Analysis Of Measured Data Research Articles

Microseismic Electronic Fencing for Monitoring of Transboundary Mining in Mines

Mine transboundary mining has been occurring frequently in recent years, and this illegal behavior has brought great potential danger to mine safety while also causing greater losses of state-owned assets. However, the current method of monitoring transboundary mining is still mainly based on underground verification by supervisors, which is far from meeting the demand for supervision. Microseismic monitoring technology is effective for monitoring transboundary mining due to its ability to locate vibration signals. For mine transboundary mining monitoring, this paper proposes a microseismic electronic fence method focusing on mine boundary locating, which differs from the routine microseismic monitoring used in mining operations. This method focuses its key monitoring area on the mine boundary. The deployment mode, number of sensors, and localization theory are analyzed, and numerical simulation and field measurement data analysis results show that the microseismic electronic fence method can achieve a localization accuracy of 15–20 m for underground microseismic events in the vicinity of mine boundaries, which can be effectively applied to the monitoring of transboundary mining activities.

Research on Precise Attitude Measurement Technology for Satellite Extension Booms Based on the Star Tracker

This paper reports the successful application of a self-developed, miniaturized, low-power nano-star tracker for precise attitude measurement of a 5-m-long satellite extension boom. Such extension booms are widely used in space science missions to extend and support payloads like magnetometers. The nano-star tracker, based on a CMOS image sensor, weighs 150 g (including the baffle), has a total power consumption of approximately 0.85 W, and achieves a pointing accuracy of about 5 arcseconds. It is paired with a low-cost, commercial lens and utilizes automated calibration techniques for measurement correction of the collected data. This system has been successfully applied to the precise attitude measurement of the 5-m magnetometer boom on the Chinese Advanced Space Technology Demonstration Satellite (SATech-01). Analysis of the in-orbit measurement data shows that within shadowed regions, the extension boom remains stable relative to the satellite, with a standard deviation of 30′′ (1σ). The average Euler angles for the “X-Y-Z” rotation sequence from the extension boom to the satellite are [−89.49°, 0.08°, 90.11°]. In the transition zone from shadow to sunlight, influenced by vibrations and thermal factors during satellite attitude adjustments, the maximum angular fluctuation of the extension boom relative to the satellite is approximately ±2°. These data and the accuracy of the measurements can effectively correct magnetic field vector measurements.

A Review of the Application of the Laser-Light Backscattering Imaging Technique to Agricultural Products

Growing concerns about food safety and waste have increased consumer demand for high-quality agricultural products, particularly at the postharvest stage. This demand has prompted the development of non-destructive methods to assess or inspect the internal quality of fruits and vegetables. The backscattering imaging technique, also known as diffuse reflectance imaging, is considered a highly promising approach. Numerous studies have focused on practical applications, using laser light at selected wavelengths to develop quick multispectral methods. Due to the rapid interaction of photons with biological tissue, together with the highly computational performance of machine vision, backscattering imaging can offer a valuable alternative to traditional methods. Its primary benefits include quick measurements without chemical sample preparation, easy integration with high-throughput automatic quality control, and reduced waste, since this non-destructive technique does not damage samples. This review presents a comprehensive overview of backscattering imaging, including the measurement geometry, data analysis, and design considerations for vision systems. Additionally, it explores this technique’s advantages, challenges, and accuracy, as demonstrated using various case studies.

An action-oriented participatory research methodology involving residents, elected representatives and researchers: a case study in Rezé, France

The management of sound environments is a major challenge for the quality of life of residents (a definition here including city users). Local authorities have regulatory tools at their disposal to manage urban noise pollution, but these are sometimes far removed from the residents' perceptions. The aim of the SonoRezé project is to build on a citizen science approach, based on a triptych of "Local authorities - Residents - Researchers", working together on the diagnosis and management of urban sound environments in the city of Rezé, in Nantes metropolitan area. The approach consists in participatory noise measurements using the NoiseCapture mobile application, and the definition and implementation of actions jointly developed by residents, elected representatives and researchers, with the aim of improving the city's sound environments. The article will present the interdisciplinary and multi-stakeholder methodology involved. Then, two initiated actions will be outlined and discussed: (i) a scientific mediation initiative in schools focusing on the development of perceptual sound maps, (ii) an action on aircraft noise, precising the methodology involving the three parties used to construct the action, as well as some of the results produced, namely a sensitive map, analyses of measurement data and the production of alternative indicators.

