Measurement Locations Research Articles

Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing

Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subsequent analysis and design. In some cases, reconstruction of missing data is required in downstream tasks such as the development of digital twins. This paper presents a data-driven sparse sensor selection algorithm, aiming to provide the most information contents for reconstructing aerodynamic characteristics of wind pressures over tall building structures parsimoniously. The algorithm first fits a set of basis functions to the training data, then applies a computationally efficient QR algorithm that ranks existing pressure sensors in order of importance based on the state reconstruction to this tailored basis. The findings of this study show that the proposed algorithm successfully reconstructs the aerodynamic characteristics of tall buildings from sparse measurement locations, generating stable and optimal solutions across a range of conditions. As a result, this study serves as a promising first step toward leveraging the success of data-driven and machine learning algorithms to supplement traditional genetic algorithms currently used in wind engineering.

Performance‐based wind engineering assessment of a telecommunication mast

AbstractThe study adopts the Performance‐Based Design concept to analyse the structural behaviour of a telecommunication steel lattice tower equipped with wind speed and strain sensors located in Central Hungary. Bayesian updating is utilised to combine the information from the relatively short measurement collected at the site with a longer dataset from a neighbouring measurement location. A finite element model is developed to analyse the performance of the tower in probabilistic terms using Monte Carlo simulation with Latin hypercube method. The probability of failure is assessed for both serviceability and ultimate limit state verifications. This study aims to demonstrate the potential of combining the performance‐based wind design framework and monitoring system.

Analysis of the Response Characteristics of Smoke and Carbon Monoxide in Non-Fire Conditions

This study confirms the applicability of cross-detection by analyzing the response characteristics of smoke and carbon monoxide detectors, to reduce non-firm alarms. Based on the results of previous studies, various scenarios reflecting non-fire conditions in Korea were selected, to conduct experiments to confirm the change in carbon monoxide concentration and the response of smoke detectors based on the measurement location. Although carbon monoxide was detected earlier than the smoke near the heating source, the smoke detector reacted earlier than the carbon monoxide meter as the distance from the heating source increased. The measured concentration of carbon monoxide was much lower than the concentration generated in the case of a fire, thus confirming the applicability of carbon monoxide cross-sensing for non-fire detection.

Deep Learning-Based Subsurface Damage Localization Using Full-Field Surface Strains.

Structures in their service life are often damaged as a result of aging or extreme events such as earthquakes or storms. It is essential to detect damage in a timely fashion to ensure the safe operation of the structure. If left unchecked, subsurface damage (SSD) can cause significant internal damage and may result in premature structural failure. In this study, a Convolutional Neural Network (CNN) has been developed for SSD detection using surface strain measurements. The adopted network architecture is capable of pixel-level image segmentation, that is, it classifies each location of strain measurement as damaged or undamaged. The CNN which is fed full-field strain measurements as an input image of size 256 × 256 projects the SSD onto an output image of the same size. The data for network training is generated by numerical simulation of aluminum bars with different damage scenarios, including single damage and double damage cases at a random location, direction, length, and thickness. The trained network achieves an Intersection over Union (IoU) score of 0.790 for the validation set and 0.794 for the testing set. To check the applicability of the trained network on materials other than aluminum, testing is performed on a numerically generated steel dataset. The IoU score is 0.793, the same as the aluminum dataset, affirming the network's capability to apply to materials exhibiting a similar stress-strain relationship. To check the generalization potential of the network, it is tested on triple damage cases; the IoU score is found to be 0.764, suggesting that the network works well for unseen damage patterns as well. The network was also found to provide accurate predictions for real experimental data obtained from Strain Sensing Smart Skin (S4). This proves the efficacy of the network to work in real-life scenarios utilizing the full potential of the novel full-field strain sensing methods such as S4. The performance of the proposed network affirms that it can be used as a non-destructive testing method for subsurface crack detection and localization.

