Measurement Data Research Articles

Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning.

As one of the most widely used spread spectrum techniques, the frequency-hopping spread spectrum (FHSS) has been widely adopted in both civilian and military secure communications. In this technique, the carrier frequency of the signal hops pseudo-randomly over a large range, compared to the baseband. To capture an FHSS signal, conventional non-cooperative receivers without knowledge of the carrier have to operate at a high sampling rate covering the entire FHSS hopping range, according to the Nyquist sampling theorem. In this paper, we propose an adaptive compressed method for joint carrier and direction of arrival (DOA) estimations of FHSS signals, enabling subsequent non-cooperative processing. The compressed measurement kernels (i.e., non-zero entries in the sensing matrix) have been adaptively designed based on the posterior knowledge of the signal and task-specific information optimization. Moreover, a deep neural network has been designed to ensure the efficiency of the measurement kernel design process. Finally, the signal carrier and DOA are estimated based on the measurement data. Through simulations, the performance of the adaptively designed measurement kernels is proved to be improved over the random measurement kernels. In addition, the proposed method is shown to outperform the compressed methods in the literature.

Wireless Data Acquisition System with Feedback Function

When operating solar–wind power plants (SWPPs) located in populated areas, cases of premature failure of expensive batteries and other power equipment often occur. The purpose of this study is to develop a wireless data acquisition system (DAS) for the operation of an SWPP with a feedback function to prevent material damage from the failure of power equipment and to increase the efficiency of natural energy use. The principles of constructing a DAS, free from some of the disadvantages of analogues, are described in this paper. Intelligent wireless current and voltage sensors and a device for receiving and recording data with an additional feedback function have been developed, providing real-time feedback when the measured parameters go outside the norm. Measurement data are displayed on the laptop screen and alphanumeric display and stored on the hard drive along with timestamps and current event messages. An example of using a reverse communication channel to implement the functions of backup battery protection and to switch SWPP loads is described. The principles and methods proposed in this article are suitable for constructing systems for remote measurements of any physical quantities; therefore, the scope of application of the described system can be significantly expanded.

Optimizing flood predictions by integrating LSTM and physical-based models with mixed historical and simulated data

The current flood forecasting models heavily rely on historical measured data, which is often insufficient for robust predictions due to practical challenges such as high measurement costs and data scarcity. This study introduces a novel hybrid approach that synergistically combines the outputs of traditional physical-based models with historical data to train Long Short-Term Memory (LSTM) networks. Specifically, the NAM hydrological model and the HD hydraulic model are employed to simulate flood processes. Focusing on the Jinhua basin, a typical plains river area in China, this research evaluates the efficacy of LSTM models trained on measured, mixed, and simulated datasets. The LSTM architecture includes multiple layers, with optimized hyperparameters tailored for flood forecasting. Key performance indicators such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Peak-relative Error (PRE) are employed to assess the predictive accuracy of the models. The findings demonstrate that LSTM models trained on mixed datasets with a simulated-to-measured data ratio of less than 2:1 consistently achieve superior performance, exhibiting significantly lower RMSE and MAE values compared to models trained on mixed data with larger data ratios. This highlights the advantage of integrating measured and simulated data, leveraging the strengths of both data types to enhance model accuracy. Despite its advantages, the approach has limitations, including dependence on the quality of simulated data and potential computational complexity. However, the development of this hybrid model marks a significant advancement in flood forecasting, offering a promising solution to the challenges of computational efficiency and data scarcity. Potential applications of this approach include real-time flood prediction and risk management in other flood-prone regions, providing a robust framework for integrating diverse data sources to improve forecasting accuracy.

Baseline assessment of macrolitter on the coastline of Algeria: Fit-for-purpose data for tailor-made measures to navigate the Plasticene Age

Curbing the growing threat of marine litter requires reliable, coherent and fit-for-purpose data. The present study reports the findings of beach macrolitter surveys carried out in seventeen sites along the coastline of Algeria. The median litter density recorded along these sites amounted to 578 items per 100 m of coastline (range: 317–2684 items/100 m). Every surveyed beach exceeded the European threshold value of 20 items per 100 m of coastline by a significant margin. In addition, the evaluation conducted employing the Mediterranean threshold value of 130 items per 100 m of coastline indicated that each of the seventeen surveyed beaches resides within the non-Good Environmental Status spectrum. A significant proportion of the litter, accounting for 43 %, is attributed to food and beverage consumption-related items, highlighting the impact of single-use food packaging, including food and beverage containers resulting from unsustainable practices mainly by beach users and inadequate waste management.

