Efficient Error Research Articles

HETEROSCEDASTICITY CORRECTION MEASURES IN STOCHASTIC FRONTIER ANALYSIS

The stochastic frontier analysis (SFA) model, designed to assess technical efficiency in production models, operates under the assumption of homoscedasticity. However, in practical scenarios, either the random error or the technical efficiency error, or both, can exhibit non-homoscedasticity. This research proposes heteroscedasticity correction measures for the random error (HCRE), technical efficiency error (HCTE), and both (HCRTE) within the SFA model. The study aims to determine which correction measure yields the most efficient parameter estimates when heteroskedasticity is present. The comparison involves evaluating the mean squared error (MSE) across different forms of heteroscedasticity and sample sizes through Monte Carlo simulations comprising 5000 replications. The findings indicate that attempting to correct for heteroskedasticity in the absence of such issues can adversely affect the parameter estimates of the SFA Model. Conversely, the HCRTE measure consistently produces the most efficient estimates when dealing with heteroskedasticity in terms of both random error and technical efficiency. Moreover, in cases where heteroskedasticity exists, applying the HCRTE measure not only enhances parameter estimates but also improves the technical efficiency measure of the SFA model.

Performance analysis of free space optical communications with FOA-WFS.

Adaptive optics (AO) technology can correct wavefront distortion in coherent free space optical communication (FSOC), with wavefront sensors playing a vital role in this process. However, traditional wavefront sensors are large and expensive. Therefore, we propose using the inexpensive and easy-to-deploy flat optics angle-based wavefront sensor (FOA-WFS) to measure the wavefront aberration. It aims to meet the needs of various FSOC applications. We first establish the relationship between the energy ratio and the Zernike coefficient through theoretical studies and analyze the feasibility of applying the FOA-WFS to the FSOC. We then generate experimental datasets based on the relevant principles. Through numerical simulation, we verify that it can reconstruct wavefront aberration accurately and improve system performance. Finally, we analyze the mixing efficiency and bit error rate based on the collected aberration data by the experimental platform. The results indicate that the AO system based on the FOA-WFS can efficiently improve the performance of the FSOC. This study provides a novel wavefront aberration detection method for designing the AO systems in the FSOC.

Optimization and influence of oil supply parameters on oil capture characteristics of radial oil scoop for high-speed bearings

Radial under-race lubrication technology offers an innovative solution for high-speed bearings in aero-engines, aiming to minimize oil flow rate and engine power loss. This study, through experimental and CFD simulation methods, evaluates the entire operational process of radial under-race lubrication — capturing, delivering, and expelling oil. It elucidates the mechanisms through which oil supply parameters, particularly the orifice diameter, affect both oil capture efficiency and flow rate, while identifying the optimal range of orifice diameters under various oil supply conditions. An improper orifice diameter can reduce oil capture efficiency by up to 45 %. In engineering applications, achieving a higher oil capture flow rate may necessitate sacrificing a portion of oil capture efficiency. Optimized oil capture efficiency consistently exceeds 80 %, with a significant improvement of over 20 %. Owing to the competitive relationship between the relative error of oil supply and capture efficiency, it is necessary to reconcile these factors to achieve optimal performance.

Analysis of the Effect of Sampling Probe Geometry on Measurement Accuracy in Supersonic Gas Flow

The accuracy of sampling of gas components has a significant impact on the measurement of various performance parameters in the combustion chamber of an aero-engine. In order to investigate the effect of the probe geometry of a six-point gas sampling probe on sampling accuracy in supersonic gas flow, a three-dimensional probe gas flow characteristic solution model is established through numerical simulation methods of components of transport and fluid–solid coupling. Probes with three angles of 28°, 30°, and 32° and an optimized conical probe are constructed. The sampling accuracy of the probes with different geometries is compared and evaluated by the deviation of the component volume fraction before and after sampling and the resulting combustion efficiency error. This paper presents a set of calculation methods for solving the relative deviation of volume fraction by an iterative method based on the ideal gas law and the Redlich–Kwong equation (R-K equation). The method is designed to solve the exact component volume fraction problem in the simulation calculation. The study results demonstrate that the 28° and optimized conical probes improve sampling accuracy more effectively than the original 30° structure. The deviation of the volume fractions of the two structures is less than 1.7%, and the combustion efficiency error is less than 0.09%. The developed iterative calculation method can significantly reduce the theoretical calculation error to less than 0.06%. The experimental data of the test bench are in good agreement with the simulation results, thereby demonstrating the reliability and accuracy of the sampling probe following structural optimization.

