Maximum Error Value Research Articles

Construction and experimental verification of the spatial attitude kinematic model of advanced support equipment

The roadway in coal mine is prone to deformation of the surrounding rock under the influence of complex disturbance, which induces the imbalance of support attitude matching between roadway surrounding rock and advanced coupling support equipment, breaks the mechanical balance of the original coupling support system, and causes safety accidents. The postures and offsets of the various working spaces of the advanced hydraulic supports under the influence of complex disturbances are systematically analyzed. The D–H parameter matrix transformation method is used to establish the spatial posture kinematic model of a single/group of advanced hydraulic supports. The posture parameters of each key node are obtained through global coordinate transformation. Based on the comparison of the working attitude parameters of the support obtained through simulation analysis and model calculation, the average absolute error of the support height calculation results of the single group of advanced hydraulic support attitude model is 3.92 mm, and that of the coordinate calculation results of the advanced hydraulic support group is (3.26 mm, 1.99 mm), which meet the accuracy requirements of the advanced hydraulic support attitude monitoring. Finally, the spatial attitude kinematic model of a single/group of advanced hydraulic supports is verified and analyzed by using the advanced hydraulic support attitude monitoring test bench. The maximum error value of the experimental results is 4.9 mm, which verifies the accuracy of the established spatial attitude kinematic model of the advanced hydraulic support. The research results can provide theoretical and technical support for the intelligentization of the advanced coupling support system and the safe and efficient mining of coal mine.

Systematic error compensation for improving accuracy of the electrosurgical unit.

This paper presents an approach to systematic error correction for improving the accuracy of the electrosurgical unit (ESU), through a method that enables safer operation for both surgeons and patients. The equations for systematic errors encountered during the calibration of the eight ESUs were obtained using regression analysis. The ESU calibration was performed using a power analyzer and the reference power value was selected on the ESU panel; the power value indicated by the ESU was noted from the power analyzer and recorded. The fitted regression models were suitable for predicting systematic errors associated with ESU output power values, as a reduction of about 84 % was noted in cutting power (CuP) and about 74 % for coagulation power (CoP). After correction, systematic error values remained below 1.9 W and 0.4 W in cutting and coagulation power, respectively. The maximum error values obtained were less than those specified for the IEC 60601-2-2 standard for all evaluated ESUs.

Experiment of Canopy Leaf Area Density Estimation Method Based on Ultrasonic Echo Signal

Variable-rate spray systems with canopy leaf area density information detection are an important approach to reducing pesticide usage in orchard management. In order to estimate the canopy leaf area density using ultrasonic sensors, this article proposed three parameter model equations based on ultrasonic echo peaks for canopy leaf area density estimation and verified the accuracy of the three parameter model equations using laboratory-simulated canopy and outdoor tree experiments. The orthogonal regression statistics results from the laboratory-simulated canopy experiment indicated that parameter Vc is more suitable for canopy leaf area density estimation compared to parameter Va and Vb when the density ranges from 0.54 to 5.4 m2m−3. The model equation from parameter Vc has minor systematic errors, and the predicted and observed values of parameter Vc have good agreement with the experimental conditions. The laboratory-simulated canopy and outdoor tree canopy leaf area density verification experiments of parameter Vc were carried out, and the results indicated that the absolute value of the mean relative error is 5.37% in the laboratory-simulated canopy and 2.84% in outdoor tree experiments. The maximum absolute value of the relative error is 8.61% in the laboratory-simulated canopy and 14.71% in the outdoor tree experiments, and the minimum absolute value of the relative error is 3.21% in the laboratory-simulated canopy and 0.56% in the outdoor tree experiments. The laboratory-simulated canopy leaf area density verification results showed that the mean relative errors under canopy leaf area density 0.98 and 4.92 m2m−3 conditions are 6.29% and 5.82%, respectively, which is larger than the mean relative error under 2.95 m2m−3; nevertheless, these results proved that this model equation is applicable for canopy information detection and advanced pesticide application development in future.