Learning a stable approximation of an existing but unknown inverse mapping: application to the half-time circular Radon transform

Supervised deep learning-based methods have inspired a new wave of image reconstruction methods that implicitly learn effective regularization strategies from a set of training data. While they hold potential for improving image quality, they have also raised concerns regarding their robustness. Instabilities can manifest when learned methods are applied to find approximate solutions to ill-posed image reconstruction problems for which a unique and stable inverse mapping does not exist, which is a typical use case. In this study, we investigate the performance of supervised deep learning-based image reconstruction in an alternate use case in which a stable inverse mapping is known to exist but is not yet analytically available in closed form. For such problems, a deep learning-based method can learn a stable approximation of the unknown inverse mapping that generalizes well to data that differ significantly from the training set. The learned approximation of the inverse mapping eliminates the need to employ an implicit (optimization-based) reconstruction method and can potentially yield insights into the unknown analytic inverse formula. The specific problem addressed is image reconstruction from a particular case of radially truncated circular Radon transform (CRT) data, referred to as ‘half-time’ measurement data. For the half-time image reconstruction problem, we develop and investigate a learned filtered backprojection method that employs a convolutional neural network to approximate the unknown filtering operation. We demonstrate that this method behaves stably and readily generalizes to data that differ significantly from training data. The developed method may find application to wave-based imaging modalities that include photoacoustic computed tomography.

Risk factors for progression to chronic kidney disease in patients with cardiac valve replacement surgery-associated acute kidney injury

Objective: To explore the risk factors for progression to chronic kidney disease (CKD) in patients with cardiac valve replacement surgery-associated acute kidney injury (AKI). Methods: A retrospective, nested case-control study was conducted at Fuwai Central China Cardiovascular Hospital. The study subjects were patients who underwent cardiac valve replacement surgery from January 1, 2018 to December 31, 2020, with a baseline estimated glomerular filtration rate (eGFR)>60 ml·min-1·(1.73 m2)-1 and postoperative complication of AKI. The patients were followed up for 90 days after discharge from hospital. The endpoint event was defined as progression to CKD 90 days after the occurrence of cardiac valve replacement surgery-associated AKI. The patients were divided into CKD group and non-CKD group based on whether they experienced endpoint event. The baseline clinical data were compared between the two groups. The measurement data with non-normal distribution was represented as M (Q1,Q3). Logistic regression model was used to analyze the risk factors of endpoint event. The receiver-operating characteristic (ROC) curve was drawn to evaluate the performance for predicting CKD in cardiac valve replacement surgery-associated AKI patients. Results: A total of 149 cardiac valve replacement surgery-associated AKI patients (86 males and 63 females) were included in the study, aged (59.0±10.2) years. There were 27 patients (18.1%) who progressed to new-onset CKD 90 days after the occurrence of cardiac valve replacement surgery-associated AKI. Compared with non-CKD group, patients in CKD group had older age [66 (58, 70) vs 59 (53, 64) years], lower baseline eGFR [76.3 (65.8, 98.5) vs 92.7 (78.5, 101.6) ml·min-1·(1.73 m2)-1], higher proportion of preoperative hypertension [51.9% (14/27) vs 27.9% (34/122)] and serum creatinine at discharge [136 (101, 165) vs 86 (65, 104) μmol/L], and the differences were statistically significant (all P<0.05). The multivariate logistic regression analysis results revealed that older age (OR=1.063, 95%CI: 1.001-1.129, P=0.047), preoperative hypertension (OR=3.070, 95%CI: 1.105-8.532, P=0.031) and higher serum creatinine at discharge (OR=1.026, 95%CI:1.013-1.038, P<0.001) were risk factors for progression to CKD in patients with cardiac valve replacement surgery-associated AKI. The clinical risk model including age, preoperative hypertension, preoperative baseline eGFR, and serum creatinine at discharge produced a moderate performance for predicting progression to CKD in patients with cardiac valve replacement surgery-associated AKI [the area under the curve (AUC)=0.865, 95%CI: 0.790-0.940, P<0.001]. Conclusion: Older age, preoperative hypertension and higher serum creatinine at discharge are risk factors for progression to CKD in patients with cardiac valve replacement surgery-associated AKI.

