Unmeasured Locations Research Articles

Directional wave spectrum estimation through onboard measurement data utilizing B-spline basis functions

The utilization of digital twin technology, which integrates real-world measurement data with a digital representation of the vessel, is garnering increasing attention within the realm of structural integrity management. Due to limitations in sensor deployment on ships, the integration of the digital twin framework, which merges measurement data with the ship's digital design model, becomes crucial for ensuring the effectiveness of structural integrity management. This investigation introduces a methodological approach for estimating localized responses at unmeasured locations, leveraging both measurement and design datasets. To approximate the response at unobserved sites, the directional wave spectrum is initially determined utilizing the least squares method, thereby, minimizing the disparity between the estimated and measured response spectra. The methodology notably incorporates cubic B-spline interpolation specifically to smooth the directional wave spectrum deployed on the heading-frequency domain. This deliberate choice of employing cubic B-spline interpolation underscores the emphasis on achieving a refined and continuous representation of the directional wave spectrum, thus enhancing the accuracy and reliability of the estimation process. To validate the proposed methodology, synthetic pseudo-measurement data derived from the wave spectrum and RAO of a 13,000 TEU container ship are employed. Subsequently, the directional wave spectrum is estimated utilizing the pseudo-measurement data, and the estimated directional wave spectrum, in conjunction with the Response Amplitude Operator (RAO), is harnessed to approximate the response spectrum at the location where sensors are not installed.

Power Quality State Estimation for Distribution Grids Based on Physics-Aware Neural Networks—Harmonic State Estimation

In the transition from traditional electrical energy generation with mainly linear sources to increasing inverter-based distributed generation, electrical power systems’ power quality requires new monitoring methods. Integrating a high penetration of distributed generation, which is typically located in medium- or low-voltage grids, shifts the monitoring tasks from the transmission to distribution layers. Compared to high-voltage grids, distribution grids feature a higher level of complexity. Monitoring all relevant nodes is operationally infeasible and costly. State estimation methods provide knowledge about unmeasured locations by learning a physical system’s non-linear relationships. This article examines a new flexible, close-to-real-time concept of harmonic state estimation using synchronized measurements processed in a neural network. A physics-aware approach enhances a data-driven model, taking into account the structure of the electrical network. An OpenDSS simulation generates data for model training and validation. Different load profiles for both training and testing were utilized to increase the variance in the data. The results of the presented concept demonstrate high accuracy compared to other methods for harmonic orders 1 to 20.

The persistent DDT footprint of ocean disposal, and ecological controls on bioaccumulation in fishes

Globally, ocean dumping of chemical waste is a common method of disposal and relies on the assumption that dilution, diffusion, and dispersion at ocean scales will mitigate human exposure and ecosystem impacts. In southern California, extensive dumping of agrochemical waste, particularly chlorinated hydrocarbon contaminants such as DDT, via sewage outfalls and permitted offshore barging occurred for most of the last century. This study compiled a database of existing sediment and fish DDT measurements to examine how this unique legacy of regional ocean disposal translates into the contemporary contamination of the coastal ocean. We used spatiotemporal modeling to derive continuous estimates of sediment DDT contamination and show that the spatial signature of disposal (i.e., high loadings near historic dumping sites) is highly conserved in sediments. Moreover, we demonstrate that the proximity of fish to areas of high sediment loadings explained over half of the variation in fish DDT concentrations. The relationship between sediment and fish contamination was mediated by ecological predictors (e.g., species, trophic ecology, habitat use), and the relative influence of each predictor was context-dependent, with habitat exhibiting greater importance in heavily contaminated areas. Thus, despite more than half a century since the cessation of industrial dumping in the region, local ecosystem contamination continues to mirror the spatial legacy of dumping, suggesting that sediment can serve as a robust predictor of fish contamination, and general ecological characteristics offer a predictive framework for unmeasured species or locations.

SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data

Spatial barcoding-based transcriptomic (ST) data require deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER tackles platform effects between ST and scRNA-seq data, ensuring a linear relationship between them while addressing sparsity and spatial correlations in cell types across capture spots. SDePER estimates cell-type proportions, enabling enhanced resolution tissue mapping by imputing cell-type compositions and gene expressions at unmeasured locations. Applications to simulated data and four real datasets showed SDePER’s superior accuracy and robustness over existing methods.

