Long-term Monitoring System Research Articles

Seismic response of a suspension bridge: Insights from long-term full-scale seismic monitoring system

This paper describes an assessment of seismic responses of Hakucho Suspension Bridge based on data obtained from long-term monitoring system. The bridge with a total span length of 1,380 m has a permanent monitoring system consisting of 27 channels of accelerometers, and seismic responses have been recorded from its opening in 1998. Between 1998 and 2006, the monitoring system has recorded about 213 earthquakes with various amplitude, including the major ones such as the 2003 Tokachi-Oki, the 2004 Kushiro-Oki, the 2004 Kushiro, and the 2005 Miyagi-Oki earthquakes. Studies by multi-input multi-ouput time-domain system identifications, time–frequency analysis, and inverse analysis were conducted to investigate seismic response characteristics. From the four largest recorded earthquakes, it was observed that girder responses are generally dominated by the modes with frequencies between 0.3 and 0.6 Hz corresponding to the higher order of vertical bending modes. Significant pylon vibration that corresponds to stand-alone pylon lateral frequencies at 0.6 Hz (pylon in phase) and 0.8 Hz (pylon out of phase) with low damping was also observed. Results of system identifications from 213 sets of seismic record show dependency of natural frequencies and damping ratios of girder-dominant modes on the amplitude of ground motion. The trends of increase in damping and decrease in natural frequency as the ground motion amplitude increases were observed. This behavior is explained by an inverse analysis method using identified modal parameters at different amplitude of excitation. Furthermore, effect of stiffness and damping variation caused by the friction force at the bearings was clarified.

Stainless Steel Microfibers for Strain-Sensing Smart Clay Bricks

Life cycle monitoring of structural health of civil constructions is crucial to guarantee users’ safety. An optimal structural health monitoring system allows to automatically detect, locate, and quantify any damage in structural elements, thus anticipating major risks of local or global failures. Critical issues affecting traditional monitoring systems are sensors’ placement, hardware durability, and long-term reliability of the measurements. Indeed, sensors’ deployment is crucial for an effective investigation of the static and dynamic characteristics of the structural system, whereby durability and long-term stability of sensing systems are necessary for long-term monitoring. A very attractive solution to some of these challenges is developing sensors made of the same, or similar, material of the structure being monitored, allowing a spatially distributed and long-term reliable monitoring system, by the use of self-sensing construction materials. Within this context, the authors have recently proposed new “smart clay bricks” that are strain-sensing clay bricks aimed at embedding intelligent monitoring capabilities within structural masonry buildings. While previous work focused on smart bricks doped with titanium dioxide and using embedded point electrodes, this work proposes an enhanced version of smart bricks based on the addition of conductive micro stainless steel fibers that possess higher electrical conductivity and a more suitable fiber-like aspect ratio for the intended application, as well as plate copper electrodes deployed on top and bottom surfaces of the bricks. The paper thus presents preparation and experimental characterization of the new smart bricks. The influence of different amounts of fibers is investigated, allowing the identification of their optimal content to maximize the gauge factor of the bricks. Both electrical and electromechanical experimental tests were performed. Overall, the presented results demonstrate that the new smart bricks proposed in this paper possess enhanced strain-sensing capabilities and could be effectively utilized as sensors within structural masonry buildings.

A long-term stable monitoring system for atmospheric carbon monoxide based on 2.3 μm laser absorption

Recent progress in the development of an atmospheric carbon monoxide (CO) monitoring system using a ∼2.33 μm laser absorption sensor is reported. The principles of detection technology and the algorithms used for removing noise are discussed. In order to eliminate the cross interferences from the effects of CO2 and H2O in the atmosphere, a Distributed Feedback (DFB) laser at a specific wavelength of 2330.18 nm was employed in the system. The CO monitoring system demonstrated excellent accuracy and stability in long-term continuous monitoring. The results obtained have validated the use of such a CO monitoring approach in practical gas monitoring applications.

