Automatic Parameter Selection Research Articles

A Fast iterative shrinkage and threshold algorithm-based least-squares deconvolution technique with application to bearing fault signals

Bearings are commonly subjected to load, transmission, and impact during operation, resulting in bearing failure that ultimately leads to mechanical breakdown. The low energy level of fault-induced impulsive signals, however, renders it vulnerable to being masked by environmental noise and other disturbances present in the vibration signal. To cope with this issue, this paper investigates the application of the finite impulse response (FIR) filter for bearing fault detection. In this work, the estimation of the FIR is formulated as a least squares optimization problem, aiming to minimize the l 2 norm of the FIR and effectively suppress noise and interference sources. The solutions of the least squares optimization problem are obtained using the fast iterative shrinkage–thresholding (FISTA) algorithm. Furthermore, to better select the regularization parameter in our method, an improved gray wolf optimizer (I–GWO) is employed to conduct the parameter selection. Based on these improvements, the proposed FISTA–LSD technique enables rapid and precise detection of fault characteristics upon their occurrence, thereby mitigating the propagation of faults and preventing accidents from happening. Furthermore, with this automated selection process, engineers can save valuable time that would otherwise be spent on trial-and-error approaches. Through its rapid response capabilities and precise fault detection abilities enabled by the FISTA–LSD technique along with I–GWO integration for automatic parameter selection; this FISTA–LSD technique greatly enhances its practical value. The proposed technique is validated through the utilization of both numerical and experimental data.

SPGMVC: Multiview Clustering via Partitioning the Signed Prototype Graph.

Multiview clustering (MVC) has been widely studied in machine learning and data mining for its capability of improving clustering performance by fusing the information from multiview data. In the past decade, a large number of MVC methods have made impressive progress, but most of them suffer from computational burdens, especially in large-scale tasks. Binary MVC (BMVC) is proposed to address this issue by representing the large-scale high-dimensional dataset as a group of consensus and low-dimensional binary codes. However, current BMVC-based approaches generate the clustering by executing binary k -means on the obtained binary codes, which fail to capture the embedded geometric information, leading to poor clustering performance. In addition, parameter selection is another "mission impossible" in unsupervised learning tasks including MVC. To tackle these challenges, a framework of multiview clustering via partitioning the signed prototype graph (SPGMVC) is proposed in this work. The SPGMVC framework offers several contributions. First, SPGMVC is designed as a unified framework for MVC. It combines effective technologies, such as consensus binary coding, code compression (CC), signed prototype graph (SPG) partitioning, and prototype-based cluster assignment. Second, SPGMVC partitions the signed graph (SG) based on the relationships between positive and negative edges. By capturing the underlying structure of the data, this partitioning strategy improves clustering accuracy (ACC). CC techniques are applied to reduce the graph's scale, enabling further partitioning and enhancing computational efficiency. Third, SPGMVC employs an alternate minimizing strategy to efficiently handle the optimization problem. This strategy has nearly linear time and space complexity with respect to the data volume, making it suitable for large-scale tasks. Fourth, SPGMVC proposes an automatic parameter selection strategy, eliminating the need for extensive parameter exploration. Comprehensive experiments illustrate the superiority of our model. The implementation of SPGMVC is available at: https://github.com/gepingyang/PSGMVC.

Mode decomposition of core dynamics transients using higher-order DMD method

Accurately predicting three-dimensional power distribution within a reactor core during reactivity transients is crucial for optimizing reactor operational control. This paper proposes a higher-order dynamic mode decomposition (DMD) method for analyzing highly nonlinear dynamic systems in nuclear reactors. Through analysis of the 3-D LWR Core Transient Benchmark (3DLWRCT) with rod ejection accident, traditional DMD reconstruction techniques were found inadequate in accurately capturing the system’s dynamic evolution using the given simulation data, primarily due to the nonlinear features and local high-order characteristics of the reactor system. In contrast, the HODMD approach demonstrated its effectiveness in predicting future variations in the core’s state. These findings establish the feasibility of employing the HODMD method for transient analysis of core dynamics, leading to promising prospects for dynamic analysis in nuclear energy systems. Future research directions for improving the reconstructive accuracy of HODMD include capturing nonlinear characteristics, automating parameter selection, enabling multi-scale analysis, enhancing anomaly detection capabilities, and providing a broader context for analysis.

