Error Method Research Articles

Low Complexity Model Predictive Flux Control Based on Discrete Space Vector Modulation and Optimal Switching Sequence for Induction Motors

In this paper, a low-computational burden model predictive flux control (MPFC) based on discrete space vector modulation (DSVM) and the optimal switching sequence (OSS) is proposed for achieving switching frequency (SF) and computational burden efficiencies in motor drives fed by two-level voltage source inverter. The DSVM is used to extend the prediction candidates of MPFC and greatly improve the performance of the controller. A generalized minimum flux error method independent of the number of virtual vectors is derived to cancel the exhaustive optimization method and lower the execution time of the proposed algorithm. In addition, new over-modulation and OSS schemes are designed to optimize the use of DC-link voltage and mitigate the inverter SF when implementing the optimal control action into switching states. The comparative experimental results show that without significant performance degradation, the proposed strategy provided about 50% SF and 25% execution time reductions compared to the classic MPFC methods.

Online system identification of global lung heat transfers

In cardiac surgery where extracorporeal circulation is used, the lungs are temporarily disconnected from the body and are connected to a device that provides air and blood. To minimize the risk of tissue damage, the lungs are subjected to mild hypothermia. Heat transfer modeling offers the potential to enhance temperature regulation through a more advanced approach. From a thermal quadrupole formalism combined with the heat equation or the bio-heat equation when considering blood perfusion according to the branch level, a more compact model is used for system identification. The feasibility of online system identification is proposed for coefficient estimation by using an extended Prediction Error Method algorithm more suited for estimating fractional order systems: the Long Memory Prediction Error Method (LMRPEM).

Development of an Optimal RTK Calculation Program Using Genetic Algorithm

Rising levels of underground water and inappropriate pipe alignment during rainy weather have led to a rise in rainfall-derived infiltration and inflow (RDII), increasing incidences of separate sewer overflows (SSOs) and reducing the efficiency of sewage treatment systems. RTK analysis based on measured RDII data of the target area through computer modeling is essential when maintaining sewer pipes in order to address this RDII problem. In the Sanitary Sewer Overflow Analysis and Planning (SSOAP) program used for RTK analysis, calculating RTK using a trial and error method is a bit challenging. Accordingly, this study introduces a program to calculate the optimal RTK at the measurement point using a genetic algorithm. In ROP, accuracy was added to the RDII prediction mechanism by incorporating error rates based on time intervals into the evaluation factors of SSOAP, namely rainfall inflow rate and peak flow error rate. The error rate for the SSOAP program and ROP was validated as 2.66%.

Teaching the Analysis of Translation Errors through Collaborative Work: The Case of 1st Year Master Students at the University of Mascara – Algeria

Given the gap noted in the Algerian official syllabus of the translation unit, and though both teachers and students are aware of the importance of translation errors, no lecture is dedicated to this concept. Hence, the present investigation attempts to testify the efficiency of collaborative work in teaching translation errors to M.A. students and thereby apply the theoretical background in real situations. It aims to train students to detect and correct errors, improve the final product, and minimise the different types of errors. The paper adopts a qualitative quantitative-mixed method to scrutinise the phenomenon under investigation. Whereas the theoretical part draws on Delisle's (1999) model of translation errors, the practical part relies on data collected via questionnaires, interviews, and document analysis. The paper's data were obtained from M.A. students' documents at Mascara University (Algeria) to examine the efficiency of collaborative work as a method of teaching translation errors. The study finds out that the error rate was reduced by 24% after adopting collaborative work, including the three types of texts (general, medical, and literary). The study also concludes that collaborative work enhances three central competencies competitiveness, autonomy, and motivation; hence, they acquire certain translation maturity and critical thinking.