Pulse Oximeter Design for SpO2 and BPM Recording on External Memory to Support the Covid-19 Diagnosis

COVID-19 (coronavirus disease) is an acute respiratory illness induced by exposure to coronavirus 2 in 2019 (SARS-CoV-2). WHO confirms that there were 1.8 million registered deaths in 2020 and that there were 3.5 million recorded deaths in 2021. People who are infected with SARS-CoV-2 without symptoms should have a pulse oximeter. Early detection of low oxygen levels in the blood can lead to fewer complications. Continuously decreasing oxygen saturation, if not controlled, will cause hypoxia (an abnormal respiratory circulation system condition that causes breathlessness). In normal conditions, oxygen levels and heart rate are related. When a person has a shortage of oxygen (breathlessness), their heart rate increases to supply the oxygen. Regulating heart rate can aid in the prevention of disorders such as arrhythmia, coronary heart disease, and hypertension. A pulse oximeter is used to measure the oxygen saturation in the blood and the patient's heart rate (BPM) with non-invasive methods. Conventional pulse oximeters do not support users by not having features such as medical records, which are required for further examination by a doctor. The purpose of this research is to make a pulse oximeter with external storage capability. The difference in wavelength between the red and infrared LED lights that will be captured by the photodiode is measured. SpO2 and HR values will be generated as a result of comparative measurements. Using a MAX30102 sensor to detect SpO2 and heart rate, and an Arduino Mega256 to process data for display on the TFT Nextion with Memory Card storage. By comparing the module to a conventional pulse oximeter, data was collected 10 times for each respondent. The maximum SpO2 error value is 0.43%, whereas the BPM parameter has the largest error value of 2.02% and the smallest error value of 0.01% based on the data collected. A significant error value is caused by finger movement. The module is usable, based on the results, because the maximum error tolerance for a pulse oximeter is 1% SpO2 and 5% BPM, according to the 2001 Ministry of Health Ministry's Guidelines for Testing and Calibrating Medical Devices.

A Collaborative Estimation Scheme for Lithium-Ion Battery State of Charge and State of Health Based on Electrochemical Model

Nowadays, many studies focus on single state of health (SOH) or state of charge (SOC) estimation. How to effectively combine the two for joint estimation is particularly important. This paper relies on our previous research foundation of SOH, and further increases the electrochemical model, and realizes the SOC estimation of the battery under different SOH stages by combining the two. An SPM model based on electrochemical mechanism is proposed and sensitivity analysis is performed for the parameters in the model. Three types of dynamic conditions, FUDS, BJDST and DST, are used to verify the feasibility of the model. Further, the synergistic estimation of SOH and SOC is realized by the change of the difference between the two lithium ion concentrations with the battery decay when SOC = 100% and SOC = 0% in the SPM model. In the offline state, a linear regression relationship is constructed between the results of the GPR model and the parameters of the SPM model. During online prediction, the parameters of SPM are obtained by calibrating the offline model, and the goal of estimating SOC based on SOH is realized. The experimental results show that the maximum error value of SOC estimation does not exceed 0.08, and the model has high accuracy.

Localization Method and Finite Element Modelling of the Mid-Point Anchor of High-Speed Railway Distributed in Long Straight Line with Large Slope

In order to ensure the safe and reliable operation of a high-speed railway, the precise positioning of the mid-point anchor in the catenary is very important. In view of the two problems in the calculation of the mid-point anchor position of the catenary in a long ramp section, the calculation accuracy is low, and the calculation of the central anchor clamp position is lacking. In this study, the predetermined location of the mid-point anchor is chosen based on the mid-point anchor location principle and the line condition, and the range of the allowable error of the mid-point anchor setting is determined according to the predetermined position of the mid-point anchor. Secondly, by considering the impact of the line ramp and using the measured span length, the tension difference of the clue in the direction of the line is calculated. Then, the theoretical location of the mid-point anchor clamp is determined using the downhill component and the tension difference. Finally, the theoretical position of the clamp is corrected according to the setting of the dropper to obtain the corrected position of the clamp. An FEM (finite element method) of the catenary is established in ANSYS software to calculate the height difference between the messenger cable and the contact wire at the point of the mid-point anchor setting, and then the length of the mid-point anchor rope is obtained. Through the calculation of actual case data, the maximum value of the relative error of the location of the mid-point anchor obtained by this proposed method is very small compared with the actual position, which verifies the effectiveness of this method.

