Random Errors Research Articles

METHODOLOGY FOR THE APPLICATION OF THE DIGITAL IMAGE CORRELATION (DIC) FOR INVESTIGATING RC BEAMS

The article discusses the improvement of the digital image correlation (DIC) method for analyzing deformations in RC beams, specifically focusing on the importance of zero-strain verification. This step is critical for ensuring high measurement accuracy, as it helps identify and minimize systematic and random errors. Before starting the research, system calibration is conducted, which includes the assessment of background noise and stability that influence the results. The study shows that proper sample preparation, pattern creation, and control of external factors allow for obtaining reliable data. The application of DIC enables remote monitoring of cracks and evaluation of the stress-strain state of structures. It has been established that this method is useful not only for scientific experiments but also in practical engineering, contributing to the increased reliability of structures.

Investigating Tropical Cyclone Warm Core and Boundary Layer Structures with Constellation Observing System for Meteorology, Ionosphere, and Climate 2 Radio Occultation Data

The Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2) collects data covering latitudes primarily between 40 degrees north and south, providing abundant data for tropical cyclone (TC) research. The radio occultation data provide valuable information on the boundary layer. However, quality control of the data within the boundary layer remains a challenging issue. The aim of this study is to obtain a more accurate COSMIC-2 radio occultation (RO) dataset through quality control (QC) and use this dataset to validate warm core structures and explore the planetary boundary layer (PBL) structures of TCs. In this study, COSMIC-2 data are used to analyze the distribution of the relative local spectral width (LSW) and the confidence parameter characterizing the random error of the bending angle. An LSW less than 20% is set as a data QC threshold, and the warm core and PBL composite structures of TCs at three intensities in the Northwest Pacific Ocean are investigated. We reproduce the warm core intensity and warm core height characteristics of TCs. In the radial direction of the typhoon eyewall, the impact height of the PBL increases from 3.45 km to 4 km, with the tropopause ranging from 160 hPa to 100 hPa. At the bottom of the troposphere, the variations in the positive and negative bias between the RO-detected and background field bending angles correspond well to the PBL heights, and the variations in the positive bias between the RO-detected and background field refractivity reach 14%. This research provides an effective QC method and reveals that the bending angle is sensitive to the PBL height.

Assessing the correlation between Gamma passing rate and clinical dosimetric variations in breast cancer IMRT plans with multi-leaf collimator errors: perspectives from the ArcCHECK QA system.

This study aimed to comprehensively investigate the influence of multi-leaf collimator (MLC) position errors on both the clinical absolute dose distribution and Gamma passing rate (%GP) in intensity-modulated radiation therapy (IMRT) plans for breast cancer. Additionally, the correlation between %GP and the clinical absolute dose relative difference (%DE) caused by MLC position errors was analysed. Ten IMRT plans for breast cancer were randomly selected. Systematic and random MLC position errors were introduced into DICOM files representing the investigated treatment plans by modifying the plan files and adjusting the MLC positions. Systematic errors were categorized as MLC opening errors, closing errors, and shift errors. The %DE in the tumour planning target volume (PTV) and organs at risk (OARs) caused by MLC errors were statistically analyzed using dose-volume histogram (DVH) analysis. The ArcCHECK quality assurance (QA) system was used to detect the %GP differences between baseline plans and plans with MLC errors. The correlation between %GP and %DE was obtained using linear regression methods. The results of this study indicate that MLC opening and closing errors have a significant impact on %DE and %GP in IMRT plans for breast cancer. Opening and closing errors can be detected at a gamma level of 3%/2mm, if error values are greater than or equal to 0.5mm, and %GP can predict DVH dosimetric changes caused by MLC opening and closing errors. It is concluded that DVH-based verification of IMRT plans can serve as an adjunct method to Gamma analysis to improve QA accuracy for breast cancer cases. Additionally, it is concluded that greater attention should be given to MLC leaf opening and closing errors in clinical practice.