Validation of a model-based dual-band modal decomposition and expansion approach for fatigue monitoring of offshore wind turbine foundations

The fatigue limit state strongly drives the lifetime of offshore wind turbine support structures. Despite steady advancements in their design, the accumulation of fatigue damage over the lifetime of the structure remains uncertain; one way to improve certainty is to directly monitor structural loads at fatigue-critical locations, e.g., near or below the mudline. However, directly monitoring the loads at these locations is often not possible, since these locations do not allow for easy installation or retrofitting of sensors. To this end, model-based virtual sensing techniques can be employed to estimate the structural response at unmeasured locations based on vibration response data and a finite element model which can accurately describe the modal and operational deflection shapes of a specific turbine [1,2,3]. For this contribution, a modal decomposition and expansion (MDE) algorithm [4] will be used to predict strain time series at fatigue-critical locations, based on acceleration and strain data obtained from the tower of an operational OWT. Emphasis will be put on sparse data situations in which a realistic amount of sensor data is used as input. Fibre-optical strain gauges close to and below the mudline level (Fig 1) will be used for direct validation of the extrapolation capabilities. The required finite element model is built using an in-house developed and verified integrated FE model class [5,6], in combination with a unique database which contains all available geotechnical and structural data of offshore wind turbines (owimetadatabase) [7,8]. This combination allows us to generate fleet-wide, turbine-specific, finite elements models for several wind farms. The results will be presented in terms of time series (e.g., Fig. 2) and fatigue damage-related metrics. Preliminary results show that strain time series estimated with the MDE method show good agreement with the validation data.

Spatial distribution of NORMs concentrations and assessing potential risk to inhabitants in western Chattogram, Bangladesh

ABSTRACT This study represents a pioneering endeavour in consolidating previous measurements of naturally occurring radioactive materials (NORMs) within the Chattogram division, Bangladesh. Employing a meticulous approach, data was overlaid onto a comprehensive grid map covering the entire Chattogram region, comprising 523 grids, each of 8.05 km x 8.05 km. The analysis revealed numerous areas where NORMs concentrations had not been previously measured. To bridge this gap, soil samples were collected from 56 previously unmeasured locations across Cumilla and Chandpur districts and analysed using high purity Germanium (HPGe) detector technology. Results indicated average activity concentrations of Ra-226, Th-232, and K-40 at 27.30 ± 5.41, 46.09 ± 7.88, and 362.50 ± 48.10 Bq.kg−1 respectively, with no detectable presence of Cs-137 or other anthropogenic radionuclides, affirming the region’s absence of fallout or accidental releases. Additionally, measured values for radiation hazard indices are well within acceptable limits set by United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). Spatial distribution maps of radionuclide concentrations and absorbed dose rates were generated not only for the immediate study area but also for the entire Chattogram division, integrating previously collected data for the first time. These distribution maps, alongside the comprehensive findings of this study, establish a pivotal dataset for future research and regulatory efforts, providing invaluable insights into the region’s radiological landscape.

DESARROLLO DE MÉTODOS BASADOS EN REDES NEURONALES EN LA ESTIMACIÓN DE RECURSOS MINERALES

Due to economic and physical limitations, our understanding of mineral resources in a specific area of interest is limited and fragmented. Traditionally, this problem has been solved using the Kriging geostatistical method, where the ore grade is estimated at unmeasured locations using known values of the grade at surrounding points. The advantage of this method lies in the calculation of weights through a spatial variability model known as a variogram. However, the method is imperfect, as it is based on the assumption of stationarity, aditivity, linearity and potential subjectivity in variographic modelling. This study proposes to approach the mineral resource estimation problem as a regression problem using neural networks, which are not subject to the restrictions of stationarity, aditivity, linearity and spatial modelling of geostatistics methods. Kriging and a radial basis function neural network and a multilayer perceptron have been compared using different validation metrics. The results show that a properly trained neural network model, with appropriate labelling of the mineral grade and its input characteristics, achieves similar results to the geostatistical approach, with a significant reduction in time, while avoiding all the aforementioned assumptions. However, neural networks do not consider the spatial correlation of ore grade or reproduce it at the locations where it was measured, characteristics that have marked distrust in its industrial implementation and that are discussed in this article, finally proposing an adjustment between both approaches at a minimum sacrifice of time and labor costs. Keywords Neural networks; machine learning; geostatistics.

