Model Parameters Research Articles

Stochastic EM algorithm for partially observed stochastic epidemics with individual heterogeneity.

We develop a stochastic epidemic model progressing over dynamic networks, where infection rates are heterogeneous and may vary with individual-level covariates. The joint dynamics are modeled as a continuous-time Markov chain such that disease transmission is constrained by the contact network structure, and network evolution is in turn influenced by individual disease statuses. To accommodate partial epidemic observations commonly seen in real-world data, we propose a stochastic EM algorithm for inference, introducing key innovations that include efficient conditional samplers for imputing missing infection and recovery times which respect the dynamic contact network. Experiments on both synthetic and real datasets demonstrate that our inference method can accurately and efficiently recover model parameters and provide valuable insight at the presence of unobserved disease episodes in epidemic data.

Wire rope isolator identification and dynamic modeling for small amplitude vibrations

Wire rope isolators are used in variety of applications to protect sensitive equipment from vibration. The nonlinear hysteretic behavior of steel wires provides advantages when compared to linear vibration isolators. This study proposes an amplitude dependent stiffness and damping model for low amplitude vibrations under axial loading, where the parameters can be determined with an experimental procedure. Comprehensive experimental results of forced vibration tests with varying loading, frequency and preload were considered in the model identification. Amplitude dependent stiffness and loss energy models were determined from the test data, and the effect of the preload and loading frequency on the model parameters were studied. It is shown, that the effect of preload and frequency is not evidently clear, while the effect of vibration amplitude is more significant. The mathematical model was further verified against measurements from base excitation loading. The proposed model can be used to study the effectiveness of the selected wire rope isolator configurations in chosen application, and to effectively perform dynamic design studies.

A novel scale-bridging method for MSMA linking continuum thermodynamics constitutive formulations to lumped system-level models

This work introduces a novel scale-bridging method between a continuum thermodynamics constitutive model and a lumped system-level model for magnetic shape memory alloys (MSMA). With this method, system models for real-time operations are generated based on virtual experiments using the constitutive model. The proposed method addresses the fact that, while constitutive models for MSMA typically only require small sets of parameters as input, their evaluation is still computationally expensive. System models for control engineering, however, require extensive experimental parameterization, while their evaluation is highly time-efficient. The proposed scale-bridging method has the potential to combine a small parameterization effort and a low computational cost of the real-time system model. Additionally, the constitutive model is utilized to investigate whether it can determine the individual behavior of MSMA samples. This is important since the inherent model parameters, while valid for ideal single crystals, deviate for non-ideal MSMA sample behavior. To this end, the MSMA constitutive model, based on a global variational principle originally proposed by Kiefer et al is supplemented by various extensions, including a more robust algorithmic treatment. A parameter identification procedure is introduced to optimize the constitutive model parameters based on an outer hysteresis curve for a particular load case. By conducting virtual experiments with the constitutive model, data sets are generated to parameterize Preisach hysteresis models as numerical approximations of the constitutive models. The resulting hysteresis models are compared with physical experiments using an MSMA test bench for different load cases. It is shown that the proposed scale-bridging method successfully generates hysteresis models derived from constitutive models. While maintaining accuracy comparable to strictly phenomenological models across various load cases (as validated through physical MSMA test bench experiments), these models require significantly less parameterization effort than classical system models. This translates to faster model creation and broader applicability.

Effect of Vitamin K2 on Blood Rheology and Vascular Responses in Diabetic Rats

Background/aim: Diabetes is manifested by endothelial dysfunction and an imbalance between vasoconstriction and vasodilation. The aim of our study is to examine the effect of vitK2 application on vascular and rheological parameters in a rat diabetes model. Materials and methods: A total of 60 male Wistar Albino rats were used to examine vascular responses and hemorheological parameters. A total of 6 groups were: control (C), control+vehicle (Cv), control+vitK2 administered (C + K2), diabetes (D), diabetes+vehicle (Dv), and diabetes+vitK2 (D + K2) group. After the animals were sacrificed, blood and vascular samples were taken and the contraction and relaxation responses of the aorta and erythrocyte deformability and aggregation were examined. Results: When KCl dose-response curves are evaluated; Increased vasoconstriction response was found in the Dv group compared to the Cv group. The increase in vasoconstriction observed in the Dv group decreased with the application of vit K2. D+vitK2 group thoracic aorta contraction responses returned to Cv group levels. In response to increasing cumulative doses of Phe, a significant increase in vasoconstriction response was observed in the Dv group compared to the Cv group. VitK2 application reduced the Phe-mediated contractile response, which was increased in the Dv group, and returned the contraction response to Cv conditions except for two intermediate Phe doses. In the Dv + K2 group, a significant decrease was observed in the aggregation index, which was tended to increase. Conclusion: Considering the cardiovascular complications frequently observed in diabetes, it can be suggested that vitK2 therapy may yield positive outcomes in diabetes.

