying-induced soil shrinkage and desiccation cracking monitoring with distributed optical fiber sensing technique

Monitoring of drying-induced volume shrinkage and desiccation cracking in clayey soils is of great importance in geological and geotechnical engineering. Compared with conventional strain monitoring methods providing discrete measurements, the Brilliouin optical time domain reflectometer (BOTDR) technique enables continuous measurement of the distributed strain generated along optical fibers. In this study, a physical model test is conducted to investigate the feasibility of monitoring drying-induced soil shrinkage and desiccation cracking using BOTDR. Three optical fibers with different surface protections (thermoplastic polyester elastomer (TPEE) jacket, nylon jacket, and acrylate coating) are tested and compared. Experimental results validate that BOTDR is applicable for the direct strain monitoring of desiccation cracking soils. Monitored strain values are strongly influenced by water content, soil cracking, and fiber types. The strain measured by the optical fiber reaches only several micro strains when the soil is over-saturated, gradually increases with the decreasing water content and the increasing soil-fiber interfacial shear stresses, and drops rapidly after the occurrence of decoupling between fiber and soil resulting from the mature development of desiccation cracks. The optical fiber with acrylate coating is not suitable because of its fragility and the poor interfacial coupling with the soil. Optical fibers covered with TPEE jacket or nylon jacket are both applicable for soil strain monitoring, with the former one more sensitive to water content variations. The study is the first attempt to apply the BOTDR technique for the direct and continuous monitoring of drying-induced soil shrinkage and desiccation cracking process. It is expected bring new insights into the fundamental understanding of volumetric shrinkage and desiccation cracking in clayey soils.

A Long-Range and Self-Powered Intelligent Food Monitoring System Based on Far-Field Energy Harvesting and Pressure Measurement

Introduction Radiofrequency (RF) energy harvesting has recently become a promising solution to power wireless sensors networks. As this emerging technology allows low-power sensors and systems to be charged wirelessly, it is advantageous in various applications with low cost and fewer requirements for periodical maintenance [1]. On the other hand, food quality is one of the most concerned problems of our society, foodborne illnesses caused by eating spoilt food has been a serious threat to human health. Therefore, development of a low cost, rapid and sensitive monitoring system is essential to ensure food safety and security. However, conventional food monitoring methods, which are usually based on measuring gas concentration show some drawbacks in terms of power consumption, cost, and reliability [2]. In this study, we developed a high-efficient battery-less system that can be used for food monitoring based on the far-field RF energy harvesting technique. Methods Our proposed system consists of an RF energy harvester and a sensing circuit. The RF energy harvester can harvest energy from radio waves at an ultra-high frequency (UHF) of 915 MHz to supply the power for the operation of all tag components. To improve the received power and enhance the distance of power transfer, a Yagi type structure antenna with three elements (driver, director, and reflector) was implemented to achieve a high gain and fit into a compact footprint [3]. Besides, to overcome the disadvantages of conventional food monitoring methods, our study focuses on measuring the increase in air pressure inside the food package caused by gas emission during storage. To confirm the effectiveness of the proposed system, we performed experiments on different sorts of food such as chicken, pork and fish. Results and Conclusions As a result, the sensor module is designed using a high accuracy pressure sensor and other ultra-low-power components, allowed it to consume low average power of only 1mW. The designed Yagi antenna has a high gain of 5.23dBi in the direction of the maximum radiation and voltage standing wave ratio (VSWR) better than 1.3 in approximately 60Mhz band (890-950Mhz). Finally, the increase in air pressure during food spoilage was used to categorize food freshness, demonstrating the feasibility of our proposed system.