Flow Field Characterisation of Gaseous Flow in a Packed Bed by Particle Image Velocimetry

AbstractPacked beds with gaseous flow are used in many industrial processes with high energy and resource consumption. This study provides velocity data of the gas flow through a model packed bed of 40 mm spheres arranged in body centred cubic packing obtained by Particle Image Velocimetry to allow for validation of numerical calculations which are often used for process optimization. Results are presented exemplarily for a particle Reynolds number range from 200 to 500. The flow fields are discussed in terms of inlet conditions to the empty reactor, symmetry and height independent flow structure above the bed and measurements in the optically accessible interstices of the bed. The measurement of a sufficient number of snap-shots for each measurement condition and location allowed also for the determination of the turbulent kinetic energy. Above the bed, low frequency oscillations of the flow are observed, that increase with the Reynolds number. In the interior of the bed, fluctuations are much less pronounced. The appearing flow structures are not significantly influenced by different particle Reynolds numbers, but the velocity magnitude changes.

Geostatistical analysis of groundwater levels in a mining area with three active mines

AbstractMining activities can significantly impact groundwater reservoirs in their vicinity. Different approaches have been employed, with varying success, to investigate the spatial variability of groundwater levels in mining areas. Typical problems include the small sample size, the non-Gaussian distribution of the data, and the clustering of sample locations near the mines. These conditions complicate the estimation of spatial dependence. Under sparse and irregular sampling conditions, stochastic methods, which can provide estimates of prediction uncertainty, are preferable to deterministic ones. This research focuses on the comparison of two stochastic methods, stochastic local interactions (SLI) and universal Kriging (UK), using water level data from 72 locations around three mines in Northern Greece. UK is a well-known, variogram-based geostatistical method, while SLI is a computationally efficient kernel-based method that can cope with large spatial datasets. The non-Gaussian distribution of the data is handled by means of a flexible, data-driven Gaussian anamorphosis method that uses kernel functions. The spatial prediction performance of both methods is assessed based on cross-validation. UK performs better than SLI, due to the fact that the former incorporates a linear trend function. On the other hand, a comparison of the two methods using data from a single mine that contains only 28 measurement locations shows that SLI performs slightly better than UK. The prediction uncertainties for both methods are also estimated and compared. The results suggest that SLI can provide better estimates than classical geostatistical methods for small sample sizes that do not allow reliable estimation of the variogram model.

What non-pharmacological and non-invasive pain management interventions are available for individuals from Turkish-speaking ethnic groups with non-malignant chronic pain? A scoping review of published literature.

A scoping review was selected to explore what non-pharmacological and non-invasive pain management interventions are available for individuals from Turkish-speaking ethnic groups with chronic pain and what represents the most appropriate intervention. Adults with non-malignant chronic pain from Turkish-speaking ethnic groups residing in or outside of Turkey. All non-pharmacological and non-invasive pain management interventions were considered. No limits were placed on geographic location, gender, sex or healthcare setting. The MEDLINE database was searched for published literature in April 2022. An English language filter was applied. No limits were placed on study design or date of publication. Data was charted from eligible studies into a data extraction table. Key concepts were identified during data extraction by DN. Eleven studies were included in the final review. All were conducted within a quantitative research paradigm. The studies were completed in Turkey (7), Belgium (1), Sweden (1) and Switzerland (1). One was a multi-country review. No studies were conducted in the UK. The primary interventions were heterogenous and included: pain science education (2), cognitive behavioural therapy (2), transcranial magnetic stimulation (1), balneotherapy (1), extracorporeal shockwave therapy (1), transcutaneous electrical nerve stimulation (1), wool therapy (1), exercise and patient dialogues (1) and aromatherapy massage and reflexology (1). Location of pain, outcome measures and timings of follow-ups were heterogeneous. Intervention heterogeneity, exclusively quantitative methodology and absence of studies completed in the UK meant no conclusions could be made on what represents the most appropriate non-pharmacological and non-invasive interventions intervention for individuals from Turkish speaking ethnic groups with non-malignant chronic pain.