Normative Data of Carpal Bone Measurements in the Sample Adult Indian Population

Abstract Introduction Radiography is the most often accessible and affordable imaging modality. Accurate assessments of wrist X-rays can aid in the diagnosis and prognostic evaluation of various wrist problems. This study aims to identify normal radiographic anthropometry reference values and variations of carpal bones and joints by gender and age in sample Indian population, with the potential to be clinically applicable. Materials and Methods Two investigators conducted a prospective analysis of normal wrist radiographs in a single center. Radiology Information Systems and Picture Archiving and Communication Systems were used to collect standard digitized normal X-rays without significant osseous pathology over a year. We conducted measurements of length, angles, and indices in a standard posteroanterior and lateral wrist X-rays in order to establish the standard dimensions and variances based on age and gender. Results A total of 18 measurements which included eight linear measurements, eight angles, and two ratios were documented. A total of 500 X-rays of 250 males and 250 females with 125 of each in two age groups of 20 and 40 years and 41 and 60 years were evaluated. Conclusion This work is a comprehensive database of the Indian population measuring parameters in normal wrist radiographs of posteroanterior and lateral views. The results of our study indicate that men and younger individuals had a significantly higher carpal height ratio. The width of distal radial ulnar joint space was significantly lesser in older subjects. Additionally, males showed a significantly higher lunate uncovered ratio and radial height. When compared to the literature, the study revealed a significant positive ulnar variance in elderly people and women. However, we noted an overall increase in the percentage of positive ulnar variance individuals in our study. We also recorded a marginal increase in radial inclination with no variations across gender and age.

Exploring the relationship between response time sequence in scale answering process and severity of insomnia: A machine learning approach

Utilizing computer-based scales for cognitive and psychological evaluations allows for the collection of objective data, such as response time. This cross-sectional study investigates the significance of response time data in cognitive and psychological measures, with a specific focus on its role in evaluating sleep quality through the Insomnia Severity Index (ISI) scale. A mobile application was designed to administer scale tests and collect response time data from 2729 participants. We explored the relationship between symptom severity and response time. A machine learning model was developed to predict the presence of insomnia symptoms in participants using response time data. The result revealed a statistically significant difference (p < 0.01) in the total response time between participants with or without insomnia symptom. Furthermore, a strong correlation was observed between the severity of specific insomnia aspects and the response times at the individual questions level. The machine learning model demonstrated a high predictive Area Under the ROC Curve (AUROC) of 0.824 in predicting insomnia symptoms based on response time data. These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures.

Characterization of low-frequency distributed acoustic sensing signals in hydraulic fracturing stimulation – A coupled flow-geomechanical simulation approach

Low-frequency distributed acoustic sensing (LF-DAS) is one of the promising diagnostic techniques for detecting and characterizing hydraulic fractures. LF-DAS signals can capture fracture hits and the strain field around the hydraulic fracture. However, the interpretation of field LF-DAS data can be challenging due to the complexity of the underground conditions. This study develops a fracture propagation model to simulate the hydraulic fracturing process. The modelling results are analyzed to examine patterns and trends observed in interpreting field LF-DAS data. The fracture propagation model, coupled with the flow and geomechanical computations, is implemented in the MATLAB Reservoir Simulation Toolbox (MRST). The flow and geomechanical calculations are discretized by the finite volume and the virtual element methods, respectively. The hydraulic fracture is set to propagate along a prescribed path with a specific propagation or activation criterion. The accuracy of our model is validated against the KGD analytical solutions for the leak-off-viscosity, storage-viscosity and leak-off-toughness dominated regimes. The simulated stress and strain features are consistent with those interpreted from field LF-DAS signals. Several case studies and sensitivity analyses demonstrate the approach's utility and examine fracture interference, closure, and stress shadowing effects. The modelling work facilitates interpreting field measurement data by investigating characteristics of fracture hits from adjacent wells. The modelling method provides insights into fracture interference and its implications on optimal designs during hydraulic fracturing stimulation.