Prognosis and fail detection in a dynamic rotor using artificial immunological system

PurposeIn recent years, the mechanical industries began to apply many investments in research and technological development to obtain efficient methods to analyze the integrity of structures and prevent disasters and/or accidents, ensuring people’s lives and preventing economic losses. Any structure, whether mechanical or aeronautical, before being put into use undergoes a structural integrity assessment and testing. In this case, non-destructive evaluations are performed, aiming to estimate the degree of safety and reliability of the structure. For this, there are techniques traditionally used such as ultrasonic inspection, X-ray, acoustic emission tests, among other techniques. The traditional techniques may even have a good instrumental apparatus and be well formulated for structural integrity assessment; however, these techniques cannot meet growing industrial needs, even more so when structures are in motion. The purpose of this paper is to demonstrate artificial immune systems (AISs), ate and strengthen the emergence of an innovative technological tool, the biological immune systems and AISs, and these are presented as computing methods in the field of structural health monitoring (SHM).Design/methodology/approachThe concept of SHM is based on a fault detection mechanism used in industries, and in other applications, involving the observation of a structure or a mechanical system. This observation occurs through the dynamic response of periodic measurements, later related to the statistical analysis, determining the integrity of the system. This study aims to develop a methodology that identifies and classifies a signal in normal signals or in faults, using an algorithm based on artificial immunological systems, being the negative selection algorithm, and later, this algorithm classifies the failures in probabilities of failure and degree of fault severity. The results demonstrate that the proposed SHM is efficient and robust for prognosis and failure detection.FindingsThe present study aims to develop different fast access methodologies for the prognosis and detection of failures, classifying and judging the types of failures based on AISs. The authors declare that the present study was neither published in any other vehicle of scientific information nor is under consideration for publication in another scientific journal, and that this paper strictly followed the ethical procedures of research and publication as requested.Originality/valueThis study is original by the fact that conventional structural integrity monitoring methods need improvements, which intelligent computing techniques can satisfy. Intelligent techniques are tools inspired by natural and/or biological processes and belong to the field of computational intelligence. They present good results in problems of pattern recognition and diagnosis and thus can be adapted to solve problems of monitoring and identifying structural failures in mechanical and aeronautical engineering. Thus, the proposal of this study demonstrates and strengthens the emergence of an innovative technological tool, the biological immune system and the AIS, and these are presented as computation methods in the field of SHM in rotating systems – a topic not yet addressed in the literature.

A Noninvasive Glucose Monitoring SoC Based on Single Wavelength Photoplethysmography.

Conventional glucose monitoring methods for the growing numbers of diabetic patients around the world are invasive, painful, costly and, time-consuming. Complications aroused due to the abnormal blood sugar levels in diabetic patients have created the necessity for continuous noninvasive glucose monitoring. This article presents a wearable system for glucose monitoring based on a single wavelength near-infrared (NIR) Photoplethysmography (PPG) combined with machine-learning regression (MLR). The PPG readout circuit consists of a switched capacitor Transimpedance amplifier with 1MΩ gain and a 10-Hz switched capacitor LPF. It allows a DC bias current rejection up to 20μA with an input-referred current noise of 7.3pA/√Hz. The proposed digital processor eliminates motion artifacts, and baseline drifts from PPG signal, extracts six distinct features and finally predicts the blood glucose level using Support Vector Regression with Fine Gaussian kernel (FGSVR) MLR. A novel piece-wise linear (PWL) approach for the exponential function is proposed to realize the FGSVR on-chip. The overall system is implemented using a 180nm CMOS process with a chip area of 4.0mm2 while consuming 1.62mW. The glucose measurements are performed for 200 subjects with R2 of 0.937. The proposed system accurately predicts the sugar level with a mean absolute relative difference (mARD) of 7.62%.

A sampling and estimation method for monitoring poultry red mite (Dermanyssus gallinae) infestation on caged-layer poultry farms.

BackgroundThe poultry red mite, Dermanyssus gallinae, is a serious problem in the laying hen industry worldwide. Currently, the foremost control method for D. gallinae is the implementation of integrated pest management, the effective application of which necessitates a precise monitoring method.ObjectivesThe aim of the study was to propose an accurate monitoring method with a reliable protocol for caged-layer poultry farms, and to suggest an objective classification for assessing D. gallinae infestation on caged-layer poultry farms according to the number of mites collected using the developed monitoring method.MethodsWe compared the numbers of mites collected from corrugated cardboard traps, regarding with length of sampling periods, sampling sites on cage, and sampling positions in farm buildings. The study also compared the mean numbers of mites collected by the developed method with the infestation levels using by the conventional monitoring methods in 37 caged-layer farm buildings.ResultsThe statistical validation provided the suitable monitoring method that the traps were installed for 2 days on feed boxes at 27 sampling points which included three vertical levels across nine equally divided zones of farms. Using this monitoring method, the D. gallinae infestation level can be assessed objectively on caged-layer poultry farms. Moreover, the method is more sensitive than the conventional method in detecting very small populations of mites.ConclusionsThis method can be used to identify the initial stages of D. gallinae infestation in the caged-layer poultry farms, and therefore, will contribute to establishment of effective control strategies for this mite.

