Asymmetric Function Research Articles

Theoretical framework and inference for fitting extreme data through the modified Weibull distribution in a first-failure censored progressive approach

The importance of biomedical physical data is underscored by its crucial role in advancing our comprehension of human health, unraveling the mechanisms underlying diseases, and facilitating the development of innovative medical treatments and interventions. This data serves as a fundamental resource, empowering researchers, healthcare professionals, and scientists to make informed decisions, pioneer research, and ultimately enhance global healthcare quality and individual well-being. It forms a cornerstone in the ongoing pursuit of medical progress and improved healthcare outcomes. This article aims to tackle challenges in estimating unknown parameters and reliability measures related to the modified Weibull distribution when applied to censored progressive biomedical data from the initial failure occurrence. In this context, the article proposes both classical and Bayesian techniques to derive estimates for unknown parameters, survival, and failure rate functions. Bayesian estimates are computed considering both asymmetric and symmetric loss functions. The Markov chain Monte Carlo method is employed to obtain these Bayesian estimates and their corresponding highest posterior density credible intervals. Due to the inherent complexity of these estimators, which cannot be theoretically compared, a simulation study is conducted to evaluate the performance of various estimation procedures. Additionally, a range of optimization criteria is utilized to identify the most effective progressive control strategies. Lastly, the article presents a medical application to illustrate the effectiveness of the proposed estimators. Numerical findings indicate that Bayesian estimates outperform other estimation methods by achieving minimal root mean square errors and narrower interval lengths.

Photometric Completeness Modelled with Neural Networks

In almost any study involving optical/near-infrared photometry, understanding the completeness of detection and recovery is an essential part of the work. The recovery fraction is, in general, a function of several variables including magnitude, color, background sky noise, and crowding. We explore how completeness can be modeled, with the use of artificial-star tests, in a way that includes all of these parameters simultaneously within a neural network (NN) framework. The method is able to manage common issues including asymmetric completeness functions and the bilinear dependence of the detection limit on color index. We test the method with two sample Hubble Space Telescope data sets: the first involves photometry of the star cluster population around the giant Perseus galaxy NGC 1275, and the second involves the halo-star population in the nearby elliptical galaxy NGC 3377. The NN-based method achieves a classification accuracy of > 94% and produces results entirely consistent with more traditional techniques for determining completeness. Additional advantages of the method are that none of the issues arising from the binning of the data are present and that a recovery probability can be assigned to every individual star in real photometry. Our data, models, and code (called COINTOSS) can be found online on Zenodo at the following link: https://doi.org/10.5281/zenodo.8306488.

Particle Swarm Optimization-Based Variable Scale Asymmetric Stochastic Resonance Bearing Diagnostic Method

Abstract A diagnostic method for bearing faults, centered around the extraction and identification of diagnostic signals, is introduced. This method utilizes a Particle Swarm Optimization (PSO) algorithm to optimize a variable-scale asymmetric stochastic resonance (SR) framework. The PSO algorithm dynamically fine-tunes the parameters of the asymmetric stochastic resonance system to align more effectively with the demands of bearing fault diagnosis. An asymmetric factor-controlled potential function for the stochastic resonance system is established, using the Signal-to-Noise Ratio Improvement (A-SNRI) of the fault signal as the objective function for the optimization algorithm. The PSO algorithm is employed for global optimization to adjust the structural parameters a 0, b 0 and the asymmetric factor of the asymmetric α bistable stochastic resonance system. Simulations and experimental validations are conducted using the optimized stochastic resonance system parameters, demonstrating the robustness and effectiveness of the algorithm through the extraction of fault characteristic frequencies. Experimental results indicate the proposed bearing fault diagnostic method can stably extract fault characteristic frequencies, effectively filter out noise, and the extracted fault frequencies align with theoretical values.

Estimating the Parameters of the Generalized Inverted Kumaraswamy Distribution through the Utilization of a First Failure-Censored Sampling Plan

ThispaperfocusesonthedevelopmentofapproximateBayesestimatorsfortheshapeparametersofthegeneralizedinverted Kumaraswamy (GIKum) distribution. The estimators are based on a progressive first-failure censored plan. The study considers both maximum likelihood and Bayesian estimations using a gamma-informative prior distribution for the parameters, as well as the reliability function, hazard rate, and reversed hazard rate functions. To obtain the estimators, the paper employs Lindley’s approximation and utilizes Markov Chain Monte Carlo (MCMC) methods. The Bayes estimators are derived with respect to both symmetric (squared error) and asymmetric (linex and general entropy) loss functions. In order to assess the performance of the proposed estimators, the paper presents numerical results obtained through a simulation study involving different sample sizes.