A multi-sensor sub-Nyquist power spectrum blind sampling approach for low-power wireless sensors in operational modal analysis applications

A novel multi-sensor power spectrum blind sampling (PSBS) approach is proposed supporting low-power wireless sensor networks (WSN) for Operational Modal Analysis (OMA) applications. The developed approach relies on arrays of wireless sensors, employing deterministic non-uniform in time multi-coset sampling to acquire structural response acceleration signals at sub-Nyquist sampling rates, treated as realizations of stationary random processes without making any assumption about the average signal frequency content and spectral support. The acquired compressed measurements are transmitted to a central server and collectively processed via a PSBS technique, herein extended to the multi-sensor case, to estimate the power spectral density matrix of an underlying spatially correlated stationary response acceleration random process directly from the compressed measurements. Structural modal properties are then extracted through standard frequency domain decomposition (FDD). The efficacy of the proposed approach to resolve closely-spaced modes is numerically tested for various data compression levels using noisy response acceleration signals of a white-noise excited finite element model of a space truss as well as field-recorded acceleration time-histories of an instrumented bridge under operational loading. It is shown that accurate mode shapes based on the modal assurance criterion can be obtained from as low as 89% less measurements compared to conventional non-compressive FDD at Nyquist sampling rate. Further, significant gains in energy consumption and battery lifetime prolongation of the order of years are estimated, assuming wireless sensors operating on multi-coset sampling at different data compression levels. It is, therefore, concluded that the proposed PSBS approach could provide long-term structural health monitoring systems with low-maintenance cost once wireless sensors with multi-coset sampling capabilities become commercially available.

Integrated Signal Encoding and Analysis System for Actigraphy-based Long-term Monitoring of Periodic Limb Movements in Sleep.

There has been a boom in the development of wearable devices for wellness and healthcare applications. Numerous studies have been conducted on the utility of employing wearable devices for the long-term monitoring of biosignals. Despite their efficacy, the potential for practical implementation faces many hurdles such as memory usage, power consumption, denoising, and efficient data transmission. Of the many wearables being used, the actigraph has been a popular choice amongst experts for identifying motion abnormalities such as periodic leg movements (PLMs) in sleep and the activities of patients suffering from various medical illnesses. In this paper, we present an efficient pulse code modulation based, 3-bit, signal encoding technique, which when applied to long-term (6-8 hours), 16-bit sleep actigraphy signals, generates 3-bit encoded, accelerometry data with an average compression ratio of 92%, an average increase in the signal-to-noise (SNR) ratio by 20 dB and an average reduction of memory usage by 92%. The proposed technique also eliminates the need to apply filters for denoising, by retaining only characteristic signal information in the quantized version. The proposed technique, in general, could be applied to accelerometer-based wearables and has the potential to provide efficient memory and power usage in long-term monitoring applications.

In Situ Monitoring of Groundwater Contamination Using the Kalman Filter.

This study presents a Kalman filter-based framework to establish a real-time in situ monitoring system for groundwater contamination based on in situ measurable water quality variables, such as specific conductance (SC) and pH. First, this framework uses principal component analysis (PCA) to identify correlations between the contaminant concentrations of interest and in situ measurable variables. It then applies the Kalman filter to estimate contaminant concentrations continuously and in real-time by coupling data-driven concentration-decay models with the previously identified data correlations. We demonstrate our approach with historical groundwater data from the Savannah River Site F-Area: We use SC and pH data to estimate tritium and uranium concentrations over time. Results show that the developed method can estimate these contaminant concentrations based on in situ measurable variables. The estimates remain reliable with less frequent or no direct measurements of the contaminant concentrations, while capturing the dynamics of short- and long-term contaminant concentration changes. In addition, we show that data mining, such as PCA, is useful to understand correlations in groundwater data and to design long-term monitoring systems. The developed in situ monitoring methodology is expected to improve long-term groundwater monitoring by continuously confirming the contaminant plume's stability and by providing an early warning system for unexpected changes in the plume's migration.

Development of technology for a long-term monitoring and automatic data acquisition system

The Central Research Institute of Electric Power Industry conducted research to develop a long-term monitoring technology to clarify pore water pressure and water chemistry in drilled boreholes. An automatic data acquisition system for the long-term monitoring was also developed. The automatic data acquisition system has an independent electric power supply and data communication system. The electric power is supplied from solar and wind power. The data communication is secured through a satellite or 3G/4G line. A very low power consumption system can be readily installed by creating personal area networks between measuring devices using Zigbee digital radio. A demonstration of the system was conducted under harsh natural conditions for 18 months. It was confirmed that the system has sufficient reliability for actual operation.