Time series prediction model using LSTM-Transformer neural network for mine water inflow

Mine flooding accidents have occurred frequently in recent years, and the predicting of mine water inflow is one of the most crucial flood warning indicators. Further, the mine water inflow is characterized by non-linearity and instability, making it difficult to predict. Accordingly, we propose a time series prediction model based on the fusion of the Transformer algorithm, which relies on self-attention, and the LSTM algorithm, which captures long-term dependencies. In this paper, Baotailong mine water inflow in Heilongjiang Province is used as sample data, and the sample data is divided into different ratios of the training set and test set in order to obtain optimal prediction results. In this study, we demonstrate that the LSTM-Transformer model exhibits the highest training accuracy when the ratio is 7:3. To improve the efficiency of search, the combination of random search and Bayesian optimization is used to determine the network model parameters and regularization parameters. Finally, in order to verify the accuracy of the LSTM-Transformer model, the LSTM-Transformer model is compared with LSTM, CNN, Transformer and CNN–LSTM models. The results prove that LSTM-Transformer has the highest prediction accuracy, and all the indicators of its model are well improved.

A new extension of the Gumbel distribution with biomedical data analysis

In the field of biomedical research, data characteristics often exhibit significant variability, challenging the applicability of classical Gumbel distribution for biomedical data modeling. To address this, this paper introduces a novel extension of the Gumbel model known as the odd beta prime Gumbel (OBP-Gum) model. Derived from the odd beta prime family, the new distribution exhibits greater kurtosis compared to the traditional Gumbel distribution. Importantly, the proposed distribution is designed to capture right-skewed, left-skewed, and nearly symmetric density functions, as well as increasing, decreasing, constant, and upside-down bathtub shapes for its hazard rate function, providing excellent curvature features for creating flexible statistical models for biomedical research. We derive the fundamental features of the OBP-Gum model, such as the quantile function, linear representations, moment generating function, moments, skewness, kurtosis, incomplete moments, and Rényi and Tsallis entropies. Parameter estimation for this new model is conducted using the maximum likelihood estimation method. A simulation study demonstrates the performance of the model parameters. The empirical findings, based on applications to two biomedical datasets, suggest that the OBP-Gum distribution outperforms existing models, particularly in handling extreme observations. Instead of relying on conventional models for decision-making, this research provides relevant stakeholders with an improved statistical distribution for more accurate biomedical data modeling.

Ionic Coulomb blockade controls the current in a short narrow carbon nanotube.

We use all-atom molecular dynamics simulations to investigate ionic conduction in a short, charged, single-wall carbon nanotube. They reveal ionic Coulomb blockade (ICB) oscillations in the current as a function of the fixed charge on the wall, and an associated occupancy staircase. Current peaks related to fluctuations around the 2 → 1 and 1 → 0 steps in occupancy are clearly resolved, in agreement with ICB theory. Current peaks were also observed at constant occupancy. These unpredicted secondary peaks are attributed to edge effects involving a remote knock-on mechanism; they are attenuated, or absent, for certain choices of model parameters. The key parameters of the system that underlie the current oscillations are estimated using ICB theory and the potential of the mean force. Future perspectives opened up by these observations are discussed.

Advancements in physiologically based pharmacokinetic modeling for fedratinib: updating dose guidance in the presence of a dual inhibitor of CYP3A4 and CYP2C19.

A physiologically based pharmacokinetic (PBPK) model for fedratinib was updated and revalidated to bridge a gap between the observed drug-drug interaction (DDI) of a single sub-efficacious dose in healthy participants and the potential DDI in patients with cancer at steady state. The study aimed to establish an appropriate dose for fedratinib in patients coadministered with dual CYP3A4 and CYP2C19 inhibitors, providing quantitative evidence to inform dosing guidance. The original minimal PBPK model was developed using Simcyp® Simulator v17. The model was updated by substituting a single distribution rate (Qsac) with 2 separate rates (CLin/CLout) and transitioning to v20. Model parameter updates were further informed with 3 clinical studies, and 3 more studies served as independent validation data. The validated model was applied to simulate potential DDIs between fedratinib and a known dual inhibitor of CYP3A4 and CYP2C19 (fluconazole). Coadministration of fedratinib with fluconazole in patients was predicted to increase fedratinib exposure by < 2-fold in all simulated scenarios. For patients with cancer receiving the approved dose of fedratinib 400mg once daily along with fluconazole 200mg daily, the model predicted an approximate 50% increase in fedratinib exposure at steady state. The updated PBPK model improved description of the observed pharmacokinetics and predicted a low risk of clinically significant DDIs between fedratinib and fluconazole. The quantitative evidence serves as a primary foundation for providing dose guidance in clinical practice for the coadministration of fedratinib with dual CYP3A4 and CYP2C19 inhibitors.

