Error Process Research Articles

Investigating the Performance of Data-Driven Ensemble Machine Learning Models in Preliminary Designing Multi-Stage Friction Pendulum Bearings

Over the last few decades, the field of base isolation systems has demonstrated enormous advancements through the innovation of novel systems, and there has been an enhancement in the performance of structures equipped with isolation under conditions of moderate and severe seismic activity. One of the most important systems is the multi-stage friction pendulum, which offers a spectrum of effective pendulums across different regimes, thereby achieving enhanced capability for energy dissipation. The difficulty in designing these bearings at the preliminary stage comes from the fairly long process of trials and errors in selecting the properties of each sliding surface so that the required effective period, effective damping, and displacement capacities are achieved. This study investigates the performance of various data-driven ensemble machine learning models in directly designing multi-stage friction pendulum bearings. Within the context of the analysis, the most recent generation of friction pendulum that has been introduced to the literature, the quintuple friction pendulum, is used as the investigation employs a case to assess the dependability of the design strategy and the efficacy of a multi-output machine learning techniques. In general, the findings of this study have shown that machine learning models provide an accurate way to directly design quintuple friction pendulum isolators and attain the necessary properties of the isolators. The practicality of this research lies in highlighting how machine learning can be applied to considerably expedite the design and optimization of multi-stage friction pendulum bearings and in defining the most suitable machine learning technique to adopt in such tasks.

Can the Manufacturing Process Be Improved by FMEA? A Study on X Company in Plastic Packaging Industry in Vietnam

This study based on the FMEA method, provides a systematic approach, which include identifying potential errors in the production process, tracking priority issues, proposing improvement solutions, and putting those solutions into practice in order to improve the flexible plastic packaging manufacturing process at a typical X company in the plastic packaging industry in Vietnam. It is found that four large errors – GP8, SM4, LM4, and BF4 – stand out as having an RPN index higher than 250. The aforementioned issues are also analyzed, and their root causes are tracked down using techniques like brainstorming, cause-effect diagram, and 5 Whys. Then, appropriate actions are taken to deal with these causes. Overall, the findings can serve as an insightful reference for Vietnamese manufacturing companies using the FMEA method to identify any potential errors in their production processes. After that, the company is able to organize proper inquiries and make recommendations for potential failure prevention strategies as well.

Research on the Configuration Optimization of All-Metal Micro Resonant Hemisphere

As the core component of the all-metal micro resonant gyroscope, the structural parameters and form and position errors of the resonator significantly influence its vibration characteristics, and consequently, the accuracy of the gyroscope. By establishing the finite element model of an ideal hemispherical resonator and optimizing the meshing method, we refined the frequency difference to 0.1 Hz, enhancing the accuracy of the simulation model. Through finite element simulation, we examined the impact of various structural parameters and processing errors on the natural frequencies of each mode. We analyzed how form and position errors, including shell thickness error, central axis error, equatorial plane error, and edge rectangular tooth position error, affect the frequency splitting of the resonator. We provided optimization suggestions for the structural parameters, ensuring frequency splitting variations of less than 1 Hz. Theoretical modeling and simulation analysis indicated that the primary factors influencing the vibration modes and frequency splitting are the rectangular tooth structure and shell thickness. Following the optimized parameters, the frequency splitting of the All-Metal Micro Resonant Hemisphere was reduced by an order of magnitude to 14 Hz, demonstrating that these optimized conditions can significantly enhance the resonator’s performance.

Research on hydroacoustic signal processing algorithm based on B-spline and Hilbert transform

Purpose This paper aims to solve the problems of baseline drift, susceptibility to abnormal data interference during baseline drift processing, and phase inconsistency in underwater acoustic target detection and signal processing of single microelectromechanical systems (MEMS) vector hydrophone. To this end, this paper proposes a baseline drift removal algorithm based on Huber regression model with B-spline interpolation (H-BS) and a phase compensation algorithm based on the Hilbert transform. Design/methodology/approach First, the Huber regression model is innovatively introduced into the conventional B-spline interpolation (B-spline) to solve the control point vectors more accurately and to improve the anti-interference ability of the abnormal data when the B-spline interpolation fitting removes baseline drift and the baseline drift components in the signals are fitted accurately and removed by the above method. Next, the Hilbert transform is applied to the three-channel output signals of the MEMS vector hydrophone to calculate the instantaneous phase and the phase compensation is performed on the vector X/Y signals with the scalar P signal as the reference. Findings Through simulation experiments, it is found that H-BS proposed in this paper has smaller processing error and better robustness than variational modal decomposition and B-spline, but the H-BS algorithm takes slightly longer than the B-spline. In the actual lake test experiments, the H-BS algorithm can effectively remove the baseline drift component in the original signal and restore the signal waveform, and after the Hilbert transform phase compensation, the direction of arrival estimation accuracy of the signal is improved by 1°∼2°, which realizes high-precision orientation to the acoustic source target. Originality/value In this paper, the Huber regression model is introduced into B-spline interpolation fitting for the first time and applied in the specialized field of hydroacoustic signal baseline drift removal. Meanwhile, the Hilbert transform is applied to phase compensation of hydroacoustic signals. After simulation and practical experiments, these two methods are verified to be effective in processing hydroacoustic signals and perform better than similar methods. This study provides a new research direction for the signal processing of MEMS vector hydrophone, which has important practical engineering application value.