Model-Free Dynamic Response Prediction at Unmeasured Locations for Three-Dimensional Structures Based on Polynomial Shape Functions

Model-Free Dynamic Response Prediction at Unmeasured Locations for Three-Dimensional Structures Based on Polynomial Shape Functions

Measuring the radiation of sound sources with the radiation mode method: Towards realistic problems.

The measurement of the pressure field radiated by a sound source has many applications in the fields of noise control and loudspeaker system design. In this paper, the radiation mode method is used to measure the field radiated by a complex acoustic source whose surface impedance is arbitrary and does not correspond to the Neumann boundary condition used for the calculation of radiation modes. The most effective radiation modes are used as test functions to calculate a pressure expansion around the source under test, an expansion that matches the measured pressure at a limited number of points close to the source. This expansion is then used to calculate the radiated pressure at a greater distance at unmeasured locations. In a first step, numerical simulations are performed to evaluate the method's most influential parameters. Then, measurements are performed in a semi-anechoic room on two real sources of increasing complexity. Obtained results show that the radiation mode method allows an accurate evaluation of the pressure field radiated by the test object over a fairly wide frequency band (between 100 Hz and 2 kHz) even for complex sources.

Singular value decomposition and unsupervised machine learning for virtual strain sensing: Application to an operational railway bridge

Metal fatigue is a major concern in civil, mechanical, and offshore structures. As a result, inspections are frequently directed to critical, fatigue prone locations using visual inspections, nondestructive evaluations, or sensor data. However, fatigue assessment may be hampered if access to these fatigue-prone areas is difficult or impossible. Furthermore, when comprehensive sensor use as part of a bridge health monitoring system is desired, cost and power requirements can be prohibitive. These identified limitations have motivated studies examining virtual sensing methods that estimate strains at unmeasured locations using indirect measurements. Kalman filtering (KF) and modal expansion (ME) are two popular strain estimation processes. However, both processes are model-based, requiring calibration of a finite element (FE) model of the structure of interest, which can be time-consuming, particularly for complex structures. This constraint has spurred the need for data-driven strain estimation methods, which depends solely on data from the structure, typically provided by sensors, without any additional a priori knowledge of the structure, such as what could be provided by a FE model. This study investigated the use of a novel, data-driven, Singular Value Decomposition (SVD) based method for strain estimation on an operational railroad bridge. Left singular vector (LSV) SVD modes, also known as Proper Orthogonal Modes (POMs), were employed for strain estimation. Machine learning (ML) was implemented to reduce POM variability and subsequently increase estimation accuracy using two classification methods: k-means clustering and root mean square (RMS); and self-organizing maps (SOM) and POMs. Strains were predicted using strain time-history POMs from snapshot matrices from clustered groups of train passages to estimate unmeasured time-histories from the same group. The method was applied to operational strain measurements from approximately 300 train passages over a steel, truss, railway bridge. Results showed that use of the data driven SVD technique in conjunction with ML could predict suitable strain time signals at unmeasured locations, data that can subsequently be utilized when performing fatigue assessment.

Soil pH mapping as a function of land use, elevation, and rainfall in the lake tana basin, northwestern of ethiopia

