Results Of Error Analysis Research Articles

Machine learning-based performance prediction for energy storage medium-deep borehole ground source heat pump systems

With the rapid development of artificial intelligence, data-driven prediction models play an important role in energy demand forecasting and performance prediction. This study established performance prediction models for medium-deep borehole ground source heat pump (MDB-GSHP) systems to predict the coefficient of performance (COP) of GSHP, utilizing back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), and particle swarm optimization-support vector machine (PSO-SVM) method that optimizes the penalty parameter (C), insensitive loss parameter (ε), and kernel parameter (γ) of SVM using PSO. Operational data were collected through field experiments, and typical parameters were selected as features to establish the feature set. The feature set was then optimized using Pearson correlation analysis and recursive feature elimination (RFE), resulting in the creation of five distinct feature sets. Error analysis and trend evaluation results indicate that the combined feature set (FS5), which incorporates the advantages of multiple feature selection methods, demonstrates the best performance. The PSO-SVM model exhibits outstanding performance under various input conditions, achieving an accuracy of over 90% within the error range. When using FS5, the PSO-SVM model achieves a mean absolute percentage error (MAPE) of 0.037, mean absolute error (MAE) of 0.103, root mean square error (RMSE) of 0.125, coefficient of multiple determinations (R2) of 0.970, and explained variance score (EVS) of 0.967, showcasing excellent predictive performance. The research results can provide a basis for the formulation of storage-related operational parameters and the optimization of system operation for energy storage in MDB-GSHP heating systems.

Comparison Of Some Estimation Methods For The Estimators Of Marshall Olkin Distribution With Simulation

The research comprised multiple simulated tests to determine the relationship between (sample size, distribution parameter value, estimation method, and pollution indivuduales). The experimental findings indicate that the estimator is influenced by sample size, the value of distribution parameter, estimation method, and pollution indivuduales. The results of the mean square error analysis indicate that (robust estimation method) produces the best results with the lowest mean square error, and the best estimation method was (191) of (243) simulation experiments. Additional statistical distributions with additional factors can be performed to demonstrate additional results.

Prediction of dredged soil settlement based on improved BP neural network

Based on the measured settlement data from the Reclamation Project of Eastern Hengsha Shoal, two improved BP neural networks optimized by the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) respectively are employed to predict the long-term consolidation settlement of the dredged soil in the reclamation area. By comparing the prediction results of the improved neural networks with those obtained from standard BP neural network and various curve fitting methods including hyperbola method, Hoshino method, and Asaoka method, as well as field-measured data, the improved neural network methods are validated to have a better predictive performance. The error analysis results indicate that compared with the standard BP neural network and curve-fitting prediction methods, the improved BP neural networks exhibit smaller prediction errors in terms of MAE, MSE, RMSE, MAPE, etc., and have a closer match between the predicted settlement values and the actual values. The selected GA-BP and PSO-BP neural network prediction methods in this study demonstrate higher prediction accuracy. Meanwhile, GA-BP demonstrates higher prediction accuracy and generalization ability than PSO-BP, but PSO-BP shows faster convergence in terms of prediction speed. The preferred methods presented in this paper exhibit good robustness and high reliability, which can achieve accurate prediction of dredged soil settlement. Additionally, unlike the traditional curve-fitting methods, the selected methods can effectively reduce the influence of subjective factors during the settlement prediction, and provide a new approach for settlement prediction, which can be effectively utilized as a substitute for long-term settlement monitoring and give reference for the design of settlement control of foundation of further construction projects.