Continuous Biosensor Based on Particle Motion: How Does the Concentration Measurement Precision Depend on Time Scale?

Continuous biosensors measure concentration-time profiles of biomolecular substances in order to allow for comparisons of measurement data over long periods of time. To make meaningful comparisons of time-dependent data, it is essential to understand how measurement imprecision depends on the time interval between two evaluation points, as the applicable imprecision determines the significance of measured concentration differences. Here, we define a set of measurement imprecisions that relate to different sources of variation and different time scales, ranging from minutes to weeks, and study these using statistical analyses of measurement data. The methodology is exemplified for Biosensing by Particle Motion (BPM), a continuous, affinity-based sensing technology with single-particle and single-molecule resolution. The studied BPM sensor measures specific small molecules (glycoalkaloids) in an industrial food matrix (potato fruit juice). Measurements were performed over several months at two different locations, on nearly 50 sensor cartridges with in total more than 1000 fluid injections. Statistical analyses of the measured signals and concentrations show that the relative residuals are normally distributed, allowing extraction and comparisons of the proposed imprecision parameters. The results indicate that sensor noise is the most important source of variation followed by sample pretreatment. Variations caused by fluidic transport, changes of the sensor during use (drift), and variations due to different sensor cartridges and cartridge replacements appear to be small. The imprecision due to sensor noise is recorded over few-minute time scales and is attributed to stochastic fluctuations of the single-molecule measurement principle, false-positive signals in the signal processing, and nonspecific interactions. The developed methodology elucidates both time-dependent and time-independent factors in the measurement imprecision, providing essential knowledge for interpreting concentration-time profiles as well as for further development of continuous biosensing technologies.

Analiza pełzania w przetwornikach momentu siły dla turbin wiatrowych

A crucial aspect to consider when assessing the effectiveness of wind turbines is the phenomenon of torque creep, both under load and without load. This phenomenon significantly affects the proper functioning of torque transducers, thus necessitating the utilization of suitable algorithms for analysing measurement data. The least squares method is well-suited for this type of analysis. Linear regression was employed to study the creep trend, while a third-degree non-linear polynomial curve enabled a more precise visualization of creep, yielding valuable insights.

Interfacial property determination from dynamic pendant-drop characterizations.

The properties of the interface between materials have practical implications in various fields, encompassing capillary action, foam and emulsion stability, adhesion properties of materials and mass and heat transfer processes. Studying the dynamics of interfaces is also fundamental for understanding intermolecular interactions, change of molecular conformations and molecular aggregations. Pendant-drop tensiometry and its extension, the oscillating drop method, are simple, versatile methods used to measure surface tension, interfacial tension and interfacial rheological properties. These methods can, however, generate unreliable results because of inadequate material preparation, an incorrect calibration method, inappropriate selection of data for analysis, neglect of optical influences or operating the system outside the linear viscoelastic regime. In addition, many studies fail to report accurate uncertainties. This protocol addresses all these critical points and provides detailed descriptions of some operation tips relating to purifying methods for different kinds of material, the time frame for analyzing measurement data, the correction method for optical effects, implementation of the oscillating method with a common programmable pump and remedies for some common problems encountered during the measurement. Examples of interfacial tension measurements for two- and three-phase systems, as well as interfacial dilational modulus measurements for N2 and surfactant solutions, are provided to illustrate procedural details and results. A single measurement takes minutes to hours to complete, while the entire protocol, including the leak test, cleaning, repeated measurements and data analysis, may take several days.