Surviving and Thriving-Shifting the Public Health Response to HIV-Exposed Uninfected Children: Report of the 3rd HIV-Exposed Uninfected Child Workshop.

Great gains were achieved with the introduction of the United Nations' Millennium Development Goals, including improved child survival. Transition to the Sustainable Development Goals (SDGs) focused on surviving, thriving, and transforming, representing an important shift to a broader public health goal, the achievement of which holds the promise of longer-term individual and societal benefits. A similar shift is needed with respect to outcomes for infants born to women living with HIV (WLHIV). Programming to prevent vertical HIV transmission has been successful in increasingly achieving a goal of HIV-free survival for infants born to WLHIV. Unfortunately, HIV-exposed uninfected (HEU) children are not achieving comparable health and developmental outcomes compared with children born to HIV-uninfected women under similar socioeconomic circumstances. The 3rd HEU Child Workshop, held as a satellite session of the International AIDS Society's 9th IAS Conference in Paris in July 2017, provided a venue to discuss HEU child health and development disparities. A summary of the Workshop proceedings follows, providing current scientific findings, emphasizing the gap in systems for long-term monitoring, and highlighting the public health need to establish a strategic plan to better quantify the short and longer-term health and developmental outcomes of HEU children.

Phase transitions in huddling emperor penguins

Emperor penguins (Aptenodytes forsteri) are highly adapted to the harsh conditions of the Antarctic winter: they are able to fast for up to 134 days during breeding. To conserve energy, emperor penguins form tight groups (huddles), which is key for their reproductive success. The effect of different meteorological factors on the huddling behaviour, however, is not well understood. Using time-lapse image recordings of an emperor penguin colony, we show that huddling can be described as a phase transition from a fluid to a solid state. We use the colony density as order parameter, and an apparent temperature that is perceived by the penguins as the thermodynamic variable. We approximate the apparent temperature as a linear combination of four meteorological parameters: ambient temperature, wind speed, global radiation and relative humidity. We find a wind chill factor of −2.9 , a humidity chill factor of −0.5 rel. humidity, and a solar radiation heating factor of 0.3 . In the absence of wind, humidity and solar radiation, the phase transition temperature (50% huddling probability) is −48.2 °C for the investigated time period (May 2014). We propose that higher phase transition temperatures indicate a shrinking thermal insulation and thus can serve as a proxy for lower energy reserves of the colony, integrating pre-breeding foraging success at sea and energy expenditure at land due to environmental conditions. As current global change is predicted to have strong detrimental effects on emperor penguins within the next decades, our approach may thus contribute towards an urgently needed long-term monitoring system for assessing colony health.

On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios.

Despite the wide literature on R-wave detection algorithms for ECG Holter recordings, the long-term monitoring applications are bringing new requirements, and it is not clear that the existing methods can be straightforwardly used in those scenarios. Our aim in this work was twofold: First, we scrutinized the scope and limitations of existing methods for Holter monitoring when moving to long-term monitoring; Second, we proposed and benchmarked a beat detection method with adequate accuracy and usefulness in long-term scenarios. A longitudinal study was made with the most widely used waveform analysis algorithms, which allowed us to tune the free parameters of the required blocks, and a transversal study analyzed how these parameters change when moving to different databases. With all the above, the extension to long-term monitoring in a database of 7-day Holter monitoring was proposed and analyzed, by using an optimized simultaneous-multilead processing. We considered both own and public databases. In this new scenario, the noise-avoid mechanisms are more important due to the amount of noise that exists in these recordings, moreover, the computational efficiency is a key parameter in order to export the algorithm to the clinical practice. The method based on a Polling function outperformed the others in terms of accuracy and computational efficiency, yielding 99.48% sensitivity, 99.54% specificity, 99.69% positive predictive value, 99.46% accuracy, and 0.85% error for MIT-BIH arrhythmia database. We conclude that the method can be used in long-term Holter monitoring systems.

Interference Assessment of Various Endogenous and Exogenous Substances on the Performance of the Eversense Long-Term Implantable Continuous Glucose Monitoring System.