Establishment of a Mass Concrete Strength-Monitoring Method Using Barium Titanate-Bismuth Ferrite/Polyvinylidene Fluoride Nanocomposite Piezoelectric Sensors with Temperature Stability.

Mass concrete is widely used in large-scale projects, including metro upper cover structures, water conservancy dams, and heavy equipment foundations, among others, necessitating the process of health monitoring in mass concrete construction. The development of reliable and simple strength-monitoring methods for mass concrete is challenging because the inner temperature of mass concrete is high and changes a lot. This study proposes a strength-monitoring approach for mass concrete using barium titanate-bismuth ferrite/polyvinylidene fluoride (BT-BFO/PVDF) nanocomposite piezoelectric sensors, wherein the new sensors are embedded as actuators and sensors in mass concrete. The stress wave generated by the BT-BFO/PVDF piezoelectric sensors is used to monitor the specimen's strength for 28 days. The piezoelectric voltage received by the sensors in mass concrete is analyzed. The experimental results indicate that the signal received by the BT-BFO/PVDF sensors is not easily affected by the internal temperature of mass concrete compared with that of the traditional PVDF piezoelectric sensors. The signal parameters sensitive to concrete strength variation and the change trend of concrete strength are closely related to the piezoelectric voltage. Therefore, the proposed approach using BT-BFO/PVDF nanocomposite piezoelectric sensors is efficient (error < 10%) in mass concrete monitoring. Moreover, the monitoring results do not need temperature compensation. The physical meaning of the obtained strength prediction formula is proposed. An experimental system based on PVDF dynamic strain-sensing characteristics is established.

Efficiently improving the performance of noisy quantum computers

Using near-term quantum computers to achieve a quantum advantage requires efficient strategies to improve the performance of the noisy quantum devices presently available. We develop and experimentally validate two efficient error mitigation protocols named ``Noiseless Output Extrapolation" and ``Pauli Error Cancellation" that can drastically enhance the performance of quantum circuits composed of noisy cycles of gates. By combining popular mitigation strategies such as probabilistic error cancellation and noise amplification with efficient noise reconstruction methods, our protocols can mitigate a wide range of noise processes that do not satisfy the assumptions underlying existing mitigation protocols, including non-local and gate-dependent processes. We test our protocols on a four-qubit superconducting processor at the Advanced Quantum Testbed. We observe significant improvements in the performance of both structured and random circuits, with up to 86&amp;#x0025; improvement in variation distance over the unmitigated outputs. Our experiments demonstrate the effectiveness of our protocols, as well as their practicality for current hardware platforms.

Extending the historical in situ gauge network over Indian river reaches utilizing the Surface Water and Ocean Topography (SWOT) mission

ABSTRACT We present a preliminary study on using the Surface Water and Ocean Topography (SWOT) mission to extend water surface elevation (WSE) measurements from a sparsely distributed in situ gauge network along a one-dimensional (1D) study reach within the Mahanadi River basin in India. We use the Centre National d’Etudes Spatiales (CNES) simulator to simulate 3 years of synthetic measurements of SWOT WSE, which are assimilated into a 1D stochastic model based on six independent configurations. For each configuration, the model is derived using historical measurements of in situ WSE to simulate daily WSE estimates every 200 m along the study reach. Assimilated outputs showed high Nash-Sutcliffe efficiency (0.697–0.811), high correlation (0.838–0.901) and low root mean square error (0.242–0.513 m) with respect to in situ validation data. Results indicate consistent performance of the SWOT-assimilated model framework and are relevant considering the declining availability of in situ monitoring stations.