Automated streamflow measurements in high‐elevation Alpine catchments

AbstractSalt dilution is a well‐established streamflow measurement method in creeks, which works particularly well downstream of turbulent flow sections as the mixing of the salt tracer is enhanced. Usually, salt dilution measurements are performed manually, which considerably limits the observations of rare peak flow events. These events are particularly important for constructing robust rating curves and avoiding large uncertainties in the extrapolation of streamflow values. An additional challenge is the variability of the river cross section, especially after larger discharge events, leading to nonstationary rating curves. Therefore, discharge measurements well distributed over time are needed to construct a reliable streamflow–water level relationship and to detect changes caused by erosion and deposition processes. To overcome these two issues, we used an automated streamflow measuring systems at three different sites with contrasting hydrological and hydraulic characteristics in the Alps. This system allowed us to measure discharge at nearly maximum flow of the observation period (2020–2021) at all three sites and to detect abrupt changes in the rating curve by performing event‐based salt injections. The uncertainty in the measurements was quantified, and the streamflow was compared with official gauging stations in the same catchment. Based on a very large dataset of almost 300 measurements, we were able to evaluate the reliability of the system and identify the primary sources of uncertainty in the experimental setup. One key aspect was the site selection for the downstream electrical conductivity sensors, as measurement location strongly controls the signal‐to‐noise ratio in the recorded breakthrough curves.

Geographic Accessibility to Primary Healthcare: Study Case Dengue Fever in Purwosari Sub-District, Gunungkidul Regency, Yogyakarta, Indonesia

Distribution and spatial analysis of public healthcare in Indonesia are rarely discussed. Most of the research tell about quantity and quality which do not mention the spatial aspect. This research purposes to know the relationship between health facilities selection and geographic conditions by combining medical facilites data, number of doctor and spatial analysis approach. The selected study areas are referral health facilities chosen by DHF (Dengue Hemorrhagic Fever) patients in Purwosari District, Gunungkidul Regency. Information was collected by interviews and field measurements (altitude and location plotting) and also secondary data collection (hospital accreditation data, number of doctors, and number of beds). GPS, Altimeter, ArcGIS, and SPSS used for data collection and analysis. The spatial analysis method in this research were kernel density, buffer, ruler, and altitude of each facility. Meanwhile, the medical analysis approach used Pearsons' correlation and multinomial logistic regression in SPSS. The results of the spatial analysis show that the DHF patients in Purwosari dominantly choose health facilities in Bantul Regency because of having closer distance, relatively short travel time, lower elevation, and many health facilities centers. At the same time, Puskesmas Purwosari (Sub-district level Health center) was only chosen as an early reference and a place for treating mild dengue patients. In addition, distance, duration of travel time, and the number of doctors had a strong correlation based on the Pearson's correlation. These three variables have the greatest linear relationship with the response variable. Meanwhile, some variables; elevation, number of doctors, accreditation, and number of beds were considered by patients in choosing a hospital based on multinomial logistic regression analysis.

Location of faults based on deep learning with feature selection for meter placement in distribution power grids

Abstract A problem of great interest for power distribution companies is ensuring uninterrupted service in extensive power distribution systems. Thus, the monitoring of networks and identification of system faults become essential. This work focuses on identifying a fault’s occurrence from a small number of low-cost measurements in a power distribution system. The determination of sensor locations is based on the recent feature selection approach LassoNet, where the measurement locations are ranked. It provides the most informative measures during a fault resulting in a shortening data set. It is used as input to a deep neural network without a significant loss in accuracy. We validate our method on the IEEE 13 and 34 node test feeders for distribution systems to conduct the suggested approach’s experimental studies.

Positioning a Measurement System for Determining the Mixing Quality in Biogas Digesters

AbstractMixing quality in agricultural biogas plants is crucial for nutrition supply to the microorganisms and needs to be ensured by an energy‐efficient mixing strategy. Various approaches have been proposed to determine the mixing quality in biogas digesters. Among them are magnetic‐inductive or force sensors brought into vertical digesters through the concrete wall. This article presents an approach to locating these sensors effectively around the circumference of the digester to avoid positioning in dead zones and maximize information quality regarding flow and mixing behavior. Particle image velocimetry is used to measure flow fields in a transparent model digester. Finally, optimum measurement locations considering three widely used agitator geometries are suggested.