AbstractSoil acidity has become a serious problem in the northern highlands of Ethiopia, limiting land productivity mainly that of crop yield. Research had been done to try to come up with results showing the area was affected by severe acidity, but based on few watersheds and districts in a haphazard way. The objective of this research was therefore to develop a comprehensive soil pH map that covers a broader area of 27 districts covering 2,810,055 ha of land to assess the degree of soil acidity in the Tana Sub‐basin, northwestern highlands of Ethiopia. Using GPS (3‐m precision), 2652 soil point data were collected. Precipitation, elevation, and land use cover were taken into account when creating an interpolated soil pH map. Using adjacent probed pH values, ordinary kriging interpolation (R2 = 0.65; root‐mean‐square error = 0.37125) technique was used to estimate the unmeasured locations, while the spatial pH variability was mapped using the geostatistical (GS+10) software. All Food and Agriculture Organization of the United Nations soil pH categories, with 11.22%, 19.97%, 18.77%, 14.59%, 9.64%, and 6.29% of the soil samples being extremely acidic, strongly acidic, moderately acidic, slightly acidic, neutral, moderately alkaline and strongly alkaline, respectively, were observed. The interpolation result showed a moderate coefficient of variation (10.52%) indicating that the research area had medium variability. In addition, the nugget‐to‐total semivariance ratio ranged from 25% to 75%, indicating moderate geographic dependence. The pH semivariograms best fitted the exponential model, which means the one with maximum (R2 = 0.834) and with minimum residual sum of squares (1.819E‐06) value for mapping of pH. Our result showed that soil pH was inversely dependent on precipitation and altitude, while it varies with land use type. The research result/map could give useful tools for effective integrated land management to minimize soil acidification by policymakers, agriculturalists, and other stakeholder groups. We also recommended future routine updates on the size and distribution of surface soil acidity in the study area.

Probabilistic bridge fatigue evaluation at virtual sensing locations using kernel density estimation

Performing bridge fatigue evaluation using field measurements can be difficult given the amount of data needed for effective assessment, access needed to effectively monitor all fatigue prone locations, and associated power requirements and cost. Several studies have been conducted that estimate strain response at unmeasured locations using indirect measurements and subsequently investigate the quality of the estimation using certain metrics [1]. There is little to no research focusing on pragmatically extending these estimation techniques to probabilistic fatigue assessment. This may be because strain estimation has primarily been successful when applied to numerical and laboratory specimens and perceived potential for difficulties associated with applying developed techniques at scale. This study investigates using data-driven, Singular Value Decomposition (SVD), estimated strains at unmeasured locations for probabilistic fatigue assessment of an in-service, railway, bridge. Before performing strain estimations, SVD Proper Orthogonal Modes (POM) variability was reduced using two classification approaches: k-means clustering and root mean square (RMS); and self-organizing maps (SOMs) and POMs. After estimated strains were obtained, reliability analyses using Kernel Density Estimation (KDE) were utilized to perform probabilistic fatigue assessments. Resulting reliability indices computed using estimated strains were compared against reliability indices obtained using measured strains at the same locations. Results showed that reliability indices computed using estimated strains matched closely with indices obtained using measured strains.

Data-Driven Distance Metrics for Kriging-Short-Term Urban Traffic State Prediction

Estimating traffic flow states at unmeasured urban locations provides a cost-efficient solution for many ITS applications. In this work, a geostatistical framework, kriging is extended in such a way that it can both estimate and predict traffic volume and speed at various unobserved locations, in real-time. In the paper, different distance metrics for kriging are evaluated. Then, a new, data-driven one is formulated, capturing the similarity of measurement sites. Then, with multidimensional scaling the distances are transformed into a hyperspace, where the kriging algorithm can be used. As a next step, temporal dependency is injected into the estimator via extending the hyperspace with an extra dimension, enabling for short horizon traffic flow prediction. Additionally, a temporal correction is proposed to compensate for minor changes in traffic flow patterns. Numerical results suggest that the spatio-temporal prediction can make more accurate predictions compared to other distance metric-based kriging algorithms. Additionally, compared to deep learning, the results are on par while the algorithm is more resilient against traffic pattern changes.

Data-driven structural identification of nonlinear assemblies: Structures with bolted joints