Background: A variety of prescriptions and over-the-counter medications interfere with transcutaneous continuous glucose monitoring (CGM) sensors. This study characterized the interference profile of the Eversense® CGM System (Senseonics, Inc., Germantown, MD), which has a different mechanism of glucose detection than other CGM systems.Materials and Methods: Sensor bias (sensor glucose concentration measurement – plasma glucose concentration measured by a reference test) was measured in vitro against 41 different substances at supratherapeutic/supraphysiologic plasma concentrations. Testing was performed using a paired-sample method adapted from the Clinical and Laboratory Standards Institute guidance document EP7-A2. Any substance producing sensor bias that exceeded the International Organization for Standardization (ISO) document 15197:2013 limits was then tested using an in vitro dose–response method to determine whether the concentration producing a significant sensor bias was within physiologic/therapeutic concentration ranges.Results: Eight of 41 substances produced a sensor bias that exceeded ISO 15197:2013 limits when tested in vitro at supratherapeutic/supraphysiologic plasma concentrations. Only two of these substances (tetracycline and mannitol) exceeded bias limits within therapeutic concentration ranges. Notably, neither acetaminophen nor ascorbic acid, which are substances reported to interfere with other CGM systems, produced sensor bias that exceeded ISO limits when used at physiologic concentrations.Conclusions: Although tetracycline and mannitol interfered with the Eversense sensor, substances frequently reported to interfere with enzymatic, electrochemical-based transcutaneous CGM systems, such as acetaminophen and ascorbic acid, did not affect Eversense readings.

Making good use of structural health monitoring systems of long-span cable-supported bridges

Many long-span cable-supported bridges have emerged in recent years, but are exposed to harsh environment conditions. The installation of long-term structural health monitoring (SHM) systems to the bridges has become a trend to monitor their loading conditions, assess their performance, detect their damage, and guide their maintenance with the utmost goals of ensuring their functionality, safety and sustainability. Nevertheless, it is not very clear how to make good use of SHM systems toward these goals. This paper takes the Tsing Ma suspension bridge in Hong Kong as an example and recapitulates the relevant works done by the author and his colleagues and students in the past 20 years to manifest how the SHM system installed in the bridge has been utilized since 1997. The SHM system installed in the bridge is briefly introduced first. How to use the SHM system for investigating highway loading, railway loading, wind characteristics, and temperature effects is then presented. Identification of time-varying natural frequencies and modal damping ratio of the bridge under strong winds using the data recorded by the SHM system is also demonstrated. Toward the performance assessment and damage detection of the bridge, SHM system-based computer simulation and damage assessment are targeted and some typical examples are given. The establishment of SHM system-based bridge rating system for bridge maintenance is also briefly introduced.

Bayesian ballast damage detection utilizing a modified evolutionary algorithm

This paper reports the development of a theoretically rigorous method for permanent way engineers to assess the condition of railway ballast under a concrete sleeper with the potential to be extended to a smart system for long-term health monitoring of railway ballast. Owing to the uncertainties induced by the problems of modeling error and measurement noise, the Bayesian approach was followed in the development. After the selection of the most plausible model class for describing the damage status of the rail-sleeper-ballast system, Bayesian model updating is adopted to calculate the posterior PDF of the ballast stiffness at various regions under the sleeper. An obvious drop in ballast stiffness at a region under the sleeper is an evidence of ballast damage. In model updating, the model that can minimize the discrepancy between the measured and model-predicted modal parameters can be considered as the most probable model for calculating the posterior PDF under the Bayesian framework. To address the problems of non-uniqueness and local minima in the model updating process, a two-stage hybrid optimization method was developed. The modified evolutionary algorithm was developed in the first stage to identify the important regions in the parameter space and resulting in a set of initial trials for deterministic optimization to locate all most probable models in the second stage. The proposed methodology was numerically and experimentally verified. Using the identified model, a series of comprehensive numerical case studies was carried out to investigate the effects of data quantity and quality on the results of ballast damage detection. Difficulties to be overcome before the proposed method can be extended to a long-term ballast monitoring system are discussed in the conclusion.