Processing of Machine Translation Errors by Korean Learners of English: An Eye-tracking Study

The present study investigates second language (L2) processing of machine translation (MT) errors in Korean-to-English translations using an eye-tracker. Twenty-five Korean learners of English participated in an Acceptability Judgment Task in which they had to rate the accuracy of Korean-to-English translated sentences while their reading times, fixations, and revisits were recorded. The effect of proficiency level (high vs. low), six error types, and language (English vs. Korean) on the participants’ responses and reading measures were examined and analyzed. The findings indicate that high-proficiency learners demonstrated a clearer differentiation between accurate and inaccurate translations and were better at recognizing errors related to proverbs and subjects, while low-proficiency learners exhibited a higher number of revisits and fixations especially when processing subject-related errors. These findings highlight the importance of proficiency level in shaping how learners process MT errors and have important pedagogical implications for MT-assisted language instruction.

Quality control for single-cell analysis of high-plex tissue profiles using CyLinter.

Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artifacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly and feature extraction. Here we show that these artifacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artifacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years before data collection, such as those from clinical trials.

Elastodynamic analysis of rotating solids by novel nodal position finite element method

This study develops a novel 8-node isoparametric hexahedral element using the Nodal Position Finite Element Method (NPFEM) for elastodynamic analysis of rotating solids. The element also incorporates the flexural modes directly into its element shape function to alleviate the shear locking when modeling the bending deformation of solids. Unlike conventional displacement-based finite element methods, which require the decoupling of elastic deformation from rigid-body motions, the NPFEM eliminates this process by directly representing strain and kinetic energies through nodal position coordinates, which avoids potential approximation errors in the decoupling process. To validate the accuracy and efficacy of this new NPFEM solid element, numerical simulations of a beam under static and dynamic loads are conducted and benchmarked against the theoretical solutions. Then, dynamic analysis of a rotating blade demonstrates that the NPFEM element can directly account for the centrifugal stiffening effect and superharmonic resonance of rotating blades without resorting to conventional methods. The successful implementation of this NPFEM element in complex simulations highlights its potential to provide significant advancements in computational mechanics.

Translated object identification for efficient ghost imaging

Alignment of a single-pixel quantum ghost imaging setup is complex and requires extreme precision. Due to misalignment, easily created by human error in the alignment process, reconstructed images are often translated off the central imaging axis. This becomes problematic for intelligent object detection and identification in fast imaging cases, as these algorithms are unable to achieve early image identification. Here, we implemented a U-net algorithm to correctly recognize images in the early reconstruction stage regardless of any off-axis translation. The U-net was trained on a uniquely curated blurred, noised, and off-axis translated dataset. We achieved a 5× reduction in imaging speeds by early image identification in four different translation directions.

Box-Behnken Design for MAFM Precision Surface Finishing of Al-6063/SiC/B<sub>4</sub>C Composites: A Comparative Study with Nature-Inspired Algorithms

Precision polishing is difficult for advanced materials like silicon carbide and boron carbide. Magnetic abrasive flow machining (MAFM) has become an effective method for cleaning, deburring, and polishing metal and high-tech engineering parts. By finishing hybrid Al/SiC/B4C-metal matrix composites (MMCs), this research uses MAFM for experimental readings. The present work is innovative due to the aluminum workpiece fixture, hybrid composites, and response surface methodology (RSM) modeling. The neural simulation of the MAFM process and nature-inspired error reduction make it unique. Using six input and two output parameters, a generic framework is created. Box-Behnken design (BBD) of response surface methodology plans and executes 54 runs of experimentation. The hybrid artificial neural network (ANN) technique is used to compare the MAFM process systematically. ANN is used to model parameter input-output relations. To anticipate the created surface accurately, regression models must be precise. These hybrid particle swarm optimization (PSO)-genetic algorithm (GA)-simulated annealing (SA) algorithms optimize the MAFM process. Additionally, trained ANN models outperform the BBD model in prediction. For optimal error reduction, the neural network uses Bayesian regularization with 112 iterations. The ANN model regression graph shows a correlation between inputs and outputs. A scanning electron microscope (SEM) with 300-magnification examines the workpiece surface. According to SEM, MAFM provides fine surface textures, thus reducing abnormalities.

Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles

The optimum performance position control of pneumatic artificial muscles (PAM) is restricted by their in-built hysteresis and nonlinearity. The hysteresis is usually depicted by a phenomenological model, while the model mentioned above always only describes the hysteresis phenomenon under certain conditions. Thus, the universality of the compensator is due to its weakness in handling disparate outside conditions. Our research employs the FN–QUPI (feedforward neural network–quadratic unparallel Prandtl–Ishlinskii) model to depict the phenomenon of pressure-displacement hysteresis in PAMs. This model has high-precision expression and generalization ability for the PAM hysteresis phenomenon. According to this, an inverse model of the QUPI operator is established as a feedforward control while combining with the feedback control of incremental PID-type iterative learning. The results show that due to the hysteresis of PAM, the compound control of feedforward control and iterative learning has better tracking performance than the ordinary PID compound control in terms of convergence rate and stability. According to the mean absolute error (MAE) and root mean square error (RMSE) of the tracking process, it can be seen that the control model can achieve accurate nonlinear compensation, and the control system shows excellent robustness to different input signals.

Impact of process parameters on the flow behavior and filling accuracy of photoresist in hot embossing for microgroove array

Hot embossing is a cost-effective and efficient method for fabricating micro-nano structures. However, challenges arise from the flow behavior and rebound phenomena of photoresists during hot embossing, leading to issues with filling accuracy and subsequently degrading optical properties. Therefore, elucidating the impact of process parameters on filling accuracy in hot embossing is crucial. Initially, a polydimethylsiloxane (PDMS) soft mold was fabricated by replicating microgroove arrays from a nickel-phosphorus (Ni-P) mold, which was produced using ultra-precision fly-cutting techniques. Subsequently, the microgroove morphology from the PDMS soft mold was transferred onto the photoresist surface through hot embossing. A numerical model for photoresist filling accuracy was established and experimentally validated. The filling mechanism of photoresist in hot embossing was elucidated. Polymer viscoelasticity decreased with increasing temperature, substantially enhancing filling accuracy, followed by a gradual decrease due to rebound phenomena. Additionally, higher embossing forces enhanced the filling ratio, but excessive pressure led to the deformation of the soft mold. Process parameters were optimized through numerical simulation and experimentation, achieving processing errors at the hundred-nanometer level and a filling ratio of 97.3 %.

Three-dimensional coupled vibration failure analysis and structural optimization of the crankshaft of the diesel engine used in the shale gas fracturing truck

The crankshaft is the key component of the shale gas fracturing truck's diesel engine. Mass imbalance due to errors in the manufacturing process and other damage caused by long periods of operation can cause vibration for the crankshaft vibration problem. The static analysis is carried out based on the ignition condition of the second cylinder. The modal analysis of the crankshaft is then carried out. The modal analysis showed that resonance could cause significant deformation of the crankshaft. Consequently, harmonic response analysis, stochastic vibration analysis, and topology optimization were carried out on the crankshaft. The results show that the modes of each order of the crankshaft of the fracturing truck diesel engine are diverse. The modal amplitude is large. Fatigue damage is easily generated. After optimization, the mass of the crankshaft is reduced, the first natural frequency is increased, and the crankshaft is far away from the resonance interval while avoiding resonance failure. This document provides theoretical guidance for the design, optimization, research, and development of crankshafts.

Integration of Metrology in Grinding and Polishing Processes for Rotationally Symmetrical Aspherical Surfaces with Optimized Material Removal Functions

Aspherical surfaces, with their varying curvature, minimize aberrations and enhance clarity, making them essential in optics, aerospace, medical devices, and telecommunications. However, manufacturing these surfaces is challenging because of systematic errors in CNC equipment, tool wear, measurement inaccuracies, and environmental disturbances. These issues necessitate precise error compensation to achieve the desired surface shape. Traditional methods for spherical optics are inadequate for aspherical components, making accurate surface shape error detection and compensation crucial. This study integrates advanced metrology with optimized material removal functions in the grinding and polishing processes. By combining numerical control technology, computer technology, and data analysis, we developed CAM software (version 1) tailored for aspherical surfaces. This software uses a compensation correction algorithm to process error data and generate NC programs for machining. Our approach automates and digitizes the grinding and polishing process, improving efficiency and surface accuracy. This advancement enables high-precision mass production of rotationally symmetrical aspherical optical components, addressing existing manufacturing challenges and enhancing optical system performance.

