Average Error Research Articles

Experimental investigation on in-situ laser-assisted mechanical ruling of single crystal silicon

Abstract In this paper, response surface methodology (RSM) was employed as a robust and convenient predictive tool to establish the correlation between process parameters of in situ laser-assisted mechanical ruling and the ductile-to-brittle transition of single-crystal silicon. The interaction effects among three factors laser power, ruling speed, and negative rake angle on the ductile-to-brittle transition of single-crystal silicon were investigated. The optimal combination of process parameters was determined to be a laser power of 30W, a ruling speed of 0.25 mm s−1, and a negative rake angle of 58°. Utilizing a self-assembled setup and the optimal process parameters, multiple processing experiments were conducted. The average error between the experimental and predicted values was found to be 2.8%.

Forecasting Grain Production in Hubei Province Based on Markov Modeling

Abstract. Grain production plays a crucial role in economic and social development. Accurate prediction of grain production can help agricultural decision-makers allocate resources rationally and do a good job at the next stage of the arrangements for the deployment of the work.This article used the production data, which was from 2023 Hubei Provincial Statistical Yearbook,to simulate and predict the grain production for several years,with the method of Markov prediction. It can be seen from the results that the average fitting error of the model is 3.47%. Using the text dataset of grain production from 2020 to 2022, the results show that the model can effectively improve the prediction accuracy of agricultural grain procuction. Finally, the model is used to predict the agricultural output value structure of China from 2023 to 2027. Markov prediction can improve the predictability and initiative of the work and provide scientific basis for the future decision-making of agricultural managers, and has guiding significance for the work of grain storage and distribution.

Positioning Sensor Data Mining and Correlation Analysis Based on Multi-mode Radio Frequency Identification

Real-time positioning and sensor data mining are critical for various Internet of things (IoT) applications, but current approaches face challenges such as limited accuracy, high cost, and energy inefficiency. This paper proposes a novel fusion intelligent structure that combines radio frequency identification (RFID) technology and wireless sensor networks (WSN) to enable accurate and efficient positioning and data mining. The proposed method consists of two key components: an energy heterogeneity strategy for optimizing network energy consumption, and a data association feature analysis technique for mining sensor data. The results demonstrated that the proposed approach achieved superior positioning accuracy compared to other algorithms, with an average error of less than 0.300 under various conditions. The data detection accuracy was also high, with a maximum false detection rate of 3%. Furthermore, the proposed energy heterogeneity strategy significantly improved network energy utilization and stability. Real-world scenario testing validated the practicality of the proposed approach, with an average positioning error of less than 0.500 meters. These results validate the effectiveness of the proposed RFID-WSN fusion structure for real-time positioning and sensor data mining, providing a promising solution for IoT applications.

A novel algorithm for generating random fiber distributions for fiber reinforced composites based on lattice partition

AbstractThis paper propose a random fiber distribution generation algorithm based on lattice partition, which addresses the issue of limited increase in fiber volume fraction caused by the random movement of fibers in previous methods. By partitioning the representative volume element (RVE) window into lattices and moving fibers directionally, the proposed algorithm significantly increases the number of fibers within the RVE window. The generated fiber distribution achieves a fiber volume fraction of up to 80%, with an average execution time of 243.4 s. The influence of algorithm parameters on the randomness of fiber distribution is examined through statistical analysis of the generated distributions. Finite element models with various parameters are established to predict the elastic properties of the composites. The average relative error between the predicted values and the experimental results is 8.6%, demonstrating the effectiveness of the proposed algorithm. This algorithm offers a simple and effective approach for generating fiber distributions in the numerical study of the micromechanics of composites.Highlights A new lattice‐partition method is proposed to generate fiber distributions. The fiber volume fraction of generated fiber distribution is up to 80%. The randomness of fiber distribution is analyzed through statistical analysis. The algorithm predicts elastic properties with an average relative error of 8.6%.