Twin support vector machines based on chaotic mapping dung beetle optimization algorithm

Abstract Twin Support Vector Machine (TSVM) is a powerful machine learning method that is usually used to solve binary classification problems. But although the classification speed and performance of TSVM is better than that of primitive support vector machine, TSVM still faces the problem of difficult parameter selection; therefore, to overcome the problem of parameter selection of TSVM, this paper proposes a Chaotic Mapping Dung Beetle Optimization Algorithm-based Twin Support Vector Machine (CMDBO-TSVM) for automatic parameter selection. Due to the uncertainty of the random initialization population of the original Dung Beetle Optimization Algorithm, this paper additionally adds chaotic mapping initialization to improve the Dung Beetle Optimization Algorithm. Experiments on the dataset through this paper show that the classification accuracy of the CMDBO-TSVM has a better performance.

Improvement of photogrammetric joint roughness coefficient value by integrating automatic shooting parameter selection and composite error model

In order to improve the accuracy of the photogrammetric joint roughness coefficient (JRC) value, the present study proposed a novel method combining an autonomous shooting parameter selection algorithm with a composite error model. Firstly, according to the depth map-based photogrammetric theory, the estimation of JRC from a three-dimensional (3D) digital surface model of rock discontinuities was presented. Secondly, an automatic shooting parameter selection algorithm was novelly proposed to establish the 3D model dataset of rock discontinuities with varying shooting parameters and target sizes. Meanwhile, the photogrammetric tests were performed with custom-built equipment capable of adjusting baseline lengths, and a total of 36 sets of JRC data was gathered via a combination of laboratory and field tests. Then, by combining the theory of point cloud coordinate computation error with the equation of JRC calculation, a composite error model controlled by the shooting parameters was proposed. This newly proposed model was validated via the 3D model dataset, demonstrating the capability to correct initially obtained JRC values solely based on shooting parameters. Furthermore, the implementation of this correction can significantly reduce errors in JRC values obtained via photographic measurement. Subsequently, our proposed error model was integrated into the shooting parameter selection algorithm, thus improving the rationality and convenience of selecting suitable shooting parameter combinations when dealing with target rock masses with different sizes. Moreover, the optimal combination of three shooting parameters was offered. JRC values resulting from various combinations of shooting parameters were verified by comparing them with 3D laser scan data. Finally, the application scope and limitations of the newly proposed approach were further addressed.

Optimizing parameter search for community detection in time-evolving networks of complex systems.

Network representations have been effectively employed to analyze complex systems across various areas and applications, leading to the development of network science as a core tool to study systems with multiple components and complex interactions. There is a growing interest in understanding the temporal dynamics of complex networks to decode the underlying dynamic processes through the temporal changes in network structures. Community detection algorithms, which are specialized clustering algorithms, have been instrumental in studying these temporal changes. They work by grouping nodes into communities based on the structure and intensity of network connections over time, aiming to maximize the modularity of the network partition. However, the performance of these algorithms is highly influenced by the selection of resolution parameters of the modularity function used, which dictate the scale of the represented network, in both size of communities and the temporal resolution of the dynamic structure. The selection of these parameters has often been subjective and reliant on the characteristics of the data used to create the network. Here, we introduce a method to objectively determine the values of the resolution parameters based on the elements of self-organization and scale-invariance. We propose two key approaches: (1) minimization of biases in spatial scale network characterization and (2) maximization of scale-freeness in temporal network reconfigurations. We demonstrate the effectiveness of these approaches using benchmark network structures as well as real-world datasets. To implement our method, we also provide an automated parameter selection software package that can be applied to a wide range of complex systems.