Pendampingan Membaca Al-Qur’an Dengan Tema Sehari Mengenal Lebih Dekat Bersama Al-Qur’an

The problem of this discussion is the low ability to read the Qur'an at MDA Nurul Hikmah Simpang Tonang and can be said to be not fluent. While the spirit and ability to memorize were found to be very good. Based on preliminary observational data, it was found that ten of the memorizers of the Qur'an only four can be said to be good at reading the Qur'an according to the law of tajweed. The purpose of this study is to guide Qur'an memorizers in reading the Qur'an correctly and precisely according to the makharijul letters and the science of tajweed. The research method is studied based on descriptive qualitative research starting from observation and lectures to find out the initial ability as a whole and provide motivation, the tahsin and talqin methods as reading reinforcement and trial and error methods in order to find out the final ability of material delivery and can form a group in understanding the law of tajweed at MDA Nurul Hikmah Simpang Tonang, West Pasaman Regency. The results of this activity show that: 1) there is a significant relationship in the success of reading the Qur'an according to the rules of tajweed to the fluency and enthusiasm of memorizing the Qur'an and it takes a trial and errol method as an acceleration of the ability to understand the laws of tajweed. Of the 18 MDA participants, it was found that 11 children could read the Qur'an better with the provision of accuracy of letters and tajweed knowledge. 2) the method and strategy of delivering the law of tajweed according to the short and long vowels of 2, 4, 5, and 6 vowels emitted from the level of humming, and the level of buzzing and vagueness can stimulate the ability to read the Qur'an faster and better than before for the memorization of the Qur'an.

Fabrication of a System to Convert Waste Plastic into Fuel

The widespread use of plastics in numerous industries over the worldwide has expanded its output throughout time. The constant demand for plastics resulted in the piling of its garbage in landfills, which occupied a lot of space and contributed to the environmental disaster. Plastics consumption increased, resulting in the depletion of petroleum products which is a non-renewable fossil energy source, as plastics were petroleum-based materials. As a result, pyrolysis is offered as a tertiary recycling method. Pyrolysis is the thermal decomposition of materials in the absence of oxygen at high temperatures (or an inert atmosphere). In the presence of heat, it is a chemical reaction process that includes the molecular breakdown of bigger molecules into smaller ones. Plastic trash pyrolysis might play an essential role in transforming this solid waste with high thermal energy into power and commercially expensive hydrocarbons that can be utilised as fuels or feed stock in the petrochemical sector. The composition of the plastic waste influences the end product yields and attributes. This work has unleashed the possibilities to obtain the fuel from the waste plastics, utilising the renewable resources with the temperature range of about 180-220 °C was achieved with our pyrolysis reactor. The fuel with different compositions of plastics were obtained through the pyrolysis method under the absence of the oxygen in a closely monitored environment obtaining a combustible fuel and later on, the fuel properties were tested and analysed with various trial and error methods.

A Novel Approach for Rapid Material Library Generation Using Laser‐Remelting

Cost‐ and time‐demanding trial‐and‐error methods have been the historical route for alloy development. A combinatorial approach can significantly simplify and accelerate the development process by characterization of composition dependent properties on material libraries, which are specimens or sets of specimens that map out a certain composition space, often employing composition profiles. Herein, a promising production method for such a material library is proposed: laser‐remelting of stacked blocks of different compositions is evaluated for its suitability to produce material libraries, using the ternary CrCoNi system for a proof‐of‐concept. The composition profiles of the successfully created CrCoNi material library were measured by electron probe micro analysis. The intermixing has a length of about 2.5 mm. An analytical model describing the intermixing process is proposed and shows value in the estimation of the intermixing length after the first melting step. The comparison of the experimental microstructure observations from this work and from literature shows mostly good agreement with some deviations related to microsegregation and finite quenching cooling rates, which is supported by thermodynamic calculations regarding phase stability. In the single‐phase region, the mechanical properties as measured via microhardness indentation are discussed as potential candidates for model validation.

