Parameter Constraints Research Articles

OPTIMIZATION OF CYBER-PHYSICAL SYSTEM PARAMETERS BASED ON INTELLIGENT IOT SENSORS DATA

The optimization of cyber-physical systems (CPS) parameters is researched with focusing on the integration of computation, networking, and physical processes with the Internet of Things (IoT). The use of intelligent IoT sensors is crucial for collecting real-time data, which is necessary for enhancing the efficiency, reliability, and performance of CPS. Various methods of CPS parameters optimization are analyzed and categorized into model-based approaches, data-driven approaches, and hybrid approaches. The model-based approaches rely on mathematical models to describe CPS behavior and use optimization algorithms like linear programming and evolutionary algorithms to predict system responses and optimize parameters. But, the limitations of model-based approaches are related to complex systems with uncertain or dynamic behavior. The data-driven approaches are more suitable for complex cyber-physical systems. These approaches utilize machine learning and data analytics techniques to extract patterns from sensor data, which are then used to adjust system parameters. The hybrid approaches combine elements of both model-based and data-driven methods. The method of cyber-physical system parameters optimization based on intelligent IoT sensors data processing is developed with using of distributed neural network. The optimization problem is formulated with constraints for the system parameters. The neural network mathematical model and learning algorithm are proposed. The performed research shows the importance of developing optimization methods for CPS parameters based on intelligent IoT sensor data, considering the evolving nature of IoT technology. The integrating intelligent sensors into CPS offers new opportunities for optimizing system performance but also presents challenges in data management and security that should be addressed in future.

Differential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression.

Here, we assess how the differential expression of low molecular weight serum peptides might predict breast cancer progression with high confidence. We apply an LC/MS-MS-based, unbiased 'omics' analysis of serum samples from breast cancer patients to identify molecules that are differentially expressed in stage I and III breast cancer. Results were generated using standard and machine learning-based analytical workflows. With standard workflow, a discovery study yielded 65 circulating biomarker candidates with statistically significant differential expression. A second study confirmed the differential expression of a subset of these markers. Models based on combinations of multiple biomarkers were generated using an exploratory algorithm designed to generate greater diagnostic power and accuracy than any individual markers. Individual biomarkers and the more complex multi-marker models were then tested in a blinded validation study. The multi-marker models retained their predictive power in the validation study, the best of which attained an AUC of 0.84, with a sensitivity of 43% and a specificity of 88%. One of the markers with m/z 761.38, which was downregulated, was identified as a fibrinogen alpha chain. Machine learning-based analysis yielded a classifier that correctly categorizes every subject in the study and demonstrates parameter constraints required for high confidence in classifier output. These results suggest that serum peptide biomarker models could be optimized to assess breast cancer stage in a clinical setting.

Hybrid calibration method for a single camera stereo digital image correlation system

In single camera stereo digital image correlation systems, the existing calibration methods have problems with poor accuracy and violation of theoretical constraints, so we have proposed a hybrid calibration method. In this study, we first explain the principles of the existing single camera stereo measurement calibration methods. Then, on the basis of the classical camera calibration method combined with the characteristics of planar mirror reflection imaging, a hybrid calibration method is proposed that not only satisfies the constraints of the same intrinsic parameters but also has higher accuracy. Next, we complete the mathematical model of the hybrid calibration method and explain the specific calibration process. Finally, the experimental results of point distance reconstruction and static displacement measurement show that, compared with the existing calibration methods, the hybrid calibration method has lower error and higher reconstruction accuracy.

Solutions of crack constraint parameter in 3D models − I: Through curve shape similarity

Two-parameter approaches of elastic plastic fracture mechanics, e.g., J-A(A2), J-Q, can be used to obtain more accurate fracture analysis results, especially under low crack constraint condition which commonly observed in practical engineering structures. While, theoretical solutions of the constraint parameters in the two-parameter approaches are not available, and substantial consumption of resources and time makes it inappropriate to numerically determine constraint parameters (e.g., A(A2), Q), especially in 3D solid models. In this work, based on the curve shape similarity, the estimation theory and formulae for constraint parameter A in 3D solid models are developed through theoretical and numerical analyses to obtain parameter A solutions quickly and efficiently, so as to enable the application of two-parameter approaches in practical engineering structures. The proposed estimation formulae here are used to predict the constraint parameter A in two actual engineering structures.

Optical Solitons for the Dispersive Concatenation Model with Polarization Mode Dispersion by Sardar's Sub-Equation Approach

This paper recovers the spectrum of optical solitons for the dispersive concatenation model with the application of the Sardar sub-equation approach. The single soliton solutions that emerge from this scheme are bright, dark, and of singular type. The parameter constraints for the existence of such solitons are also included.