Numerical Study of the Cavitation Performance of an Ice-Blocked Propeller Considering the Free Surface Effect

Propeller cavitation performance can be predicted based on model tests or simulations. However, the cavitation performance of an ice-blocked propeller near the free surface differs from that of a propeller in the cavitation tunnel. Therefore, research on the cavitation performance simulation of propellers near the free surface holds crucial scientific significance. In this study, a coupled model was established using Computational Fluid Dynamics (CFD) and the Volume of Fluid (VOF) coupling method. The CFD-VOF model weighted the overlapping grids and simulated the cavitation performance of an ice-blocked propeller using various immersion depths, cavitation numbers, and advance coefficients. The propeller inflow ahead of the propeller and the wake field behind it were controlled to accurately obtain the propeller cavitation performance. Moreover, a comparison was conducted between the cavitation tunnel test results and the numerical simulation results at various immersion depths. When the immersion depth was at a distance of 1D, the effect of the free surface on the propeller cavitation performance became significant. When the immersion depth was at a distance of 9D, the average errors between the numerical simulation and the model test data were within 10%. This study analyzed the cavitation performance of ice-blocked propellers near the free surface and provided valuable insights for the design of ice-class propellers.

Robust pollination for tomato farming using deep learning and visual servoing

Abstract In this work, we propose a novel approach for tomato pollination that utilizes visual servo control. The objective is to meet the growing demand for automated robotic pollinators to overcome the decline in bee populations. Our approach focuses on addressing this challenge by leveraging visual servo control to guide the pollination process. The proposed method leverages deep learning to estimate the orientations and depth of detected flower, incorporating CAD-based synthetic images to ensure dataset diversity. By utilizing a 3D camera, the system accurately estimates flower depth information for visual servoing. The robustness of the approach is validated through experiments conducted in a laboratory environment with a 3D printed tomato flower plant. The results demonstrate a high detection rate, with a mean average precision of 91.2 %. Furthermore, the average depth error for accurately localizing the pollination target is impressively minimal, measuring only 1.1 cm. This research presents a promising solution for tomato pollination, showcasing the effectiveness of visual-guided servo control and its potential to address the challenges posed by diminishing bee populations in greenhouses.

Assessing Project Resilience Through Reference Class Forecasting and Radial Basis Function Neural Network

A project needs to be able to anticipate potential threats, respond effectively to adverse events, and adapt to environmental changes. This overall capability is known as project resilience. In order to make efficient project decisions when the project is subjected to disruption, such as adjusting the project budget, reformulating the work plan, and rationalizing the allocation of resources, it is necessary to quantitatively understand the level of project resilience. Therefore, this paper develops a novel approach for forecasting project performance, illustrating the changes in performance levels during the disruption and recovery phases of a project and thus quantitatively assessing project resilience. While there are several methods for assessing project resilience in existing research, the majority of assessment approaches originate from within projects and are highly subjective, which makes it difficult to objectively reflect the level of project resilience. Moreover, the availability of project samples is limited, which makes it difficult to forecast the level of project performance. In view of the fact that the Reference Class Forecasting (RCF) technique avoids subjectivity and the Radial Basis Function (RBF) neural network is known to be better at forecasting small sample datasets, this paper therefore combines the RCF technique and the RBF neural network to construct a model that forecasts the project performance of the current project after experiencing a disruption, further assessing the level of the project resilience. Specifically, this paper first presents a conceptual model of project resilience assessment; subsequently, an RBF neural network model that takes into account project budget, duration, risk level of disruption, and performance before disruption based on the RCF technique is developed to forecast project performance after experiencing disruption; and finally, the level of project resilience is assessed through calculating the ratio of recovery to loss of project performance. The model is trained and validated using 64 completed construction projects with disruptions as the datasets. The results show that the average relative errors between the forecast results of schedule performance index (SPI) and the real values are less than 5%, and the R2 of the training set and the testing set is 0.991 and 0.964, respectively, and the discrepancy between the forecasted and real values of project resilience is less than 10%. These illustrate that the model performs well and is feasible for quantifying the level of project resilience, clarifying its impact on project disruption and recovery situations, and facilitating the decision-makers of the project to make reasonable decisions.