LT-PEM Fuel Cells diagnosis based on EIS, clustering, and automatic parameter selection

In the field of fuel cells, early detection of faulty conditions can significantly improve the lifetime. Then, signal analysis techniques such as electrochemical impedance spectroscopy combined with machine learning algorithms can generate a representation of the system state of health space using data in known conditions. Although the onboard measurement of EIS can be done by controlling the harmonic content of the power converter at the output of the fuel cell, the implementation of in-vehicle diagnostic algorithms is still limited by the absence of a large database listing the evolution of performance throughout the life cycle. This paper presents a fast-diagnostic method able to consider the occurrence of new data to adapt the dimensional space representing the health state and compensate for the lack of data. Available measurements come from two low-temperature proton exchange membrane fuel cell technologies characterized by two laboratories. The results presented in the paper show that the automatic parameter selection provides performances as good as the ones obtained by an expert. The feasibility of the approach has also been demonstrated on a low-cost embedded platform.

Constrained Regularization by Denoising With Automatic Parameter Selection

Constrained Regularization by Denoising With Automatic Parameter Selection

Hyper-parameter Optimization in the context of Smart Manufacturing: a Systematic Literature Review

Smart manufacturing involves the use of a variety of automation solutions, such as robotics, machines with embedded software, and advanced sensors collecting vast quantities of data. Efficient control of a complex composition of such solutions, as well as the analysis of the collected data, are essential for improving the efficiency of the production processes and decision-making. Data analysis and process optimization are enabled through the application of state-of-the-art optimization and machine learning algorithms. However, the efficient use of these algorithms often depends on the careful selection of the parameters, which on its own is a process that requires a high degree of expertise and time. Therefore, another class of algorithms can be applied that is designed to discover the optimal parameter configuration given the specific nature of the manufacturing process and the used algorithm. In this work, we systematically analyze the published literature to discover which parameter selection techniques are used in the context of Industry 4.0, for which processes, and how these benefit from automated parameter selection. Within our literature review, we discover nine relevant publications, most of which concentrate on parameter selection for machine learning algorithms through various numerical optimization and metaheuristic techniques.

AAUConvNeXt: Enhancing Crop Lodging Segmentation with Optimized Deep Learning Architectures

Rice lodging, a phenomenon precipitated by environmental factors or crop characteristics, presents a substantial challenge in agricultural production, notably impacting yield prediction and disaster assessment. Despite that the application of conventional methodologies like visual assessment, mathematical models, and satellite remote sensing technologies has been employed in the segmentation of crop lodging, these approaches are still constrained in precision, immediacy, and capacity for large-scale evaluation. This study introduces an innovative convolutional neural network architecture, AFOA + APOM + UConvNeXt, that integrates intelligent optimization algorithms for automatic selection of optimal network parameters, thereby enhancing the accuracy and efficiency of crop lodging segmentation. The proposed model, empirically validated, outperforms recent state-of-the-art models in crop lodging segmentation, demonstrating higher accuracy, lower computational resource requirements, and greater efficiency, thereby markedly reducing the cost of segmentation. In addition, we investigated the segmentation on half lodging rice, and the results indicate that the model exhibits commendable performance on the half lodging dataset. This outcome holds significant implications for the prediction of rice lodging trends. The fusion of deep learning with intelligent optimization algorithms in this study offers a new effective tool for crop lodging monitoring in agricultural production, providing strong technical support for accurate crop phenotypic information extraction, and is expected to play a significant role in agricultural production practices.

Adaptive sparse estimation of nonlinear chirp signals using Laplace priors.