Experimental Study of the TVOC Distribution in a Car Cabin

The vehicle in-cabin is subject to several types of pollutants infiltrating from the outdoors or emitted directly inside it, such as Volatile Organic Compounds (VOCs). The concentration of TVOC (total volatile organic compounds) is the result of the emission from different equipment surfaces that compose the car cabin. In the present study, the experimental characterization of TVOC emission from the interior surfaces of a car cabin is discussed by considering the influence of two parameters: the temperature and ventilation modes. A measurement location grid was used to measure TVOC’s emissions from 267 points on all surfaces of the car’s interior equipment. Three different temperatures and two ventilation modes (recirculation and outdoor air) were investigated. The results indicate that the concentration of TVOC increases with the temperature inside the cabin with a contribution that varies with the type of cabin equipment including the dashboard, center console, seats, and carpets. On the other hand, the concentration distributions of TVOC showed relative differences of 10–13% and 2–5% for surface and volumetric measurements, respectively. This implies no preferential positioning of the in-cabin probe for TVOC volumetric concentration measurements. In addition, the recirculation ventilation mode results in a higher accumulation of TVOC; therefore, higher concentrations are measured.

SEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters.

For assistive devices such as active orthoses, exoskeletons or other close-to-body robotic-systems, the immediate prediction of biological limb movements based on biosignals in the respective control system can be used to enable intuitive operation also by untrained users e.g. in healthcare, rehabilitation or industrial scenarios. Surface electromyography (sEMG) signals from the muscles that drive the limbs can be measured before the actual movement occurs and, hence, can be used as source for predicting limb movements. The aim of this work was to create a model that can be adapted to a new user or movement scenario with little measurement and computing effort. Therefore, a biomechanical model is presented that predicts limb movements of the human forearm based on easy to measure sEMG signals of the main muscles involved in forearm actuation (lateral and long head of triceps and short and long head of biceps). The model has 42 internal parameters of which 37 were attributed to 8 individually measured physiological measures (location of acromion at the shoulder, medial/lateral epicondyles as well as olecranon at the elbow, and styloid processes of radius/ulna at the wrist; maximum muscle forces of biceps and triceps). The remaining 5 parameters are adapted to specific movement conditions in an optimization process. The model was tested in an experimental study with 31 subjects in which the prediction quality of the model was assessed. The quality of the movement prediction was evaluated by using the normalized mean absolute error (nMAE) for two arm postures (lower, upper), two load conditions (2 kg, 4 kg) and two movement velocities (slow, fast). For the resulting 8 experimental combinations the nMAE varied between nMAE = 0.16 and nMAE = 0.21 (lower numbers better). An additional quality score (QS) was introduced that allows direct comparison between different movements. This score ranged from QS = 0.25 to QS = 0.40 (higher numbers better) for the experimental combinations. The above formulated aim was achieved with good prediction quality by using only 8 individual measurements (easy to collect body dimensions) and the subsequent optimization of only 5 parameters. At the same time, just easily accessible sEMG measurement locations are used to enable simple integration, e.g. in exoskeletons. This biomechanical model does not compete with models that measure all sEMG signals of the muscle heads involved in order to achieve the highest possible prediction quality.

Pressure Fluctuations in the Base of a Typical Re-Entry Module

Unsteady pressure measurements were carried out on the flow side and around the base of a blunt-nosed bluff body representing a spacecraft re-entry module in the Mach number range of 0.8 to 4.0. The results show that the pressure fluctuations in the base region are much higher than in the attached flow region of the body. Most of the energy in the pressure fluctuations in the base region is in the low-frequency band, while fluctuations in the attached flow region occur over a wider bandwidth. In the separated flow region at the base, highest pressure fluctuations were observed around the rear stagnation point. The frequency spectra do not show any dominant tone attributable to the configuration at any measurement location. The observations suggest that vortex shedding behind such 3-d bluff bodies could be relatively weak and that most of the fluctuating energy in the separated flow region could be due to random oscillations of the rear stagnation point.