A graphics-based digital twin (GBDT) framework for accurate UAV localization in GPS-denied environments

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) is crucial for effective assessment of large-scale civil infrastructure, as manual UAV control is time consuming and prone to mishaps. Autonomous navigation outdoors typically employs GPS signals to enable accurate localization and reduce long-term drifts. However, in many civil engineering applications, GPS signals are either poor or unavailable due to interference and/or multi-path effects. Current approaches for UAV localization in GPS-denied environments, track geometric features such as corners; however, these natural features can be misrepresented due to the presence of occlusions and/or image processing errors, resulting in significant localization errors that can grow with time. AprilTags can reduce localization errors, but installation on large-scale civil infrastructure can be challenging. Therefore, this paper proposes a framework that leverages the wealth of visual and geometric information encoded in a Graphics-Based Digital Twin (GBDT) of a target infrastructure to provide accurate localization of a UAV. The GBDT is comprised of a computer graphics model that faithfully represents a target structure’s geometry, structural features, and visual textures. The visual details of the GBDT are exploited to design an object recognition algorithm that detects GBDTtags, which are distinctive objects (or a collection of components) on the target structure in advance; GBDTtags provide functionality similar to AprilTags. When the camera attached to a UAV detects one or more GBDTtags, the vertices of the GBDTtags are mapped from the image plane into the GBDT coordinate system, allowing for the UAV to be localized. The framework, termed herein as GBDTpose, is first validated numerically using Blender, Gazebo, and Mavros software-in-the-loop (SITL). Subsequently, field validation is carried out using the Kavita and Lalit Bahl Smart Bridge at the University of Illinois Urbana-Champaign (UIUC). Results show that localization in GPS-denied environments can be achieved with 5-50 cm accuracy without the need for physical markers being placed on the structure.

Menakar Kebijakan Pendampingan Self Declare Halal oleh Perguruan Tinggi (Studi Kasus di Universitas Nahdlatul Ulama Sidoarjo dan Uin Maulana Malik Ibrahim Malang)

This study examines various government efforts to encourage halal product assurance, especially through the creation of derivative regulations and the establishment of the Halal Product Assurance Organizing Agency (BPJPH). However, small to micro-scale business actors still need help, especially related to registration fees and understanding the importance of halal certification. The study aims to explain the role of universities in East Java in assisting halal certification for Micro and Small Enterprises (MSEs) while analyzing the legal aspects and maqashid sharia. Using field research methods and qualitative approaches, it was found that the role of universities is as PPH assistants for MSEs who meet the halal certification criteria through the self-declare route. The implementation of self-declare halal assistance is faced with the obstacle of maintaining the activeness of registered PPH assistants, and there are still business actors who need to meet the requirements in the assistance process, such as lack of information on materials and expired material certification. An effective monitoring system is required to overcome potential errors in the halal certification process. Universities are also expected to be able to create program innovations to strengthen and activate the role of PPH assistants. The government, in this case, especially BPJPH, also needs to create a more consistent policy regarding halal certification registration through the application so that the criteria of the Halal Product Assurance System (SJPH), which is built on five basic principle frameworks (arkanul halal) can be met.

The Influence of Chinese and Western Cultural Differences on English Translation and Its Countermeasures

Abstract English plays an irreplaceable role as a bridge of global communication. However, the cultural differences between China and the West have a profound impact on English learning and translation practice, resulting in the reduction of semantic accuracy and information richness, which damages the effect of communication and restricts the style of translation. These cultural differences not only lead to understanding errors in the translation process, but also lead to the loss of cultural connotation, the conflict of expression and the mismatch of logical structure, thus increasing the complexity and challenge of English translation. To solve these problems, this paper suggests some strategies to alleviate the influence of Chinese and Western cultural differences on English translation, such as enhancing the training of cultural awareness, improving the accurate grasp of the context, flexibly adjusting the ways of language expression and optimizing the logical expression structure. In addition, it is emphasized that while mastering language skills, English learners should deeply understand and respect the cultural background of the source language and the target language, so as to improve the quality of translation and the effectiveness of communication. These measures will help English learners to translate and apply English more accurately and freely, not only in the literal sense of the language, but also in the cultural level.