The identification of nonlinear chirp signals has attracted notable attention in the recent literature, including estimators such as the variational mode decomposition and the nonlinear chirp mode estimator. However, most presented methods fail to process signals with close frequency intervals or depend on user-determined parameters that are often non-trivial to select optimally. In this work, we propose a fully adaptive method, termed the adaptive nonlinear chirp mode estimation. The method decomposes a combined nonlinear chirp signal into its principal modes, accurately representing each mode's time-frequency representation simultaneously. Exploiting the sparsity of the instantaneous amplitudes, the proposed method can produce estimates that are smooth in the sense of being piecewise linear. Furthermore, we analyze the decomposition problem from a Bayesian perspective, using hierarchical Laplace priors to form an efficient implementation, allowing for a fully automatic parameter selection. Numerical simulations and experimental data analysis show the effectiveness and advantages of the proposed method. Notably, the algorithm is found to yield reliable estimates even when encountering signals with crossed modes. The method's practical potential is illustrated on a whale whistle signal.

Comparative Study of Algorithms for the Conversion From Surface Mesh Models to Volume Mesh Models

The study delves into the performance of different algorithms in the process of converting surface mesh models (SMMs) into volume mesh models (VMMs) required for finite element analysis. The experiment compared the Initial mesh, Delaunay, Frontal, and HXT algorithms, analyzing their central processing unit (CPU) runtime, node count, element count, and model size when processing simple models under default control parameters. The results showed that the Frontal algorithm performed the best in the conversion of simple models with a CPU runtime of 0.001 s, while the Delaunay algorithm took relatively longer. For complex models, the Initial mesh, Delaunay, and Frontal algorithms were all successful in completing the conversion, but the HXT algorithm failed, highlighting the importance of algorithm selection. The Delaunay algorithm performed best in terms of efficiency and model size, while the Frontal algorithm had an advantage in model refinement. Although the Initial mesh algorithm had a more balanced number of nodes and elements, its longer CPU runtime indicates potential for efficiency improvement. In addition, the randomness of the HXT algorithm in mesh generation led to variations in results, which requires further optimization to improve the consistency and robustness of the algorithm. Future research will focus on algorithm optimization, particularly enhancing the HXT algorithm’s ability to handle complex models, developing automated parameter selection mechanisms, exploring the application of algorithms in other engineering fields, and achieving better integration of algorithms with computer‐aided design (CAD)/computer‐aided engineering (CAE) workflows.

Simulation research on breakdown diagnosis based on least squares support vector machine optimized by simulated annealing genetic algorithm

The application of support vector machine to the aircraft power supply system breakdown diagnosis is one of the research focuses in the tomorrow. Support vector machine (SVM) belongs a new type of machine learning technique, which uses structural hazard minimization rule to substitute for the conventional empirical hazard minimization according to great specimen. The powerful performance of the least squares support vector machine (LSSVM) needs to be reflected in the optimal selection of appropriate parameters, and the quality of parameters greatly affects the accuracy and efficiency of breakdown diagnosis. The traditional LSSVM parameters selection method is inefficient, the calculation is huge, and it takes expensive time to select the satisfactory solution. Aiming at the problem of parameters selection of LSSVM, the way of optimizing the parameters of LSSVM by apply simulated annealing genetic algorithm (SAGA) is proposed. SAGA uses the parallel sampling process of genetic algorithm to improve the time performance of optimization, and makes use of the simulated annealing algorithm to dominate the constriction of majorization to prevent precocity. On the one hand, genetic algorithm has strong ability to grasp the overall search process, on the other hand, simulated annealing algorithm is used to control the convergence of the algorithm to prevent premature phenomenon. The automatic optimal selection of LSSVM parameters is achieved. The Iris system classification and recognition in the UCI machine learning database and the breakdown diagnosis of the autopilot flight control box are used as experimental platforms to obtain data samples for simulation research. The simulation results show that LSSVM optimized by SAGA improves both the accuracy and efficiency of classification recognition. It not only effectively overcomes the problem of low efficiency caused by searching optimal parameters by experience, but also effectively improves the accuracy of classification recognition. The false alarm rate and redundant breakdowns are effectively reduced.