Q-Learning-Incorporated Robust Relevance Vector Machine for Remaining Useful Life Prediction

Accurate and reliable remaining useful life (RUL) prediction is crucial for improving equipment reliability and safety, realizing predictive maintenance. The relevance vector machine (RVM) method is commonly utilized for RUL prediction, profiting from its sparse property under a Bayesian framework. However, the RVM faces the issue of poor robustness, which is mainly manifested as poor prediction accuracy and difficulty in fitting when the predicted data fluctuate greatly. This is due to weights and random errors following Gaussian distributions, which are highly sensitive to outliers. Also, the traditional model training process heavily relies on an additional feature extraction process, which suffers from the problem of effective data loss as well as the risk of overfitting. Thus, a robust regression framework against outliers is developed by incorporating t-distribution into the RVM. And a Q-learning (QL) algorithm is embedded into the constructed robust RVM model to replace the feature extraction process. In addition, this paper firstly predicts the degradation trend of RUL to enhance the accuracy and interpretability of RUL prediction. Finally, a comparative experiment on the performance degradation of capacitors in the traction system is designed, and the root mean square errors for the QL-RRVM, QL-RVM, RRVM, and RVM models are obtained as 0.751, 8.599, 38.316, and 41.892, respectively. The experimental results confirm the superiority of the proposed method.

A Machine-Learning Approach to Mitigate Ground Clutter Effects in the GPM Combined Radar-Radiometer Algorithm (CORRA) Precipitation Estimates

Abstract In this study, a machine-learning based methodology is developed to mitigate the effects of ground clutter on precipitation estimates from the Global Precipitation Mission Combined Radar-Radiometer Algorithm. Ground clutter can corrupt and obscure precipitation echo in radar observations, leading to inaccuracies in precipitation estimates. To improve upon previous work, this study introduces a general machine learning (ML) approach that enables a systematic investigation and a better understanding of uncertainties in clutter mitigation. To allow for a less restrictive exploration of conditional relations between precipitation above the lowest clutter-free bin and surface precipitation, reflectivity observations above the clutter are included in a fixed-size set of predictors along with the precipitation type, surface type, and freezing level to estimate surface precipitation rates, and several ML-based estimation methods are investigated. A Neural Network Model (NN) is ultimately identified as the best candidate for systematic evaluations, as it is computationally fast to apply while effective in applications. The NN provides unbiased estimates; however, it does not significantly outperform a simple bias correction approach in reducing random errors in the estimates. The similar performance of other ML approaches suggests that the NN’s limited improvement beyond bias removal is due to indeterminacies in the data rather than limitations in the ML approach itself.

Analysis of the accuracy of the inverse marching method used to determine thermal stresses in cylindrical pressure components

This paper analyses the inverse marching method used to determine the thermal stresses on the inner surface of a thick-walled cylindrical element not weakened by holes in the transient state. The heat conduction problem was considered one-dimensional, i.e. it was assumed that heat is transferred only in the radial direction. The method is based on measuring the temperature inside the pipeline wall at a single point and assuming that the pipeline is thermally insulated. The paper undertook an evaluation of the influence of the measuring point's distance from the inner surface, the number of control volumes into which the inverted area was divided and the length of the time step on the accuracy of the calculated temper-ature, heat transfer coefficient and thermal stresses on the inner surface of the pressure element. Verification was performed by comparing the calculation results obtained from the direct analytical method perturbed by random errors with those obtained from the numerical inverse step method.

Statistical analysis of the matrix method for measuring the radar characteristics of objects

The matrix method for measuring the radar characteristics of objects is examined. The method involves irradiating the object with a system of independent test fields, receiving the set of scattered fields, and performing posterior processing of the obtained results based on the superposition principle to determine the required radar characteristics of the object. An analysis of the information capabilities of the matrix method for measuring linear and nonlinear radar characteristics of objects is conducted, considering random errors in the registration of the amplitude and phase of the test signals. The variances of the desired linear and nonlinear radar characteristics of objects are determined, assuming a high signal-to-noise ratio in each test signal reception channel and maximum likelihood estimation of the amplitude and phase of the test signals. It is established that the matrix method for measuring radar characteristics of objects is functional under real conditions involving noise and interference.