Moving horizon estimation for pipeline leak detection, localization, and constrained size estimation

Advanced pipeline leak detection and localization techniques are needed to reduce greenhouse gas emissions from hydrocarbon transportation pipelines. Developing effective leak detection and localization methods is challenging due to the spatiotemporal dynamics of process variables, the presence of process/measurement disturbances and constraints, and the limited measurement data. To address this issue, this manuscript proposes a novel moving horizon estimation design for pipeline leak detection, constrained estimation of leak size and location by using an infinite-dimensional pipeline hydraulic model. Based on the mass and momentum balance laws and the Cayley–Tustin time-discretization method, an infinite-dimensional discrete-time pipeline hydraulic model is proposed considering (unknown but bounded) disturbance and leak. By introducing a coordinate transformation, we decouple the leak size and location estimation problems. The implementable discrete-time moving horizon estimator and observer are designed for constrained leak size and location estimation. The effectiveness of the proposed designs is validated via simulation examples.

A surgical instrument motion measurement system for skill evaluation in practical laparoscopic surgery training.

This study developed and validated a surgical instrument motion measurement system for skill evaluation during practical laparoscopic surgery training. Owing to the various advantages of laparoscopic surgery including minimal invasiveness, this technique has been widely used. However, expert surgeons have insufficient time for providing training to beginners due to the shortage of surgeons and limited working hours. Skill transfer efficiency has to be improved for which there is an urgent need to develop objective surgical skill evaluation methods. Therefore, a simple motion capture-based surgical instrument motion measurement system that could be easily installed in an operating room for skill assessment during practical surgical training was developed. The tip positions and orientations of the instruments were calculated based on the marker positions attached to the root of the instrument. Because the patterns of these markers are individual, this system can track multiple instruments simultaneously and detect exchanges. However due to the many obstacles in the operating room, the measurement data included noise and outliers. In this study, the effect of this decrease in measurement accuracy on feature calculation was determined. Accuracy verification experiments were conducted during wet-lab training to demonstrate the capability of this system to measure the motion of surgical instruments with practical accuracy. A surgical training experiment on a cadaver was conducted, and the motions of six surgical instruments were measured in 36 cases of laparoscopic radical nephrectomy. Outlier removal and smoothing methods were also developed and applied to remove the noise and outliers in the obtained data. The questionnaire survey conducted during the experiment confirmed that the measurement system did not interfere with the surgical operation. Thus, the proposed system was capable of making reliable measurements with minimal impact on surgery. The system will facilitate surgical education by enabling the evaluation of skill transfer of surgical skills.

A novel symplectic geometry-based modal decomposition technique for accurate modal identification of tall buildings with close-spaced modes

The modal decomposition technique is of crucial importance for accurate modal parameter identification of tall buildings with close-spaced modes. However, most of the existing decomposition techniques are proposed for long-span structures, and the techniques specifically focused on tall buildings have rarely been explored. To this end, this paper proposes a novel modal decomposition technique called Symplectic Geometry Close-Spaced Mode Decomposition (SGCD) for the close-spaced modal identification of tall buildings. A numerical study conducted using a two-degree-of-freedom model is utilized to evaluate the effectiveness of the proposed approach. The results demonstrate that the SGCD method can effectively decompose close-spaced modes, and the modal parameters can be accurately identified with high robustness. Furthermore, the proposed modal decomposition technique is employed to analyze the field measurements obtained during a typhoon event on a 420-m-tall building. The results demonstrate the applicability and efficacy of the proposed modal decomposition technique to field measurement data. The main objective of this paper is to provide an effective modal decomposition technique for accurately identifying close-spaced modes of tall buildings.