Correlation between the sound speed ratio and physical properties of seafloor sediments in the northwestern shelf of the south China sea

Based on a substantial number of seabed sediment samples and corresponding acoustic characteristic and physical property test data collected in the continental shelf area of the northwestern South China Sea, this study employed a sound speed ratio correction model to analyze the correlation between the sound speed ratio of seabed sediments and parameters such as porosity, density, mean grain size, and water content. This study resulted in the development of both single-parameter and double-parameter empirical formulas for the sound speed ratio of seabed sediments in this marine region. The determination coefficients (r2) for the single-parameter empirical formulas based on physical properties, excluding the mean grain size (r2 ≤ 0.75), were all above 0.78, indicating a strong correlation. Additionally, the fitting curve at 100 kHz was slightly greater than that at 50 kHz, consistent with the acoustic dispersion characteristics of sediments. To assess the applicability of the empirical formulas to different maritime areas, the formulas derived from the South Yellow Sea, East China Sea, and South China Sea were compared with those of the study area. The results indicated that the empirical formulas for the sound speed ratio established in this study have general applicability, especially for sediments with low porosity, high density, and low mean grain size. In addition, the determination coefficient of the dual-parameter empirical formula for the sound speed ratio–porosity–mean grain size is superior to that of the single-parameter empirical formula for the sound speed ratio–mean grain size. Through comparative studies with other marine areas, it was found that there are certain differences in the relationships between porosity and mean grain size established in different marine areas. Therefore, based on the rough sphere model, this study transforms the GS model into a single-parameter model built on either porosity or mean grain size. The error analysis results indicate that the single-parameter GS model has a smaller error than the measured data. However, the GS model controlled by a single parameter performs poorly compared to the predicted curves of the sound speed ratio–porosity and the sound speed ratio–mean grain size fitted in this study. This discrepancy may be attributed to the fact that the input parameters of the GS model are reference values, emphasizing the need for further research to determine suitable input parameters for the GS model in this marine area.

Investigation of Error Margin of Some Path Loss Models Over Digital Terrestrial Television Channel in Katsina Metropolis

Due to rapid development in mobile communication technology in recent times, strong signal coverage has become a major necessity. However, path loss is one of the major challenges against strong signal coverage. Several path loss models have been developed for predicting wireless signal coverage for urban, sub-urban and rural areas. However, the need to investigate which model can best predict losses in an environment becomes necessary. This work is aimed at comparing the Error Margin of some Path loss Models over Digital Terrestrial Television (DTTV) channel using the Star Times DTTV Channel within Katsina metropolis. Seven path loss predicting models for outdoor macro cell wireless communication were used. They are; Free Space model, COST-231 model, (developed by the European Union Co-operative for Scientific and Technical Research Team) Hata - Okurmura model, Plane Earth model, Okumura model, Ericson model and ECC model. The measurement campaigns were in two seasons, the wet season was in August and dry season in November, 2021. Both measured and predicted path loss values were computed using empirical models. Statistical error analysis based on the RMSE was carried out to determine the error margins between measured and predicted values. Based on the result of path loss assessment and error analysis, COST-231 with the lowest RMSE value of 13.49 dB which is within the acceptable range for sub-urban city is the most preferred amongst the investigated models for path loss prediction over digital UHF channel in Katsina city

A new optimized method in wellbore to improve gas recovery in shale reservoirs

With the scale development of shale gas, the importance of selecting appropriate deliquification process has become increasingly evident in maintaining well productivity and improving shale gas recovery rate. At present, the preferred deliquification process are macro-control plate method and field experience method. The existing methods can only qualitatively select the deliquification process by considering limited influencing factors, resulting in poor process implementation. Based on the results of error analysis, the Gray model was optimized to calculate the pressure distribution in the shale gas wellbore and determine the applicable pressure limit. The W.Z.B. empirical model, which fully considers the influence of wellbore inclination, is used to calculate the gas-liquid carrying situation and determine the applicable liquid carrying limit. By analyzing the technical limits of five commonly used deliquification processes in the Changning shale gas field, namely, plunger lift, optimizing pipe string, gas lift, foam drainage, and intermittent production, a quantitative optimization method for deliquification processes was established. This method was then used to obtain the optimization chart for deliquification processes in shale gas wells. This method was applied in Well Ning 209-X, where the corresponding optimization chart for deliquification processes was drawn based on the production characteristics of the gas well. By quantitatively optimizing the deliquification processes and adjusting to suitable techniques, it effectively guided the production of the gas well and improved the gas field recovery rate.