Integrating Copernicus LMS with ground measurements data for leaf area index and biomass assessment for grasslands in Poland and Norway

ABSTRACT The integration of satellite data from the Copernicus Land Monitoring Service (CLMS) with ground-based measurements represents a pioneering approach to the assessment of leaf area index (LAI) and in situ biomass at grasslands in Poland and Norway. The aim of this study was to develop a method for assessing grass growth conditions and predicting biomass yield based on Sentinel-2 data and CLMS products. LAI values derived from CLMS were compared with in-situ measurements on grassland plots in the two regions of Podlaskie (PL84) and Wielkopolskie (PL41). Small random statistical errors observed in the differences represent a significant opportunity to use Copernicus data to develop a relationship model for biomass prediction. The NDII index calculated using Sentinel2 was considered important for biomass assessment in the humid areas of Norway. This relational biomass prediction model could provide valuable information to farmers, improving their ability to manage grasslands effectively. As a result of the research, relational models were developed has been developed to predict grassland fresh biomass yield with an R 2 accuracy of 0.72 for the first cut, 0.81 for the second cut and 0.91 for the third cut. This allows farmers to effectively manage and monitor grassland throughout the growing season.

Enhancing positioning accuracy in adjuvant radiotherapy for left breast cancer using cervical-thoracic integrated bracket combined with deep inspiration breath holding.

This study aimed to investigate the accuracy of three fixation methods in patients with left breast cancer receiving whole breast radiotherapy: conventional breast bracket (BB), breast bracket combined with deep inspiration breath holding (DIBH), and cervical-thoracic integrated bracket (CTIB) combined with DIBH. From January 2023 to September 2023, 84 patients who underwent left breast cancer radiotherapy with supraclavicular radiation after conservative surgery were included in this study, of which 25 patients were fixed by conventional BB, 34 patients by BB & DIBH, and 25 patients by CTIB & DIBH. Image registration was conducted around the treatment area, using the sternoclavicular joint and acromioclavicular joint as landmarks. Systematic and random errors were calculated to assess the accuracy of these fixation methods. Compared to the conventional BB group, the CTIB & DIBH group demonstrated significant improvements in accuracy across multiple dimensions, including left-right, superior-posterior, and anterior-posterior directions, as well as rotational errors in the sagittal and coronal planes. The CTIB & DIBH group showed a significant reduction of setup error in the anterior-posterior direction compared to the BB & DIBH group. The displacement of the acromioclavicular joint varied, with the CTIB & DIBH method showing more favorable outcomes. DIBH method exhibited lower setup errors and more effective fixation of the acromioclavicular joint, especially when combined with CTIB, making it a recommended fixation method in adjuvant radiotherapy following breast-conserving surgery.

Optimization and Numerical Verification of Microseismic Monitoring Sensor Network in Underground Mining: A Case Study

A scientific and reasonable microseismic monitoring sensor network is crucial for the prevention and control of rockmass instability disasters. In this study, three feasible sensor network layout schemes for the microseismic monitoring of Sanshandao Gold Mine were proposed, comprehensively considering factors such as orebody orientation, tunnel and stope distributions, blasting excavation areas, construction difficulty, and maintenance costs. To evaluate and validate the monitoring effectiveness of the sensor networks, three layers of seismic sources were randomly generated within the network. Four levels of random errors were added to the calculated arrival time data, and the classical Geiger localization algorithm was used for locating validation. The distribution of localization errors within the monitoring area was analyzed. The results indicate that when the arrival time data are accurate or the error is between 0% and 2%, scheme 3 is considered the most suitable layout; when the error of the arrival time data is between 2% and 10%, scheme 2 is considered the optimal layout. These research results can provide important theoretical and technical guidance for the reasonable design of microseismic monitoring systems in similar mines or projects.

Structural damage detection using a sensitivity-based model updating approach with autocorrelation function data

ABSTRACT This study presents a novel sensitivity equation for estimating structural parameters using autocorrelation function (ACF) data of dynamic responses. The proposed method reduces the need for extensive excitation frequencies, making structural parameter identification more practical and cost-effective, especially for large-scale structures. Furthermore, due to the quadratic relationship between ACF and the structural dynamic response, this approach exhibited greater sensitivity to damage than time-history-based methods. The method’s effectiveness was evaluated using simulated data from truss and offshore jacket platform models. Incomplete measurements were addressed by using partially measured structural characteristics without model reduction and data expansion processes. Measurement and modelling errors were simulated by adding uniformly distributed random error into the damaged structure data. The low values of the coefficient of variation indicated the method’s robustness against these errors. The accuracy of the method was confirmed through low root mean square error (RMSE) values and high variance accounted for (VAF) values.