Autonomous Ultrasonic Thickness Measurement of Steel Bridge Members Using a Climbing Bicycle Robot

This paper presents the development of a steel-climbing bicycle robot integrated with an ultrasonic sensing device towards autonomous thickness measurement of steel bridge members. The bicycle-like robot has two magnetic wheels controlled by two independent steering actuators, which navigates on various steel surfaces (flat, curved, rough, concave, and convex). The compact ultrasonic sensing device is capable of high-voltage pulse excitation, filtering/amplification of the received ultrasonic signal, and high-speed analog-to-digital conversions (up to 80 MHz). Along with the ultrasonic sensing device, the robot carries a dual element transducer, an automatic gel couplant dispenser, a position tracking sensor, and a camera. A mounting/retrieving mechanism of the transducer is designed to ensure the reliable contact of the transducer on steel surfaces. The camera view assists an operator to safely navigate the robot to measurement locations, properly dispense gel couplant, and firmly press the transducer on a measurement surface. The automated robot localization provides accurate thickness measurement locations, which are mapped real-time in three-dimensional (3D) space together with the robot’s travel path. We validate the performance of the developed system in several indoor and outdoor tests. Weblink to a supplementary video demonstrating the robot’s maneuverability and ultrasonic thickness measurements is available in the Data Availability Statement of this article.

Virtual Metrology Modeling for Wafer Edges via Graph Attention Networks

Quality monitoring is an essential element of defect detection in semiconductor manufacturing processes, but semiconductor companies use virtual metrology (VM) in addition to actual metrology to prevent productivity degradation due to the time and costs required to obtain measurements. Past VM studies aimed to predict average wafer measurement values via equipment sensor data and focused on achieving improved predictive performance by selecting or extracting important variables among high-dimensional variables such as equipment sensor data. However, the management of wafer chip quality requires not only average measurement values but also measurement value predictions for chips located at the edges, which are vulnerable to defects. In this paper, we therefore propose a graph attention (GAT) network-based VM model that predicts the measurement values of chips located at wafer edges by constructing graph data with measurement data (i.e., the measurement location information in wafers and the measurement values). To verify the performance of the proposed model, we conduct a comparative experiment with conventional machine learning methods. The experimental results show that the proposed VM model contributes to a predictive performance improvement in terms of the measurement values of chips located at wafer edges.

Dynamic Pressure Measurements in a Rectangular Cavity with Multiple Stores

An experimental investigation is carried out to study the effects of Mach number on the acoustic resonance characteristics of a rectangular cavity with and without multiple stores. Measurements are conducted for a cavity with a length-to-depth ratio of 4.5 at Mach numbers of 2, 3, 4, and 5. Dynamic pressures are measured within the cavity and on the store surfaces. An increase in Mach number results in a decrease in overall sound pressure levels at all measurement locations within the cavity. Pressure spectra indicate that increasing the Mach number decreases both the peak and broadband amplitudes of the cavity pressure fluctuations. At Mach 5, the amplitude of the cavity modes is significantly reduced in the upstream half of the cavity, and the modes are either indistinguishable from broadband levels or absent in the downstream half. The addition of stores reduces cavity acoustics, with two stores offering greater reduction than a single store. The effect of Mach number and the presence of stores on the aeroacoustic feedback loop in a cavity align with literature. Additionally, the pressure fluctuations on the windward store surface scale with Mach number in a similar trend as the cavity acoustics.

Internet of agriculture: Analyzing and predicting tractor ride comfort through supervised machine learning