Challenges by latent heat calculation – Competition among analytical and computational methods

Determining the latent heat of multi-component cast aluminum alloys is complex. These alloys solidify within the mushy zone, with their latent heat release influenced by factors like composition, cooling rate, and microstructure. Techniques such as DSC and DTA, along with software tools like JMatPro, FactSage, and ThermoCalc, can determine this value, understand solidification parameters, and calculate latent heat. This paper introduces a novel approach using Newtonian, Fourier, and Energy Balance methods simultaneously, for the first time, to calculate the latent heat of pure aluminum and its AlSi7Cu1 alloy. These methods offer advantages in foundry conditions and require no specialized operator for data interpretation. Commercial software typically provides parameters for standard alloys only, necessitating alternative sources for accurate data. Thermal analysis techniques offer a reliable method to acquire missing parameters and calculate latent heat with high precision. Comparing the accuracy of these methods for pure aluminum and its AlSi7Cu1 alloy, using DSC measurement data and commercial software values, indicates successful application on the foundry floor to determine thermophysical properties accurately.

Radial artery pulse wave age-related assessment for diabetic patients based on multiple linear regression time domain analysis method

The global incidence of diabetes increases yearly. About 10 % of adults are affected. This study established a multiple linear regression (MLR) time domain analysis model of radial artery pulse wave characteristic parameters in CUN, GUAN and CHI based on traditional Chinese medicine (TCM) to assess diabetes. Pulse signals of diabetic subjects were collected by a pulse instrument. The locations of CUN, GUAN and CHI were determined by a TCM practitioner, and all pulse signals were normalized. Measurement data from 100 male and 100 female patients were used to establish a new time domain analysis method for extracting pulse wave characteristic parameters called equal pressure pulse transit time (EP-PTT). For these mentioned samples, the EP-PTT is different for male and female diabetic subjects, it was smaller for male diabetic subjects than those of female diabetic subjects. EP-PTT1 and EP-PTT6 are relevant to predicting age based on CUN, GUAN and CHI signals, which showed a linear negative correlation (P < 0.05). The EP-PTTi (i = 2, 3, 4) of female diabetic subjects are linearly correlated with age, and all of these are positively correlated (P < 0.05). These observations were clearly different from observations in healthy controls. The pulse waveform of CUN, GUAN and CHI of diabetic subjects can explore the relationship between the age of diabetic subjects and EP-PTT. The preliminary findings will provide foundational data for type 2 diabetes assessment based on CUN, GUAN and CHI pulse signals of radial artery, and the proposed new analysis method can be used for assessing type 2 diabetes.

All‐nonlinear static‐dynamic neural networks versus Bayesian machine learning for data‐driven modelling of chemical processes

AbstractIn recent decades, the utilization of machine learning (ML) and artificial intelligence (AI) approaches have been explored for process modelling applications. However, different types of ML models may have contrasting advantages and disadvantages, which become critical during the optimal selection of a specific data‐driven model for a particular application as well as estimation of parameters during model training. This paper compares and contrasts two different types of data‐driven modelling approaches, namely the series/parallel all‐nonlinear static‐dynamic neural network models and models from a Bayesian ML approach. Both types of AI modelling approaches considered in this work have shown to significantly outperform several state‐of‐the‐art steady‐state and dynamic data‐driven modelling techniques for various performance measures, specifically, model sparsity, predictive capabilities, and computational expense. The performances of the proposed model structures and algorithms have been evaluated for two nonlinear dynamic chemical engineering systems—a plug‐flow reactor for vapour phase cracking of acetone for production of acetic anhydride and a pilot‐plant for post‐combustion CO2 capture using monoethanolamine as the solvent. For the validation data from the CO2 capture pilot plant, root mean squared error (RMSE) for flue gas outlet temperature, flowrate and CO2 concentration is 0.05%, 1.07%, and 5.0%, respectively, for the all‐nonlinear static‐dynamic neural networks and 0.1%, 1.75%, and 14.14%, respectively, for the Bayesian ML models. For the plug flow reactor data, the Bayesian ML models yield superior RMSE compared to the all‐nonlinear static‐dynamic neural networks when the measurement data are corrupted with Gaussian, auto‐correlated, or cross‐correlated noise.