Wavelet denoising analysis on vacuum-process monitoring signals of aerospace vacuum vessel structures

Abstract The monitoring of micro-defects or external actions induced vacuum degradation in aerospace vacuum vessels is an important challenge. A vacuum-process monitoring method based on quasi-distributed fiber Bragg grating (FBG) sensing technology is proposed. Due to the influence of environmental noise and vacuum pump operation noise, the raw signals measured by FBG sensors contain a large amount of noises, which affects the measurement accuracy and data analysis. Therefore, the wavelet threshold (WT) denoising method is proposed to analyze the influence of noises on the monitoring signals measured by two different kinds of FBG sensors. The evaluation of the monitoring signals after denoising indicates that the proposed method can effectively remove the noise and significantly improve signal quality. The highest signal-to-noise ratio of the processed signals can reach 37.61 dB and the mean square error is 3.68 × 10−7, while retaining the key features of the original signal. The proposed WT denoising method demonstrates better performance and feasibility compared with moving average filtering and Kalman filtering methods. The study provides critical supports for improving the performance and reliability of the vacuum vessel monitoring system.

Scientific preparation for JRT: Wind pressure prediction model for large radio telescope based on real data from multi-sensors

Jingdong 120-meter radio telescope (JRT) is poised to become the world's largest single-aperture fully steerable medium-low frequency radio telescope. However, like other large-aperture radio telescopes, the JRT is vulnerable to wind loads, which can cause structural deformation and pointing errors. Addressing this challenge requires the ability to predict dynamic winds in real-time. This study developed a wind pressure preprocessing and prediction model using sensor data collected from the Kunming 40-meter radio telescope (KRT), enabling real-time prediction of wind pressure on the telescope. The model employs adaptive noise and Variational Mode Decomposition (VMD) techniques to eliminate random noise from the original wind pressure data. Subsequently, wind pressure predictions are made using a Bidirectional Long Short-term Memory (BiLSTM) model. By conducting predictions under various stabilization conditions and conducting a thorough analysis of measurement data from five sensors, the study has achieved impressive results in predicting wind pressure on the KRT reflector surface. The proposed model demonstrates the lowest MAE, RMSE, and MAPE, while achieving the highest R2 across various data sets. Where the average R2 of the proposed model is 0.9392 at 45° pitch angle attitude and the RMSE, MAE and MAPE values are 1.4923, 1.2377 and 1.82% respectively. This model helps wind load monitoring of real-time wind pressure monitoring of the telescope surface, to study the effects of wind load on pointing accuracy. By adjusting the control parameters to reduce wind load interference, to ensure the high-precision work of a large radio telescope, such as JRT.

Wasserstein principal component analysis for circular measures

We consider the 2-Wasserstein space of probability measures supported on the unit-circle, and propose a framework for Principal Component Analysis (PCA) for data living in such a space. We build on a detailed investigation of the optimal transportation problem for measures on the unit-circle which might be of independent interest. In particular, building on previously obtained results, we derive an expression for optimal transport maps in (almost) closed form and propose an alternative definition of the tangent space at an absolutely continuous probability measure, together with fundamental characterizations of the associated exponential and logarithmic maps. PCA is performed by mapping data on the tangent space at the Wasserstein barycentre, which we approximate via an iterative scheme, and for which we establish a sufficient a posteriori condition to assess its convergence. Our methodology is illustrated on several simulated scenarios and a real data analysis of measurements of optical nerve thickness.

Analysis of ultrasonic measurement data from medical models

This study's primary goal is to improve the accuracy and efficiency of acoustic wave propagation simulations using circular probe models in ultrasonography. This research aims to develop a detailed understanding of how variations in boundary conditions and wave characteristics influence the fidelity of ultrasound imaging. A range of simulation techniques were employed, focusing on non-dispersive and dispersive scenarios, to model acoustic wave behavior comprehensively. The study utilized Gaussian beamforming techniques and improved kernel functions to refine the resolution and decrease computational overhead. Various scenarios were simulated to analyze the impact of wave scattering and dispersion on imaging outcomes. The simulations demonstrated significant improvements in image resolution and accuracy. The refined methods allowed for more apparent distinctions in wave behavior under different boundary conditions, providing deeper insights into wave propagation dynamics. The results confirmed that controlling dispersion and scattering is critical for enhancing imaging quality. This research contributes to the field of ultrasonic imaging by presenting advanced simulation methods that offer more accurate and efficient imaging solutions. The study provides valuable insights into optimizing ultrasonic probes and imaging techniques by focusing on the impact of wave characteristics and boundary conditions. The findings have significant implications for medical diagnostics and material characterization, suggesting potential improvements in ultrasound technology for better patient outcomes and more precise material assessments.