Rotational motion investigation in seismology – remote sensing by an optical fiber seismograph

This paper aims to present data recorded by a three-axial Fiber-Optic Rotational Seismograph (FORS). The laboratory and field tests showed a high correlation coefficient above 99% between two seismographs signals during the external disturbance, with an amplitude ranging from 0.5 mrad/s to 1.2 rad/s. This is achieved by ensuring a 100 ns time synchronization in the systems. At the same time, the Allan Variance analysis was applied to determine the basic parameters of random errors of the presented seismographs. The performed analysis indicates the angular random walk of 35–45 nrad/s/√Hz and a bias instability below 50 nrad/s. FORS offers the widest dynamic range available, at 170 dB, which is crucial during rotational seismology exploration due to its wide range of interest from seismology to engineering applications. Finally, a field research is also presented during an explosion in a closed limestone quarry in the border area.

Modified TRIAD Method for Solving the Problem of a Moving Object Orientation

Currently, it is relevant to create moving objects orientation systems based on the integration of various types of primary information sensors. One way to solve the orientation problem is to use the TRIAD (Tri-Axial Orientation Determination) method which allows determining the matrix of direction cosines between two coordinates. The traditional TRIAD force method is based on the use of two reference vectors – of gravitational and geomagnetic fields, measured by accelerometers and magnetometers, respectively. The disadvantage of this method is – appearance of additional deviations during the accelerated movement of the object and influence of primary information sensors’ random errors. Modified TRIAD method which is based on measurements of three triads of sensors: magnetometers, accelerometers and gyroscopes was proposed in the article. Estimates of acceleration vectors of gravitational and geomagnetic fields were calculated taking into account gyroscope measurements. Then these estimates were combined with the accelerometers’ and magnetometers’ data. The complex gravitational and geomagnetic fields’ accelerations were used to form the direction cosine matrix by the TRIAD method. The suggested modified method can be used to implement free-form moving objects’ orientation systems, since it is 6–8 times more accurate compared to the classic TRIAD method. Attenuation of random sensor errors and disturbances due to object acceleration can be adjusted by use of weigh factors.

Comparison and analysis of denoising method in TBM key tunnelling data

ABSTRACT Environmental vibrations and other factors cause significant random errors in raw data collected during TBM tunnelling. Such errors can reach up to 25% and will severely reduce the performance of data mining and machine learning model. To improve the data quality and find a suitable denoising method for TBM data, this study first proposed two assurance indicators to evaluate the effectiveness of data denoising, such as distortion degree (θ) and denoising magnitude (λ). Then, the effects of the data denoising schemes were compared. The results demonstrate that: (1) The Convlove19 scheme has better performance and is recommended for the denoising processing for TBM data. It can preserve the statistical characteristics of the raw data to the maximum extent possible and can improve the performance of the machine learning model by 15∼25%. (2) “Boring cycle segmentation after denoising strategy” (SAD) has better performance than “Boring cycle segmentation before denoising strategy” (SBD) and is recommended. The findings in this study can help with TBM data cleaning and quality improvement, as well as provide a reference for noise reduction of other data with similar features.

A study of automatic output power control methods for stand-alone photovoltaic power generation systems

Abstract The conventional power generation system’s output power automatic control structure is generally unidirectional, and the control coverage is small, leading to the increase in the random error obtained in the final test. Therefore, the design and research of this article are proposed. According to the current control demand, the generation system shall be stabilized first, and the power compensation shall be calculated. The multi-step mode shall be adopted to improve the control coverage and design the multi-step automatic control structure. According to this, it is constructed, and the automatic control processing is realized using jump balance. The test results show that compared with the traditional DFIG-DRWT system power output automatic control method and the traditional three-phase asynchronous generation system power output automatic control method, the random error finally obtained by the designed independent photovoltaic power station output power automatic control method is relatively effective. This indicates that the application effect and coverage of the designed power automatic control method are high, that controllability has been significantly improved, and that control flexibility has also been strengthened.