Load-independent creep constraint analysis and solutions for surface cracks in pressurized pipes

Using load-independent creep constraint parameters Q* and Qm*, the variations of crack front stress field and constraint level are analyzed for various crack sizes of pipelines containing internal surface cracks under high temperature conditions. The results show that the trends of stress changes in the axial and circumferential internal surface crack front for different crack sizes are similar. For axial internal surface cracks, when the crack depth to length ratio a/c is the same, the maximum values of crack front Q* and Qm* are shifted from the deepest part of the crack (2Φ/π = 1) to the vicinity of the free surface (2Φ/π = 0.2) as the crack depth a/t increases. As for the circumferential inner surface cracks, when the crack depth a/t exceeds 0.2, the locations where the maximum values of crack fronts Q* and Qm* appear are mainly concentrated in the deepest part of the crack (2Φ/π = 1). Based on the calculation results of the constraint parameters, the empirical formulas for the engineering calculation of the relationship between the average constraint parameters (Qavg* and Qm‐avg*) and the crack size are obtained by fitting.

Efficient recognition of fish feeding behavior: A novel two-stage framework pioneering intelligent aquaculture strategies

In the domain of aquaculture, the act of feeding fish is a pivotal factor influencing both the growth of the fish and the associated cultivation costs. The implementation of intelligent feeding strategies is a crucial prerequisite for maintaining fish health and minimizing costs, with the accurate discernment of fish feeding behavior serving as the fundamental basis for the realization of such strategies. Addressing the issues of high data redundancy and substantial noise content inherent in the datasets utilized by existing identification models, as well as the intricate design and suboptimal execution efficiency of the model structures, this study introduces a two-stage framework for discriminating fish feeding behavior. In the initial stage, a re-parameterizable multi-scale object detection model is established, facilitating the acquisition of the spatial distribution of fish schools. This process effectively eliminates redundant data and noise, decouples the training and inference processes of the model, equivalently transforms the complex network training weights, and simplifies the inference process of the model. An analysis of the dataset characteristics is conducted, leading to the optimization of the model detector’s design and a reduction in both the model parameters and computational requirements. In the subsequent stage, responding to the key data characteristics of the spatial distribution of fish schools, a lightweight behavior recognition model is designed. This model enables the rapid and accurate identification of fish feeding behavior. A plethora of experimental results demonstrate that the proposed method can achieve a high recognition accuracy (Acc 83.33%) while operating under the constraints of minimal model parameters and computations (6.45M Params, 8.135G FLOPs). This provides a robust model foundation for the industrial application of the algorithm, underscoring the significant potential of the proposed method in advancing intelligent feeding strategies in aquaculture.

How informative are summaries of the cosmic 21 cm signal?

The cosmic 21 cm signal will bring data-driven advances to studies of Cosmic Dawn (CD) and the Epoch of Reionization (EoR). Radio telescopes such as the Square Kilometre Array (SKA) will eventually map the HI fluctuations over the first billion years -- the majority of our observable Universe. With such large data volumes, it becomes increasingly important to develop optimal summary statistics, which will allow us to learn as much as possible about the CD and EoR. In this work we compare the astrophysical parameter constraining power of several 21 cm summary statistics, using the determinant of the Fisher information matrix, $ F $. Since we do not have an established fiducial model for the astrophysics of the first galaxies, we computed for each summary the distribution of $ F $ across the prior volume. Using a large database of cosmic 21 cm light cones that include realizations of telescope noise, we compared the following summaries: (i) the spherically averaged power spectrum (1DPS), (ii) the cylindrically averaged power spectrum (2DPS), (iii) the 2D wavelet scattering transform (WST), (iv) a recurrent neural network (RNN) trained as a regressor; (v) an information-maximizing neural network (IMNN); and (vi) the combination of 2DPS and IMNN. Our best performing individual summary is the 2DPS, which provides relatively high Fisher information throughout the parameter space. Although capable of achieving the highest Fisher information for some parameter choices, the IMNN does not generalize well, resulting in a broad distribution across the prior volume. Our best results are achieved with the concatenation of the 2DPS and IMNN. The combination of only these two complimentary summaries reduces the recovered parameter variances on average by factors of sim 6.5 -- 9.5, compared with using each summary independently. Finally, we point out that that the common assumption of a constant covariance matrix when doing Fisher forecasts using 21 cm summaries can significantly underestimate parameter constraints.