Deconvolution of Cyclic Voltammograms in the Hydrogen Region of the Platinum-Black Electrodes with the Asymmetric Double Sigmoid Function

Deconvolution of Cyclic Voltammograms in the Hydrogen Region of the Platinum-Black Electrodes with the Asymmetric Double Sigmoid Function

Nanoscale morphology imaging for arbitrary surfaces by optical coherence tomography

The imaging of nanoscale morphology using optical methods on arbitrary surfaces poses challenges, particularly in the presence of large axial distances and irregular shapes. In this work, we introduce a novel nano-resolution imaging method along the longitudinal direction, utilizing optical coherence tomography (OCT) to measure arbitrary surface morphology comprehensively. Based on the sparsity of the primary profile, our approach allows the adjustment of parameters such as the asymmetric penalty function ϕ, the regularization parameters αi and a high-pass filter to accommodate the micro-structure of any arbitrary surface. Remarkably, our method achieves an exceptional spatial resolution of 3.04 nm. The technique has been validated by observing the surface morphology of spherical and aspherical lenses with nanometer resolution in the longitudinal direction. With the ability to detect impurities and scratches with a height of tens of nanometers, this method can be used to study the fine structure of complex surfaces. This makes it an ideal choice for experimental requirements demanding high-resolution imaging and a broad en-face perspective.

Severity influences categorical likelihood communications: A case study with Southeast Asian weather forecasters

Risk assessments are common in multiple domains, from finance to medicine. They require evaluating an event’s potential severity and likelihood. We investigate the possible dependence of likelihood and severity within the domain of impact-based weather forecasting (IBF), following predictions derived from considering asymmetric loss functions. In a collaboration between UK psychologists and partners from four meteorological organisations in Southeast Asia, we conducted two studies (N = 363) eliciting weather warnings from forecasters. Forecasters provided warnings denoting higher likelihoods for high severity impacts than low severity impacts, despite these impacts being described as having the same explicit numerical likelihood of occurrence. This ‘Severity effect’ is pervasive, and we find it can have a continued influence even for an updated forecast. It is additionally observed when translating warnings made on a risk matrix to numerical probabilities.

Utilizing Bayesian inference in accelerated testing models under constant stress via ordered ranked set sampling and hybrid censoring with practical validation

This research investigates the application of the ordered ranked set sampling (ORSSA) procedure in constant-stress partially accelerated life-testing (CSPALTE). The study adopts the assumption that the lifespan of a specific item under operational stress follows a half-logistic probability distribution. Through Bayesian estimation methods, it concentrates on estimating the parameters, utilizing both asymmetric loss function and symmetric loss function. Estimations are conducted using ORSSAs and simple random samples, incorporating hybrid censoring of type-I. Real-world data sets are utilized to offer practical context and validate the theoretical discoveries, providing concrete insights into the research findings. Furthermore, a rigorous simulation study, supported by precise numerical calculations, is meticulously conducted to gauge the Bayesian estimation performance across the two distinct sampling methodologies. This research ultimately sheds light on the efficacy of Bayesian estimation techniques under varying sampling strategies, contributing to the broader understanding of reliability analysis in CSPALTE scenarios.

Event-triggered finite-time fuzzy tracking control for a time-varying state constrained quadrotor system based on disturbance observer

In contrast with most existing results concerning quadrotor unmanned aerial vehicle (UAV) wherein time-triggered controllers or only symmetric state constraints are considered, this paper deals with the event-triggered finite-time trajectory tracking control problem for quadrotor UAV with asymmetric time-varying state constraints and unknown external disturbances. Firstly, the fuzzy logic system is used to approximate the nonlinear relationship of the quadrotor UAV system, and a disturbance observer is designed to compensate for composite disturbances. Secondly, the command filter and the event-triggered mechanism are introduced into the controller design, while asymmetric time-varying Lyapunov functions are constructed. Thus, the computational complexity and communication burden are reduced, and the event-triggered finite-time adaptive fuzzy controller is guaranteed to stabilize the quadrotor UAV in finite-time without violating all asymmetric time-varying state constraints. Finally, the simulation results demonstrate the validity of the proposed control strategy.