Regional Healthcare Resource Allocation and Decision-Making: Evaluating the Effectiveness of the Three-Stage Super-Efficiency DEA Model

This study addresses the challenge of achieving a more rational allocation of medical resources at the regional level, using Guangxi Province, China, as a case study. A three-stage super-efficiency Data Envelopment Analysis (DEA) model is employed to assess and analyze the effectiveness of resource allocation. The research methodology involves identifying input, output, and environmental variable indicators to construct a healthcare resource allocation index system. The indicator data are processed using Excel software. The three-stage super-efficiency DEA model is then applied to evaluate the healthcare system in Guangxi Province, focusing on Pure Technical Efficiency Change (PEC), Scale Efficiency Change (SEC), Efficiency Change (EC), Technological Change (TC), and Total Factor Productivity (TFP). Finally, the Malmquist index method is utilized to measure and dynamically analyze the efficiency of healthcare resource allocation. The study's results show that, from a static perspective, the average comprehensive efficiency is 1.067 before adjustment and 1.054 after adjustment, indicating relatively high overall efficiency in healthcare resource allocation in Guangxi Province. However, environmental factors and random errors have led to an overestimation of healthcare resource allocation efficiency, which the three-stage super-efficiency DEA model effectively corrects. Additionally, the average SEC and PEC values are 0.997 and 0.998, respectively, both below 1. This indicates that both scale efficiency and pure technical efficiency contribute to a decline in technical efficiency. Based on the results of the sensitivity analysis, the conclusions regarding the efficiency of healthcare resource allocation in Guangxi are deemed highly reliable. Despite the influence of uncertain factors, the model consistently provides stable and coherent assessment results in most scenarios. Therefore, special attention is needed to improve scale efficiency in healthcare resource allocation within the region, alongside enhancing management and technological capabilities in the healthcare sector. Overall, this study provides valuable reference and guidance for researchers and practitioners in related fields and offers scientific decision support for healthcare resource allocation.

Effect of artificial insemination, ruminal incubation, and esophageal tubing on cortisol concentration in blood of lactating dairy cows

Cortisol is a hormone associated with pain, fear, distress, or discomfort. We hypothesized that human interventions increase cortisol concentrations in plasma. Therefore, the objective of this study was to determine the concentration of cortisol in plasma before and after animals were subjected to routine on-farm and research procedures including artificial insemination, ruminal incubation, or esophageal tubing. Forty lactating Holstein cows were used in this study. Experimental treatments consisted of 4 interventions. Before any intervention, a first blood sample was collected from all cows. Following this bleeding, all cows were assigned to 1 of 4 treatments. A negative control treatment consisted of bleeding for a second time without any human intervention at least 30 min after the previous bleeding. Artificial insemination was performed by the herd managers from the Virginia Tech Dairy Complex. A rumen incubation treatment consisted of bleeding for a second time 30 min after opening the rumen cannula, inserting the operator's arm, and pulling ruminal contents out of the rumen for 2 min mimicking a ruminal in situ incubation, and placing back the cannula plug. An esophageal tubing treatment consisted of bleeding for a second time 30 min after inserting an esophageal tube into the esophagus and maintaining it inserted for 2 min mimicking a ruminal drenching procedure. Blood samples were collected from the coccygeal vessels. Cortisol concentration in plasma was measured by an independent laboratory using a chemiluminescence assay. The experiment was designed as a completely randomized design with repeated measures, where cow was the subject and the pre- and post-intervention bleeding were the repeated observations. The statistical model included the fixed effect of treatment, the random effect of cow, the fixed effect of time, the fixed effect of the treatment by time interaction, and the random residual error. According to Akaike's criterion, compound symmetry was utilized as the covariance structure for the repeated measures. Cortisol concentrations in plasma after the interventions did not differ from the cortisol concentrations in plasma before the interventions. Cows subjected to artificial insemination had greater cortisol concentrations than cow subjected to the other treatments. No interaction existed between treatment and time. Most cows subjected to insemination in a palpation rail had elevated cortisol concentrations before the intervention. Therefore, animal restraint seems to affect cortisol concentrations in plasma more than the actual human intervention. In conclusion, human interventions like artificial insemination, ruminal incubation, and esophageal tubing did not elevate the cortisol concentrations in the plasma of lactating dairy cattle under the conditions of this study.