Highly dispersive optical solitons with differential group delay and multiplicative white noise for Fokas–Lenells equation

AbstractThe current article addresses highly dispersive optical solitons with polarization mode dispersion, modeled by the Fokas–Lenells equation in presence of white noise. The corresponding soliton solutions are retrieved with the usage of two integration algorithms. They are the enhanced Kudryashov’s method and the enhanced direct algebraic method. The intermediary solutions are in terms of Jacobi’s elliptic functions and Weierstrass’ elliptic functions. The existence criteria of soliton solutions, by virtue of parameter constraints, are also presented.

A novel fuzzy algorithm for assembly precision management

The assembly precision of a specified product is subject to epistemic uncertainty due to manufacturing and measurement errors. The efficient and accurate assembly precision management is essential for realizing smart production lines, which rely on the robust assembly precision analysis model and calculation method. Parametric models are extensively used for assembly precision analysis in industrial products, typically integrating worst-case and statistical methods for calculations. Nevertheless, the combined application of these methods presents inherent limitations. Therefore, a novel fuzzy algorithm for assembly precision management is proposed in this paper by using fuzzy sets to quantify epistemic uncertainty in assembly precision, which integrates the proposed fuzzy-based assembly precision analysis model and fuzzy-based calculation method. The proposed fuzzy small displacement torsor model is a fuzzy-based model for deviation representation and tolerance classification using decomposed fuzzy numbers, where an assembly reliability index is always established for the hierarchical evaluation and management of assembly precision. Subsequently, a comprehensive assembly precision analysis model is developed for precision prediction and contribution quantification by integrating the Jacobian model for deviation propagation. A new constrained transformation method is proposed as a fuzzy-based calculation method, offering efficient and highly accurate assembly precision computation. It accounts for torsor parameter constraints to ensure greater prediction accuracy than the worst-case method and improves computational efficiency compared to statistical methods. An assembly case of the centering pin mechanism is applied to verify the superiority of the proposed novel fuzzy algorithm compared with the Jacobian-Torsor model with worst-case or statistical methods.

Impact of high dispersion and cubic-quintic-septic nonlinearity on optical solitons perturbations of the resonant nonlinear Schrödinger equation with multiplicative white noise

This study investigates the effect of high dispersion, perturbations, cubic-quintic-septic nonlinearity, and multiplicative white noise on the resonant nonlinear Schrödinger equation in the Bohm potential. The model being studied is novel and requires investigation into its behavior and characteristics. Two integration schemes, namely the unified Riccati expansion method and the Jacobi elliptic function method, are employed in this study to seek soliton solutions for the governing model. Various types of solitons, such as bright, dark, and singular solitons, are observed. Moreover, solutions shaped like Jacobi's elliptic functions are identified. Parametric constraints characterize the obtained soliton solutions. Furthermore, the results are presented comprehensively, accompanied by visual representations.

State Control Design of Ostensible Metzler Linear Systems with Unsigned Input Parameters

This paper deals with the design of a complete state control for input unsigned, rank deficient matrix parameters of a linear system with system dynamics defined by ostensible structures of Metzler matrices. The proposed solution is based on the principle of diagonal stabilization of positive systems and uses a stabilizing additional component over the decomposition of the Metzler matrix in solving the incomplete internal positivity of such linear system structures. The novelty of the proposed approach is the unified representation of the parametric constraints of the Metzler matrix and the structurally constrained system inputs using linear matrix inequalities, which guarantees that the closed-loop system will be asymptotically stable. Despite the complexity of the constraint conditions on this class of linear continuous systems, the design conditions are formulated using sharp linear matrix inequalities only. A detailed design process is presented using a system-linearized mathematical model to verify the superiority and practicality of the proposed method.

Modified effective butterfly optimizer for solving optimal power flow problem

The optimal power flow (OPF) problem remains a popular and challenging work in optimizing power systems. Although researchers have suggested many optimization algorithms to solve this problem in the literature, their comparison studies lack fairness and transparency. As these studies increase in number, they deviate from a standard test system, considering a common security and technical constraints., there is a growing trend away from a standard test system. Different studies used different search ranges for the same decision and constraint parameters, different than the standard ranges suggested by IEEE systems. This caused many unfair comparisons in literature. Furthermore, these studies are generally not transparent enough so that their results cannot be verified. This has resulted in numerous infeasible solutions in the literature, violating the limits of constraint parameters. The recent incorporating of renewable energy sources in OPF studies has made this situation more complicated. Sorting through the literature and identifying those OPF applications having exactly the same test conditions is a challenging process. The main contribution is this paper adapts the modified effective butterfly algorithm (MEBO) to solve OPF problem under the common parameter constraints and sufficient transparency. The focus is on a transparent comparison with works in the literature with the same constraint values. This paper compares the performance of the proposed algorithm with other state-of-the-art algorithms in the literature, focusing on the wind energy and without wind energy IEEE 30-bus and IEEE 57-bus systems and the most commonly used constraints. The results demonstrate the efficiency and superiority of the proposed algorithm. For instance, in the 30-bus test system, compared to the initial case, fuel cost has been reduced by 11.42 %, emission by 14.33 %, L-index by 45.10 %, active power losses by 51.60 %, and voltage deviation by 92.70 %.