Automated Processing Method for Chinese NOTAMs Based on Knowledge Graph

Notice to airmen (NOTAMs) constitutes a vital element in civil aviation operational intelligence. Historically, the processing of these notices has been manual. However, with the significant increase in the number of NOTAMs, issues including low efficiency, time-consuming processes, and high error rates associated with manual processing have become apparent. To address these challenges, we propose an enhanced approach utilizing the Bi-GRU-CRF-Attention model, based on a dataset of 105,797 NOTAMs collected from the Intelligence Center between September 2020 and April 2023. In this methodology, we employ preprocessing techniques to train the model using processed NOTAMs. Subsequently, the trained model is utilized for named entity recognition, identifying entities within the notices, such as status, facilities, and reasons and segmenting sentences into words. Following this, an advanced BERT-DPCNN method is employed to classify the identified entities, yielding triplets comprising NOTAM entities, their categories, and corresponding processing methods. By integrating rule-based approaches, we configure a NOTAM knowledge graph using neo4j. This process establishes an automated NOTAM processing system. This system can autonomously determine the category of a NOTAM upon reception and utilize the Cypher language to query for the appropriate processing method.

Modeling soil erosion dynamic processes along hillslopes with vegetation impact across different land uses on the Loess Plateau of China

Developing a process-based soil erosion model that comprehensively considers the effects of vegetation is complex but crucial for evaluating sediment reduction during vegetation restoration. Current understanding of the effect of vegetation on sediment reduction along hillslopes across different land uses is limited. In this paper, we developed a dynamic model of hillslope erosion that integrates the effects of vegetation on soil detachability and hydrodynamics (VED) based on the feedback mechanism between soil detachment and sediment transport. The VED was calibrated and validated with runoff plot data for woodland, grassland, and farmland in the Wuding, Yan, Jing and Wei Rivers of the Loess Plateau in China. Unified baseline parameters and decay coefficients of vegetation were obtained. The model validation results indicated that the coefficient of determination, Nash–Sutcliffe simulation efficiency and relative error ranged from 0.59 to 0.99, 0.56 to 0.90, and −45.99 % to 46.36 %, respectively. The decay coefficients for soil detachment capacity (Φ), sediment transport capacity (Tc), and the sediment reduction effect of vegetation exhibited the following order: woodland > grassland > farmland. Compared with VED, existing process-based soil erosion models failed to effectively characterize the influence of vegetation on Φ and Tc on the Loess Plateau, with differences of several orders of magnitude. The individual contributions of runoff, soil detachability, and hydrodynamics with vegetation impact on sediment yield were quantified by VED. The contribution rates of vegetation to sediment reduction decreased gradually with increasing slope length because the soil detachment capacity of bare slopes exceeded that of vegetated slopes, resulting in a faster increase in sediment yield. The contribution rates of vegetation to sediment reduction for woodland, grassland, and farmland with 20 %–60 % vegetation covers were 66.75 %–99.95 %, 57.43 %–99.63 %, and 27.91 %–88.63 % in the boundary conditions of this study, respectively. The results reveal the potential for integrating VED into other distributed watershed hydrological and sediment models to assess the effects of vegetation restoration on sediment reduction at a watershed scale.

Sea surface temperature prediction by stacked generalization ensemble of deep learning

Accurate sea surface temperature (SST) prediction is of great significance for fishery farming, marine ecological protection, and planning of maritime activities. In this paper, the stacked generalization ensemble is demonstrated for SST prediction to improve every single deep-learning model. Long-term high-resolution satellite-derived SST is used with the sub-regions of the Taiwan Strait and East China Sea taken as the study area. We select the Multilayer Perceptron, Long short-term memory (LSTM), Convolutional Neural Networks (CNN), CNN-LSTM as individual learners, and Convolutional LSTM as the meta-learner. The individual learners are trained and validated on the retained data subset I, while the meta-learner is trained and validated by constructing the samples with the predictions of validated individual learners on the retained data subset II. The two types of models are evaluated on the same test dataset with root-mean-square error and coefficient of efficiency as the scoring criteria. We find that the meta-model outperforms any individual model and other baselines for the one-day-/three-day-ahead forecasts in the Taiwan Strait and one-day-/three-day-/five-day-ahead predictions in the East China Sea. Furthermore, when the lead time is 1 day and 3 days, the meta-model has a better spatial distribution of prediction metrics across all grid points in the Taiwan Strait sub-area. For the East China Sea sub-region, the meta-model advantage is extended to the lead time of 5 days. Probably due to the higher quality of offshore satellite data, the prediction ability enhancement of the stacked ensemble applied in the East China Sea is better than that in the Taiwan Strait. The better-performing meta-model prediction suggests that the stacked generalization ensemble is encouraging and promising for improving the short-term prediction of the daily SST field.