Determination of Modified Mohr-Coulomb Damage Model Parameters for DH780 Steel in Finite Element Analysis

In sheet metal forming processes, tearing problems might be occasionally encountered due to many reasons such as incorrect forming parameters. The trial and error methods that are used to solve such problems, on many occasions, are time-consuming and inefficient in terms of finding the correct forming parameters or die design for the forming process. The finite element analysis method, on the other hand, can be used as a tool that is both time and cost-saving. However, in order to effectively exploit the use of finite element analysis in sheet metal forming operations, the material that is used to be formed needs to be well characterized in terms of its hardening behaviour and failure criteria. In this study, a TRIP-aided DP steel (DH780) has been tensile tested in three different deformation conditions (uniaxial, plane stress and shear) and the parameters of its hardening model (Hollomon) and failure criteria (Modified Mohr-Coulomb) have been determined. According to the simulation results, obtained hardening parameters are able to describe the flow behaviour of the steel and the used failure criterion is able to predict the experimental failure correctly in each deformation condition.

On-machine measurement method of hole parallelism error based on multi-sensor fusion

The hole parallelism error (HPE) is one of the key quality parameters for mechanical parts. However, HPE can usually be out-of-tolerance due to the coupling effect of residual stress, cutting stress, and geometric structure during its machining process. Thus, on-machine measurement and timely adjustment are important guarantees for HPE. This paper proposes an on-machine measurement method of HPE based on multi-sensor fusion that is low-cost, efficient, and suitable for mass production of parts. First, the axis of the cylindrical surface is directly extracted using five points according to the spatial line position relation. Then, the mathematical model of the HPE of two holes’ axes was established through the theory of spatial analytic geometry. The HPE is characterized using two parameters: parallelism magnitude and parallelism direction. Finally, the feasibility of the measurement method in principle and practical application is verified by simulation and experiment. The simulation tests show that the maximum value of the relative error of parallelism magnitude and parallelism direction is −1.38‱. The actual experiments show that the relative error of the measurement results compared with the coordinate measuring machine is 6.23%. None of the standard deviations exceeds the order of magnitude of 0.01 mm. The difference in measurement uncertainty is 0.0003 mm.

Uncertainty-aware error modeling and hierarchical redundancy optimization for robotic surface machining

Industrial robots are commonly utilized in in-situ machining, but their inherent limitations in stiffness and positioning accuracy can pose challenges in achieving precise freeform surface milling. Previous research primarily focused on robot stiffness optimization and positioning error prediction separately, neglecting the uncertainty of robot errors and the modeling of surface profile errors. To address these issues, this work introduces a novel evaluation method for surface profile errors in robotic milling. It combines geometric error prediction models, based on kinematic and stiffness models, with non-geometric errors using Gaussian process regression. Moreover, collaborative optimization of multiple redundancies in robotic surface milling systems is crucial for reducing machining errors effectively. To achieve this, this work proposes a collaborative optimization method for optimizing the robot’s posture change sequence and the workpiece’s orientation, considering the motion constraints and error requirements of the robot. Among them, this work introduces the improved Whale Optimization Star Algorithm (WOA*) method to address the optimization problem of coupling robot posture and workpiece’s orientation. The proposed method’s effectiveness is confirmed through simulations and experiments, which clearly demonstrate its capability in improving error prediction and machining accuracy in robotic milling.

Validasi Data Satelit Tropical Rainfall Measurement Mission dengan Menggunakan Pengamatan Curah Hujan

The availability of hydrological data poses a challenge in water infrastructure development. Common issues in this domain often arise due to the lack of comprehensive data availability. Utilizing high-resolution satellite-based rainfall measurements covering extensive areas presents a potential solution. However, variations in the observed rainfall data resolution may impact the accuracy of the data. This study employs Tropical Rainfall Measurement Mission (TRMM) rainfall data and compares it with rainfall data from observation stations to assess the suitability of TRMM as a hydrological data source. Validation analysis is conducted using the Root Mean Squared Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Correlation Coefficient (R), and relative bias (RB) methods. Validation results indicate that corrected TRMM data yields better outcomes compared to uncorrected TRMM data, with lower RMSE, higher NSE, and increased RB values. The most favorable findings occur at the Batu Busuk station, with RMSE = 22.554, NSE = 0.181, R = 0.95, and RB = 0.413. These findings suggest that corrected TRMM data can be effectively used as a hydrological data source in water infrastructure development.