ICM-SHOX. I. Methodology Overview and Discovery of a Gas–Dark Matter Velocity Decoupling in the MACS J0018.5+1626 Merger

Galaxy cluster mergers are rich sources of information to test cluster astrophysics and cosmology. However, cluster mergers produce complex projected signals that are difficult to interpret physically from individual observational probes. Multi-probe constraints on the gas and dark matter (DM) cluster components are necessary to infer merger parameters that are otherwise degenerate. We present Improved Constraints on Mergers with SZ, Hydrodynamical simulations, Optical, and X-ray (ICM-SHOX), a systematic framework to jointly infer multiple merger parameters quantitatively via a pipeline that directly compares a novel combination of multi-probe observables to mock observables derived from hydrodynamical simulations. We report a first application of the ICM-SHOX pipeline to MACS J0018.5+1626, wherein we systematically examine simulated snapshots characterized by a wide range of initial parameters to constrain the MACS J0018.5+1626 merger geometry. We constrain the epoch of MACS J0018.5+1626 to the range 0–60 Myr post-pericenter passage, and the viewing angle is inclined ≈27°–40° from the merger axis. We obtain constraints for the impact parameter (≲250 kpc), mass ratio (≈1.5–3.0), and initial relative velocity when the clusters are separated by 3 Mpc (≈1700–3000 km s−1). The primary and secondary clusters initially (at 3 Mpc) have gas distributions that are moderately and strongly disturbed, respectively. We discover a velocity space decoupling of the DM and gas distributions in MACS J0018.5+1626, traced by cluster-member galaxy velocities and the kinematic Sunyaev–Zel'dovich effect, respectively. Our simulations indicate this decoupling is dependent on the different collisional properties of the two distributions for particular merger epochs, geometries, and viewing angles.

Improving constraints on inflation with CMB delensing

The delensing of cosmic microwave background (CMB) maps will be increasingly valuable for extracting as much information as possible from future CMB surveys. Delensing provides many general benefits, including sharpening of the acoustic peaks, more accurate recovery of the damping tail, and reduction of lensing-induced B-mode power. In this paper we present several applications of delensing focused on testing theories of early-universe inflation with observations of the CMB. We find that delensing the CMB results in improved parameter constraints for reconstructing the spectrum of primordial curvature fluctuations, probing oscillatory features in the primordial curvature spectrum, measuring the spatial curvature of the universe, and constraining several different models of isocurvature perturbations. In some cases we find that delensing can recover almost all of the constraining power contained in unlensed spectra, and it will be a particularly valuable analysis technique to achieve further improvements in constraints for model parameters whose measurements are not expected to improve significantly when utilizing only lensed CMB maps from next-generation CMB surveys. We also quantify the prospects of testing the single-field inflation tensor consistency condition using delensed CMB data; we find it to be out of reach of current and proposed experimental technology and advocate for alternative detection methods.

Wavelet-based statistics for enhanced 21cm EoR parameter constraints

We propose a new approach to improve the precision of astrophysical parameter constraints for the 21cm signal from the epoch of reionisation (EoR). Our method introduces new sets of summary statistics, hereafter ‘evolution compressed’ statistics, which quantify the spectral evolution of the 2D spatial statistics computed at fixed redshift. We defined such compressed statistics for power spectrum (PS), wavelet scattering transforms (WST), and wavelet moments (WM), which also characterise non-Gaussian features. To compare these different statistics, along with the 3D power spectrum, we estimated their Fisher information on three cosmological parameters from an ensemble of simulations of 21cm EoR data, both in noiseless and noisy scenarios using Square Kilometre Array (SKA) noise levels equivalent to 100 and 1000 h of observations. We also compare wavelet statistics, in particular WST, built from standard directional Morlet wavelets, as well as from a set of isotropic wavelets derived from the binning window function of the 2D power spectrum. For the noiseless case, the compressed wavelet statistics give constraints that are up to five times more precise than those obtained from the 3D isotropic power spectrum. At the same time, for 100 h SKA noise, from which it is difficult to extract non-Gaussian features, compressed wavelet statistics still give over 30% tighter constraints. We find that the wavelet statistics with wavelets derived from the power-spectrum binning window function provide the tightest constraints of all the statistics, with the WSTs seemingly performing better than the WMs, in particular when working with noisy data. The findings of this study demonstrate that evolution-compressed statistics extract more information than usual 3D isotropic power-spectra approaches and that our wavelet-based statistics can consistently outmatch power-spectrum-based statistics. When constructing such wavelet-based statistics, we also emphasise the need to choose a set of wavelets with an appropriate spectral resolution concerning the astrophysical process studied.

