Measurement Data Research Articles

A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant information in measurement data, present challenges to forecasting methods. The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced data-driven forecasting models. Focus on the seasonal variation characteristics of wind energy, a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed. The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

Data-Driven Continuous-Time Hammerstein Modeling with Missing Data Using Improved Archimedes Optimization Algorithm

This research introduces the improved Archimedes optimization algorithm (IAOA) for data-driven modeling of continuous-time Hammerstein models with missing data. It addresses the limitations of the original Archimedes optimization algorithm (AOA) through two key modifications: rebalancing the exploration and exploitation phases and mitigating local optima trapping issues. The primary focus is on developing a novel data-driven approach for modeling continuous-time Hammerstein models, particularly in the presence of missing output data. Four levels of missing measurement data (5%, 15%, 35%, and 50%) were considered, with data points randomly replaced with zeros. Models were tested with both complete and missing output data to evaluate the robustness of the IAOA-based method. The proposed based method identified linear and nonlinear subsystem variables in a continuous-time Hammerstein model leveraging input and output data, validated through two practical experiments: a Twin Rotor System and an Electromechanical Positioning System. The performance was assessed by examining various factors, including the convergence curve of the fitness function and its statistical analysis, responses in the frequency and time domains, Wilcoxon's rank-sum test, and computational time. Across all experiments, the IAOA-based method demonstrated superior performance compared to AOA and other methods, including a hybrid approach combining the average multi-verse optimizer and sine cosine algorithm, particle swarm optimizer, the sine cosine algorithm, multi-verse optimizer and grey wolf optimizer. The findings showed that the proposed IAOA-based method delivered highly accurate and consistent solutions, proving it to be the most effective and reliable method compared to the others assessed.

Image quality safety model for the safety of the intended functionality in highly automated agricultural machines

Achieving safe and reliable environmental perception is crucial for the success of highly automated or even autonomous agricultural machinery. However, developing such a system is challenging due to the inherent limitations of perception sensors. In certain conditions, these sensors may fail to capture accurate data, leading to erroneous perceptions of the environment and potentially compromising safety. Monitoring the functional insufficiencies of the measurement data is crucial for ensuring the safety and reliability of perception systems.This article introduces ISO standards, which provide guidelines for ensuring functional safety in highly automated mobile machines and vehicles. It also proposes an Image Quality Safety Model (IQSM) for monitoring the safety of the intended functionality in perception systems. The IQSM estimates the confidence level with which a camera can safely perform a specific object detection task. If the confidence level falls below a predefined threshold, the IQSM can trigger actions, alert operators, and prevent potential safety hazards. The IQSM exhibits remarkable performance, achieving a validation accuracy of about 90%, demonstrating its ability to effectively distinguish the safety of the intended functionality under a variety of image quality conditions.

Hybrid building energy modeling method with parameterized prototype models and rapid calibration

Building energy models were widely used in building energy performance analysis and efficiency improvement, such as building demand response assessment and energy system optimization. However, building energy modeling was a time-consuming and complex process. Therefore, this paper proposed a hybrid building energy modeling method based on parameterized prototype models and rapid calibration. The main feature of the method was the rapid generation of target building energy models using the prototype building energy performance database. The parameterized prototype building energy model considered 13 uncertainty parameters. Based on the range of 13 uncertainty parameters, such as wall U-value and solar heat gain coefficient, 1000 simulations were performed by Monte Carlo sampling to generate the prototype building energy performance database. The target building energy model was quickly calibrated by learning from this database. It filtered the models that met the requirements based on the actual building measurement data. Based on this hybrid modeling method, the rapid modeling tool AutoBPS-Hybrid was developed in a ruby environment. Shopping mall buildings located in three different climate zones were selected as case studies for this study. The results showed that if only one building energy model meeting the percentage errors was needed, only 3–4 simulations were required. If it was necessary to match the real uncertainty parameter distributions, an average of about 53 simulations was needed. The building energy models were applied to plug load and lighting control in buildings. The shopping mall buildings in Harbin, Beijing and Chengdu could reduce energy consumption by 10.18–13.33 kWh/m2, 14.43–18.15 kWh/m2 and 11.54–14.69 kWh/m2 per year, respectively.