Test–retest Reliability and Responsiveness of the Machine Learning-based Short-form of the Berg Balance Scale in Persons with Stroke

ObjectiveTo examine the test–retest reliability, responsiveness, and clinical utility of the machine learning-based short-form of the Berg Balance Scale (BBS-ML) in persons with stroke. DesignRepeated measures design. SettingA department of rehabilitation in a medical center. ParticipantsThis study recruited two groups: 50 persons who were more than 6 months post-stroke to examine the test–retest reliability, and 52 persons who were within 3 months post-stroke to examine the responsiveness. Test–retest reliability was investigated by administering assessments twice at a 2-week interval. Responsiveness was investigated by gathering data at admission and discharge from hospital. InterventionsNot applicable. Main outcome measureBBS-ML. ResultsThe BBS-ML exhibited excellent test–retest reliability (intraclass correlation coefficient = 0.99), acceptable minimal random measurement error (minimal detectable change % = 13.6%), and good responsiveness (Kazis' effect size and standardized response mean values ≥ 1.34). On average, the participants completed the BBS-ML in around 6 minutes per administration. ConclusionsOur findings indicate that the BBS-ML appears an efficient measure with excellent test–retest reliability and responsiveness. Moreover, the BBS-ML may be utilized as a substitute for the original BBS to monitor the progress of balance function in persons with stroke.

A hybrid model for accurate prediction of composite longitudinal elastic modulus

This research presents a novel hybrid model that integrates a physical-based empirical model with an Artificial Neural Network (ANN) to accurately predict the longitudinal modulus of elasticity for composites under compression. The study focuses on a composite material with a pore inclusion within an ABS plastic matrix, exploring various pore volumes, orientations, and shapes. As part of the proposed hybrid model, a regression-type neural network was trained in MATLAB® to predict and correct discrepancies between the Generalized Stiffness Formulation (GSF) homogenization-based modeling method and the collected compression experimental test results. Using MATLAB® neural network, random error datasets were used to train the feed-forward neural network, and the remaining error datasets were used for validating the performance of the proposed hybrid modeling scheme.The hybrid model demonstrated superior performance, achieving the lowest Mean Error (ME) of 0.1684864, Mean Absolute Error (MAE) of 1.051846, Mean Squared Error (MSE) of 3.500952, and highest R-squared of 0.998797. The proposed hybrid model outperformed both the Generalized Stiffness Formulation (GSF) and standalone ANN models. The significant improvement in prediction accuracy underscores the novelty and robustness of the hybrid approach in composite material modeling. Furthermore, this method can be used to refine any existing physical model by focusing on improving these established models to match experimental results and reducing the discrepancies, which offers a more efficient and attractive strategy for accurate predictions.

Enhancing and mapping thermal boundary conductance across bonded Si-SiC interface

SiC is a promising substrate for Si-on-insulator (SOI) wafers for efficient thermal management owing to its high thermal conductivity and large bandgap. However, fabricating a Si device layer on a SiC substrate with a high and uniform thermal-boundary conductance (TBC) at the wafer scale is challenging. In this study, a 4-inch Si-on-SiC wafer was fabricated using a room-temperature surface-activated bonding method, and the TBC was measured using the time-domain thermoreflectance (TDTR) method. The obtained TBC was 109 MW/m2K in the as-bonded sample, improving to 293 MW/m2K after annealing at 750 °C, representing a 78 % increase compared to previously reported values for a Si–SiC interface formed by bonding methods. Such enhancement is attributed to the absence of an oxide layer at the interface. Furthermore, we assessed the actual spatial distribution of the TBC in the SOI system by combining the TDTR mapping with a mathematical model to remove the influence of random errors in the experiment. The spatial distributions before and after annealing were 7 % and 17 %, respectively. Such variation highlights the need to consider the TBC distribution when designing thermal management systems.