An Extended Kalman Filter Design for State-of-Charge Estimation Based on Variational Approach

State of charge (SOC) is a very important variable for using batteries safely and reliably. To improve the accuracy of SOC estimation, a novel variational extended Kalman filter (EKF) technique based on least square error method is herein provided by establishing a second-order equivalent circuit model for the battery. It was found that when SOC decreased, resistance polarization occurred in the electrochemical model, and the parameters in the equivalent RC model varied. To decrease the modeling error in the equivalent circuit model, the system parameters were identified online depending on the SOC’s estimated result. Through the SOC-estimation process, the variation theorem was introduced, which enabled the system parameters to track the real situations based on the output measured. The experiment results reveal the comparison of the SOC-estimation results of the variational EKF algorithm, the traditional EKF algorithm, the recursive least square (RLS) EKF algorithm, and the forgotten factor recursive least square (FFRLS) EKF algorithm based on different indices, including the mean square error (MSE) and the mean absolute error (MAE). The variational EKF algorithm provided in this paper has higher estimation accuracy and robustness than the traditional EKF, which verifies the superiority and effectiveness of the proposed method.

Recurrent neural network based on attention mechanism in prediction of glass forming ability by element proportion

The traditional trial and error method is time-consuming and laborious to study glass forming ability (GFA), so it is necessary to design a fast and accurate method to predict GFA. In this paper, four deep learning models of BidiRNN, BidiLSTM, Attention-BidiRNN and Attention-BidiLSTM are constructed by using recurrent neural network (RNN) and Attention mechanism, and the GFA of alloys is directly predicted by element proportion. After model selection and model evaluation through cross-validation, the prediction accuracy of the four models on the test set is 0.727, 0.796, 0.822 and 0.843, respectively, which has good generalization performance. Since amorphous alloys usually have high similarity to similar alloy systems, the attention mechanism can dynamically identify alloys with high similarity to the current prediction task. By weight matching, the feature extraction ability of the model is enhanced, and the prediction accuracy of Attention-BidiRNN and Attention-BidiLSTM is 9.05% and 4.7% higher than that of BidiRNN and BidiLSTM, respectively. Finally, two systems of CuZrAl and CuCeGa are predicted in this paper, and the reliability of the model is verified by experimental data. This realization of predicting GFA only by combination of elements is expected to provide important guidance for the development and preparation of novel amorphous alloys.

Research on the measurement method of crankshaft coaxiality error based on three-dimensional point cloud

Aiming at the demand of rapid detection for coaxiality of crankshaft journal, a method of visual measurement of coaxiality error based on 3D point cloud is proposed. In this research, a laser scanner is utilized to obtain a 3D point cloud model of the part to be measured, and a circular method of rotary cutting is put forward to obtain equally spaced optimal slices of the journal. The center of the slice is then obtained using the Pratt circle fitting method optimized in this paper. After that, the center of the slice is used to obtain the common reference axis using the least squares method, which finally realizes the measurement of the coaxiality error. The experimental results show that the proposed method of getting the slices is more accurate, the maximum absolute error of the circle fitting method compared with the results of the coordinate measuring machine (CMM) is no more than 4.2 μm, and the maximum absolute error of the coaxiality detection results and the results of the CMM is 5.5 μm, which can satisfy the practical needs of fast and efficient measurement of the coaxiality error of crankshafts.