The identification of nonlinearities that have a significant impact on dynamic behaviour of complex mechanical structures is necessary for ensuring structural efficiency and safety. A new methodology for structural identification of nonlinear assemblies is proposed in this paper that enables the discovery of stiffness and damping nonlinear models especially when it is not possible to directly measure the degrees of freedom where non-trivial nonlinearities are located. Input-output time-domain data collected at accessible locations on the structure are used to learn nonlinear models in the unmeasured locations. This is accomplished by making use of virtual sensing and model reduction schemes along with a physics-informed identification method recently developed by the authors (Safari and Londoño 2021). The methodology is suited for weakly nonlinear systems with localised nonlinearities for which their location is assumed to be known. It also takes into account dominant modal couplings within the identification process. The proposed methodology is demonstrated on a case study of a nonlinear structure with a frictional bolted joint, in numerical and experimental settings. It is shown that the model selection and parameter estimation for weakly nonlinear elements can be carried out successfully based on a reduced-order model which includes only a modal equation along with relevant modal contributions. Using the identified localised nonlinear models, both the reduced and full-order models can be updated to simulate the dynamical responses of the structure. Results suggests that the identified nonlinear model, albeit simple, generalises well in terms of being able to estimate the structural responses around modes which were not used during the identification process. The identified model is also interpretable in the sense that it is physically meaningful since the model is discovered from a predefined library featuring different nonlinear characteristics.

Evaluation of 3-D spatial distribution of dissolved oxygen concentrations in a eutrophic lake

In this study, three-dimensional (3-D) ordinary kriging of dissolved oxygen (DO) concentrations was performed for a eutrophic reservoir based on 81 sampling points using Stanford Geostatistical Modeling Software (SGeMs). Potential hotspots (problematic zones in terms of water quality with high/low DO concentrations) not only at the surface but also in deeper layers of Porsuk Dam Reservoir (PDR) were evaluated. Moreover, 3-D distributions of DO, and specific conductivity (SC) were examined against the thermocline layer identified using the 3-D temperature data. Thermocline layer existed between 10 and 14m below the surface based on 3-D temperature data. This result showed that the traditional approach of collecting samples from mid-depths may cause incomplete characterization and evaluation of water quality as thermocline layer may not coincide with mid-depth. Although the variation in SC values and temperatures above and below the thermocline layer were relatively homogeneous, this was not the case for DO. 3-D DO distribution suggested a better location for water withdrawal for domestic purposes. 3-D DO maps generated by predicting data at unmeasured locations at different depths could be used as input for 3-D water quality estimation in the reservoir through model simulations in future. Moreover, the outcomes can also be useful in the segmentation (physical configuration) of the water body for future water quality modeling studies.

DIST: spatial transcriptomics enhancement using deep learning.

Spatially resolved transcriptomics technologies enable comprehensive measurement of gene expression patterns in the context of intact tissues. However, existing technologies suffer from either low resolution or shallow sequencing depth. Here, we present DIST, a deep learning-based method that imputes the gene expression profiles on unmeasured locations and enhances the gene expression for both original measured spots and imputed spots by self-supervised learning and transfer learning. We evaluate the performance of DIST for imputation, clustering, differential expression analysis and functional enrichment analysis. The results show that DIST can impute the gene expression accurately, enhance the gene expression for low-quality data, help detect more biological meaningful differentially expressed genes and pathways, therefore allow for deeper insights into the biological processes.

Estimation of Unmeasurable Vibration of a Rotating Machine Using Kalman Filter

Rotating machines are typically equipped with vibration sensors at the bearing location and the information from these sensors is used for condition monitoring. Installing additional sensors may not be possible due to limitations of the installation and cost. Thus, the internal condition of machines might be difficult to evaluate. This study presents a numerical and experimental study on the case of a rotor supported by four rolling element bearings (REBs). As such, the study resembles a complex real-life industrial multi-fault scenario: a lack of information, uncertainties, and nonlinearities increase the overall complexity of the system. The study provides a methodology for modeling and analyzing complicated systems without prior information. First, the unknown model parameters of the system are approximated using measurement data and the linearized model. Thereafter, the Unscented Kalman Filter (UKF) is applied to the estimation of the vibration characteristics in unmeasured locations. As a result, the estimation of unmeasured vibration characteristics has a reasonable agreement with the rotor whirling, and the estimated results are within a 95% confidence interval. The proposed methodology can be considered as a transfer learning method that can be further used in other identification problems in the field of rotating machinery.