A method for monitoring solar diffuser’s bidirectional reflectance distribution function degradation in geostationary orbit

ABSTRACT The advanced geosynchronous radiation imager (AGRI) is a geostationary sensor whose reflective solar band is calibrated by a solar diffuser (SD). The SD bidirectional reflectance distribution function (BRDF) degrades over time in the space environment. This degradation can be measured through the SD reflectance degradation monitor (SDRDM). The SDRDM calibration data are normally collected by three filtered detectors, covering wavelengths from 0.45μm to 0.90μm. The SD reflectance degradation can be derived by trending the ratio of the background-subtracted solar-angle corrected SDRDM sun and SD view responses. The conventional monitoring methods rely on geometry factors of the sun view port and the relative BRDF of the SD, and these parameters can cause uncertainties. Aiming at these uncertain factors, the present study collects calibration data at the same solar angle. This method compares the detector digital counts obtained at different times but with the same solar angle. Consequently, it cancels out the angle-dependent parameter to obtain the ratio of the BRDF degradation factors. The obtained results show that monitoring uncertainty of the proposed method is less than 0.18%, while the corresponding monitoring error is less than 0.66%. This method can be applicated in SD's BRDF monitoring.

An industrial process monitoring scheme with moving window slow feature analysis

With the development of the modern industries, the requirement for comprehensive and effective monitoring scheme of the industrial production process is growing significantly. Conventional monitoring methods treat the deviations as the abnormities and thus result in the invalid monitoring results, because the dynamic information cannot be extracted accurately, which may be caused by the transient process or new operation conditions, and real faults cannot be separated from the normal process changes. To cope with this limitation, a moving window slow feature analysis is proposed in this paper. First, the temporal dynamic features of the industrial production process are extracted to separate the temporal dynamics from the steady state. Second, an adaptive monitoring strategy is presented to accurately acquire the normal changes of the production process, including the normal shift of operation conditions and the slow time-varying behaviors, through updating model parameters and monitoring statistics when a query sample comes. In this way, the real dynamic anomalies can be distinguished from the normal dynamic behaviors and reduce the false alarms effectively. Finally, the effectiveness and practicality are demonstrated through an evaporation process.

Inversion of seepage channels based on mining-induced microseismic data

Water inrush in mines has always been a major technical problem restricting the safe production of mines with abundant water. Seepage channels formed by fractures in rock masses provide the necessary conditions for water inrush to occur. Effectively controlling the formation of fractures in rock masses is essential for preventing water inrush disasters. In response to the shortcomings of conventional water inrush monitoring methods, a set of methods for the inversion of seepage channels in rock masses based on mining-induced microseismic (MS) data was proposed in this paper. A discrete fracture network (DFN) was built based on moment tensor (MT) theory, and a method for extracting reasonable fractures associated with different failure mechanisms was proposed. To enhance the applicability of the fractures derived from MS data (MS-derived fractures), the MS-derived fractures were transformed into a graph structure by graph theory. Subsequently, the hydraulic properties of each node and edge in the graph structure were obtained based on Darcy's law. In addition, the fluid flow time in the MS-derived fractures was weighted to detect the shortest seepage channel. Finally, the method was applied to a mine that experienced a water inrush disaster. The seepage channel formation process, water pressure distribution and main seepage channel in the study area of the mine were analyzed. The results may provide references for decision-makers regarding when and where to implement water shutoff measures in mines.