The aim of this study is to improve ride comfort among tractor drivers by utilizing Internet of Things (IoT) technology and Machine Learning (ML) techniques to analyze and predict vibration exposure. The study explores the association between tractor ride characteristics, including average speed, tool depth, and pulling force, and ride comfort during actual rotary soil tillage operations. Ride comfort was evaluated by calculating the Overall Vibration Value (OVV) at three measurement locations (i.e., the floor, seat pan, and seat backrest). The study employed spectral analysis i.e., Fast Fourier Transform (FFT) and Power Spectral Density (PSD) to determine the predominant resonant frequencies. In addition, ML approaches i.e., Linear Regression (LR), Decision Tree Regressor (DTR), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) were used to predict ride comfort response. Three hyperparameter optimization techniques, including Grid Search, Random Search, and Bayesian optimization were used. Tractor-tiller system was operated on a real agricultural field by five male tractor drivers. Vibration levels were measured along three translational axes at three measurement locations using a smart monitoring device based on the IoT concept. Average OVV magnitude was computed as 0.84 m/s2 i.e., exceeding the ISO 2631-1 (1997) threshold limits. FFT and PSD analysis revealed a variety of dominant frequencies, including resonance peaks at 2 Hz, 6–7 Hz, and 9–11 Hz. The analysis of variance (ANOVA) indicates that tractor speed and tool depth are significant variables (at the 5% level) in determining ride comfort. Regarding predictive accuracy, the ANN model exhibits the highest coefficient of determination of 0.90, followed by the SVM and GPR models with a coefficient of determination of 0.89. The DTR and LR models demonstrate slightly lower coefficient of determination values of 0.83 and 0.82, respectively. Furthermore, Bayesian optimization proved to be an effective approach in achieving more accurate predictions of tractor ride comfort. In general, the study outcomes can be utilized by tractor designers in numerous ways to optimize tractor design (such as suspension system, cab design, seat design, etc.) to improve tractor ride comfort in real field applications.

Remote Bio Vision: Perfusion Imaging Based Non-Contact Autonomic Biosignal Measurement Algorithm

Due to the outbreak of new viruses in recent years, the need for contactless medical systems has significantly increased for monitoring, treating, managing, and preventing diseases. Systems are constantly being developed to replace conventional biosignal measuring methods with contactless medical systems. Several systems that substitute attachable sensors in conventional systems have been developed to measure diverse biosignals using images captured with visible cameras. Most non-contact biometric systems have certain limitations, for example, they can only measure a single biosignal, such as the heart rate, oxygen saturation, or blood pressure. They are also easily affected by external factors such as lighting conditions and noises. Furthermore, a blood pressure measuring system based on the pulse transit time requires physical contact and accuracy depends on the measurement location. This study examined a system that provides highly accurate biosignals to overcome these limitations of conventional biosignal measurement systems. This study proposes a biosignal measurement system that uses an infra-red camera to reduce visible light noise. The proposed system uses light sources outside the visible range and measures the amplified reflected light using a near infra-red camera to calculate the heart rate and oxygen saturation. The blood flow value is calculated by the measured heart rate and oxygen saturation. Based on measured data, blood pressure is estimated without contact with the body. We compared the proposed system with an existing non-contact blood pressure measurement system. The proposed system showed an average error rate of 2.07% in blood pressure measurement, which increased by 3.86% compared to the conventional system. Also, this study enabled the early detection of pressure ulcers, which is difficult to measure quantitatively.

Validation of remote assessment of preschool children's anthropometrics and motor skills.

Remotely delivered treatment and research procedures were rapidly adopted in response to the COVID-19 pandemic. However, it is unclear if these measures are valid. The purpose of this study was to compare the validity of anthropometry and motor skill proficiency measurements collected in a remote-setting to in-person setting among a sample of children ages 3-4 years. Child anthropometry and motor skill performance were measured in-person by trained assessors and by parents at home with remote supervision via videoconference by trained assessors. The following measures from the National Institutes of Health Toolbox were collected: anthropometry (height and weight), manual dexterity/manipulation (9-hole pegboard), motor coordination and agility (supine timed up and go), lower body strength (standing long jump), and postural stability (one-leg standing balance). Differences in expert and parent-based measurements were assessed using Bland-Altman plots, paired samples t-tests, and Pearson correlations. A total of n = 14 children completed the assessments. No significant differences were observed between measurement locations for weight and motor skills (p > .05). Remote measurement of height (M = 101.1 cm, SD = 5.40) was significantly greater than in-person measurements (M = 98.2 cm, SD = 5.16); p < .0001. Remote measurements of motor skills and weight are valid assessments for researchers and clinicians to utilize in young children. Remote assessment with guidance offers comparable and valid estimates as in-person assessment, potentially offering a solution to resource-constricted barriers in research and access to care. There is an opportunity for researchers to fine-tune remote height and individual-level assessment strategies.