Effects of batroxobin on the antithrombotic system in patients with cerebral venous thrombosis: Clues to mechanisms.

More evidence supports the benefits of batroxobin combined with anticoagulation in correcting acute cerebral venous thrombosis (CVT). The dynamic fluctuations of peripheral blood platelets, fibrinolysis, and coagulation biomarkers during this therapy were analyzed. We investigated batroxobin's effects on the antithrombotic system under two regimens. The pretreatment group included patients on anticoagulants for at least 1 week before starting batroxobin. The simultaneous treatment group began both treatments upon admission. The control group received only anticoagulation. Batroxobin was given on alternate days at doses of 10BU, 5BU, and 5BU, totaling three doses. Anticoagulation was continuous. Baseline data were T0; the next day after each batroxobin dose was T1, T2, and T3. Data from these four time points was analyzed. The time-point paired sample T-test results of the pretreatment group [n = 60; mean age (SD), 43.3(16.5); 38 (63.35%) women] showed that batroxobin significantly inhibited ADP-induced platelet aggregation rate (T1-T0: p = 0.015; T2-T0: p = 0.025; T3-T0: p = 0.013), decreased fibrinogen level (T1-T0: p < 0.001; T2-T0: p < 0.001; T3-T0: p < 0.001), and increased D-dimer (T1-T0:p < 0.001; T2-T0: p < 0.001; T3-T0: p < 0.001), TT (T1-T0:p = 0.046; T2-T0: p = 0.003; T3-T0: p < 0.001), and APTT (T1-T0:p = 0.021; T2-T0: p = 0.012; T3-T0: p = 0.026). Compared to the control group, the simultaneous treatment group showed significantly higher TT (T2: p = 0.002; T3: p = 0.004) and D-dimer (T1: p < 0.001; T2: p < 0.001; T3: p < 0.001) values, while fibrinogen (T2: p < 0.001; T3: p < 0.001) levels were significantly lower. Using batroxobin can alleviate the amplitude of changes in coagulation indicators other than TT caused by anticoagulants. The above conclusions are consistent with the results of repeated measurement data analysis. Batroxobin can significantly inhibit ADP-induced platelet aggregation rate, increase D-dimer, decrease fibrinogen, and prolong TT and APTT in the presence of anticoagulant agents. Using batroxobin can reduce the amplitude of changes in coagulation indicators caused by anticoagulants. These results reveal the potential mechanism of batroxobin combined with anticoagulation in the safe and effective treatment of CVT.

Label-free surface-enhanced Raman spectroscopy analysis method for liquid biopsy and its application in serum-based lung cancer diagnosis and classification

Liquid biopsy offers promise for the diagnosis of malignant tumors or their precancerous lesions at the subclinical stage, which is crucial for improving the survival rate of cancer. Label-free surface-enhanced Raman spectroscopy (SERS) has become an emerging detection method in liquid biopsy. The accuracy of SERS-based diagnosis relies on both precise measurement and effective data analysis. In our previous study, a large laser spot SERS method was proposed for the precise detection of complex liquids. In this work, a logical Raman data analysis method was developed, to address the lack of Raman characteristics and biological significance in current SERS-based diagnosis data analysis. The measured Raman data is classified to obtain a weighting matrix of classification criteria, and this matrix is used to establish a material correspondence with the Raman spectra, extract key Raman peaks, determine corresponding biomolecules and biological processes, thus enabling a logical analysis of the data. This method has been used to analyze the SERS data of lung cancer, it can not only show exceptional performance in diagnosing lung cancer and differentiating small cell lung cancer, but also obtain the Raman diagnostic and classification criteria with clear numerical values and reasonable biological significance, demonstrating its great potential value in precise biosensing.

Real Driving Emissions—Event Detection for Efficient Emission Calibration

The systematic analysis of measurement data allows a large amount of information to be obtained from existing measurements in a short period of time. Especially in vehicle development, many measurements are performed, and large amounts of data are collected in the process of emission calibration. With the introduction of Real Driving Emissions Tests, the need for targeted analysis for efficient and robust calibration of a vehicle has further increased. With countless possible test scenarios, test-by-test analysis is no longer possible with the current state-of-the-art in calibration, as it takes too much time and can disregard relevant data when analyzed manually. In this article, therefore, a methodology is presented that automatically analyzes exhaust measurement data in the context of emission calibration and identifies emission-related critical sequences. For this purpose, moving analyzing windows are used, which evaluate the exhaust emissions in each sample of the measurement. The detected events are stored in tabular form and are particularly suitable for condensing the collected measurement data to a required amount for optimization purposes. It is shown how different window settings influence the amount and duration of detected events. With the example used, a total amount of 454 events can be identified from 60 measurements, reducing 184,623 s of measurements to a relevant amount of 12,823 s.

