Parameter Selection Approach Research Articles

Exploration of weak-PGML Method for Efficient Stability Control During Machining Operations

Stable control of the machining process can be difficult to maintain in production environments due to many factors, but especially uncertainty about selection of optimal machining parameters. The true underlying stability model for production machines on the shop floor is generally unknown, so parameters are typically selected by operators based on manufacturer recommendations or, in most cases, their accumulated shop floor experience. A class of methods referred to as physics-guided machine learning (PGML) offers a new approach for optimal parameter selection that incorporates theoretical knowledge about machining dynamics into the data-driven machine learning workflow. The focus of this paper is a version of PGML appropriate to shop floor operations in small and medium-sized machine shops (SMMs)—referred to as weak-PGML—that does not require a priori theoretical knowledge of machining dynamics to approximate the true underlying stability model for a particular machining scenario. Rather, the weak-PGML leverages the operator’s accumulated domain knowledge to uncover the true stability model and informs parameter selection. Weak-PGML reflects the limited resources and lack of technical expertise typical of most SMMs with legacy machines and a new generation of workforce without the accumulated experience of operators who have spent many years on the shop floor.

An improved similarity-based RUL prediction method considering degradation degree of multiple condition monitoring parameters for aero-engines

Abstract The aero-engine is the heart of an airplane. Predicting the remaining useful lifetime (RUL) of an aero-engine bears great significance, not only for improving the reliability and safety of the aero-engine but also for ensuring aircraft safety and performance. However, both issues, namely the selection of uncorrelated parameters for RUL estimation and the lack of a standard theoretical methodology for Health Index (HI) construction, inevitably impact the prediction accuracy. Here, we proposed an improved similarity-based RUL prediction method considering the degradation degree of multiple condition monitoring parameters for aero-engines. This method includes the improved minimum-redundancy maximum-relevancy approach for the quantitative selection of key parameters, and the similarity matching approach which takes into account the degradation degree of multiple parameters instead of constructing HI. The effectiveness of the proposed method was evaluated on turbine engine datasets. Experimental results show that (1) compared with other feature selection methods, the root mean square error (RMSE) of the proposed method is reduced by 12.3%; (2) compared with other RUL prediction methods, the RMSE of the proposed method is smaller than most methods but without a complicated training process. The results demonstrate that the proposed method achieves highly competitive prediction performance. By employing the proposed method, it is possible to significantly reduce the risk of engine failures, thereby improving safety and economic efficiency.

Diagnostics of viral infections using high-throughput genome sequencing data.

Plant viral infections cause significant economic losses, totalling $350 billion USD in 2021. With no treatment for virus-infected plants, accurate and efficient diagnosis is crucial to preventing and controlling these diseases. High-throughput sequencing (HTS) enables cost-efficient identification of known and unknown viruses. However, existing diagnostic pipelines face challenges. First, many methods depend on subjectively chosen parameter values, undermining their robustness across various data sources. Second, artifacts (e.g. false peaks) in the mapped sequence data can lead to incorrect diagnostic results. While some methods require manual or subjective verification to address these artifacts, others overlook them entirely, affecting the overall method performance and leading to imprecise or labour-intensive outcomes. To address these challenges, we introduce IIMI, a new automated analysis pipeline using machine learning to diagnose infections from 1583 plant viruses with HTS data. It adopts a data-driven approach for parameter selection, reducing subjectivity, and automatically filters out regions affected by artifacts, thus improving accuracy. Testing with in-house and published data shows IIMI's superiority over existing methods. Besides a prediction model, IIMI also provides resources on plant virus genomes, including annotations of regions prone to artifacts. The method is available as an R package (iimi) on CRAN and will integrate with the web application www.virtool.ca, enhancing accessibility and user convenience.

Practical fixed-time non-singular sliding mode control of second order nonlinear dynamic systems with chattering and overshooting avoidance

This paper introduces a Practical Fixed-Time Stabilization technique (PFxT) designed for a specific class of nonlinear second-order systems. The closed-loop systems, when appropriately parameterized, exhibit convergence within a predetermined time frame to a confined region near the origin. This outcome is achieved by amalgamating a nonlinear sliding mode approach with a practical fixed-time tracking virtual trajectory. The control algorithmś design incorporates considerations for overshoot reduction and chattering cancellation. Furthermore, the Lyapunov method is employed to establish the practical fixed-time stability of the proposed solution, providing insights into the limits within which the algorithm can be effectively deployed. To complete the algorithm specification, a parameter selection approach is introduced, enabling customization of the desired settling time. Comprehensive simulations are conducted to validate the effectiveness and viability of the proposed PFxT technique.