Flattening behaviour of weft-knitted spacer fabrics

AbstractWeft-knitted spacer fabrics are thick 3D knitted structures prized for their cushioning properties which have gathered increasing attention in the last decade. The thickness of a spacer fabric is one of its most influential parameters and strongly impacts its cushioning properties, wearability, thermal insulation or permeability. However, the fabric's natural undulation and high deformability make its thickness measurement uneasy. The current standard measurement methods require to measure the fabric thickness after compressing it until a fixed threshold stress value is reached to flatten it. The diversity of these threshold values is confusing, and each of them is unsuitable to variety of fabric rigidity. In this article, a standard for thickness evaluation was proposed and used to measure the thickness of 20 samples knitted with 5 independent parameters. The measured thickness was compared to the thickness measured at a threshold value of 1 kPa and to a theoretical thickness. The proposed measurement standard was proved reproducible and efficient for all fabrics when the threshold measures showed large errors on the softer and stiffer samples. The flattening stress of the fabrics ranged from 86 to 5262 Pa and could not be approximated by a single standard value. The theoretical thickness was more accurate, predicting the thickness with an average error of 3.8%.

A Monocular Ranging Method for Ship Targets Based on Unmanned Surface Vessels in a Shaking Environment

Aiming to address errors in the estimation of the position and attitude of an unmanned vessel, especially during vibration, where the rapid loss of feature point information hinders continuous attitude estimation and global trajectory mapping, this paper improves the monocular ORB-SLAM framework based on the characteristics of the marine environment. In general, we extract the location area of the artificial sea target in the video, build a virtual feature set for it, and filter the background features. When shaking occurs, GNSS information is combined and the target feature set is used to complete the map reconstruction task. Specifically, firstly, the sea target area of interest is detected by YOLOv5, and the feature extraction and matching method is optimized in the front-end tracking stage to adapt to the sea environment. In the key frame selection and local map optimization stage, the characteristics of the feature set are improved to further improve the positioning accuracy, to provide more accurate position and attitude information about the unmanned platform. We use GNSS information to provide the scale and world coordinates for the map. Finally, the target distance is measured by the beam ranging method. In this paper, marine unmanned platform data, GNSS, and AIS position data are autonomously collected, and experiments are carried out using the proposed marine ranging system. Experimental results show that the maximum measurement error of this method is 9.2%, and the average error is 4.7%.

Predicting Natural Gas Production in Various Nations Using a Fractional Grey Bernoulli Approach

Accurate forecasting of natural gas production is crucial for economic stability, environmental sustainability, and market investment. This study presents an advanced forecasting method using the fractional grey Bernoulli model, which combines fractional accumulation and Bernoulli processes to enhance the predictive accuracy for nonlinear datasets. The model’s versatility and flexibility allow it to adapt to various data characteristics and complexities, thereby outperforming traditional grey models in forecasting performance. To optimize the model parameters, this study employs the Particle Swarm Optimization (PSO) algorithm, further improving the model’s effectiveness. Empirical analysis of natural gas production data from Brazil, Italy, and Qatar demonstrates that the model exhibits significant advantages in both fitting and forecasting capabilities. The findings indicate that the fractional grey Bernoulli model achieves high accuracy and reliability in predicting natural gas production in these countries, providing a robust framework for strategic energy planning and investment decision-making. With average forecast errors of 1.9113%, 4.0353%, and 1.8902% for natural gas production in Brazil, Italy, and Qatar respectively, this study underscores the model’s effectiveness in enhancing forecast reliability and minimizing risk, providing valuable insights for sustainable energy development.

Enhancing the performance of a resonance-based sensor network for soft robots using binary excitation

Embedded, flexible, multi-sensor sensing networks have shown the potential to provide soft robots with reliable feedback while navigating unstructured environments. Time delay associated with extracting information from these sensing networks and the complexity of constructing them are significant obstacles to their development. This paper presents a novel enhancement to an existing class of embedded sensor network with the potential to overcome these challenges. In its original version, this sensor network extracts information from multiple reactive sensors on a two-wire electrical circuit simultaneously. This paper proposes to change the excitation signal applied to this sensor network to a binary signal. This change offers two key advantages: it provides the ability to employ small, inexpensive microcontrollers and results in a faster data extraction process. The potential of this enhanced system is demonstrated here with a proof of concept implementation. The self-inductance of all inductance-based sensors within this proof of concept sensor network can be measured at a rate of over 5000 times per second with an average measurement error of less than 2%.