A mixed finite element method using a biorthogonal system for optimal control problems governed by a biharmonic equation

In this article, we consider an optimal control problem governed by a biharmonic equation with clamped boundary conditions. We use the Ciarlet--Raviart formulation combined with a biorthogonal system to obtain an efficient numerical scheme. We discuss the a priori error analysis and present results of the numerical experiments that validate the theoretical estimates.

Research on the influence of an adhesive layer on the monitoring signal of piezoelectric sensors

Electromechanical impedance (EMI) technology, as one of the important methods for analyzing and studying the interaction between sensors and structures, has the characteristics of clear physical meaning and high computational efficiency. However, for complex structures, it is difficult to quantitatively combine the measured impedance signal with the physical parameters of the tested structure. Therefore, establishing an appropriate EMI model is crucial to facilitate the quantitative analysis of structural health monitoring. The impedance models developed so far assume perfect adhesion between PZT and the structure, with displacement compatibility, and ignore the shear lag effect of the adhesive layer. Therefore, this research focuses on analyzing how surface-bonded PZT couples with the structure through the adhesives and conducting a shear layer analysis on two-dimensional (2-D) circular PZT to derive a closed-form solution for shear lag distribution. The shear lag effect is integrated into the 2D impedance formula. The influence of the adhesive layer on bolt loosening monitoring is studied, and it is proven that the modified EMI model can effectively predict the shear lag effect of the adhesive layer. The result of relative error analysis proves that conventional adhesive PZT also exhibits the shear lag phenomenon. The influence of different thicknesses of adhesive layers on sensor sensitivity is studied. The experimental results show that as the thickness of the adhesive layer decreases, the monitoring sensitivity of the sensor increases. Moreover, for slight bolt loosening, the thickness of the adhesive layer can affect the monitoring frequency.

Efficient Newton‐multigrid FEM solver for multifield nonlinear coupled problems applied to thixoviscoplastic flows

AbstractThis note is concerned with efficient Newton‐multigrid finite element method (FEM) solver for multifield nonlinear flow problems. In our approach, for efficient FEM solver, we advantageously use the delicate symbiosis aspects of the problem settings for FEM approximations, and the algorithmic tools to obtain the numerical solutions. We concretize our ideas on thixoviscoplastic flow problems. It is a two‐field coupled nonlinear problem. And beside the integrated nonlinearity within momentum and microstructure equations, thixoviscoplastic problems induce a nonlinear two‐way coupling. As far as FEM numerical solutions are concerned, we set the problem in a suitable variational form to use the corresponding wellposedness analysis to develop FEM techniques for the solver. Indeed, the wellposedness study is not an intellectual exercise, rather it is the foundation for the approximate thixoviscoplastic problem as well as for the development of an efficient solver. We base our investigations for the solver on our wellposedness and error analysis results of thixoviscoplastic flow problems published in Proc. Appl. Math. Mech. We continue our series, and proceed to develop a monolithic Newton‐multigrid thixoviscoplastic solver. The solver is based on Newton's method and geometric multigrid techniques to treat the coupling of the problem. So, we use Local Pressure Schur Complement (LPSC) concept to solve the coupled problem on mesh's elements, and proceed with outer blocks Gauss‐Seidel iteration to update the global solutions. Furthermore, we handle the nonlinearity of the problem with the combined adaptive discrete Newton's and multigrid methods. The adaptivity within discrete Newton's method is based on the adaptive step‐length control for the discrete differencing in the Jacobian calculations, while the convergence of linear multigrid solver is made to match the convergence requirement of nonlinear solver, accordingly. And the solver's update parameters are solely dependent on the actual convergence rate of the nonlinear problem. We provide the numerical results of solver performance for thixoviscoplastic lid‐driven cavity flow.

An Iterative High-Accuracy ADI Method for the 3D Parabolic Equation

The alternating direction implicit parabolic equation (ADI-PE) method and the Crank–Nicolson parabolic equation (CN-PE) method have been widely used for solving the 3D parabolic equation (3D-PE) in radio wave propagation. The ADI-PE method is more computationally efficient than the CN-PE method. The accuracy of the ADI-PE method is improved by the higher-order Mitchell–Fairweather (MF)-ADI method. This paper presents an iterative high-accuracy (IHA)-ADI method for the 3D parabolic equation. A derivation of the proposed method is presented. The convergence and stability of the proposed method are estimated. Several numerical examples are considered to illustrate the advantages of the proposed method. The results of error analysis and a comparative study show that the proposed method is unconditionally stable and computationally efficient. The proposed method is more numerically accurate than the MF-ADI method.