Corn grain yield forecasting by satellite remote sensing and machine‐learning models

AbstractThis study aimed to evaluate the performance of six machine‐learning models in forecasting corn (Zea mays L.) grain yield before harvest using, as input, variables in the models, some of the most‐used vegetation indices (VIs) and spectral bands in the literature, as well as using data at 770 and 980 sum of degree days (SDD). The field study was carried out in a commercial area in the 2017–2018 and 2018–2019 harvests. Spectral data were obtained from Sentinel‐2 satellite images and were used as input variables in the proposed models: artificial neural networks (ANN), k‐nearest neighbors (KNN), random forest (RF), and support vector machine (SVM). The maximum R2 and minimum values of mean absolute error (MAE) and RMSE were 0.89, 0.33, and 0.42 t ha−1, respectively, for the RF algorithm using all input variables. The results obtained in the present study show that it is possible to predict corn grain yield 80 d before harvest using only VIs for the crop. Testing the various combinations of spectral bands and VIs resulted in obtaining the GREEN band and the VI global environment monitoring index (GEMI) as the best predictor variables in the present study. The use of more than one SDD did not improve the performance of the models tested. The models developed using data at 980 SDD obtained the best precision and accuracy performance both in the scenario with all model input variables and with the two best predictors. The KNN algorithm obtained the best performance in the precision and accuracy metrics for most of the scenarios studied in the present work.

Development and Application of Standard Device for Calibrating Steel Measuring Tape Based on Machine Vision

Aiming to address the disadvantages of the low automation degree of the traditional steel measuring tape verification device, as well as the high labor intensity of metrologists, high time costs, and low measurement accuracy, this paper develops a new automatic steel measuring tape calibration device based on machine vision technology. In this study, the key components, such as the tape-winding mechanism, machine vision system, force measuring device, and tape-retracting device, were designed. Three different scribed line identification and location algorithms, processing the longest line, the closest to the starting line, the highest line, etc., were designed to adapt to different types of steel measuring tape. Lastly, indication error and measurement repeatability tests were carried out. The results showed that the standard device can carry out the verification and calibration of steel measuring tape, and the maximum repeatability of the indication error of the test objects was 0.025 mm, 0.038 mm, and 0.030 mm, respectively, and the indication error of each object was within the allowable error limits. The uncertainty evaluation of the indication error of each test object was 0.16 mm, 0.54 mm, 0.78 mm, which was not more than one third of the maximum permissible error value. Hence, the measuring principle, method, and procedure of the calibration device are shown to be scientific and feasible.

Gray Wolf Optimization for Scheduling Irrigation Water

Although various optimization algorithms have been developed for operational purposes, the development of new optimization algorithms is still an open problem due to the complex system of irrigation canals that must be addressed. Recently, a new algorithm named gray wolf optimization (GWO) has been introduced and applied in different contexts. It mimics the social hierarchy and hunting behavior of gray wolves in nature. In this research, GWO was formulated, developed, and linked to irrigation canal system simulation (ICSS) to schedule water delivery. A fitness (optimization) function was defined according to the standard water delivery performance indicators. Normal and water shortage operational scenarios in the E1R1 Dez canal in Iran were tested and evaluated. The results revealed that GWO is a powerful optimization method and avoids local optimal points when normal conditions exist. However, it has relatively poor performance in water shortage conditions in which there is not enough water. Water depth variations remain inside acceptable margins. Its results were comparable to fuzzy state, action, reward, state, action (SARSA) learning (FSL) in the same canal, showing a value of maximum absolute error (MAE) and integral absolute error (IAE) of 10.7% and 9.2%, respectively, and it can distribute water between turnouts adequately, efficiently, equitably, and dependably in normal conditions.