Review of Fourth-Order Maximum Entropy Based Predictive Modeling and Illustrative Application to a Nuclear Reactor Benchmark: II. Best-Estimate Predicted Values and Uncertainties for Model Responses and Parameters

This work continues the review and illustrative application to energy systems of the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters in the combined phase space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21,976 imprecisely known parameters. This benchmark is representative of “large-scale computations” such as those involved in the modeling of energy systems. The result (“response”) of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for “high-precision” parameters (2% standard deviations) and “typical-precision” parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st—through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems modeled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, and MC). At least second-order sensitivities should be retained in order to obtain reliable predictions.

Review of Fourth-Order Predictive Modeling and Illustrative Application to a Nuclear Reactor Benchmark. I. Typical High-Order Sensitivity and Uncertainty Analysis

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to nuclear energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, while yielding best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC), as will be illustrated in this work by means of the above-mentioned OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation involving 21,976 imprecisely known parameters, the solution of which is representative of “large-scale computations”. The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP

In this work, we demonstrate how Machine learning (ML) techniques can be employed to externalize the knowledge and time intensive process of material parameter identification. This is done on the example of a recent data driven material model for the non-linear shear behavior of glass fiber reinforced polypropylene (GF/PP) (Gerritzen, 2022). A convolutional neural network (CNN) based model architecture is trained to predict material modeling parameters based on the input of stress–strain-curves. The optimal model architecture and training setup are determined by hyperparameter optimization (HPO). Solely virtual data, generated using the target material model, is used throughout the training and HPO. The final CNN is capable of calculating model parameter combinations from experimental stress–strain-curves which lead to excellent agreement between experimental and associated model curve.

Economic evaluation of a psychological surveillance and support programme in the UK police force

PurposePolice officers in the UK frequently experience traumatic events and work pressures, and these can have a detrimental impact on mental health. A psychological surveillance programme has been implemented and clinically evaluated. This paper reports an economic evaluation of the programme.Design/methodology/approachA decision analytic model was developed to explore programme costs, outcomes and return on investment for a notional cohort of 1,000 police personnel (officers and staff). The model parameters were obtained from a large data set of sequential psychological surveillance results. Changes in individual work productivity were derived from a workability measure included in the survey.FindingsThe modelled expected total cost incurred to screen 1,000 participants was £84,287 ($106,971). The expected net increase in work productivity for those receiving interventions was valued at £241,672 ($306,713). This result represents a return on investment of 187%. Sensitivity analyses showed that the model was robust to changes in key parameters.Research limitations/implicationsPolice personnel experiencing traumatic events may need specialist support from occupational health services or psychological therapy. Screening or surveillance can detect mental health problems, but its cost-effectiveness has been previously unknown.Practical implicationsThe value for money of providing surveillance within the police force to detect mental health problems is assessed in this research. The gains in terms of increased work are evident. In similar environmental working conditions, this could extend to emergency services globally.Social implicationsThe police are often called upon to deal with the lowest points in people’s lives. The findings have implications for police forces wishing to support officers to be able to provide the best of themselves in these situations.Originality/valueThere have been no previous studies in policing that have concluded that running a psychological surveillance programme in policing is financially viable. Although this requires initial investment, this paper models that the extra costs incurred are more than offset by improved work productivity.

EFS-YOLO: a lightweight network based on steel strip surface defect detection

Abstract With the advancement of deep learning technologies, industrial intelligent detection algorithms are gradually being applied in practical steel surface defect detection. Addressing the issues of high computational resource consumption and poor detection performance faced by existing models in large-scale industrial production lines, this paper proposes an EFS-YOLO model based on improved YOLOv8s architecture. Firstly, the EfficientViT is employed as the feature extraction network, effectively reducing the model's parameters and calculations while enhancing its capability to represent defect features. Secondly, the designed lightweight C2f-Faster-EffectiveSE Block (CFE-Block) was integrated into the model neck, accelerating feature fusion and better preserving detailed defect features at lower levels. Finally, the model detection head was reconstructed using the concept of shared parameters to address the high computational cost of the original detection head. Experimental results on the NEU-DET and GC10-DET datasets demonstrate that compared to the baseline model, the proposed model achieves a reduction in parameters, calculations and size by 49.5%, 62.7% and 46.9% respectively. It also exhibits an improvement in accuracy by 2.4% and 2.3% on the two datasets. The EFS-YOLO model effectively enhances precision in steel surface defect detection while maintaining lightweight characteristics, offering superior performance.&amp;#xD;