Automatically optimized radiomics modeling system for small gastric submucosal tumor (

The clinical management of small gastric submucosal tumors (SMTs) (<2 cm) faces a non-negligible challenge because of the lack of guideline consensus and effective diagnostic tools. This article develops an automatically optimized radiomics modeling system (AORMS) based on EUS images to diagnose and evaluate SMTs. A total of 205 patients with EUS images of small gastric SMTs (<2 cm) were retrospectively enrolled in the development phase of AORMS for the diagnosis and the risk stratification of GI stromal tumor (GIST). A total of 178 patients with images from different centers were prospectively enrolled in the independent testing phase. The performance of AORMS was compared to that of endoscopists in the development set and evaluated in the independent testing set. AORMS demonstrated an area under the curve (AUC) of 0.762 for the diagnosis of GIST and 0.734 for the risk stratification of GIST, respectively. In the independent testing set, AORMS achieved an AUC of 0.770 and 0.750 for the diagnosis and risk stratification of small GISTs, respectively. In comparison, the AUCs of 5 experienced endoscopists ranged from 0.501 to 0.608 for diagnosing GIST and from 0.562 to 0.748 for risk stratification. AORMS outperformed experienced endoscopists by more than 20% in diagnosing GIST. AORMS implements automatic parameter selection, which enhances its robustness and clinical applicability. It has demonstrated good performance in the diagnosis and risk stratification of GISTs, which could aid endoscopists in the diagnosis of small gastric SMTs (<2 cm).

Autonomous Electron Tomography Reconstruction with Machine Learning.

Modern electron tomography has progressed to higher resolution at lower doses by leveraging compressed sensing (CS) methods that minimize total variation (TV). However, these sparsity-emphasized reconstruction algorithms introduce tunable parameters that greatly influence the reconstruction quality. Here, Pareto front analysis shows that high-quality tomograms are reproducibly achieved when TV minimization is heavily weighted. However, in excess, CS tomography creates overly smoothed three-dimensional (3D) reconstructions. Adding momentum to the gradient descent during reconstruction reduces the risk of over-smoothing and better ensures that CS is well behaved. For simulated data, the tedious process of tomography parameter selection is efficiently solved using Bayesian optimization with Gaussian processes. In combination, Bayesian optimization with momentum-based CS greatly reduces the required compute time-an 80% reduction was observed for the 3D reconstruction of SrTiO3 nanocubes. Automated parameter selection is necessary for large-scale tomographic simulations that enable the 3D characterization of a wider range of inorganic and biological materials.

The Watermark Imaging System: Revealing the Internal Structure of Historical Papers

This paper introduces the Watermark Imaging System (WImSy) which can be used to photograph, document, and study sheets of paper. The WImSy provides surface images, raking light images, and transmitted light images of the paper, all in perfect alignment. We develop algorithms that exploit this alignment by combining several images together in a process that mimics both the “surface image removal” technique and the method of “high dynamic range” photographs. An improved optimization criterion and an automatic parameter selection procedure streamline the process and make it practical for art historians and conservators to extract the relevant information to study watermarks. The effectiveness of the method is demonstrated in several experiments on images taken with the WImSy at the Metropolitan Museum of Art in New York and at the Getty Museum in Los Angeles, and the results are compared with manually optimized images.

FLUST: A fast, open source framework for ultrasound blood flow simulations

Background and objective:Ultrasound based blood velocity estimation is a continuously developing frontier, where the vast number of possible acquisition setups and velocity estimators makes it challenging to assess which combination is better suited for a given imaging application. FLUST, the Flow-Line based Ultrasound Simulation Tool, may be used to address this challenge, providing a common platform for evaluation of velocity estimation schemes on in silico data. However, the FLUST approach had some limitations in its original form, including reduced robustness for phase sensitive setups and the need for manual selection of integrity parameters. In addition, implementation of the technique and therefore also documentation of signal integrity was left to potential users of the approach.Methods: In this work, several improvements to the FLUST technique are proposed and investigated, and a robust, open source simulation framework developed. The software supports several transducer types and acquisition setups, in addition to a range of different flow phantoms. The main goal of this work is to offer a robust, computationally cheap and user-friendly framework to simulate ultrasound data from stationary blood velocity fields and thereby facilitate design and evaluation of estimation schemes, including acquisition design, velocity estimation and other post-processing steps.Results: The technical improvements proposed in this work resulted in reduced interpolation errors, reduced variability in signal power, and also automatic selection of spatial and temporal discretization parameters. Results are presented illustrating the challenges and the effectiveness of the solutions. The integrity of the improved simulation framework is validated in an extensive study, with results indicating that speckle statistics, spatial and temporal correlation and frequency content all correspond well with theoretical predictions. Finally, an illustrative example shows how FLUST may be used throughout the design and optimization process of a velocity estimator.Conclusions: The FLUST framework is available as a part of the UltraSound ToolBox (USTB), and the results in this paper demonstrate that it can be used as an efficient and reliable tool for the development and validation of ultrasound-based velocity estimation schemes.