A Continuous Single-Layer Discrete Tiling System for Online Detection of Corn Impurities and Breakage Rates

In order to improve the accuracy and efficiency of the methods that are used for the detection of impurities in and the breakage rate of harvested corn grains, we propose a classification and identification method using a feature threshold and a backpropagation (BP) neural network that is based on a genetic algorithm. We also constructed a continuous single-layer discrete tile detection system for application to harvested corn grains containing impurities and broken kernels. We conducted an evaluation of the proposed approach with a three-factor and three-level orthogonal experimental design. By setting the working parameters, we realized the continuous single-layer discrete tiling of the grains and 50 grain materials were collected on average in a single picture. In the static test, the error between the system monitoring value and the manual detection value was small, the maximum absolute errors of the breakage and impurity rates were 2.16% and 1.03%, and the average time that was required for each image recognition was 1.71 s. In the experimental environment, the maximum absolute error values of the breakage and impurity rates were 3.48% and 1.78%. The system’s identification accuracy and processing time meet the requirements of the online detection of corn characteristics in grain harvesting.

A novel path tracking method of tractor based on improved second-order sliding mode considering front wheel steering angle compensation

Existing agricultural tractor path tracking methods rarely consider tracking accuracy and driving stability simultaneously. To solve this problem, a novel path tracking method of tractor is proposed in this paper. The method contains two layers of controllers. The first layer is the desired front wheel steering angle controller based on path prediction, and the desired yaw rate of tractor can be obtained by reference model expression. The second layer is the front wheel steering angle compensation controller based on improved second-order sliding mode (SOSMC) to improve tractor driving stability. The innovation of SOSMC is a combination of nonlinear integral sliding mode (NISM) surface and improved Super-Twisting algorithm, which can not only improve the rate of the control system to the sliding mode surface, but also suppress chattering. To verify the performance of SOSMC, the simulations are carried out on three paths at the constant speed of 10 km/h through Matlab/Simulink and Carsim. Three working paths including arched, pear-shape and lemniscate are constructed in Carsim. The results show that the control effect under three controllers is better than no control. Compared with no control, the maximum value and root mean square (RMS) of lateral error under SOSMC are reduced by 13.09% and 25.07% on average in three paths, and that of yaw rate are decreased 53.29% and 54.64%. The tracking accuracy of SOSMC is the best of three controllers, and that of NISMC is better than SMC. Compared with NISMC, the maximum value and RMS of yaw rate under SOSMC are reduced by 37.74% and 43.62% on average. The yaw rate, front wheel steering angle compensation value and chattering amplitude of front wheel steering angle under SOSMC are significantly smaller than those of SMC and NISMC. In summary, the novel path tracking method can simultaneously satisfy the accuracy and stability of the tractor when it tracks the field working path.

Creating orthophotos with unmanned aerial vehicles and examining its accuracy and usability in geodetic applications

SummaryUnmanned aerial vehicles, one of the technologies that aim to produce data in many scientific studies, has widespread use in many engineering applications, especially in cartography applications. UAV, which can meet the high accuracy required in many studies, offer users the opportunity to produce point cloud and maps for various aims. It is aimed to produce a high‐resolution orthophoto map of the study area in this study. In addition, the accuracy of the orthophoto map produced for the areas classified according to topographic structures in the study area has been checked with GNSS measurements, and its compliance with the standards specified in the regulation has been examined. According to the results; Considering the root square mean error values of three axes, it has been searched whether the accuracy values of the products obtained are suitable according to the regulation and it has been determined that these values are within the limits. Then, the application of testing the accuracy of the orthophoto map has been started. When all checkpoints have been examined, the maximum error values in x, y, and h directions have been found to be 5.9, 4.5, and 4.9 cm, respectively.