3D point cloud regularization method for uniform mesh generation of mining excavations

Mine excavation systems are usually dozens of kilometers long with varying geometry on a small scale (roughness and shape of the walls) and on a large scale (varying widths of the tunnels, turns, and crossings). In this article, the authors address the problem of analyzing laser scanning data from large mining structures that can be used for various purposes, with focus on ventilation simulations. Together with the quality of the measurement data (diverse point-cloud density, missing samples, holes induced by obstructions in the field of view, measurement noise), this creates problems that require multi-stage processing of the obtained data. The authors propose a robust methodology to process a single segmented section of the mining system. The presented approach focuses on obtaining a point cloud ready for application in the computational fluid dynamics (CFD) analysis of airflow with minimal need for additional manual corrections on the generated mesh model. This requires the point cloud to have evenly distributed points and reduced noise (together with removal of objects inside) while keeping the unique geometrical properties and shape of the scanned tunnels. Proposed methodology uses trajectory of the excavation either obtained during the measurements or by skeletonization process explained in the article. Cross-sections obtained on planes perpendicular to the trajectory are processed towards the equalization of point distribution, removing measurement noise, holes in the point cloud and objects inside the excavation. The effects of the proposed algorithm are validated by comparing the processed cloud with the original cloud and testing within the CFD environment. The algorithm proved high effectiveness in improving skewness rate of the obtained mesh and geometry mapping accuracy (standard deviation below 5 centimeters in cloud-to-mesh comparison).

A framework for conducting GWAS using repeated measures data with an application to childhood BMI.

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in GWAS. Using childhood BMI as an example trait, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS of the 12 estimated phenotypes identified 28 genome-wide significant variants at 13 loci, one of which (in DAOA) has not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover unique biological mechanisms influencing quantitative traits.

Evaluating Evaporation Using Measurement Data from Experimental Grounds in Crimean Peninsula

Evaluating Evaporation Using Measurement Data from Experimental Grounds in Crimean Peninsula

Body Contouring With Electromagnetic Treatment Plus Radiofrequency

Background Electromagnetic treatments have recently been combined with radiofrequency to reduce the fat layer and simultaneously increase muscle thickness. Studies report treatment efficacy, using photographs and imaging methods. Methods A literature review was conducted. Measurement data were tabulated. The EmSculpt Neo device (BTL Industries, Boston, MA) was used in all studies. Results Seven studies evaluated the fat thickness and muscle thickness using either ultrasound (1 study) or magnetic resonance imaging methods (6 studies). The abdomen was treated in 2 studies. The other studies treated the lateral thighs, flanks, buttocks, inner thighs, and upper arms. The mean reduction in fat thickness was 8.0 mm, and the mean increase in muscle thickness was 5.5 mm. All 7 studies were authored by BTL medical advisors. Measured changes were likely within the margin of error and of questionable clinical relevance. Few adverse events were reported. Discussion Photographs were frequently not standardized, showing changes in treated areas, but unexplained changes in nontreated areas as well. The magnetic resonance imaging scans were affected by differing levels of the axial slice. Measured changes were incremental and likely not indicative of a true treatment effect. The scientific basis for efficacy is tenuous, both for fat reduction and muscle hypertrophy. Conclusions A scientific evaluation of the results fails to produce reliable evidence of a clinically meaningful result for this combination energy-based treatment. Financial conflict is a major factor. Liposuction remains the gold standard.

Estimation of long-term stability of precision inductance measures using different methods

Abstract Inductance is a physical quantity that characterizes the ability of a conductor to accumulate magnetic field energy when electric current flows. Considering the small number of standards of a similar level of accuracy in the world, it is interesting to study the behavior of individual characteristics of such standards using known research methods. The long-term stability of measures is an important metrological characteristic that characterizes the change in the nominal value of a standard or measure over time. Regular metrological research of the long-term stability of precision inductance measures with nominal values of 10 mH and 100 mH have been carried out since 2009. Analysis of the long-term behavior of precision inductance measures included checking the primary measurement data for randomness and outliers using Abbe’s and Grubb’s tests. The detection of anomalous results significantly affects the estimation of the random error component and the standard uncertainty of the reproduction of the inductance measure. There is a significant change in the inductance of both measures over the entire observation period, which is confirmed by Abbe’s test. At the same time, this change is most significant in the first years, and then the change in inductance occurs slowly. The analysis of the functional change in inductance using the Grabb’s test showed that, depending on the research object itself, different analytical dependencies can be used for describing the functional trend. The criterion for choosing the best approximation can be both the “criterion of the reliability of the approximation” and the standard deviation of the “cleaned” measured data. The practical value of this work is the possibility of accumulating and integrating the obtained experimental data for standards of a similar accuracy class in different countries, which makes it possible to further improve the methods of their construction, stabilization of reproducible characteristics and operational features.

Direct moment estimation of intensity distribution of magnetic fields with quantum sensing network

Abstract A quantum sensing network is used to simultaneously detect and measure physical quantities, such as magnetic fields, at different locations. However, there is a risk that the measurement data is leaked to the third party during the communication. Many theoretical and experimental efforts have been made to realize a secure quantum sensing network where a high level of security is guaranteed. In this paper, we propose a protocol to estimate statistical quantities of the target fields at different places without knowing individual value of the target fields. We generate an enanglement between L quantum sensors, let the quantum sensor interact with local fields, and perform specific measurements on them. By calculating the quantum Fisher information to estimate the individual value of the magnetic fields, we show that we cannot obtain any information of the value of the individual fields in the limit of large L. On the other hand, in our protocol, we can estimate theoretically any moment of the field distribution by measuring a specific observable and evaluated relative uncertainty of k-th (k=1,2,3,4) order moment. Our results are a significant step towards using a quantum sensing network with security inbuilt. 