Proximity-based activation of AURORA A by MPS1 potentiates error correction.

Faithfull cell division relies on mitotic chromosomes becoming bioriented with each pair of sister kinetochores bound to microtubules oriented toward opposing spindle poles. Erroneous kinetochore-microtubule attachments often form during early mitosis, but are destabilized through the phosphorylation of outer kinetochore proteins by centromeric AURORA B kinase (ABK) and centrosomal AURORA A kinase (AAK), thus allowing for re-establishment of attachments until biorientation is achieved. MPS1-mediated phosphorylation of NDC80 has also been shown to directly weaken the kinetochore-microtubule interface in yeast. In human cells, MPS1 has been proposed to transiently accumulate at end-on attached kinetochores and phosphorylate SKA3 to promote microtubule release. Whether MPS1 directly targets NDC80 and/or promotes the activity of AURORA kinases in metazoans remains unclear. Here, we report a novel mechanism involving communication between kinetochores and centrosomes, wherein MPS1 acts upstream of AAK to promote error correction. MPS1 on pole-proximal kinetochores phosphorylates the C-lobe of AAK thereby increasing its activation at centrosomes. This proximity-based activation ensures the establishment of a robust AAK activity gradient that locally destabilizes mal-oriented kinetochores near spindle poles. Accordingly, MPS1 depletion from Drosophila cells causes severe chromosome misalignment and erroneous kinetochore-microtubule attachments, which can be rescued by tethering either MPS1 or constitutively active AAK mutants to centrosomes. Proximity-based activation of AAK by MPS1 also occurs in human cells to promote AAK-mediated phosphorylation of the NDC80 N-terminal tail. These findings uncover an MPS1-AAK cross-talk that is required for efficient error correction, showcasing the ability of kinetochores to modulate centrosome outputs to ensure proper chromosome segregation.

Application of image processing technology based on field programmable gate array in mechanical part inspection

Introduction: In the field of industrial manufacturing, accurate inspection of mechanical components such as gears and bearings is of Paramount importance. However, the traditional mechanical testing methods are often disturbed by human factors, which not only affects the stability of the test results, but also leads to low efficiency and large error. In order to solve these problems, this research focuses on developing a new edge detection model.Methods: A novel edge detection model based on field-programmable gate array image processing technology was used in this study. The model uses adaptive threshold multi-directional edge detection technology to identify the edge features of mechanical gears and bearings, aiming at improving the precision of detection.Results and Discussion: After performance verification, the running time of the model was controlled within 11 s, and the detection error was limited to less than 9%. Compared with the control group and the experimental group, their performance was superior. Further analysis data show that the detection accuracy of this model is as high as 0.9004, its internal resource utilization rate is 88%, and the detection rate is as high as 91%, which are better than the comparison model.Conclusion: The proposed test model not only significantly improves the efficiency and accuracy of the test, but also fully meets the requirements of the test. This new edge detection model has potential application value in industrial manufacturing field, and provides a new solution for industrial manufacturing quality inspection.

High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor

Due to the discrepancy in resolution between existing global climate model output and the resolution required by decision-makers, there is a persistent need for climate downscaling. We conducted a study to determine the effectiveness of Relevant Vector Machine (RVM), one of the machine learning approaches, in outperforming existing statistical methods in downscaling historical rainfall data in the complex terrain of Selangor, Malaysia. While machine learning eliminates the requirement for manual feature selection when extracting significant information from predictor fields, considering multiple pivotal factors is essential. These factors include identifying relevant atmospheric features contributing to rainfall, addressing missing data, and developing a significant model to predict daily rainfall intensity using appropriate machine-learning techniques. The Principal Component Analysis (PCA) technique was employed to choose relevant environmental variables as input for the machine learning model, and various imputation methods were utilized to manage missing data, such as mean imputation and the KNN algorithm. To assess the performance of the RVM-based rainfall model, we collected a dataset from the Department of Irrigation and Drainage Malaysia. We used Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) as evaluation metrics. This study concluded that Relevance Vector Machine (RVM) models are suitable for forecasting future rainfall since they can support large rainfall extremes and generate reliable daily rainfall estimates based on rainfall extremes. In this study, the RVM model was employed to determine a predictive association between predictand variables and predictors.