Meta model-based importance sampling combined with adaptive Kriging method for estimating failure probability function

The failure probability function (FPF) can provide the entire effect of the distribution parameter in the concerned region on failure probability (FP), which is important for decoupling the optimal parameter search and constraint analysis in the FP-constrained design optimization. To improve the efficiency of solving the FPF, this paper combines meta model-based importance sampling with adaptive Kriging (Meta-IS-AK). First, based on the theoretical optimal importance sampling density (ISD) for estimating the FP (ISD-FP) at the given distribution parameter, the theoretical optimal ISD for solving FPF (ISD-FPF) is constructed using an integral operator. Then, a Kriging model of the performance function is adaptively trained to establish the quasi-optimal ISD-FPF for approximating the optimal performance function, and a simple rejection sampling (SRS) method is designed to extract the samples of the quasi-optimal ISD-FPF. For recognizing the state of the ISD-FPF sample and completing the IS estimation of FPF, the Kriging model is secondly updated to establish ISD-FPF in the sample pool comprising these ISD-FPF samples. Using the proposed method, the whole FPF corresponding to arbitrary distribution parameters can be obtained using one IS reliability analysis based on the constructed ISD-FPF. Four examples and an application illustrate the advantages of the proposed method.

Parameter constraints on traversable wormholes within beyond Horndeski theories through quasiperiodic oscillations

compact astrophysical objects such as black holes and wormholes, as well as testing gravity theories, are important issues in relativistic astrophysics. In this sense, theoretical and observational studies of quasiperiodic oscillations (QPOs) observed in (micro)quasars become helpful in exploring their central object, which can be a black hole or a wormhole. In the present work, we study the throat properties of traversable wormholes beyond Horndeski theory. Also, we investigate the circular motion of test particles orbiting the wormhole. We analyze the test particle’s effective potential and angular momentum for circular orbits. Frequencies of radial and vertical oscillations of the particles around stable circular orbits have also been studied and applied in explaining the quasiperiodic oscillations mechanism in the relativistic precession (RP) model. Finally, we obtain constraint values for the parameters of beyond Horndeski gravity and the mass of the wormhole candidates using QPOs observed in the microquasars GRO J1655-40, GRS 1915+105 & XTE J1550-564 and at the center of Milky Way galaxy through Monte-Carlo-Markovian-chain (MCMC) analyses. Published by the American Physical Society 2024

Gap solitons in optical fibers with the concatenation model

AbstractThis paper recovers gap solitons from Bragg gratings of the concatenation model. The new mapping method recovers a full spectrum of optical solitons. The parameter constraints for the existence of such solitons are enumerated.

Candl: cosmic microwave background analysis with a differentiable likelihood

We present candl, an automatically differentiable python likelihood for analysing cosmic microwave background power spectrum measurements. candl is powered by JAX, which makes it fast and easy to calculate derivatives of the likelihood. This facilitates, for example, robust Fisher matrices without finite-difference methods. We show the benefits of candl through a series of example calculations, covering forecasting, robustness tests, and gradient-based Markov chain Monte Carlo sampling. These also include optimising the band power bin width to minimise parameter errors of a realistic mock data set. Moreover, we calculate the correlation of parameter constraints from correlated and partially overlapping subsets of the SPT-3G 2018 TT/TE/EE data release. In a traditional analysis framework, these tasks are slow and require careful fine-tuning to obtain stable results. As such, a fully differentiable pipeline allows for a higher level of scrutiny; we argue that this is the paradigm shift required to leverage incoming data from ground-based experiments, which will significantly improve the cosmological parameter constraints from the Planck mission. candl comes with the latest primary and lensing power spectrum data from the South Pole Telescope and Atacama Cosmology Telescope collaborations and will be used as part of the upcoming SPT-3G TT/TE/EE and ϕϕ data releases. Along with the core code, we release a series of auxiliary tools, which simplify common analysis tasks and interface the likelihood with other cosmological software. candl is pip-installable and publicly available on Github.