Feasibility of Using V2I Sensing Probe Data for Real-Time Monitoring of Multi-Class Vehicular Traffic Volumes in Unmeasured Road Locations

Portions of dynamic traffic volumes consisting of multiple vehicle classes are accurately monitored without vehicle detectors using vehicle-to-infrastructure (V2I) communication systems. This offers the feasibility of online monitoring of the total traffic volumes with multi-vehicle classes without any advanced vehicle detectors. To evaluate this prospect, this article presents a method of monitoring dynamic multi-class vehicular traffic volumes in a road location where road-side equipment (RSE) for V2I communication is in operation. The proposed method aims to estimate dynamic total traffic volume data for multiple vehicle classes using the V2I sensing probe volume (i.e. partial vehicular traffic volumes) collected through the RSE. An experimental study was conducted using real-world V2I sensing probe volume data. The results showed that traffic volumes for vehicle types I and II (i.e. cars and heavy vehicles, respectively) can be effectively monitored with average errors of 6.69% and 10.89%, respectively, when the penetration rates of the in-vehicle V2I device for the two vehicle types average 0.384 and 0.537, respectively. The performance of the method in terms of detection error is comparable to those of widely used vehicle detectors. Therefore, V2I sensing probe data for multi-vehicle classes can complement the functions of vehicle detectors because the penetration rate of in-vehicle V2I devices is currently high.

Non-intrusive estimation of model error and discrepancy in dynamics models

Errors in formulating the system physics model, due to inability to rigorously account for nonlinear behavior, boundary conditions, and multi-component and multi-physics interactions result in discrepancies between model predictions of system responses and the corresponding experimentally measured values. In our previous work, we have developed a Bayesian state estimation-based framework for the estimation of these model errors, and the subsequent prediction of model discrepancies at unmeasured locations, for untested inputs, and for untested configurations. In this approach, we represent model errors as external inputs in the dynamic system model, and estimate them using Bayesian state estimation. However, this approach is intrusive in the sense that it requires access to the governing system of equations, the ability to modify the system model by introducing an external system input, and the ability to represent system inputs and responses as probabilistic quantities. In this paper, we develop semi-intrusive and non-intrusive approaches to extend the model error estimation to black-box models. The semi-intrusive method does not need access to the governing equations, but requires the ability to interrupt the simulation at any time instant, update the system responses at that moment, and resume the simulation using the updated value of system responses. For situations where this is not permitted, we propose a fully non-intrusive approach where, a surrogate model is constructed and used in the Bayesian state estimation procedure. The proposed approaches are illustrated with three examples: structural deformation of a Timoshenko beam, heat transfer across a rigid bar, and deformation of a Timoshenko beam subjected to heating.

Development of spatiotemporal high-resolution temperature data for São Paulo, Brazil; a valuable resource for epidemiologists

Background and aim Most studies investigating the temperature-mortality association in cities rely on temperature recordings from few meteorological stations or models at coarse spatial resolution, implicitly assuming an homogenous spatial distribution across. Evidence from urban heat island studies, remote sensing data, and atmospheric and physical sciences suggest this assumption does not hold. Overlooking the spatial variability of temperature exposure in cities may lead to biased epidemiological findings and limit our ability to identify areas of high risk. Herein, we aim to develop an open-access dataset of daily ambient temperature at 500 meters resolution for the municipality of São Paulo (2015-2020). Methods We obtained daily mean temperature data from 60 ground stations within the city and used spatiotemporal regression kriging to predict temperature at unmeasured locations. This technique uses multiple linear regression to model the spatiotemporal trend in temperature, and spatiotemporal kriging to model the regression residuals spatiotemporal autocorrelation. For the multilinear regression, we used several earth observation products including data relating to topography, the built environment and atmospheric processes, as explanatory covariates. The regression residuals were modelled by fitting a sum-metric spatiotemporal variogram model. We validated the model using a leave-one-out and 5-fold cross-validation. Results Of the 60 monitoring stations, 36 had data >75% valid data. Temperature records showed some spatial heterogeneity (interstation standard deviation: 1.4 ̊ C), supporting the need for spatially resolved temperature data. Of all covariates used in the multilinear regression model, remotely sensed land surface temperature was the best predictor. Any residual variability was modelled through spatiotemporal kriging. Validation using R2 and root-mean-square-error indicators is ongoing. Conclusions This dataset provides epidemiologists with a unique opportunity to investigate exposure to daily mean temperature in São Paulo at high spatio-temporal resolution, and to identify areas of high risk for certain health outcomes, e.g., mortality, over time and space.