A New Data Processing Method for High-Precision Mining Subsidence Measurement Using Airborne LiDAR

Coal resources are the principal energy in China, and the surface subsidence caused by coal mining has a serious impact on the safe production and life of human beings. The traditional observation method of rock movement is slow and laborious, while the accuracy of airborne LiDAR, InSAR and other methods is relatively low. In this paper, aiming at the problem of the low accuracy of deformation monitoring of airborne LiDAR, the data registration of LiDAR point cloud is analyzed by combining theoretical analysis with field experiment. An advanced distribution mode of control points is discussed, and a current method of multi-period point cloud registration using seven-parameter transformation is proposed to obtain a surface subsidence model for mining area with high precision. The results show that the RMSE of airborne LiDAR is decreased from 0.013 m to 0.008 m by using the new method for data registration, and the maximum error value is reduced from 0.022 m to 0.014 m, which effectively enhances the deformation monitoring capability of airborne LiDAR.

Comparative analysis of algorithms for estimating traffic parameters in corporate multiservice communication networks

Adequate management of modern corporate communication networks is possible if many control procedures function in near real time. In this case, the processed network monitoring data must have accurate characteristics sufficient for making objective management decisions. This fully applies to the data monitoring of network traffic parameters, which determines the relevance of the proposed work. The proposed in the paper algorithm for on-line estimation of traffic parameters in corporate multiservice communication networks is based on the concept of conditional nonlinear Pareto-optimal filtering V. C. Pugachev. Its essence is that the estimation of traffic parameters is performed in two stages - in the first stage, we evaluate the forecast values of parameters, and in the second, with the next observations of random sequences, we make adjustments to their values. Traffic parameter values forecasts are constructed in a small-sized sliding window, and the adjustment is implemented on the basis of pseudo-gradient procedures whose parameters are adjusted using a fuzzy control algorithm based on the Takagi-Sugeno method. The proposed algorithm belongs to the class of adaptive algorithms with prior learning. The maximum value of the average relative error of estimation of traffic parameters was less than 8.2%, which is a sufficient value for the implementation of operational network management tasks. At the same time, the actual scientific and technical task is to analyze the comparison of the characteristics of the developed adaptive algorithm with the characteristics of the optimal algorithms, the characteristics of which are the maximum achievable. Translated with www.DeepL.com/Translator (free version). The results of a comparison of the proposed method with the optimal Coleman filtration (OKF) are presented.

Force-distribution sensitivity to cable-tension errors in overconstrained cable-driven parallel robots

Cable-driven parallel robots (CDPRs) employ extendable cables to move their end-effectors (EEs) throughout the workspace. Since cables can only sustain tensile stresses, the EE is often overconstrained to keep cable tensions within positive limits during motion. In this case, to control the overall cable-tension distribution, one can force-control a particular set of cables, and length-control the others. This work aims at evaluating the maximum value of tension error caused on the length-controlled cables, while force-controlling a chosen cable set, by computing a performance index called force-distribution sensitivity to cable-tension errors. This index informs about the maximum expected cable-tension error on the length-controlled cables, if a unitary tension error is committed on a specific set of force-controlled cables, and allows one to determine which set of cables are best to be force-controlled, to ensure the lowest error in the overall tension distribution. The force-distribution sensitivity is derived for a generic overconstrained CDPR, with arbitrary geometry and number of cables. As an application example, its minimum value is computed to characterize the workspace of three exemplifying overconstrained CDPRs.

Improved Heart Rate Measurement Accuracy by Reducing Artifact Noise from Finger Sensors Using Digital Filters

Heart rate is an important indicator in the health sector that can be used as an effective and rapid evaluation to determine the health status of the body. Motion or noise artifacts, power line interference, low amplitude PPG, and signal noise are all issues that might arise when measuring heart rate. This study aims to develop a digital filter that reduces noise artifacts on the finger sensor to improve heart rate measurement accuracy. Adaptive LMS and Butterworth are the two types of digital filters used in this research. In this study, data were collected from the patient while he or she was calm and moving around. In this research, the Nellcor finger sensor was employed to assess the blood flow in the fingers. The heart rate sensor will detect any changes in heart rate, and the measurement results will be presented on a personal computer (PC) as signals and heart rate values. The results of this investigation showed that utilizing an adaptive LMS filter and a Butterworth low pass filter with a cut-off frequency of 6Hz, order 4, and a sampling frequency of 1000Hz, with the Butterworth filter producing the least error value of 7.57 and adaptive LMS maximum error value of 27.65 as predicted by the researcher to eliminate noise artifacts. This research could be applied to other healthcare equipment systems that are being monitored to increase patient measurement accuracy.