The Interplay between Forest Management Practices, Genetic Monitoring, and Other Long-Term Monitoring Systems

The conservation and sustainable use of forests and forest genetic resources (FGR) is a challenging task for scientists and foresters. Forest management practices can affect diversity on various levels: genetic, species, and ecosystem. Understanding past natural disturbance dynamics and their level of dependence on human disturbances and management practices is essential for the conservation and management of FGR, especially in the light of climate change. In this review, forest management practices and their impact on genetic composition are reviewed, synthesized, and interpreted in the light of existing national and international forest monitoring schemes and concepts from various European projects. There is a clear need and mandate for forest genetic monitoring (FGM), while the requirements thereof lack complementarity with existing forest monitoring. Due to certain obstacles (e.g., the lack of unified FGM implementation procedures across the countries, high implementation costs, large number of indicators and verifiers for FGM proposed in the past), merging FGM with existing forest monitoring is complicated. Nevertheless, FGM is of paramount importance for forestry and the natural environment in the future, regardless of the presence or existence of other monitoring systems, as it provides information no other monitoring system can yield. FGM can provide information related to adaptive and neutral genetic diversity changes over time, on a species and/or on a population basis and can serve as an early warning system for the detection of potentially harmful changes of forest adaptability. In addition, FGM offers knowledge on the adaptive potential of forests under the changing environment, which is important for the long-term conservation of FGR.

Robust QRS detection for HRV estimation from compressively sensed ECG measurements for remote health-monitoring systems

Objective: To present a new compressive sensing (CS)-based method for the acquisition of ECG signals and for robust estimation of heart-rate variability (HRV) parameters from compressively sensed measurements with high compression ratio. Approach: CS is used in the biosensor to compress the ECG signal. Estimation of the locations of QRS segments is carried out by applying two algorithms on the compressed measurements. The first algorithm reconstructs the ECG signal by enforcing a block-sparse structure on the first-order difference of the signal, so the transient QRS segments are significantly emphasized on the first-order difference of the signal. Multiple block-divisions of the signals are carried out with various block lengths, and multiple reconstructed signals are combined to enhance the robustness of the localization of the QRS segments. The second algorithm removes errors in the locations of QRS segments by applying low-pass filtering and morphological operations. Main results: The proposed CS-based method is found to be effective for the reconstruction of ECG signals by enforcing transient QRS structures on the first-order difference of the signal. It is demonstrated to be robust not only to high compression ratio but also to various artefacts present in ECG signals acquired by using on-body wireless sensors. HRV parameters computed by using the QRS locations estimated from the signals reconstructed with a compression ratio as high as 90% are comparable with that computed by using QRS locations estimated by using the Pan–Tompkins algorithm. Significance: The proposed method is useful for the realization of long-term HRV monitoring systems by using CS-based low-power wireless on-body biosensors.

Threshold selection for extreme value estimation of vehicle load effect on bridges

In the design and condition assessment of bridges, the extreme vehicle load effects are necessary to be taken into consideration, which may occur during the service period of bridges. In order to obtain an accurate extrapolation of the extreme value based on limited duration, threshold selection is a critical step in the peak-over-threshold method. Overly high threshold results in little information to be used and excessively low threshold leads to large bias in parameters estimation of generalized Pareto distribution. To investigate this issue, 417 days of strain data acquired from the long-term structural health monitoring system of Taiping Lake Bridge in China are employed in this article. According to the tail distribution of the strain data induced by vehicle loads, four homothetic distributions are chosen as its parent distribution, from which lots of random samples are generated by the Monte Carlo method. For each parent distribution, the 100-yearly extreme values at different thresholds are estimated and compared with the theoretical value based on those samples. Then a simple and empirical threshold selection method is proposed and applied to estimate the weekly extreme strain due to vehicle loads on the Taiping Lake Bridge. Results show that the estimate on the basis of the threshold obtained by the proposed method is closer to the measured result than the commonly used methods. The proposed method can be an effective threshold selection tool for the extreme value estimation of vehicle load effect in future engineering practice.

Inspection of the effect of the crow deterrent system using camera system for long-term monitoring of bird flight trajectories at photovoltaic power plant

Abstract We put effect inspection of a crow deterrent system with alarm sound using crow observation system at photovoltaic power plant.This number of an area concerned in a photovoltaic generation plant could get the few effect by the alarm sound this crow deterrent system takes out of this inspection result.