Normalised similarity assessment to inform grouping of advanced multi-component nanomaterials by means of an Asymmetric Sigmoid function

This manuscript presents a procedure for similarity assessment as a basis for grouping of multi component nanomaterials (MCNMs). This methodology is an adaptation of the approach by Zabeo et al. (2022), which includes an impactful change: the calculated similarities are normalised in the [0,1] domain by means of asymmetric Logistic scaling to simplify comparisons among properties' distances. This novel approach allows for grouping of nanomaterials that is not affected by the dataset, so that group membership will not change when new candidates are included in the set of assessed materials. It can be applied to assess groups of MCNMs as well as mixed groups of multi and single component nanomaterials as well as chemicals. To facilitate the application of the proposed methodology, a software script was developed by using the Python programming language, which is currently undergoing migration to a user-friendly web-based tool. The presented approach was tested against a real industrial case study provided by the Andalusian Innovation Centre for Sustainable Solution (CIAC): SiO2-ZnO hybrid nanocomposite used in building coatings, which is designed to facilitate photocatalytic removal of NOx gases from the atmosphere. The results of applying the methodology in the case study demonstrated that ZnO is dissimilar from the other candidates mainly due to its different dissolution profiles.

Few-layer bifunctional metasurfaces enabling asymmetric and symmetric polarization-plane rotation at the subwavelength scale

We introduce and numerically validate the concept of few-layer bifunctional metasurfaces comprising two arrays of quasiplanar subwavelength resonators and a middle grid (array of rectangular holes) that offer both symmetric and asymmetric transmissions connected, respectively, with symmetric and asymmetric polarization-plane rotation functionalities. The proposed structures are thinner than λ/7\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda /7$$\\end{document} and free of diffractions. Usually, the structure’s symmetry or asymmetry, i.e. unbroken or broken spatial inversion symmetries, are considered for metasurfaces as prerequisites of the capability of symmetric or asymmetric conversion of linearly polarized waves, respectively. Due to the achieved adjustment of the resonances enabling the rotation of the polarization plane simultaneously for both orthogonal polarizations of the incident wave, the symmetric polarization-plane rotation functionality can be obtained within one subwavelength band, whereas the asymmetric polarization-plane rotation functionality associated with the asymmetric transmission is obtained within another subwavelength band. This combination of the functionalities in one subdiffraction structure is possible due to the optimal choice of the grid parameters, since they may strongly affect the coupling between the two resonator arrays. Although normal incidence is required for the targeted bifunctionality, the variations of the incidence angle can also be exploited for the enrichment of the overall functional capability. Variations of the polarization angle give another important degree of freedom. The connection between the polarization-angle dependence of cross-polarized transmission and capability of symmetric and asymmetric polarization-plane rotation functionalities is highlighted. The feasible designs of the bifunctional metasurfaces are discussed.

Impact of natural antioxidant (silybin) on the thermal stability of ultra high molecular weight polyethylene: a thermogravimetric study

The natural antioxidant (Silybin), with different concentrations, is introduced in Ultra High Molecular Weight Polyethylene (UHMWPE) and impact on thermal stability is observed. For this, thermograms are recorded at 5 ℃/min heating rate in temperature region 50–600 ℃ through Thermogravimetric Analysis (TGA) technique. The model fitting (Coats and Redfern) kinetic approach is adopted to determine activation energy of each recorded thermograms to identify optimum silybin concentration. UHMWPE, with optimum silybin concentration, are further subjected to three other heating rates (10, 15 and 20 ℃) in the same temperature region. By employing deconvolution (bi-Gaussian asymmetric function) approach, two iso-conversional kinetic models (Starink (SR) and Friedman (FR)) are utilized to obtain activation energies of the deconvoluted peaks. Further, the reaction mechanism involved in thermal decomposition, change in entropy ΔS#\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left({\\Delta \ ext{S}}^{\\#}\\right)$$\\end{document}, change in enthalpy ΔH#\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left({\\Delta \ ext{H}}^{\\#}\\right)$$\\end{document} and change in Gibbs free energy ΔG#\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left(\\Delta {\ ext{G}}^{\\#}\\right)$$\\end{document} are determined.