Weighted-Combined Model for Reclaimed Foundation Settlement Prediction in Coastal Sludge-Bearing Composite Stratum

Foundation settlement prediction is significant to the reuse and management of the reclaimed land from the sea by dykes where composite stratum of many strata with different physical and mechanical properties is encountered. Toward the foundation settlement analysis of reclaimed land in the Yellow Sea composite stratum in eastern China, a weighted-combination model is proposed in this paper combining the hyperbolic model, exponential curve model, and Asaoka model. First, the weight coefficient of the weighted-combination model is calculated by the reciprocal square method of average absolute error (MAE). Second, the settlement prediction results of different models are evaluated by the absolute error, MAE, root-mean-square error, and mean absolute percentage error. Finally, the settlement mechanism of reclaimed foundations in a composite stratum is analyzed from the point of view of the multistratum coupling, and the adaptability of different models to the settlement prediction of reclaimed foundations in a composite stratum is discussed. The results show that the predicted settlement duration curves of the weighted-combined model are in good agreement with the measured settlement duration curves, and the prediction performance is better than that of the hyperbolic model, exponential curve model, and Asaoka model. The MAE of the weighted-combination model is 75.7% lower than that of the exponential curve model, 90.2% lower than that of the hyperbolic model, and 70% lower than that of the Asaoka model. This model provides a new way to predict the settlement of reclaimed foundations in a similar composite stratum.

Development of the Phaseless Calibration Algorithm for a Digital Antenna Array

In this paper, we will discuss a calibration algorithm for a digital antenna array that diagnoses its real performance. It can be applied at such stages of the antenna system life cycle as design, tuning, and especially maintenance. A calibration implementation using a scalar method for a multi-beam digital antenna array is proposed and investigated. On-the-fly calibration ensures a continuous improvement in beam pointing accuracy by reducing internal errors in the receiving (transmitting) channels. The purpose of the study is to experimentally examine the capabilities of digital beamforming to increase the angle-of-arrival estimation accuracy. A simulation model of the receiving antenna was created in an anechoic chamber with a planar antenna positioner. The possibility of precise estimation of the direction of arrival using the digital beamforming with electronic scanning was demonstrated. The proposed simulation model made it possible to observe the convergence of the antenna array calibration process using the proposed method for various errors in the signal paths, as well as different signal-to-noise ratios. It has been proven that even under adverse conditions early in the calibration algorithm, the phase error detection converges with high accuracy, and its value decreases uniformly even to the fractions of an angular degree.

Machine Learning Aided Numerical and Experimental Investigation of Hydrodynamic Performance in the Circulating Fluidized Bed Boiler

Abstract This study attempts to illustrate the benefits of integrating the concepts of machine learning algorithms with the field of thermo-fluidic applications. The current work is aimed at identifying effective or significant hydrodynamic input parameters, which are capable of deriving full benefit of fluidization that could yield a better circulating fluidized bed (CFB) furnace design using the Apriori algorithm. For this, historical datasets from the literature are collected and pretreated based on the design under observation. Association analysis performed by this Apriori algorithm measures the comparative strength of parameters under consideration. Also, this algorithm is capable of identifying the right combinations of parameters that can produce maximum fluidization performance. The end results suggested by this Apriori algorithm are validated using the computational fluid dynamics package. For this, the transient behavior of a scaled-down (1:20) reactor model of a real-time industrial CFB boiler is simulated using ansys fluent 18.0. In specific, the effects of fluidizing velocities, inventory heights of the bed, and particle sizes recommended by the Apriori algorithm are investigated. Here, the effects are assessed in terms of volume fraction distribution and axial velocity profile distribution profiles. From the results of simulations, it was clearly found that 2 m/s inlet velocity produced good circulating fluidized bed patterns on a bed inventory height of 0.5 m for a mean particle size of 200 µm. The results obtained from the simulations are once again validated visually against snapshots obtained during real-time laboratory fluidization experimental runs. Among all the cases of comparisons, the best agreement is demonstrated by Apriori algorithm compared to the numerical and the experimentally obtained results. Also, it is found that the manual time taken to identify the right combinations of parameters is drastically reduced by this method compared to conventional optimization algorithm and trial error methods.