Early Quality Prediction of Complex Double-Walled Hollow Turbine Blades Based on Improved Whale Optimization Algorithm

Abstract The precision in forming complex double-walled hollow turbine blades significantly influences their cooling efficiency, making the selection of appropriate casting process parameters critical for achieving fine-casting blade formation. However, the high cost associated with real blade casting necessitates strategies to enhance product formation rates and mitigate cost losses stemming from the overshoot phenomenon. We propose a machine learning (ML) data-driven framework leveraging an enhanced whale optimization algorithm (WOA) to estimate product formation under diverse process conditions to address this challenge. Complex double-walled hollow turbine blades serve as a representative case within our proposed framework. We constructed a database using simulation data, employed feature engineering to identify crucial features and streamline inputs, and utilized a whale optimization algorithm-back-propagation neural network (WOA-BP) as the foundational ML model. To enhance WOA-BP’s performance, we introduce an optimization algorithm, the improved chaos whale optimization-back-propagation (ICWOA-BP), incorporating cubic chaotic mapping adaptation. Experimental evaluation of ICWOA-BP demonstrated an average mean absolute error of 0.001995 mm, reflecting a 36.21% reduction in prediction error compared to conventional models, as well as two well-known optimization algorithms (particle swarm optimization (PSO), quantum-based avian navigation optimizer algorithm (QANA)). Consequently, ICWOA-BP emerges as an effective tool for early prediction of dimensional quality in complex double-walled hollow turbine blades.

STGRL: SNN based two-stage geomagnetic road localization method

Abstract Geomagnetic navigation is a widely used positioning method capable of correcting the cumulative errors of odometers and inertial navigation systems, thereby ensuring long-distance positioning for vehicles in GPS-denied environments. However, common geomagnetic road navigation algorithms are susceptible to measurement noise, which hinder improvements in positioning efficiency and accuracy. To address this issue, this paper proposes a Siamese Neural Network (SNN) based two-stage geomagnetic road localization method. First, attitude angle information is combined with geomagnetic scalar and vector value to establish geomagnetic reference database to increase the feature dimensions of geomagnetic matching. Then, we use the Random Forest algorithm to perform a coarse matching of the data sequence to determine the current road, balancing the increased computational load resulting from the addition of feature dimensions. Finally, to further reduce the impact of random noise, this paper employs the SNN algorithm based on Transformer Encoder for fine matching of the data sequence. Experiments show that compared to existing methods, the average absolute positioning error of our algorithm has been reduced from 32.36 m to 4.07 m, and the increase in computational load is kept within an acceptable range.

Performance of repair pre-damaged RC columns using steel straps tensioning techniques (SSTT) confinement: experimental data and modelling

PurposeA model that extends study parameters to predict repaired column behaviour is efficient. Three-dimensional nonlinear finite element models were created in ABAQUS to simulate steel strap confinement with inclusion of pre-damaged levels.Design/methodology/approachExperimental and analytical studies demonstrate that restored reinforced concrete (RC) columns usually crush at mid-height under axial compressions. Numerical models verified RC column load-deformation. Although some specimens have considerable column stiffness differences, a numerical model based on statistical analysis matches experimental test results.FindingsIt shows that, finite element model exhibited a tendency to overestimate the stiffness of the columns, with an average absolute error (AAE) of 23.1%. The validation results indicate that the AAE values for strength and ductility were 15.1% and 12.3%. It has been demonstrated that the combination of strength and ductility is capable of yielding predictions with an error rate of approximately 20%. A parametric study focused on finite element model-predicted load bearing capacity reduction.Originality/valueA numerical analysis employing finite element modelling has been formulated to investigate the behaviour of confined columns. The model underwent validation through comparison with the experimental results. The validated model is utilised to perform additional parametric investigations on the confined column.

Research on a Method for Measuring the Pile Height of Materials in Agricultural Product Transport Vehicles Based on Binocular Vision