Robust Fisher-regularized extreme learning machine with asymmetric Welsch-induced loss function for classification

Robust Fisher-regularized extreme learning machine with asymmetric Welsch-induced loss function for classification

Parameter estimation of Chen distribution under improved adaptive type-II progressive censoring

ABSTRACT This study focuses on the estimation of the two unknown parameters of Chen distribution, characterized by a bathtub-shaped hazard rate function, within an improved adaptive type-II progressive censored data framework. Maximum likelihood estimation is proposed for the two parameters, and the establishment of approximate confidence intervals is based on asymptotic normality. Bayesian estimation is also conducted under both symmetric and asymmetric loss functions, utilizing the proposed importance sampling and Metropolis–Hastings algorithm. Lastly, the performance of various estimation methods is evaluated through Monte Carlo simulation experiments, and the proposed estimation approach is illustrated using a real dataset.

Unilateral boundary time series forecasting.

Time series forecasting is an essential tool across numerous domains, yet traditional models often falter when faced with unilateral boundary conditions, where data is systematically overestimated or underestimated. This paper introduces a novel approach to the task of unilateral boundary time series forecasting. Our research bridges the gap in existing methods by proposing a specialized framework to accurately forecast within these skewed datasets. The cornerstone of our approach is the unilateral mean square error (UMSE), an asymmetric loss function that strategically addresses underestimation biases in training data, improving the precision of forecasts. We further enhance model performance through the implementation of a dual model structure that processes underestimated and accurately estimated data points separately, allowing for a nuanced analysis of the data trends. Additionally, feature reconstruction is employed to recapture obscured dynamics, ensuring a comprehensive understanding of the data. We demonstrate the effectiveness of our methods through extensive experimentation with LightGBM and GRU models across diverse datasets, showcasing superior accuracy and robustness in comparison to traditional models and existing methods. Our findings not only validate the efficacy of our approach but also reveal its model-independence and broad applicability. This work lays the groundwork for future research in this domain, opening new avenues for sophisticated analytical models in various industries where precise time series forecasting is crucial.

Reliability inference based on inverted exponentiated Rayleigh lifetime distribution under unified hybrid censored scheme

In this study, we investigated the Inverted Exponentiated Rayleigh Distribution (IERD), a significant and efficient continuous lifetime distribution commonly applied in lifespan research. Our focus was on estimating unknown parameters for a two-parameter inverted exponentiated Rayleigh distribution using unified hybrid censored data. We considered both maximum likelihood and Bayesian estimation approaches. Specifically, we employed the Gibbs within Metropolis–Hastings samplers method to develop approximate Bayes estimators utilizing informative and non-informative priors, along with symmetric and asymmetric loss functions. In addition, we utilized Markov chain Monte Carlo (MCMC) samples to derive maximum posterior density credible intervals. Simulation experiments were conducted to assess the efficacy of the proposed methodologies, and actual data analysis was performed to validate the proposed estimators.

Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning.

Long waiting time in outpatient departments is a crucial factor in patient dissatisfaction. We aim to analytically interpret the waiting times predicted by machine learning models and provide patients with an explanation of the expected waiting time. Here, underestimating waiting times can cause patient dissatisfaction, so preventing this in predictive models is necessary. To address this issue, we propose a framework considering dissatisfaction for estimating the waiting time in an outpatient department. In our framework, we leverage asymmetric loss functions to ensure robustness against underestimation. We also propose a dissatisfaction-aware asymmetric error score (DAES) to determine an appropriate model by considering the trade-off between underestimation and accuracy. Finally, Shapley additive explanation (SHAP) is applied to interpret the relationship trained by the model, enabling decision makers to use this information for improving outpatient service operations. We apply our framework in the endocrinology metabolism department and neurosurgery department in one of the largest hospitals in South Korea. The use of asymmetric functions prevents underestimation in the model, and with the proposed DAES, we can strike a balance in selecting the best model. By using SHAP, we can analytically interpret the waiting time in outpatient service (e.g., the length of the queue affects the waiting time the most) and provide explanations about the expected waiting time to patients. The proposed framework aids in improving operations, considering practical application in hospitals for real-time patient notification and minimizing patient dissatisfaction. Given the significance of managing hospital operations from the perspective of patients, this work is expected to contribute to operations improvement in health service practices.

Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach

Loss function plays a vital role in supervised learning frameworks. The selection of the appropriate loss function holds the potential to have a substantial impact on the proficiency attained by the acquired model. The training of supervised learning algorithms inherently adheres to predetermined loss functions during the optimization process. In this paper, we present a novel contribution to the realm of supervised machine learning: an asymmetric loss function named wave loss. It exhibits robustness against outliers, insensitivity to noise, boundedness, and a crucial smoothness property. Theoretically, we establish that the proposed wave loss function manifests the essential characteristic of being classification-calibrated. Leveraging this breakthrough, we incorporate the proposed wave loss function into the least squares setting of support vector machines (SVM) and twin support vector machines (TSVM), resulting in two robust and smooth models termed as Wave-SVM and Wave-TSVM, respectively. To address the optimization problem inherent in Wave-SVM, we utilize the adaptive moment estimation (Adam) algorithm, which confers multiple benefits, including the incorporation of adaptive learning rates, efficient memory utilization, and faster convergence during training. It is noteworthy that this paper marks the first instance of Adam’s application to solve an SVM model. Further, we devise an iterative algorithm to solve the optimization problems of Wave-TSVM. To empirically showcase the effectiveness of the proposed Wave-SVM and Wave-TSVM, we evaluate them on benchmark UCI and KEEL datasets (with and without feature noise) from diverse domains. Moreover, to exemplify the applicability of Wave-SVM in the biomedical domain, we evaluate it on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The experimental outcomes unequivocally reveal the prowess of Wave-SVM and Wave-TSVM in achieving superior prediction accuracy against the baseline models. The source codes of the proposed models are publicly available at https://github.com/mtanveer1/Wave-SVM.

BAYES ESTIMATION OF EXPONENTIALLY DISTRIBUTED SURVIVAL DATA UNDER SYMMETRIC AND ASYMMETRIC LOSS FUNCTIONS

In a research study, population data are often not available, so the population parameter is unknown. Meanwhile, knowledge about the population parameter is needed to know the characteristics of the studied population. Therefore, it is needed to estimate the parameter of the population which can be estimated by sample data. There are several methods of parameter estimation which are generally classified into classical and Bayesian method. This research studied the Bayesian parameter estimation method to determine the parameters of the exponentially distributed survival data associated with the reliability measure of the estimates under symmetric and asymmetric loss functions for complete sample data in a closed form. The symmetric loss functions used in this research are Squared Error Loss Function (SELF) and Minimum Expected Loss Function (MELF). The asymmetric loss functions used are the General Entropy Loss Function (GELF) and Linex Loss Function (LLF). Performance of some loss functions used in this research are then compared through numerical simulation to select the best loss function in determining the parameter estimation of the exponentially distributed survival data. We also studied which loss function is best for underestimation and overestimation modeling. Based on simulation results, the Bayes estimates using MELF is the best method to estimate population parameters of the exponentially distributed survival data for the overestimation modeling, while LLF is the best for the underestimation modeling. We provided direct application in a case study of fluorescence lamp survival data. The results show that the best method to estimate the parameter of the standard fluorescence life data is using LLF for underestimation with and MELF for overestimation with .

An Efficient Multi-Label Classification-Based Municipal Waste Image Identification

Sustainable and green waste management has become increasingly crucial due to the rising volume of waste driven by urbanization and population growth. Deep learning models based on image recognition offer potential for advanced waste classification and recycling methods. However, traditional image recognition approaches usually rely on single-label images, neglecting the complexity of real-world waste occurrences. Moreover, there is a scarcity of recognition efforts directed at actual municipal waste data, with most studies confined to laboratory settings. Therefore, we introduce an efficient Query2Label (Q2L) framework, powered by the Vision Transformer (ViT-B/16) as its backbone and complemented by an innovative asymmetric loss function, designed to effectively handle the complexity of multi-label waste image classification. Our experiments on the newly developed municipal waste dataset “Garbage In, Garbage Out”, which includes 25,000 street-level images, each potentially containing up to four types of waste, showcase the Q2L framework’s exceptional ability to identify waste types with an accuracy exceeding 92.36%. Comprehensive ablation experiments, comparing different backbones, loss functions, and models substantiate the efficacy of our approach. Our model achieves superior performance compared to traditional models, with a mean average precision increase of up to 2.39% when utilizing the asymmetric loss function, and switching to ViT-B/16 backbone improves accuracy by 4.75% over ResNet-101.