Dynamic inspection data-based analysis of rail base metal irregularities for engineering failure prevention

With the continual increase in train speeds, the requirement for track smoothness has become increasingly critical. Within the railway system of China, the prevalence of short-wave defects, attributed to the rail base metal irregularity, adversely affects the stability of train movement and constitutes a considerable safety risk. However, research into the rail base metal irregularity remains markedly scant, with a conspicuous absence of effective evaluation and identification methodologies. This deficiency critically restricts the capability to mitigate short-wave disturbances arising from such irregularities, negatively impacting the safety and operational stability of railway systems. Therefore, this paper proposes a method for evaluating and identifying the rail base metal irregularity based on dynamic inspection data. Initially, this paper conducts a comprehensive assessment of rail base metal irregularities by analysing wavelength characteristics, frequency amplitude features, and dynamic responses of vehicles. Subsequently, this paper utilises the Synchrosqueezed Wavelet Transform (SWT) technique, which refines the frequency resolution by compressing frequency components within the frequency domain, eliminating cross-frequency interference, and minimising energy dispersion across scale domains, thus accurately delineating the time–frequency attributes of rail base metal irregularity. Building upon the foundation provided by SWT, this paper introduces an identification method employing the Summed Wavelet Power (SWP) across various frequency bands. This approach evaluates the energy variations of different wavelength components relative to changes in mileage, facilitating the identification of track sections with rail base metal irregularity. The efficacy of the proposed methodology is corroborated through the analysis of actual measurement data. The results show that the SWT and SWP techniques can effectively identify rail base metal irregularity, thereby offering substantial support for enhancing the safety and comfort of railway operations.

Acoustic emission monitoring for industrial pressure vessels and infrastructure

Acoustic emission (AE) is a phenomenon that occurs in materials that are exposed to stress. Defects in the material, such as cracks can grow and release energy in the form of a mechanical wave that propagates through the material. With the help of piezoelectric sensors, the mechanical wave is converted into an electrical signal. The signals are recorded by the measuring system and are available for further processing. Acoustic emission as a method that records and processes the incoming signals in real time is therefore very suitable for 24/7 long-term structural health monitoring. With new approaches in data processing, acoustic emission can be moved from the classic periodic test approach to an online monitoring process. The in-service monitoring brings measuring challenges with it, like application on hot surfaces and inside explosion hazard areas. New developments in hardware and software provide tailor-made solutions for a variety of monitoring tasks. The use of artificial intelligence algorithms helps to improve the analysis of measurement data and guarantees safe operation of pressure equipment and infrastructure around-the-clock. Some examples of continuous monitoring are shown on pressure vessels from the industry sector and on infrastructure buildings e.g., steel bridges. Also, a new lean system approach in structural health monitoring with acoustic emission is presented.

Development of physical test norms for early age Pencak Silat

Background and Study Aim. The evaluation serves as a cornerstone in the continuous improvement of sports, significantly relying on the analysis of measurement data. The primary objective of this research is to develop tailored norms for physical tests specifically designed for early-age Pencak Silat practitioners. Material and Methods. The development of the test consisted of several key stages: design of the instrument, testing of the instrument, and finally, assembly of the instrument. The study included a total of 210 participants, with a breakdown of 111 male and 109 female martial artists. The techniques for data collection involved a variety of tests and measurements. The instruments used in this research included: sit-and-reach test for assessing flexibility, 30-meter sprint for speed evaluation, side step test for agility, sit-ups for abdominal strength, push-ups for arm and chest strength, wall sit test for leg muscle strength, and beep test for cardiovascular endurance. Data analysis was performed using a technique that grouped values into five standard categories, aided by the use of Microsoft Excel. Results. The findings of the study led to the establishment of norms for each component of the physical tests tailored to early-age Pencak Silat practitioners. These components have been classified into five distinct levels: very poor, poor, average, good, and excellent. Conclusions. The outcomes of this research facilitate the development of specific norms for each test item, incorporating weighting for every physical test component. These weightings have been meticulously adjusted in accordance with the primary energy systems utilized in the sport of Pencak Silat.