Optimizing Communication System Performance through Hybrid Modulation: A Comprehensive Analysis and Guideline

There is a clear need for dependable high-speed communication systems in light of 5G's recent rapid development. The enormous data that recently developed immersive media generate need to be transmitted, and traditional modulation techniques are unable to provide a suitable level of communication. Consequently, to improve communication system performance, hybrid modulation is needed. This paper examines the communication process and conducts a performance analysis to establish a reasonable method for choosing a hybrid modulation scheme. The constellation diagram, power spectral density, bandwidth efficiency, eye diagram, and bit error rate are the main components that determine a communication system's performance. Based on the analysis, the author concludes that to achieve a better-performing communication system, there must be a compromise between these performance factors. Specific recommendations are provided regarding performance indicators listed in the article, such as the constellation diagram, power spectrum density, bandwidth efficiency, eye diagram, and bit error rate. This work aims to address these issues to provide new researchers with guidance when selecting a hybrid modulation approach and to suggest a line of inquiry in the increasingly complicated and time-consuming age of large data volumes.

Double Bias of Mistakes: Essence, Consequences, and Measurement Method

There is no learning without mistakes. However, there is a clash between ‘positive attitudes and beliefs’ regarding learning processes and the ‘negative attitudes and beliefs’ toward these being accompanied by mistakes. This clash exposes a cognitive bias toward mistakes that might block personal and organizational learning. This study presents an advanced measurement method to assess the bias of mistakes. The essence of it is the detection of the existing contradictions between attitude and behavior toward mistakes at the personal and organizational levels, as well as combined. This study is based on empirical evidence from a sample of 768 knowledge workers, divided into biased and non-biased subsamples following the procedure proposed in this paper. Those subsamples were next applied to the structural model, examining knowledge, learning, and collaboration cultures (the KLC approach) 's influence on organizational intelligence to validate the proposed method. Results showed that the applied method efficiently detects the DBM and exposes that in doubly mistakes-biased knowledge-driven organizations, the influence of knowledge culture on the mistakes acceptance component of learning culture is negative. So, organizations with a dominated double bias of mistakes do not accept the affirmation of learning from mistakes. Summing up, this study constitutes the Double Bias of Mistakes Theory, which states that the clash between positive attitudes and beliefs regarding learning processes and negative attitudes and beliefs toward mistakes exposed by focusing on control managers (bosses) might block organizational learning from mistakes and, as a consequence, negatively affect organizational intelligence. Without the empirical support for this theory, there was a risk that the idea of accepting mistakes as a potential source of learning would be simplified by biased minds to mistakes tolerance and rejected as ridiculous. Accepting that mistakes can be a source of precious learning does not equal mistake tolerance. On the contrary, it is the first step to managing mistakes and creating efficient error avoidance systems thanks to lessons learned from failures. This study introduces the method of measurement and detection of the Double Bias of Mistakes phenomenon, contributing to the science of organizational learning and collective intelligence-building.

An Efficient, Fast and Accurate Online Signature Verification Using Blended Feature Vector and Deep Learning

Online signature verification is one of the biometric authentication techniques. Online signature verification reduces the human error that may occur due to physical signature verification. Many researchers have provided a method of online signature verification still there is a scope for improvement. In this paper, a novel blended feature vector (BFV) is formed by combining two feature vectors. The first of these feature vectors is formed using a raw online signature database and the other feature vector is formed of the features extracted from the grayscale images obtained from the signature database. The algorithm makes use of the probability distribution of the features to provide a feature vector that results in an enhanced verification system. Two online signature databases, namely SVC2004 and ATVS-SSig are used. BFV is used to train the proposed mixed sequence deep neural network (MS-DNN). The use of a blended feature vector in the training of MS-DNN, instead of a whole online signature database, improves the training time of deep neural network to a great extent. This reduction in training time might be useful for new user registration. The performance parameters considered are validation efficiency and equal error rate. Results are compared with the existing state of technology and the comparison shows that the use of a blended feature vector as a classification vector improves the validation efficiency (99.5%) and also there is an improvement in the verification success rate in terms of equal error rate (EER 1.5%) as compared to the existing research.