A modified inertial proximal alternating direction method of multipliers with dual-relaxed term for structured nonconvex and nonsmooth problem

In this research, we introduce a novel optimization algorithm termed the dual-relaxed inertial alternating direction method of multipliers (DR-IADM), tailored for handling nonconvex and nonsmooth problems. These problems are characterized by an objective function that is a composite of three elements: a smooth composite function combined with a linear operator, a nonsmooth function, and a mixed function of two variables. To facilitate the iterative process, we adopt a straightforward parameter selection approach, integrate inertial components within each subproblem, and introduce two relaxed terms to refine the dual variable update step. Within a set of reasonable assumptions, we establish the boundedness of the sequence generated by our DR-IADM algorithm. Furthermore, leveraging the Kurdyka–Łojasiewicz (KŁ) property, we demonstrate the global convergence of the proposed method. To validate the practicality and efficacy of our algorithm, we present numerical experiments that corroborate its performance. In summary, our contribution lies in proposing DR-IADM for a specific class of optimization problems, proving its convergence properties, and supporting the theoretical claims with numerical evidence.

Parameter selection on a multi-exposure fusion method for measuring surfaces with varying reflectivity in microscope fringe projection profilometry

As industrial and scientific advancements continue, the demand for precise measurement of three-dimensional (3D) shapes and surfaces is steadily increasing. However, accurate 3D measurement of certain surfaces, especially those with varying reflectivities, has always been a challenging issue. Multi-exposure fusion methods have shown stable, high-quality measurement results, but the selection of parameters for these methods has largely been based on experience. To address this issue, this paper has improved the multi-exposure fusion method and introduced a guided approach for parameter selection, significantly enhancing the completeness of measurement results. Additionally, a comparative model is developed to experimentally validate the specific impacts of Gaussian window variance, optimal grayscale range, and attenuation factor variance on the integrity of 3D reconstruction. The experimental results demonstrate that under the guidance of the parameter adjustment method proposed in this paper, the multi-exposure fusion for measuring the 3D topography of high-dynamic surfaces improves the restoration coverage from the original 86% (bright areas) and 50% (dark areas) to over 99%. This provides a selection strategy for parameter adjustment guidance in precise measurements based on the multi-exposure method.

Dynamic modeling and parameter sensitivity analysis of AUV by using the POD method and the HB-AFT method

Autonomous Underwater Vehicle (AUV) parameterization is a geometric modification method, and can quickly obtain excellent structure with low vibration and noise performance. Blindness and inefficiency are common outcomes of the lack of an efficient parameter selection approach. But not enough research has been done in this area. In this paper, the finite element (FE) method is used to establish the propeller–shaft–shell double beam system with ball bearings, which is more similar to the real model. The basic theory of the proper orthogonal decomposition (POD) method is introduced in detail, and the original full order model (FOM) is reduced to 4 DOFs. It is found that the accuracy of the reduced order model (ROM) is high enough and the calculation time is greatly reduced. The semi-numerical semi-analytical expression of the ROM system is solved by the harmonic balance and alternating frequency/time domain (HB-AFT) method, whose results are in good agreement with those by the Runge–Kutta method. The sensitivities of the shell responses to the spring stiffness are discussed. The AUV setup is designed to analysis of numerical and experimental results. These results in this paper can provide an advanced theoretical basis for the structural parameter design of the AUV.

A novel approach for power system stabilizer control parameter selection: a case-study on two-area four-machine system

A novel approach for power system stabilizer control parameter selection: a case-study on two-area four-machine system

Parameter development for Laser Powder Bed Fusion of nickel-based Haynes 282 using diverse density determination methods

To manufacture functional components using laser powder bed fusion (PBF-LB/M), the execution of a parameter development with the specific material and machine is an indispensable step. Typically, in the search for the optimal parameter set, the volumetric energy density (VED) used to melt the material serves as an adjustment variable, while the resulting part density as the target parameter. Although this approach effectively reduces the process parameters search-space, additional criteria concerning part quality should be considered in the development. This paper introduces a systematic parameter selection approach, refining the characterization process for processing the nickel-based superalloy Haynes® 282®. The presented strategy not only incorporates the density as target condition but also considers the surface quality and dimensional accuracy of the manufactured samples, crucial for near-net-shape manufacturing. Additionally, three porosity measurement methods (Archimedes method, microscopy, gaspycnometry) for AM metal parts are compared, and their validity for this purpose is discussed.