The advancement of unloading technology in combine harvesting is crucial for the intelligent development of agricultural machinery. Accurately measuring material pile height in transport vehicles is essential, as uneven accumulation can lead to spillage and voids, reducing loading efficiency. Relying solely on manual observation for measuring stack height can decrease harvesting efficiency and pose safety risks due to driver distraction. This research applies binocular vision to agricultural harvesting, proposing a novel method that uses a stereo matching algorithm to measure material pile height during harvesting. By comparing distance measurements taken in both empty and loaded states, the method determines stack height. A linear regression model processes the stack height data, enhancing measurement accuracy. A binocular vision system was established, applying Zhang’s calibration method on the MATLAB (R2019a) platform to correct camera parameters, achieving a calibration error of 0.15 pixels. The study implemented block matching (BM) and semi-global block matching (SGBM) algorithms using the OpenCV (4.8.1) library on the PyCharm (2020.3.5) platform for stereo matching, generating disparity, and pseudo-color maps. Three-dimensional coordinates of key points on the piled material were calculated to measure distances from the vehicle container bottom and material surface to the binocular camera, allowing for the calculation of material pile height. Furthermore, a linear regression model was applied to correct the data, enhancing the accuracy of the measured pile height. The results indicate that by employing binocular stereo vision and stereo matching algorithms, followed by linear regression, this method can accurately calculate material pile height. The average relative error for the BM algorithm was 3.70%, and for the SGBM algorithm, it was 3.35%, both within the acceptable precision range. While the SGBM algorithm was, on average, 46 ms slower than the BM algorithm, both maintained errors under 7% and computation times under 100 ms, meeting the real-time measurement requirements for combine harvesting. In practical operations, this method can effectively measure material pile height in transport vehicles. The choice of matching algorithm should consider container size, material properties, and the balance between measurement time, accuracy, and disparity map completeness. This approach aids in manual adjustment of machinery posture and provides data support for future autonomous master-slave collaborative operations in combine harvesting.

Evaluation of Machine Learning Models for Predicting the Hot Deformation Flow Stress of Sintered Al-Zn-Mg Alloy

Abstract In predicting flow stress, machine learning (ML) offers significant advantages by leveraging data-driven approaches, enhancing material design, and accurately forecasting material performance. Thus, the present study employs various supervised ML models, including Linear Regression (Lasso and Ridge), Support Vector Regression (SVR), Ensemble Methods (RF, GB, XGB), and Neural Networks (ANN, MLP), to predict flow stress in the hot deformation of an Al-Zn-Mg alloy. The ML methodology involves sequential steps from data extraction to cross-validation and hyperparameter tuning, which is conducted using the hyperopt library. Model performance is assessed using average absolute relative error (AARE), root mean squared error (RMSE), and mean squared error (MSE). The results show that ensemble methods (RF, GB, XGB) and neural networks outperform traditional regression methods, demonstrating superior predictive accuracy. Visualization using half violin plots reveals the models' error ranges, with XGB consistently exhibiting the best performance. SVR, RF, GB, XGB, ANN, and MLP showed better performance than the Arrhenius model in the context of AARE and MSE metrics. Interestingly, SVR had a somewhat higher AARE of 1.89% and an MSE of 0.251 MPa2, while XGB had the lowest AARE of 0.2% and the lowest MSE of 0.011 MPa2. When ML models were evaluated using the skill score in relation to the Arrhenius model, XGB scored higher than SVM at 0.714, with a score of 0.986. In contrast Lasso and Ridge exhibited negative scores of −0.847 and −0.456, respectively.

A Method for Measuring the Error Rules in Visual Inertial Odometry Based on Scene Matching Corrections

To address problems in the integrated navigation error law of unmanned aerial vehicles (UAVs), this paper proposes a method for measuring the error rule in visual inertial odometry based on scene matching corrections. The method involves several steps to build the solution. Firstly, separate models were constructed for the visual navigation model, the Micro-Electromechanical System (MEMS) navigation model, and the scene matching correction model. Secondly, an integrated navigation error measurement model based on scene matching corrections and MEMS navigation was established (the MEMS+SM model). Finally, an integrated navigation error measurement model based on scene matching corrections, visual navigation, and MEMS navigation was constructed (the VN+MEMS+SM model). In the experimental part, this paper first calculates the average error of the VN+MEMS+SM model and the MEMS+SM model under different scene matching accuracies, scene matching times, and MEMS accuracies. The results indicate that, when the scene matching accuracy is less than 10 m and the scene matching time is less than 10 s, the errors of the VN+MEMS+SM model and the MEMS+SM model are approximately equal. Furthermore, the relationship between the scene matching time and the scene matching accuracy in the EMS+SM model was calculated. The results show that, when the scene matching time is 10 s, the critical values of the image matching accuracies required to achieve average errors of 10 m, 30 m, and 50 m are approximately 160 m, 240 m, and 310 m. Additionally, when the MEMS accuracy is 150, the scene matching accuracy is 50 m, and the scene matching time exceeds 135 s, the average error of the VN+MEMS+SM model will be smaller than that of the MEMS+SM model.