Experimental study on the dynamic characteristics of bi-block ballastless track under the combined action of vehicle load and temperature gradient

The unit ballastless track structure, persistently exposed to environmental conditions, experiences significant vertical temperature gradients, leading to warping deformation of the track bed and impacting track smoothness. This study investigates the relationship between temperature gradients and warping deformation of the track bed through the analysis of on-site measurement data combined with a dynamic simulation model. It also examines how temperature gradients influence the dynamic response and the interlayer contact area of the ballastless track. Findings reveal that vertical displacement of the track bed follows a diurnal cycle and shows a positive correlation with temperature gradients, with pronounced differences between the slab's corners and center. Under a positive temperature gradient, the vertical displacement of the track bed is high in the middle and low at the ends, with the vehicle load causing longitudinal tensile strain in the slab’s side surface center. Conversely, under a negative temperature gradient, the vertical displacement is low in the middle and high at the ends, with the vehicle load inducing longitudinal compressive strain in the slab’s side surface center. Additionally, warping deformation of the track bed leads to incomplete contact with the base plate, creating a local support state and reducing interlayer contact area as the gradient increases. Positive temperature gradients result in an "oval" distribution of interlayer delamination areas, while negative gradients mainly affect the sides and ends. Dynamic loads from trains temporarily counteract warping deformation, briefly increasing the interlayer contact area, but their effectiveness diminishes as temperature gradients rise.

40‐3: Waveform Analysis System for GAN‐Based Anomaly Detection of Coater Pressure in Photolithography

With the advancement of manufacturing equipment and the development of sensing technology, the measurement cycle of data is becoming very short, and the characteristics of data observed at the same time are also diversifying. A Fault Detection and Classification (FDC) system is in operation to collect and analyze measurement data in real time in the display manufacturing factory. However, the anomaly detection function provided by the FDC system is based on a threshold comparison method for data in seconds, so there is a limit to accuracy in processing measurement data in milliseconds. In particular, if the process for one panel is divided into several steps and each of them shows various characteristics, it is difficult to manage because it takes a lot of airlift to set search conditions and thresholds for anomaly detection. To overcome this, a waveform analysis system is implemented to assist the FDC system. This system extracts waveforms from milliseconds of measurement data during the processing time of one panel in the equipment, and diagnoses whether the equipment processing is normal through GAN‐based waveform pattern analysis. Generative models are used in the anomaly detection process in consideration of the manufacturing environment in which the normal data is overwhelmingly larger than the abnormal data. The DCGAN‐based model that is excellent in image processing and the TadGAN‐based model that combines Auto encoder and GAN were implemented and used. In this paper, the anomaly detection performance of models is compared and evaluated for the display photo process Coater Pressure sensor using the waveform analysis system implemented in this way.

Data analysis of the TetraSpar demonstrator measurements

Floating offshore wind turbines can extract energy from deep offshore locations, typically unfit for fixed bottom designs. The complex interaction between the structural behavior of the floating offshore wind turbine and the stochastic site conditions, however, is an active area of research. Characterizing the relationship between the environmental conditions and loads may help design reduced-order models, surrogate models, and physics-based engineering models for floating wind turbines. This study uses data from the TetraSpar prototype equipped with a 3.6 MW Siemens Gamesa wind turbine. One-to-one simulations performed using an aero-servo-hydro-elastic software are included for comparison. Various tools, including linear correlation, mutual information, feature ordering using conditional independence, and sensitivity analysis using a data-driven variogram fit, are used for the assessment. This study is also helpful in validating the engineering model for future global sensitivity analysis using elementary effects or Sobol indices that require a rigid sampling of features and can, therefore, only be calculated with simulation tools. We find a good agreement between the experiments and simulations. The 10-min. damage equivalent loads on the tower show a correlation, particularly with the wind speed statistics and the significant wave height.