Empirical Pathloss Model Analysis of Television White Space in Lagos, Western Nigeria

To conduct feasible unlicensed communications in the television band, radio equipment must first identify transmission possibilities, or the proportion of the permitted spectrum for broadcasting services that is vacant at a given time in a particular location, which is referred to as Television White Space (TVWS). A drive test was conducted to determine the signal strength of three cell towers in three distinct areas of Lagos state (rural, suburban, and urban). The signal power parameters were measured in rural, urban, and suburban parts of Lagos in order to provide empirical values for the parameters that unlicensed radio devices can utilize to distinguish between vacant and occupied television channels in a real-world scenario. This research presents the results of field measurements in the UHF television band (470-860 MHz) conducted in Lagos. This work investigated and compared several propagation models (COST 231 model, Egli model, Okumura-Hata model, and Plain Earth model). The Standard Deviation Error (SDE), Root Mean Square Error (RMSE), and Mean Error (ME) analysis showed that the COST-231 Hata model is the optimal model for calculating path loss based on path loss exponents. This discovery led to the development of an improved model, the Oressuff TV model, which uses COST-231 Hata parameters to predict path loss in the 470–870 MHz spectrum for rural, urban, and suburban locations. From the result of Root Mean Square Error analysis, the proposed model predicted path loss in rural, urban, and suburban stations with low RMSE of 3.06dB, 3.08dB, and 1.19dB respectively. These figures indicate that the model optimization was successful, and that the Oressuff TV proposed model can estimate the path loss incurred by television signals with greater precision. Telecommunications firms may improve their service by utilizing the proposed model.

Theoretical connection from the dielectric constant of films to the capacitance of capacitors under high temperature

AbstractIn the process of coping with energy and environmental protection issues, technologies such as energy materials, energy devices, and energy systems have made great progress. With excellent performance, film capacitors play an increasingly important role in energy‐related fields. With the increase of application scenarios and the continuous development of film material technology, it is urgent to establish a better theoretical connection from films to capacitors. First, the main components of the capacitor including the film and the positional relationships among them are given. Then, from the two perspectives of indirect calculation according to the volume and the direct calculation according to the winding process, the equation between the dielectric constant of films and the corresponding capacitance of capacitors is established. Further, the measurement data and error analysis results of the built test platform prove the accuracy and great potential of the proposed calculation methods. In addition, error sources, including film thickness uniformity, are listed. Finally, the challenges faced by the proposed calculation methods and the paths that can be referenced for future research are summarised and discussed.

Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery

By one of the most fundamental principles in physics, a dynamical system will exhibit those motions which extremise an action functional. This leads to the formation of the Euler-Lagrange equations, which serve as a model of how the system will behave in time. If the dynamics exhibit additional symmetries, then the motion fulfils additional conservation laws, such as conservation of energy (time invariance), momentum (translation invariance), or angular momentum (rotational invariance). To learn a system representation, one could learn the discrete Euler-Lagrange equations, or alternatively, learn the discrete Lagrangian function Ld which defines them. Based on ideas from Lie group theory, we introduce a framework to learn a discrete Lagrangian along with its symmetry group from discrete observations of motions and, therefore, identify conserved quantities. The learning process does not restrict the form of the Lagrangian, does not require velocity or momentum observations or predictions and incorporates a cost term which safeguards against unwanted solutions and against potential numerical issues in forward simulations. The learnt discrete quantities are related to their continuous analogues using variational backward error analysis and numerical results demonstrate the improvement such models can have both qualitatively and quantitatively even in the presence of noise.