Applicability of Eddy Covariance to Estimate Methane Emissions from Grazing Cattle.

Grazing systems represent a significant source of enteric methane (CH), but available techniques for quantifying herd scale emissions are limited. This study explores the capability of an eddy covariance (EC) measurement system for long-term monitoring of CH emissions from grazing cattle. Measurements were made in two pasture settings: in the center of a large grazing paddock, and near a watering point where animals congregated during the day. Cattle positions were monitored through time-lapse images, and this information was used with a Lagrangian stochastic dispersion model to interpret EC fluxes and derive per-animal CH emission rates. Initial grazing paddock measurements were challenged by the rapid movement of cattle across the measurement footprint, but a feed supplement placed upwind of the measurements helped retain animals within the footprint, allowing emission estimates for 20% of the recorded daytime fluxes. At the water point, >50% of the flux measurement periods included cattle emissions. Overall, cattle emissions for the paddock site were higher (253 g CH m adult equivalent [AE] d, SD = 75) and more variable than emissions at the water point (158 g CH AE d, SD = 34). Combining results from both sites gave a CH production of 0.43 g kg body weight, which is in range of other reported emissions from grazing animals. With an understanding of animal behavior to allow the most effective use of tower placement, the combination of an EC measurement platform and a Lagrangian stochastic model could have practical applications for long-term monitoring of fluxes in grazing environments.

Historical perspectives on the mangroves of Kakadu National Park

Mangroves are a major ecosystem within Kakadu National Park in Australia’s Northern Territory, providing coastal protection, high biodiversity and an important resource for Aboriginal people. In the late Holocene (from c. 6000 before present), mangroves occupied much of the estuarine and coastal plains, but their range has subsequently contracted to the main river systems (the West Alligator, South Alligator and East Alligator Rivers, and the Wildman River), tributary creeks and offshore islands (Field and Barrow Islands). On the basis of maps of mangrove extent generated from aerial photography (1950, 1975, 1984 and 1991), compact airborne spectrographic imagery (CASI; 2002), light detection and ranging (LIDAR; 2011) and RapidEye data (2014 onward), changes in net area have been minor but significant redistribution has occurred, with this being attributed to both inland intrusion and seaward colonisation of mangroves. The greatest area changes have been associated with lower-stature mangroves dominated by Avicennia marina and Sonneratia alba, as determined from these datasets. Aerial surveys, conducted using a remote piloted aircraft (RPA) and fixed wing aircraft in September 2016, showed dieback of mangroves, with spaceborne RapidEye observations suggesting this occurred between late 2015 and 2016 and at the same time as the extensive mangrove losses reported in the Gulf of Carpentaria. Given the recent dieback and the associated need to better monitor and protect mangroves and proximal ecosystems in the World Heritage- and Ramsar-listed Kakadu National Park, the study recommends the development and implementation of a robust and long-term monitoring system that better utilises existing and ongoing earth observation and ground data, and is supported by a national approach.

An improved genetic algorithm for increasing the addressing accuracy of encoding fiber Bragg grating sensor network

Abstract Large-capacity encoding fiber Bragg grating (FBG) sensor network is widely used in modern long-term health monitoring system. Encoding FBG sensors have greatly improved the capacity of distributed FBG sensor network. However, the error of addressing increases correspondingly with the enlarging of capacity. To address the issue, an improved algorithm called genetic tracking algorithm (GTA) is proposed in the paper. In the GTA, for improving the success rate of matching and reducing the large number of redundant matching operations generated by sequential matching, the individuals are designed based on the feasible matching. Then, two kinds of self-crossover ways and a dynamic variation during mutation process are designed to increase the diversity of individuals and to avoid falling into local optimum. Meanwhile, an assistant decision is proposed to handle the issue that the GTA cannot solve when the variation of sensor information is highly overlapped. The simulation results indicate that the proposed GTA has higher accuracy compared with the traditional tracking algorithm and the enhanced tracking algorithm. In order to address the problems of spectrum fragmentation and low sharing degree of spectrum resources in survivable.