Decision Support System for Permanent Employee Selection at PT XYZ with Moora Method

Using the multi-objective optimization method based on ratio analysis (MOORA), the purpose of this research is to develop a decision support system that will help the selection process of permanent employees at PT XYZ. In order to improve the productivity and operational efficiency of the company, a proper employee selection process is essential. Nonetheless, many factors influence the decision taken, which is often subjective. Therefore, a systematic approach is needed to help managers assess and select potential employees based on predefined standards. The MOORA method was chosen because it is able to provide optimal solutions for a variety of different criteria. The identification of employee selection criteria, the collection of employee candidate data, and the use of the MOORA method to generate employee candidate rankings are all actions performed in this study. The criteria used include work experience, education The purpose of this research is to create a system that supports the process, skills, and attitudes in decision making. The results of this system show that MOORA has the ability to provide more objective and transparent recommendations in the employee selection process than conventional methods. The results of the analysis show that this system helps decision making and increases managers' confidence in choosing employees who match the company's needs. PT XYZ hopes to use this decision support system to reduce errors in the employee selection process and help achieve the company's strategic goals. It is hoped that this research will help the process of developing decision support systems in the field of human resources.

Tri-training algorithm based nuclear power systems semi-supervised fault diagnosis under multiple restricted data conditions

An improved tri-training semi-supervised classification method was proposed as a solution to the restricted data conditions of supervised classification models relying on labeled data, semi-supervised models not considering imbalanced proportion of faults data and the presence of faults data with similar feature or varying severity in existing data-driven nuclear power system fault diagnosis studies. Based on the structure of three identical sub-classifiers in the original tri-training algorithm, data update paths have been added, the updated data selection has been strengthened, and the initial information and permissions of sub-models have been differentiated. The novel method only requires the dataset to meet the Smoothness Assumption, and through multiple strict data update paths, it improves the utilization of unlabeled training data while reducing Pseudo-labeling process errors. The difference in permissions and initial information between sub-classifiers is utilized to improve the training rigor for fault types with smaller sample sizes, in order to alleviate the problem of imbalanced data. Based on a marine nuclear power system secondary circuit model and a widely recognized nuclear power plant dataset, multiple nuclear power system common restricted fault diagnosis datasets faced in ships and power plants were obtained to verify the novel method's advantages and generalization. The conclusion is that under the same conditions, compared with the original tri-training method and other semi-supervised learning methods, the Accuracy, AUC, Precision Rate and Recall Rate of the novel method have improved by about 21 %, 10 %, 20 %, and 21 %, respectively, and can reduce about 25 % of misjudgments between similar feature faults.

Interacting multiple model adaptive robust Kalman filter for process and measurement modeling errors simultaneously

This paper proposes an effective Interactive Multiple Model Adaptive Robust Kalman Filter (IMMARKF) without time delay to handle situations where both process modeling errors and measurement modeling errors exist simultaneously. Building upon the robust Centered Error Entropy Kalman Filter (CEEKF) for outlier measurements and the Adaptive Kalman Filter (AKF) for process modeling errors, the IMMARKF method combines the Gaussian optimality of the KF, the adaptability of AKF, and the robustness of CEEKF using the interacting multiple model (IMM) principle to adapt reasonably to changing application environments, and can obtain estimation results in the absence of time delay. Target tracking simulations show that compared to existing methods, the proposed method can better adapt to non-stationary noise and application environments where process anomalies and measurement anomalies occur simultaneously.

The Effect of 6 weeks Physical Activity on Cognitive Control and Trait Impulsivity in Multi-problem Young Adults: First Findings of an RCT-study.

This study aimed to report the effect of a 6-week light-active versus moderate-active physical activity intervention embedded in a multimodal day treatment program on selected measures of cognitive control (i.e., response inhibition, error processing, and cognitive interference) and trait impulsivity. A randomized controlled design was implemented, including male multi-problem young adults (aged 18-27) assigned to either light-active (N = 12) or moderate-active physical activity lessons (N = 11). A repeated measures design was used to examine treatment effects between the two groups over time on response inhibition, error processing, and cognitive interference (measured respectively with a Go/NoGo task, a Flanker task, and the Stroop) and trait impulsivity (measured with the Dutch Baratt Impulsiveness Scale). Cognitive control, but not trait impulsivity, improved over time. Specifically, enhancements in inhibition and reduced cognitive interference were observed after 6 weeks. Error processing did not improve, but we did observe improved performance on an error-processing task. No interaction with physical activity intensity was found, suggesting similar treatment effects regardless of intensity. Results should be interpreted with caution due to several limitations, including the small sample size. Overall, due to current limitations (i.e., physical activity embedded in a larger treatment program, small sample size at follow-up, and low intervention adherence), it is not possible to draw any definite conclusions. However, the current findings contribute to a growing body of evidence suggesting potential benefits of physical activity (embedded in a multi-modal day treatment program) in the enhancement of cognitive control deficits in at-risk populations, independent of exercise intensity.