An online teaching process monitoring method of MOOC platform based on video recognition

In order to overcome the problem of low monitoring accuracy of traditional methods, a new online teaching process monitoring method based on video recognition for MOOC platform is proposed. The weight coefficient of online teaching process monitoring information of MOOC platform is calculated, and the monitoring information is reconstructed. On this basis, video recognition method is used to mine the online teaching process monitoring information of MOOC platform. Through the construction of monitoring model, the online teaching process monitoring of MOOC platform is realised. The experimental results show that the mean square error analysis results of the monitoring method based on video recognition verify that the effect of different gender students is different. The method has high correction coefficient, high monitoring accuracy and good application effect, which can effectively improve the effect of students' online learning.

Teachers of English and their Perceptions towards ESL Errors

This study entitled 'Teachers of English and their Perceptions towards ESL Errors' aimed at evaluating and determining gravity of grammatical errors in terms of acceptability and intelligibility judgments. Quantitative research methodology was utilized in this study. The data was gathered with the aid of a questionnaire prepared on the basis of the result of error analysis conducted earlier. The questionnaires were mailed to English teachers of different private and public schools, colleges and universities of Nepal and worldwide. Two hundred twenty (220) useable surveys were collected altogether for the study: 100 surveys from Nepali English teachers, 100 surveys from native English speaking teachers, 20 each from Britain, Australia, New Zealand, Canada and America, and likewise, 20 surveys from non-Nepali English teachers. Received responses were analyzed and explained descriptively. The findings of the study showed that the native English teachers evaluated the errors far more leniently than Nepali and non- Nepali English teachers. There is no any significant difference in evaluation of errors found in between Nepali and non-Nepali English teachers, but compared to native English teachers, they were found statistically and significantly different in their judgments of errors in acceptability and intelligibility both. On the other hand, while comparing judgments of learners' errors in between the native English teachers; it was revealed that there were not any significant differences found there. It is recommended that the native English speaking teachers’ perception of errors be rightly explored and accordingly evaluation scales be developed and the teachers be made aware of such universal rating scales of grammatical errors while evaluating learners' errors.

FPGA Tabanlı LogSig ve TanSig Transfer Fonksiyonlarının IQ-Math Sayı Standardında Tasarımı ve Gerçeklenmesi

Yapay Sinir Ağları (YSA), günümüzde sinyal işleme, optimizasyon, tahmin, karar verme ve kontrol gibi bir çok mühendislik alanında yoğun şekilde kullanılmaktadır. YSA yapılarının çalışmasını doğrudan etkileyen önemli bölümlerinden birisi de Transfer Fonksiyonlarıdır (TF). Doğrusal olmayan TF tasarımlarının gerçek zamanlı tasarımı üstel fonksiyon içerdiğinden oldukça zor bir şekilde gerçekleştirilmektedir. Bu çalışmanın ilk aşamasında, literatürdeki çalışmalardan farklı olarak doğrusal olmayan Logaritmik Sigmoid (LogSig) ve Tanjant Sigmoid (TanSig) TF, 32-bit (16I-16Q) IQ-Math formatında VHDL dilinde tasarlanmıştır. İkinci aşamada, VHDL dilinde FPGA çiplerinde çalışmak üzere örnek bir üç giriş-üç çıkışlı bir YSA (ileri beslemeli) yapısı 32-bit IQ-Math formatında modellenmiştir. Tasarımın gizli katmanında 8 adet nöron, gizli katmanda LogSig ile TanSig TF ve çıkış katmanında ise PureLin TF kullanılmıştır. Örnek YSA tasarımı iki farklı TF tasarımı için ayrı bir testbench dosyası oluşturulmuş ve bu tasarımlar VHDL ile Xilinx ISE DS programı ile test edilmiştir. Elde edilen simülasyon sonuçlarına göre nümerik tabanlı LogSig ve TanSig TF içeren YSA tasarımları için MSE ve RMSE hata analizleri yapılarak sonuçlar sunulmuştur. Ardından her bir tasarım XC7K70T-3FBG676 FPGA (Kintex-7) için sentezlenerek Place-Route prosesi gerçekleştirilmiştir. Place-Route prosesinden elde edilen FPGA çip kaynak kullanımı istatistikleri sunulmuştur. Tasarımlara ait hata analizi sonuçlarına göre YSA-LS (LogSig-tabanlı YSA) tasarımı 8.86E-06 MSE ve YSA-TS (TanSig-tabanlı YSA) tasarımı 7.92E-02 MSE sonuçları elde edilmiştir. Bu çalışmadan elde edilen sonuçlar ile IQ-Math tabanlı LogSig ve TanSig aktivasyon fonksiyonu tasarımlarının gerçek zamanlı YSA uygulamalarında güvenli bir şekilde kullanılabileceği gösterilmiştir.