Bioinformatic analysis of peripheral blood RNA-sequencing sensitively detects the cause of late graft loss following overt hyperglycemia in pig-to-nonhuman primate islet xenotransplantation

Clinical islet transplantation has recently been a promising treatment option for intractable type 1 diabetes patients. Although early graft loss has been well studied and controlled, the mechanisms of late graft loss largely remains obscure. Since long-term islet graft survival had not been achieved in islet xenotransplantation, it has been impossible to explore the mechanism of late islet graft loss. Fortunately, recent advances where consistent long-term survival (≥6 months) of adult porcine islet grafts was achieved in five independent, diabetic nonhuman primates (NHPs) enabled us to investigate on the late graft loss. Regardless of the conventional immune monitoring methods applied in the post-transplant period, the initiation of late graft loss could rarely be detected before the overt graft loss observed via uncontrolled blood glucose level. Thus, we retrospectively analyzed the gene expression profiles in 2 rhesus monkey recipients using peripheral blood RNA-sequencing (RNA-seq) data to find out the potential cause(s) of late graft loss. Bioinformatic analyses showed that highly relevant immunological pathways were activated in the animal which experienced late graft failure. Further connectivity analyses revealed that the activation of T cell signaling pathways was the most prominent, suggesting that T cell-mediated graft rejection could be the cause of the late-phase islet loss. Indeed, the porcine islets in the biopsied monkey liver samples were heavily infiltrated with CD3+ T cells. Furthermore, hypothesis test using a computational experiment reinforced our conclusion. Taken together, we suggest that bioinformatics analyses with peripheral blood RNA-seq could unveil the cause of insidious late islet graft loss.

Pressure Measurement-Based Method for Battery-Free Food Monitoring Powered by NFC Energy Harvesting.

A novel approach for battery-free food freshness monitoring is proposed and demonstrated in this study. The aim is to track the freshness of different sorts of food such as pork, chicken, and fish during storage. To eliminate the drawbacks of conventional food monitoring methods, which are normally based on measuring gas concentration emitted from food in a container, this approach measures the gradual increase in air pressure caused by the gas emission during storage. Additionally, we aim to design a smart sensor tag that can operate in fully passive mode without an external power source. To achieve this goal, near-field communication (NFC)-based energy harvesting is utilized in this work to achieve a self-powered operation of the sensor tag. To demonstrate the feasibility of the proposed method, experiments with the above-mentioned food were tested at room and refrigerated temperatures in 2 and 4 days, respectively. For each experiment, 200 g of the target food was placed in a 2-L container with the smart sensor tag. The experiments were conducted with both rigid and flexible containers to consider real food packaging environments. The air pressure inside the container was monitored as an indicator of food freshness by a sensitive pressure sensor on the smart sensor tag. The experimental results showed a remarkable increase in air pressure, which was able to be detected with high accuracy by the pressure sensor. The fabricated battery-free smart sensor tag is small (2.5 cm × 2.5 cm) and is capable of less than 1 mW of power consumption, which is ultra-low relative to other ordinary approaches that have a power consumption that normally surpasses 10 mW. The pressure value was used to classify food freshness into different levels on a mobile display to provide food freshness status using an NFC-enabled smartphone.

Application of optical-fiber sensing to concrete support and continuous wall strain monitoring

Optical-fiber sensing technologies are being theoretically and practically researched. These technologies, possessing linear and real-time properties, realize the sectional analysis of strain and temperature and improve point monitoring methods in deep foundation pit supporting systems. In this paper, a field experimental model of the deep foundation pit supporting system was designed in the subway station in Suzhou, China, which utilizes optical-fiber sensing technology. In this field experiment, conventional monitoring methods, namely steel-bar meters, inclinometer, and water-level observation hole were firstly employed for comparative study. Distributed optical-fiber sensors (DOFS) were employed at the same time based on Brillouin optical time-domain analysis (BOTDA). The experimental results show that the DOFS can more comprehensively monitor the pit support system than the conventional methods. The fiber sensors data reflect the positive and negative strain distribution of the walls in a linear section and detect a leakage by analyzing abnormal data. The fiber sensors data response a linear increase of the axial strain with excavation depth, and a bending phenomenon is discovered by the data curves because of the excavation depth difference of the testing support. With temperature compensation, the fiber monitoring data are more accurate to the excavation result than the steel-bar does. Thus, the sensors can get a combined action function of steel-bar which used for testing axial force and temperature, inclinometer which used for testing wall horizontal displacement, and water-level observation holes with accurately monitoring data.