The prediction model for haze pollution based on stacking framework and feature extraction of time series images

In this paper, we propose a new model called “image-feature-stacking prediction model” to study the prediction problem of univariate time series data. Its main idea is to convert univariate time series data into corresponding images, and then use the optimized Inception-v1 network to extract hidden features from the images as input variables, based on these features, a two-layer stacking ensemble learning framework is constructed to output the final predicted values. The main contribution of the newly proposed model is to convert one-dimensional time series data into two-dimensional images, and automatically extract features from images. This method can truly mine the intrinsic relationship between the data instead of simply relying on descriptive statistical features to replace the original time series, thereby improving the prediction performance of the model. We use the new prediction model to predict daily PM2.5 concentration, for one-step prediction, the results show that compared with the other three time series prediction models, the proposed prediction model reduces the mean absolute percentage error and mean absolute scaled error to 19.204% and 1.242, respectively, which is 76.607% and 77.004% lower than the maximum value of mean absolute percentage error and mean absolute scaled error of four prediction models. We also make two-step and three-step predictions, and the newly proposed model also shows encouraging performance.

Estimation of the compressive strength of green concretes containing rice husk ash: a comparison of different machine learning approaches

To mitigate the environmental issues related to the utilisation of ordinary portland cement (OPC) in concrete mixtures, attempts have been carried out to find alternative binders such as rice husk ash (RHA) as replacements for OPC. This study contributes to moving from the traditional laboratory-based methods for the determination of compressive strength (CS) towards machine learning-based approaches by developing three accurate models (i.e. artificial neural network (ANN), multivariate adaptive regression spline (MARS) and M5P model tree) for the estimation of the CS of concretes containing RHA. For this purpose, the models were developed employing 909 data records collected through technical literature. The results indicate that all three techniques provide reliable estimations of the CS for both training and testing datasets. However, the ANN-based model outperforms the other two models, while the MP5-based model is associated with the least accuracy and the maximum error values among all three techniques. The parametric study revealed that by increasing the contents of cement, coarse aggregate and age, and decreasing the contents of water, fine aggregate, RHA and superplasticizer, the CS of concrete increases while the sensitivity analysis demonstrated that the coarse aggregate content was the most influential parameter affecting the values of CS.

The Development and Management Work Innovation of University Students by Artificial Intelligence and Deep Learning in the Wireless Network Era

This study is aimed at promoting the growth and development of college students from two aspects: independent student learning and school management. Firstly, deep neural networks (DNNs) are used to build a personalized learning resource recommendation model for college students based on an online education platform. The model is tested and passed the experiment. A new comprehensive quality evaluation index system for college students is established in school management based on the traditional complete quality evaluation method. The thorough fuzzy evaluation (FCE) method and the backpropagation neural network (BPNN) model are combined to construct a comprehensive quality evaluation model for college students. The students’ comprehensive quality evaluation scores obtained using this model are compared with the actual scores through experiments. The results show that the number of learners is 50 in the constructed personalized learning resource recommendation model for college students. When the number of learning resources is 180, F 1 ‐ score = 3 is more significant than other programs. The FCE method and the comprehensive quality evaluation model constructed by BPNN scored six students. The maximum error value between the evaluation result and the actual value is only 0.039. This shows that the effect of the constructed personalized learning resource recommendation model and college students’ comprehensive quality evaluation model is excellent and practical. This provides a feasible method for improving students’ independent learning ability and school management efficiency.

Analysing the accuracy of asymptotic approximations in incomplete contact problems

The error incurred in the representation of the contact pressure at the edges of incomplete contacts by first order asymptotes is treated, and the maximum value of the relative error found for a range of geometries, both symmetric and non-symmetric. For a symmetric power-law geometry, we identify when the first-order asymptote achieves maximum fidelity. Shear tractions are excited by both the application of a shear force and the application of bulk tension in one body. An asymptotic representation of the shear traction distribution under conditions of full stick is presented.