Parameterization of a WRF Model Based on Microwave Measurements of Temperature Inversion Characteristics in PBL over Moscow City

In this work the WRF-ARW model was tested with several different combinations of physical parameterizations to assess the quality of temperature inversion parameter predictions over the Moscow city. The dynamic and statistical characteristics of temperature inversions have been calculated and analysed in selecting criteria for comparisons. The calculated of estimating of the dissipation conditions in dependence on the type of temperature inversions are presented. The data source was the results of temperature profiles measurements in a layer up to 1 km, obtained by the MTP-5 passive microwave profiler from 2018 to 2021. One MTP5 on the North of Moscow was used to tune the model parameters and another one on the East of Moscow for validation. The comparison results show that several parameterization options can be chosen to reproduce the main inversion parameters.

Innovative thermal management in the presence of ferromagnetic hybrid nanoparticles

In the present work, a simple intelligence-based computation of artificial neural networks with the Levenberg-Marquardt backpropagation algorithm is developed to analyze the new ferromagnetic hybrid nanofluid flow model in the presence of a magnetic dipole within the context of flow over a stretching sheet. A combination of cobalt and iron (III) oxide (Co-Fe2O3) is strategically selected as ferromagnetic hybrid nanoparticles within the base fluid, water. The initial representation of the developed ferromagnetic hybrid nanofluid flow model, which is a system of highly nonlinear partial differential equations, is transformed into a system of nonlinear ordinary differential equations using appropriate similarity transformations. The reference data set of the possible outcomes is obtained from bvp4c for varying the parameters of the ferromagnetic hybrid nanofluid flow model. The estimated solutions of the proposed model are described during the testing, training, and validation phases of the backpropagated neural network. The performance evaluation and comparative study of the algorithm are carried out by regression analysis, error histograms, function fitting graphs, and mean squared error results. The findings of our study analyze the increasing effect of the ferrohydrodynamic interaction parameter β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document} to enhance the temperature and velocity profiles, while increasing the thermal relaxation parameter α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} decreases the temperature profile. The performance on MSE was shown for the temperature and velocity profiles of the developed model about 9.1703e−10, 7.1313ee−10, 3.1462e−10, and 4.8747e−10. The accuracy of the artificial neural networks with the Levenberg-Marquardt algorithm method is confirmed through various analyses and comparative results with the reference data. The purpose of this study is to enhance understanding of ferromagnetic hybrid nanofluid flow models using artificial neural networks with the Levenberg-Marquardt algorithm, offering precise analysis of key parameter effects on temperature and velocity profiles. Future studies will provide novel soft computing methods that leverage artificial neural networks to effectively solve problems in fluid mechanics and expand to engineering applications, improving their usefulness in tackling real-world problems.

Prediction method of long-term creep deformation of mobile phone resin lens module in a harsh temp environment

In recent years, the replacement cycle of digital products such as mobile phones in the market has significantly increased, and the accurate prediction of attenuation under long-term usage conditions of the lens module, storage, and other components has become important. The vast majority of mobile phone lens modules are processed from optical resins, and the degradation of their imaging performance is mainly caused by assembly deformation and long-term creep deformation after a high-temperature reliability test. Therefore, there is an urgent need for a reliable model to describe the long-term creep deformation of the mobile phone resin lens module in a harsh temperature environment. This study proposed a modified derivation method for the parameters of the Maxwell constitutive model to predict the long-term creep deformation of the mobile phone resin lens module in a high-temperature environment. The fitting accuracy of the modified generalized Maxwell model and the time-hardening model were compared through uniaxial compression creep experiments. Long-term creep simulations and experiments were conducted on the mobile phone resin lens module; the deformation law of each component of the mobile phone resin lens module was identified; and the creep deformation mechanism was elucidated. The result shows that the modified generalized Maxwell model has much higher accuracy than the time-hardening model in predicting the long-term creep deformation of different resin materials. When considering the influence of the creep phenomenon, the simulation result matches well with the experimental result, and the high-temperature creep mechanism of the mobile phone resin lens module was clarified. In addition, it was found that the maximum deformation of the lens occurred at the lens with the largest size, which was 2.1 µm, and the position with the largest surface deviation was at the edge of the lens. The prediction method proposed in this study can provide guidance and correction suggestions for the structural design of a mobile phone resin lens module and the rationality of optical design; thus, the imaging accuracy of lens modules will be guaranteed to the greatest extent possible.