Heterogeneous Quasi-Continuous Spiking Cortical Model for Pulse Shape Discrimination

The present study introduces the heterogeneous quasi-continuous spiking cortical model (HQC-SCM) method as a novel approach for neutron and gamma-ray pulse shape discrimination. The method utilizes specific neural responses to extract features in the falling edge and delayed fluorescence parts of radiation pulse signals. In addition, the study investigates the contributions of HQC-SCM’s parameters to its discrimination performance, leading to the development of an automatic parameter selection strategy. As HQC-SCM is a chaotic system, a genetic algorithm-based parameter optimization method was proposed to locate local optima of HQC-SCM’s parameter solutions efficiently and robustly in just a few iterations of evolution. The experimental results of this study demonstrate that the HQC-SCM method outperforms traditional and state-of-the-art pulse shape discrimination algorithms, including falling edge percentage slope, zero crossing, charge comparison, frequency gradient analysis, pulse-coupled neural network, and ladder gradient methods. The outstanding discrimination performance of HQC-SCM enables plastic scintillators to compete with liquid and crystal scintillators’ neutron and gamma-ray pulse shape discrimination ability. Additionally, the HQC-SCM method outperforms other methods when dealing with noisy radiation pulse signals. Therefore, it is an effective and robust approach that can be applied in radiation detection systems across various fields.

One-click bending stiffness: Robust and reliable automatic calculation of moment–curvature relation in a cantilever bending test

The cantilever bending test is one of the simplest and most widely used methods to estimate the bending stiffness of textile materials. A nonlinear moment–curvature relationship can be computed from a single image of a cantilevered textile specimen. However, the calculation of curvature involves second-order differentiation of noisy data, which leads to noise amplification. Traditionally, this is handled by subjectively choosing one of many functions to fit the data or by manual tuning of fitting parameters. The user choices ultimately lead to uncertainties in the data fit. This paper presents a novel automatic data processing method for the cantilever test using smoothing splines with automatic parameter selection through cross-validation. The method is verified on a simulated deflection curve with known bending stiffness and then used to characterise real textile specimens. Finally, the method is validated by simulating the deflection curve using the computed stiffness. This method makes it possible, for the first time, to accurately predict the textile curvature even in the presence of severe noise, without needing user inputs prone to human error. The code used for this paper is freely available with sample data on the repository at https://doi.org/10.5281/zenodo.7376939.

Automatic ROI recognition and parameters selection for digital image correlation in measuring structures with complex shapes

For the digital image correlation (DIC) method, the measurement of specimens with complex shapes may encounter difficulties due to the time-consuming recognition of region of interest (ROI), and the indeterminate parameter selection caused by the non-uniform deformation. This paper proposes an automatic DIC for the measurement of structures with complex shapes. An automatic ROI segmentation is developed by combining a convolutional neural network and image morphology, so the boundary of the specimen can be acquired accurately and efficiently. In dealing with the non-uniform deformation, a strain-related automatic selection of DIC parameters is developed, in which the sampling intervals and the subset sizes at different areas can be automatically determined. Both results of the simulated experiment and real experiment show that, by combing the two approaches with segmentation-aided DIC, the proposed automatic DIC can characterize the complex deformation including the boundary of the structures effectively.