The data-driven rogue waves of the Hirota equation by using Mix-training PINNs approach

Recently, solving differential equations by deep neural networks has attracted lots of attention, however, when the solution of differential equations has localized areas such as the rogue waves, the classical physics-informed neural networks (PINNs) cannot guarantee its prediction accuracy. In this paper, we propose two neural networks methods: mix-training physics-informed neural networks (MTPINNs) and prior information mix-training physics-informed neural networks (PMTPINNs), two deep learning models with more approximation ability based on PINNs. A series of numerical experiments show that the learning efficiency and absolute error accuracy of our proposed model can be improved significantly. The PMTPINNs model can so as to make up for the shortcomings of PINNs in this aspect. Furthermore, by testing the robustness of these two methods, for two kinds of rogue waves of the Hirota equation, it is surprising that these models also have good performance and thereby may provide more possibilities for solving mathematical physics problems better.

Real-time detection of surface cracking defects for large-sized stamped parts

This study presents a framework for the real-time detection of surface cracking in large-sized stamped metal parts. The framework aims to address the challenges of low detection efficiency and high error rates associated with manual cracking detection. Within this framework, a novel network, SNF-YOLOv8, is proposed to efficiently detect cracking while ensuring that the detection speed matches the production speed. The network incorporates a convolutional spatial-to-depth module to enhance the detection of small-sized cracking and mitigate surface interference during inspections. Furthermore, a visual self-attention mechanism is introduced to improve feature extraction. A combination of standard convolutional and depth-wise separable convolutional layers in the neck network enhances speed without compromising accuracy. Experimental validation conducted using a dataset from actual production lines, in collaboration with a multi-national corporation, demonstrates that SNF-YOLOv8 achieves an average precision of 85.2% at a detection speed of 164 frames per second. The framework achieves an accuracy rate of 98.8% in detecting large-sized cracking and 96.4% in detecting small-sized cracking, meeting the requirements for high-precision and real-time detection applications.

Critical Examination of 3D Building Modelling through UAV Frame and Video Imaging

Abstract. Data capture in UAV photogrammetry is carried out using two main methodologies: frame frame-based and video video-based. Frame Frame-based data gathering is the preferred method among UAV projects because to its inherent reliability in calibration. Nonetheless, circumstances involving moving objects or occlusions inside the measured region may produce unsatisfactory results utilizing this method. I In response to these challenges, video video-based data collecting appears as a potential option, capable of creating a series of successive images that together alleviate the constraints outlined above. In this study we aim to compare the usefulness of frame and video images in building 3D models, using both oblique and vertical image orientations. Rigorous evaluations produced many outputs, including dense point clouds, digital surface models (DSM), meshes, and orthophotographs. The evaluation criteria included da ta acquisition velocity, processing efficiency, calibration precision, distortion analysis, residual plots, scale correctness, and reprojection error. The empirical results demonstrated the advantages of video video-frame captures in improving the quality of the resulting 3D models. Notably, the use of video frames resulted in a significant reduction in reprojection error by 16%, calibration residuals by 36%, distortions by up to 51%, and processing time by 27%. Thus, it seems that integrating video frames improves data gathering accuracy and speeds up processing, replacing standard frame images with video counterparts for increased efficacy.

On Some Problems with Multivalued Mappings

We consider some problems with a set-valued mapping, which can be reduced to minimization of a homogeneous Lipschitz function on the unit sphere. Latter problem can be solved in some cases with a first order algorithm—the gradient projection method. As one of the examples, the case when set-valued mapping is the reachable set of a linear autonomous controlled system is considered. In several settings, the linear convergence is proven. The methods used in proofs follow those introduced by B.T. Polyak for the case where Lezanski– Polyak–Lojasiewicz condition holds. Unlike algorithms that use approximation of the reachable set, the proposed algorithms depend far less on dimension and other parameters of the problem. Efficient error estimation is possible. Numerical experiments confirm the effectiveness of the considered approach. This approach can also be applied to various set-theoretical problems with general set-valued mappings.