The double-edged sword of self-tracking: investigating factors of technostress in performance-oriented cycling and triathlon

This study is dedicated to the investigation of technostress caused by self-tracking in performance-oriented amateur sports and thus addresses a significant research gap in the understanding of stress factors operating in this specific context. Although technostress in occupational and private settings has been extensively researched, there is a lack of knowledge about the effects and specifics of technostress caused by the use of self-tracking technologies such as wearables and performance monitoring apps in sports. A total of 16 stress factors were identified, eight of which - information overload, distraction, unavailability, loss of control, lack of sense of achievement, unreliability, complexity and self-monitoring - are already known from the professional context and were transferred to the sports context. In addition, eight new stress factors specific to performance-oriented amateur sport were identified: Performance enhancement imperative, lack of context, digital visibility, feedback incorporation, measurement data fixation, comparison pressure, permanent monitoring and perception discrepancy. The study is based on a qualitative research approach with guided interviews conducted with performance-oriented amateur triathletes. The findings of this study contribute to a deeper understanding of the dynamic and contextual nature of technostress in sport and provide a basis for the development of targeted intervention strategies aimed to reducing technostress, such as adaptive training programs or personalized feedback systems. The results thus provide a valuable starting point for future research, particularly for the investigation of coping strategies in relation to the identified stress factors. In addition to identifying eight new sport-specific technostress factors, this study clearly delineates how traditional work-related technostress factors are applicable to amateur sports. This contextual adaptation helps in understanding the unique pressures faced by amateur athletes and distinguishes this study within the field.

Dirichlet problem for semilinear partial integro-differential equations: the method of orthogonal projection

AbstractWe study the Dirichlet problem for semilinear equations on general open sets with measure data on the right-hand side and irregular boundary data. For this purpose we develop the classical method of orthogonal projection. We treat in a unified form equations with operators belonging to the broad class of integro-differential operators associated with symmetric regular Dirichlet forms.

Experimental measurement of track irregularities in EMS maglev systems using the mid-chord offset method and inverse filtering technology

Track irregularities are the main factors causing changes in suspension stability and vibrations in EMS (Electro-magnetic Suspension) maglev trains. Due to the scarcity of actual measurement data on EMS maglev track irregularities, this paper utilizes the principles of the mid-chord offset method to develop a track irregularity detector with a 1-m test chord length. Tests were conducted on the maglev line in Qingyuan, Guangdong. Based on the “using small to predict large” algorithm, data simulating a 3-m test chord length were output. The experimental data were processed to remove trend items and outliers, and an inverse filter was designed to counteract the amplitude gain error of the mid-chord offset method, obtaining accurate data on vertical and lateral track irregularities. The results show that the amplitude and power spectral density (PSD) fluctuations of track irregularities on the left and right rails of the measured line section are similar, with height irregularities of 2.5–3 mm and alignment irregularities of 1–1.5 mm. Periodic wavelength components related to the longitudinal spacing of sleepers, rail length, and beam span, such as 1.2 m, 6.5 m, 12.5 m, and 25 m, are evident, highlighting the need for enhanced inspection and maintenance of these structural components in engineering projects.

Enhancing Predictive Maintenance Through Detection of Unrecorded Track Work

Predictive maintenance can help infrastructure managers to reduce costs and improve railway availability while ensuring safety. However, its accuracy depends on reliable data from various sources, especially track measurement data. When analysing track data over time, historical maintenance actions must be considered, as otherwise the interpretation of the data would be misleading. This research aims to address inconsistencies in recorded maintenance data by detecting unrecorded track works through track geometry evaluations. The main goal is to provide the foundations for accurate descriptions of track behaviour, supporting the implementation of effective predictive maintenance regimes. As part of the research, three different approaches are analysed and evaluated, whereby two of them are based on cross-sectional analyses and the third one detects track works in longitudinal track dimension. The results show that the CRAB algorithm produces the most statistically significant results. Conversely, the cumulative track geometry-based algorithm provides a homogeneous representation of past maintenance work and a result that is statistically only marginally inferior. Consequently, these two methods are best suited to build the foundation for making accurate cross-sectional conclusions about track geometry behaviour. This allows for the verification and enhancement of existing maintenance databases.

Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method

The paper considers the neural network application to detect microstructure defects in dissimilar welded joints using the acoustic emission (AE) method. The peculiarity of the proposed approach is that defect detection is carried out taking into account a priori information about the properties of the AE source and the acoustic waveguide parameters of the testing structure. Industrial process pipelines with dissimilar welded joints were studied as the testing object, and diffusion interlayers formed in fusion zones of welded joints were considered microstructure defects. The simulation of AE signals was carried out using a hybrid method: the signal waveform was determined based on a finite element model, while the amplitudes of AE hits were determined based on a physical experiment on mechanical testing of dissimilar welded joints. Measurement data from industrial process pipelines were used as noise realizations. As a result, a data sample was formed that considered the parameters of the AE source and the parameters of the acoustic waveguide with realistic noise parameters and a signal-to-noise ratio. The proposed method allows for a more accurate determination of the waveform, spectrum, and amplitude parameters of the AE signal. Greater certainty in the useful signal parameters allows for achieving a more accurate and reliable classification result. When using a backpropagation neural network, a percentage of correct classification of more than 90% was obtained for a data set in which the signal-to-noise ratio was less than (−5 dB) in 90% of cases.

Filtered partial differential equations: a robust surrogate constraint in physics-informed deep learning framework

Embedding physical knowledge into neural network (NN) training has been a hot topic. However, when facing the complex real world, most of the existing methods still strongly rely on the quantity and quality of observation data. Furthermore, the NNs often struggle to converge when the solution to the real equation is very complex. Inspired by large eddy simulation in computational fluid dynamics, we propose an improved method based on filtering. We analysed the causes of the difficulties in physics-informed machine learning, and proposed a surrogate constraint (filtered partial differential equation, FPDE) of the original physical equations to reduce the influence of noisy and sparse observation data. In the noise and sparsity experiment, the proposed FPDE models (which are optimized by FPDE constraints) have better robustness than the conventional PDE models. Experiments demonstrate that the FPDE model can obtain the same quality solution with 100 % higher noise and 12 % quantity of observation data of the baseline. Besides, two groups of real measurement data are used to show the FPDE improvements in real cases. The final results show that the FPDE still gives more physically reasonable solutions when facing the incomplete equation problem and the extremely sparse and high-noise conditions. The proposed FPDE constraint is helpful for merging real-world experimental data into physics-informed training, and it works effectively in two real-world experiments: simulating cell movement in scratches and blood velocity in vessels.

Study on intelligent recognition of urban road subgrade defect based on deep learning.

China's operational highway subgrades exhibit a trend of diversifying types and an increasing number of defects, leading to more frequent urban road safety incidents. This paper starts from the non-destructive testing of urban road subgrade defects using geological radar, aiming to achieve intelligent identification of subgrade pathologies with geological radar. The GprMax forward simulation software is used to establish multi-layer composite structural models of the subgrade, studying the characteristics of geological radar images for different types of subgrade diseases. Based on the forward simulation images of geological radar for subgrade defects and field measurement data, a geological radar subgrade defect image database is established. The Faster R-CNN deep learning algorithm is applied to achieve target detection, recognition, and classification of subgrade defect images. By comparing the loss value, total number of identified regions, and recognition accuracy as metrics, the study compares four improved versions of the Faster R-CNN algorithm. The results indicate that the faster_rcnn_inception_v2 version is more suitable for the intelligent identification of non-destructive testing of urban road subgrade defects.

Data analysis software for medical wearables

Abstract. The medical wearable device data analysis software of this product analyzes the measurement data through the data connection with various types of medical wearable devices, compares the standard values of various data of each age group, and a number of data that do not meet the standard values or a certain item that does not meet their daily average value for many days will be identified as an abnormal situation, and the user will be reminded to do a personalized physical examination, and the software will recommend a suitable time for the user to conduct a personalized physical examination according to the users location. And through the introduction of blockchain technology, the safe storage and sharing of medical wearable device data is realized while protecting user privacy. In addition, the medical wearable device data analysis software is built into a social health management platform, where users can share and exchange health data, experiences and recommendations. By socializing the app, you can increase user engagement and loyalty, and attract more users to use the app.

Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data

Abstract. Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot effectively be achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range light detection and ranging (lidar) data, which enables the measurement of individual tree heights in forest plots with high precision. Apart from lidar data, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and synthetic aperture radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining the basal area of individual tree within plots using uncrewed aerial vehicle (UAV) lidar data, this study calculated the weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at a large spatial scale, we also developed a machine-learning-based mixed-effects (MLME) model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 and 13.3 m with standard deviations of 2.9 and 3.3 m, respectively. The accuracy of mapped products was validated utilizing lidar and field measurement data. The correlation coefficient (r) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while the root mean square error (RMSE) ranged from 2.6 to 4.1 and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024).

ANALYSIS OF THE CHARACTERISTICS OF AUTOMOTIVE MASS AIR FLOW SENSORS

An analysis of the characteristics of automotive mass air flow sensors is made. A test system has been developed to study the sensors. The measurement data is processed with MATLAB using curve fitting.