Determination of Particle Size for Optimum Biogas Production from Ouagadougou Municipal Organic Solid Waste

Anaerobic digestion’s contribution to sustainable development is well established. It is a sustainable production process that enables energy to be saved and produced and efficient pollution control processes to be implemented, thereby contributing to the sustainable development of our societies. Optimizing biogas yields from the anaerobic digestion of municipal organic waste is crucial for maximum energy recovery and has become an important topic of interest. Substrate particle size is a key process parameter in biogas production and precedes other pretreatment methods for most organic materials. This study aims to evaluate the impact of particle size and incubation period on biomethane production from municipal solid waste. Sampling of municipal solid waste was carried out in waste pre-collection in the city of Ouagadougou, Burkina Faso. Waste characterization showed lignocellulolytic green waste (grass, dead leaves), waste composed of fruit and leafy vegetables and leftover food waste. TableCurve 3D v4.0 software was used to develop an optimal mathematical model to correlate particle size and biomethane productivity to describe optimal production parameters. Particle sizes ranging from 2000 to 63 µm high biogas production values, specifically 385.33 and 201.25 L·kg−1 of MSV. PCA analysis clearly showed a high correlation between particle size and biogas production, with optimum production recorded for size 250 µm with a biomethane production value of 187.53 L·kg−1 of MSV. The average relative errors and RMSE for CH4 content were improved by 24.31% and 44.97%, respectively. The data calculated with the developed mathematical model and the existing experimental data were compared and permutated to validate the model. This work enabled the identification of a mathematical model that describes the correlations between the input parameters of an experiment and the monitored parameters, as well as the definition of the particle size that allows for the optimal production of biomethane.

Broaching Digital Twin to Predict Forces, Local Overloads, and Surface Topography Irregularities

Broaching is a key manufacturing process that directly influences the surface integrity of critical components, impacting their functional performance in sectors such as aeronautics, automotive, and energy. Such components are subjected to severe conditions, including high thermomechanical loads, fatigue, and corrosion. For this reason, the development of predictive models is essential for determining the optimal tool design and machining conditions to ensure proper in-service performance. This study, therefore, presents a broaching digital twin based on hybrid modelling, which combines analytical, numerical, and empirical approaches to provide rapid and accurate predictions of the forces per tooth, local overloads, and surface topography irregularities. The digital twin was validated with a critical industrial case study involving fir-tree broaching of turbine discs made of forged and age-hardened Inconel 718. The accuracy of the digital twin was demonstrated by the results: the average error in force predictions was below 10%, and the model effectively identified the most critical teeth and zones prone to failure. It also predicted surface topography irregularities with an error of less than 15%. Interestingly, the relationship between surface topography irregularities and surface residual stress variations across the machined surface was observed experimentally for the first time.

EXPRESS: A Framework for Execution Time Prediction of Concurrent CNNs on Xilinx DPU Accelerator

Deep learning Processor Unit (DPU) is a highly configurable CNN accelerator that supports a variety of CNNs and can be implemented with multiple instances on the same FPGA. Many applications deploy concurrent execution of different CNNs and in such a setting, an execution time predictor can help “optimize” the DPU configurations to meet the performance requirements of different tasks. We characterize CNN execution on DPUs and reduce the variability in execution time due to interference from the operating system. Subsequently, we propose a machine learning-based framework (EXPRESS) to predict the execution time of any given CNN on a DPU configuration, considering CNN, DPU, and bus characteristics. We improvise EXPRESS to support heterogeneous CNNs in EXPRESS-2.0 by making features independent of the number of CNNs. Our entire experimentation is based on data from a real FPGA board for 16 standard CNNs. Our frameworks, EXPRESS and EXPRESS-2.0, significantly outperform state-of-the-art by achieving an average execution time prediction error of 2.2% and 0.7%, respectively. We illustrate the effectiveness of this low prediction error for design space exploration, which is very useful for embedded system application developers.