Uncertainty simulation of large-scale discrete grassland fire spread based on Monte Carlo

An improved fire spread model was proposed for large-scale grassland areas. In this study, based on a semi-physical model, the fuel moisture was calculated using grassland area weather data. The fuel continuity was introduced into grassland spread model to judge whether fire can burn continuously across the grassland. To overcome the uncertainty in grassland fire spread simulation, Monte Carlo (MC) method was used to deal with the uncertainty of the key factors of the model, e.g. fuel characteristics (continuity, load, and moisture) and wind speed. The fire spread rate was established based on the grassland fire behaviour field experiment and the Rothermel model. Finally, according to the spread patterns of large-scale grassland fires, meteorological and remote sensing (RS) data were used to simulate the propagation process of a grassland fire. Based on Monte Carlo method, a method to effectively improve the maximum and minimum possible range of grassland fire spread simulation is proposed. The fire spread field experiment error analysis and simulation results revealed that the results are highly consistent. This study can provide decision support for fire management departments during grassland firefighting.

A priori and a posteriori error analysis of hp spectral element discretization for optimal control problems with elliptic equations

. A priori and a posteriori error estimates of hp spectral element method for an elliptic optimal control problem with L2-norm control constraint are investigated in this paper. We then present the optimality conditions and set up hp spectral element discretization scheme. Based on some important interpolation operators and suitable immediate variable, a priori error estimates for this problem in hp spectral element discretization are established carefully. Furthermore, using some interpolation operators, a rigorous posteriori error estimates of hp spectral element approximation are also proved for control and the coupled state approximation in L2−H1-norm and L2−L2-norm, respectively. Such estimators can be used to construct reliable adaptive spectral element methods for optimal control problems. Finally, the error analysis results are confirmed by numerical results.

Errors of Class VIII Junior High School Students in Solving Mathematical Communication Problems Based on the Newman Procedure

Mathematical communication skills are the main capital in solving math problems. Mathematical communication skills help students in conveying ideas or solutions to a mathematical problem in the form of oral, written, and picture. The research objective is to identify student errors in solving mathematical communication problems. The type of research is descriptive qualitative so that the researcher is the main instrument. Technical data analysis in this research is descriptive qualitative data analysis technique of Miles and Huberman model with stages of data reduction, data presentation, conclusion drawing and verification. The data reduction stage aims to select, focus, discard unnecessary data and simplify raw data from the results of student error analysis. The research subjects were students of class VIII A SMP Negeri 2 Tanjung Palas Utara. The research showed that the students did not make reading errors but made mistakes in understanding, transforming, processing skill, and solving mathematical communication problems.

Error Analysis of Digital Electric Energy Metering Based on FPGA

With the emergence and development of smart grid, digital meters have been widely used in substations, so the research on digital energy metering error has become a research hotspot. The purpose of this paper is to analyze the error of digital power measurement based on FPGA, mainly take the analog input fusion module and digital power meter in the digital power measurement system as the research object, and analyze the source of measurement error. An FPGA-based energy metering error detection platform is built to analyze the energy metering error when there are harmonics in the full energy metering method currently used by most digital energy meter manufacturers. The comparative analysis of numerical integration algorithms shows that the asymmetric distortion rates of the simulated waveforms are approximate. The results of measurement error analysis under harmonic conditions show that when the number of sampling points per cycle is 60, the power measurement errors of the 2nd to 4th harmonics are equivalent. The magnitude of the measurement error is still consistent with other low-order harmonics, and the simulation results are consistent with the theoretical analysis results.