Performance degradation and reliability analysis of a MEMS flow sensor with accelerated degradation testing

In this article, we research the performance degradation of a MEMS flow sensor with accelerated degradation testing (ADT). The degradation of temperature stress (Ts) on sensor is studied. Based on the performance in the aging test, resistance degradation ∆R is selected as a more suitable degradation characteristic parameter. Furthermore, the degradation process of the characteristic parameter results from resistivity decreases, in which Ts leads to stress relief within the bulk of the resistor. By implementing the Constant-stress accelerated degradation test (CSADT), we estimate the lifetime of this MEMS flow sensor based on the Arrhenius model. The calculated lifetime is 6.833 years for the 120°C group, 3.942 years for the150°C group, and 2.492 years for the180°C group. In addition, using statistics analysis, the Weibull distribution (WD) is proved to be the better mode for the MEMS flow sensor acceleration life with the Anderson-Darling (AD) being 235.910. The scale and shape of WD was evaluation. On the other hand, to verify the rationality of the distribution, the Standard residual error method and the Cox-Snell residual error method have been used separately, to perform the Goodness-of-fit tests. The AD value of Standard residual error and Cox-Snell residual error is 3.476. It proves that the selected life distribution model of this MEMS flow sensor is befitting. Finally, in this research the CSADT results are original and novelty. The lifetime distribution results reveal that considering the Goodness-of-fit tests can significantly improve the accuracy of reliability analysis of this MEMS flow sensor. Based on those results, a reliability method for this MEMS flow sensor is established.

Optimasi Mix Design Beton Melalui Teknologi Machine Learning

The concrete mix design is a crucial step in ensuring the quality of concrete used in construction projects. Traditional mix design methods rely on trial and error, which can be time-consuming and escalate construction costs. In recent years, machine learning technology has been developed to predict concrete properties and optimize mix designs for high-quality concrete. This study aims to explore the application of machine learning in high-quality concrete mix design, considering theoretical conditions, methods, and related research. The focus of this research is to test the use of machine learning techniques in predicting concrete properties and optimizing mix designs for high-quality concrete. The theory and concepts of machine learning will be applied to concrete mix design datasets and relevant properties. The methods employed will include data pre-processing, feature selection, and model training and evaluation. The performance of the machine learning model will be compared with traditional mix design methods to determine its effectiveness. Furthermore, the results and benefits of this study will demonstrate the potential advantages of using machine learning in determining high-performance concrete mix designs. By accurately predicting concrete properties and optimizing mix designs, construction projects can be completed more efficiently and with higher quality. This technology also has the potential to reduce costs associated with trial and error methods and minimize the environmental impact of concrete production. The success of this study will pave the way for further research and development in the field of machine learning for high-performance concrete mix designs.

Modeling and analysis of radial throw of micro-milling tool based on tool-attachment errors in high-speed rotating

In the process of micro-milling complex structures, radial throw is likely to cause lower dimensional/form accuracy, which will severely affect the performance of the components. Therefore, it is necessary to investigate the source contributing to the radial throw. According to the kinematic process of the radial throw arising from the attachment errors of the micro milling cutters, a mathematical model considered the attachment errors and the variable section geometry of the micro milling cutters was established to predict the radial throw on the basis of the principle of minimum total potential energy and the Timoshenko beam theory. In comparison to the mathematical model, the proposed finite element model (FEM) presetting the attachment errors shows the effect of modal vibration and the variation of radial throw with attachment errors. To further investigate the environmental factors influencing the radial throw, a measurement method based on the laser measurement system is presented to achieve the on-machine measurement of runout, and a specific extraction method of attachment errors was proposed to help validate the mathematical model. The difference between the results of the mathematical model, FEM, and the experiment is not exceeding 10 % within 3 μm of the maximum runout. Radial throw varies strongly with attachment errors, and the high rotation speed will aggravate the system instability caused by the attachment errors. The main contributor among experimental factors to the variation of runout is the form accuracy of micro milling cutters, accounting for 4.7 % at the highest. This method can provide a theoretical basis for the sources of unbalanced cutting force analysis and the improvement of dimensional/form accuracy.