Analysis of a raise boring reamer in operation based on acceleration measurements

AbstractThe understanding of the complex dynamic phenomena occurring during the reaming operations of the raise boring process is limited. Any operational damage of the reamer is very likely to be caused by component failure due to fatigue effects from the dynamic processes. Therefore, an innovative measurement setup consisting of acceleration sensors in three axes was installed on a reamer to capture its movements and vibrations during operation. The capture of the reaming process by acceleration sensors was successful, however no operational damage on the reamer was observed during the reaming of three vertical shafts. The collected vibration data was implemented into a dynamic finite element assessment, which is intended to serve for lifetime predictions of components and predictive maintenance. A further step is investigating the potential for the implementation of the measurement data into a condition monitoring system and for optimizing the reaming operation.

Review of Modern Neural Network Compression Methods for the Task of Processing Measurement Data

In the article, the authors consider neural network methods of data compression, both lossy and lossless. The principles of operation and training of the following architectures are described in detail: transformer, recurrent neural networks with “long short-term memory”, bottleneck autoencoder, variational autoencoder and generative adversarial network. The efficiency of their work has been compared with classical compression algorithms and a conclusion has been made about the potential and prospects of their application.

Non-linear influences of track dynamic irregularities on vertical levelling loss of heavy-haul railway track geometry under cyclic loadings

Abstract With an emphasis on the combined degradation of railway track geometry and components, a new numerical-analytical method is proposed for predicting the track geometrical vertical levelling loss (VLL). In contrast to previous studies, this research unprecedentedly considers the influence of initial track irregularities (ITI) on VLL under cyclic loadings, elastic-plastic behaviour, and different operational dynamic conditions. The non-linear numerical models are simulated using an explicit finite element package known as LS-Dyna, and their results are validated by full-scale experimental and field measurement data. The outcomes are iteratively regressed by an analytical logarithmic function that cumulates permanent settlements, which innovatively extends the effect of ITI on VLL in a long-term behaviour. For a typical heavy-haul railway operating under 30 tons axle load and 60 km/h train velocity, the result indicates that the set of ITI with the highest standard deviation (SD) of vertical profile (VP) degrades faster (37% on average) than that with the lowest SD. Additionally, our new findings reveal that the worst scenario is related to a train running at 60 km/h and carrying a load of 20 tons/axle in an uneven track whose SD of VP evolves from 3.23 mm at N = 0 (ITI) to 7.20 mm, whereas the best one corresponds to a train at 60 km/h and 30 ton axle load in an uneven track whose SD of VP downgrades from 0.48 to 1.50 mm, both at 3 M cycles (or 60 million gross tons). These findings indicate the importance of considering the ITI for predicting track geometrical VLL under cyclic loadings. Therefore, based on this research, an acceptable condition (thresholds) of ITI can be redefined for a minimum effect on VLL, which can support the development of practical maintenance guidelines to extend the railway track service life.

Contrastive learning based on hierarchical graph of microstructures through directed energy deposition process to establish process-structure–property relationship via autoencoder

n this study, an innovative algorithm is developed to transform microstructures to hierarchical graphs without manual feature engineering. The hierarchical graph comprises two layers. Initially, pixel data, which includes Euler angles, phase, and position, forms the pixel-wise graphs within individual grains. Following this foundation, the grains serve as nodes, constituting the second layer. Importantly, the hierarchical graph preserves essential measurement data and structural details for training machine learning models. After that, a contrastive learning model based on hierarchical graph is designed to capture representations of microstructures obtained by the electron backscatter diffraction (EBSD) technique. This model can be directly extended for other microscopy techniques, such as Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray spectroscopy (EDX), and Transmission Electron Microscopy (TEM). Using the learned microstructure representations, an autoencoder model for the directed energy deposition (DED) is developed to establish the relationship among process parameters, microstructures, and material properties, completing the process-structure–property cycle quantitatively. The performance of the present model is naturally benchmarked against two other models: a contrastive learning model based on pixel-wise graph (without manual feature engineering) and a contrastive learning model based on grain-wise graph (employing manual feature engineering). The results of the present model highlight the potential in decoding the process-structure–property relationships in the DED process.

Batch-Layered Method for Transforming Measurement Data

The article proposes a method for preprocessing measurement data that transforms a set of source data samples into a formally unrelated data set formed from bits of different initial data samples. The results of the study are presented, showing that the proposed method allows to increase the overall orderliness of the generated set of sam-ples and, as a consequence, leads to an increase in the efficiency of their compression.