COVID-19 Diagnosis Using Chaotic Logistic Map Based Modified Whale Optimization: A Robust Feature and Parameter Selection Approach

COVID-19 Diagnosis Using Chaotic Logistic Map Based Modified Whale Optimization: A Robust Feature and Parameter Selection Approach

Refining analytic approximation based estimation of mixed multinomial probit models by parameter selection

Applying analytic approximations for computing multivariate normal cumulative distribution functions has led to a substantial improvement in the estimability of mixed multinomial probit models, both in terms of accuracy and especially in terms of computation time. This paper makes a contribution by presenting a possible way to improve the accuracy of estimating mixed multinomial probit model covariances based on the idea of parameter selection using cross-validation. Comparisons to the MACML approach indicate that the proposed parameter selection approach is able to recover covariance parameters more accurately, even when there is a moderate degree of independence between the random coefficients. The approach also estimates parameters efficiently, with standard errors tending to be smaller than those of the MACML approach, which can be observed by means of a real data case.

Spot Welding Parameter Tuning for Weld Defect Prevention in Automotive Production Lines: An ML-Based Approach

Spot welding is a critical joining process which presents specific challenges in early defect detection, has high rework costs, and consumes excessive amounts of materials, hindering effective, sustainable production. Especially in automotive manufacturing, the welding source’s quality needs to be controlled to increase the efficiency and sustainable performance of the production lines. Using data analytics, manufacturing companies can control and predict the welding parameters causing problems related to resource quality and process performance. In this study, we aimed to define the root cause of welding defects and solve the welding input value range problem using machine learning algorithms. In an automotive production line application, we analyzed real-time IoT data and created variables regarding the best working range of welding input parameters required in the inference analysis for expulsion reduction. The results will help to provide guidelines and parameter selection approaches to model ML-based solutions for the optimization problems associated with welding.

StabJGL: a stability approach to sparsity and similarity selection in multiple-network reconstruction.

In recent years, network models have gained prominence for their ability to capture complex associations. In statistical omics, networks can be used to model and study the functional relationships between genes, proteins, and other types of omics data. If a Gaussian graphical model is assumed, a gene association network can be determined from the non-zero entries of the inverse covariance matrix of the data. Due to the high-dimensional nature of such problems, integrative methods that leverage similarities between multiple graphical structures have become increasingly popular. The joint graphical lasso is a powerful tool for this purpose, however, the current AIC-based selection criterion used to tune the network sparsities and similarities leads to poor performance in high-dimensional settings. We propose stabJGL, which equips the joint graphical lasso with a stable and well-performing penalty parameter selection approach that combines the notion of model stability with likelihood-based similarity selection. The resulting method makes the powerful joint graphical lasso available for use in omics settings, and outperforms the standard joint graphical lasso, as well as state-of-the-art joint methods, in terms of all performance measures we consider. Applying stabJGL to proteomic data from a pan-cancer study, we demonstrate the potential for novel discoveries the method brings. A user-friendly R package for stabJGL with tutorials is available on Github https://github.com/Camiling/stabJGL.

Functional time series forecasting: Functional singular spectrum analysis approaches

AbstractWe introduce two novel nonparametric forecasting methods designed for functional time series (FTS), namely, functional singular spectrum analysis (FSSA) recurrent and vector forecasting. Our algorithms rely on extracted signals obtained from the FSSA method and innovative recurrence relations to make predictions. These techniques are model‐free, capable of predicting nonstationary FTS and utilize a computational approach for parameter selection. We also employ a bootstrap algorithm to assess the goodness‐of‐prediction. Through comprehensive evaluations on both simulated and real‐world climate data, we showcase the effectiveness of our techniques compared to various parametric and nonparametric approaches for forecasting nonstationary stochastic processes. Furthermore, we have implemented these methods in the Rfssa R package and developed a shiny web application for interactive exploration of the results.

An optimization approach for parameter selection in natural history of breast cancer in China

Breast cancer is one of the most common cancers for females. In order to propose efficient screening strategies and evaluate the effect, one fundamental and important step is to choose proper parameters, i.e., transition rates, in the natural history model of breast cancer in China. There are two challenges to choosing reasonable transition rates. Firstly, the transition rates used in other countries may be not applicable to China, due to the cancer epidemiology. Secondly, little screening sample data are available, making the traditional statistical based approaches such as maximum likelihood methods fail. In this paper, we propose an optimization approach for parameter selection in the natural history of breast cancer in China. A mathematical optimization model is established based on the published statistical data. A coordinate descent algorithm is proposed to solve the resulting box-constrained and highly nonlinear problem, whose objective function does not have an explicit form. Finally, the best matching method is proposed to estimate the distributions of each transition rate. Our approach provides solid foundations to further propose reasonable screening strategies for breast cancer in China and analyze the economic burden of breast cancer.