3D modelling of ground magnetic-source airborne TEM data for anisotropic media based on the rational Krylov method

With the rapid development of the transient electromagnetic method (TEM), the numerical simulations of three-dimensional anisotropic media have garnered attention. This study used the rational Krylov method to realise the three-dimensional modelling of the ground magnetic-source airborne transient electromagnetic data for arbitrary anisotropic media. First, based on Maxwell's equations, a time-domain electromagnetic equation was derived for anisotropic media. The finite difference method was employed to discretise the equation, and the electromagnetic field was then expressed as the product of an exponential matrix and a vector related to the source. Subsequently, the rational Arnoldi algorithm was adopted to construct the rational Krylov subspace orthogonal basis vectors, and the electromagnetic fields at different times were obtained based on a model reduction strategy. The accuracy of the proposed algorithm was verified through the comparisons of its results with those of other software packages. We calculated the response for different anisotropic parameters of the typical three-dimensional anisotropic models, and analysed the influence of the anisotropic media on the ground magnetic-source airborne transient electromagnetic data. The proposed three-dimensional forward TEM algorithm provides important technical support for identifying the anisotropic geological bodies and lays the foundation for further inversion research.

Development of machine learning models for forecasting the strength of resilient modulus of subgrade soil: genetic and artificial neural network approaches

Accurately predicting the Modulus of Resilience (MR) of subgrade soils, which exhibit non-linear stress–strain behaviors, is crucial for effective soil assessment. Traditional laboratory techniques for determining MR are often costly and time-consuming. This study explores the efficacy of Genetic Programming (GEP), Multi-Expression Programming (MEP), and Artificial Neural Networks (ANN) in forecasting MR using 2813 data records while considering six key parameters. Several Statistical assessments were utilized to evaluate model accuracy. The results indicate that the GEP model consistently outperforms MEP and ANN models, demonstrating the lowest error metrics and highest correlation indices (R2). During training, the GEP model achieved an R2 value of 0.996, surpassing the MEP (R2 = 0.97) and ANN (R2 = 0.95) models. Sensitivity and SHAP (SHapley Additive exPlanations) analysis were also performed to gain insights into input parameter significance. Sensitivity analysis revealed that confining stress (21.6%) and dry density (26.89%) are the most influential parameters in predicting MR. SHAP analysis corroborated these findings, highlighting the critical impact of these parameters on model predictions. This study underscores the reliability of GEP as a robust tool for precise MR prediction in subgrade soil applications, providing valuable insights into model performance and parameter significance across various machine-learning (ML) approaches.

Quantum support vector data description for anomaly detection

Abstract Anomaly detection is a critical problem in data analysis and pattern recognition, finding applications in various domains. We introduce quantum support vector data description (QSVDD), an unsupervised learning algorithm designed for anomaly detection. QSVDD utilizes a shallow-depth quantum circuit to learn a minimum-volume hypersphere that tightly encloses normal data, tailored for the constraints of noisy intermediate-scale quantum (NISQ) computing. Simulation results on the MNIST and Fashion MNIST image datasets, as well as credit card fraud detection, demonstrate that QSVDD outperforms both quantum autoencoder and deep learning-based approaches under similar training conditions. Notably, QSVDD requires an extremely small number of model parameters, which increases logarithmically with the number of input qubits. This enables efficient learning with a simple training landscape, presenting a compact quantum machine learning model with strong performance for anomaly detection.

Simulated annealing algorithm for the optimal design of diffractive structures under fluctuation models

In this paper, to improve the optimization capability of the simulated annealing algorithm (SA) in the optimal design of diffractive structures, the random fluctuation problem in the algorithm is investigated, and a new fluctuation calculation model is proposed. The optimal design of phase-type diffraction gratings and beam-shaping devices verified the fast accuracy of the proposed model. The simulation experimental results show that the model’s introduction is particularly significant in substantially reducing computation time (a threefold increase in computation speed) and improving the algorithm’s optimization finding capability. Finally, the setting of the model parameters is discussed analytically to make the proposed model more applicable and robust. The study results provide guidance and assistance for higher speed and effectiveness in the optimal design using simulated annealing algorithms. It can be applied to any complex and large-scale optimization calculation problem.