Iterative hybrid regularization for extremely noisy full models in single particle analysis

Cryo-electron microscopy single particle analysis (SPA) represents a vital tool for structure determination of macromolecules. Discrete inverse problems arising in this field are extremely large and seriously contaminated by noise. The model matrix is highly structured and can not be stored explicitly. Iterative regularization methods are used here only rarely, since it is believed that they are computationally expensive and require complicated stopping criteria. In this paper, we overcome these difficulties and demonstrate that Hybrid Krylov subspace methods can be used to solve SPA inverse problems efficiently. We propose an application-driven regularization parameter selection approach and present a matrix-free implementation of the hybrid solver for GPU computations in single precision arithmetic. In comparison to Fourier-based techniques, the hybrid approach allows to compute reconstructions from full (nonreduced) SPA models even for highly noisy data sets.

Autonomous and data-efficient optimization of turning processes using expert knowledge and transfer learning

Process parameters in machining are predominantly selected by following manual tuning procedures. Using data from the system and dedicated performance indicators combined with learning-based approaches enables automating these procedures while reducing the costs of the machining process. This study investigates efficient data-driven approaches for autonomous parameter selection in turning. The number of experimental trials for finding optimal process parameters is reduced by incorporating expert knowledge and transferring knowledge between different tasks. The turning process costs are modeled using Gaussian process models, and the selection of informative experiments is achieved by Bayesian optimization. In this study, all tested methods using expert knowledge or transfer of knowledge reduced the number of experiments by at least 40% compared to a standard approach for parameter selection based on Bayesian optimization without expert knowledge, confirming the efficiency of the applied methods.

An Adaptive Time-Varying Impedance Controller for Manipulators.

Aiming at the situation that the structural parameters of the general manipulators are uncertain, a time-varying impedance controller based on model reference adaptive control (MRAC) is proposed in this article. The proposed controller does not need to use acceleration-based feedback or to measure external loads and can tolerate considerable structure parameter errors. The global uniform asymptotic stability of the time-varying closed-loop system is analyzed, and a selection approach for control parameters is presented. It is demonstrated that, by using the proposed control parameter selection approach, the closed-loop system under the adaptive controller is equivalent to an existing result. The feasibility of the presented controller for the general manipulators is demonstrated by some numerical simulations.

A New Approach for Assessing the Reliability of Electronic Assemblies Using Fuzzy Logic for Multi-criteria Optimization

Abstract The reliability of electronic assemblies is a vital criterion used to assure product quality over its lifetime. Weibull distribution is the most common distribution utilized to describe the reliability data. Most of the studies use the Weibull scale parameter, or characteristic life, to compare alternatives and make a selective decision. This may not lead to achieving the optimal parameters which can be problematic because this method doesn’t consider the variability behavior of the fatigue life. In this study, a new approach for process parameters selection is proposed to find the optimal parameter values that improve the micro-optimality selection process based on reliability data. In this study, a new approach is proposed based on examining the solder joint reliability by using a multi criteria analysis. The fuzzy logic is utilized as a tool to solve the multi criteria problem that is presented from the proposed approach. The reliability of microelectronic connections in thermal cycling operating conditions is used as a validation case study. In the validation case study, the optimal process parameters are found for ball grid array electronic components. Two levels of the solder sphere materials, three levels of the surface finish, and 10 levels of solder paste alloys are studied as process parameters. Using the proposed approach, four quality responses are employed to assess the reliability data, including the scale parameter, the B10 (life at 10% of the population failure), mean-standard deviation response, and the signal to noise ratio (SNR). The fuzzy logic is applied to solve the multiresponse problem. An optimal process parameter setting that considers different quality characteristics was found for the validation case study. ENIG surface finish, SAC305 solder sphere, and material six were the optimal factor levels that are obtained for the aged CABGA208 component using the proposed approach.

Transferability of the working envelope approach for parameter selection and optimization in thin wall WAAM

This work aims to propose and assess a methodology for parameterization for WAAM of thin walls based on a previously existing working envelope built for a basic material (parameter transferability). This work also aimed at investigating whether the working envelope approach can be used to optimize the parameterization for a target wall width in terms of arc energy (which governs microstructure and microhardness), surface finish and active deposition time. To reach the main objective, first, a reference working envelope was developed through a series of deposited walls with a plain C-Mn steel wire. Wire feed speed (WFS) and travel speed (TS) were treated as independent variables, while the geometric wall features were considered dependent variables. After validation, three combinations of WFS and TS capable of achieving the same effective wall width were deposited with a 2.25Cr-1Mo steel wire. To evaluate the parameter transferability between the two materials, the geometric features of these walls were measured and compared with the predicted values. The results showed minor deviations between the predicted and measured values. As a result, WAAM parameter selection for another material showed to be feasible after only fewer experiments (shorter time and lower resource consumption) from a working envelope previously developed. The usage of the approach to optimize parameterization was also demonstrated. For this case, lower values of WFS and TS were capable of achieving a better surface finish. However, higher WFS and TS are advantageous in terms of production time. As long as the same wall width is maintained, variations in WFS and TS do not significantly affect microstructure and microhardness.