Showing papers on "Parametric statistics published in 2022"

Mechanical properties and energy absorption of bio-inspired hierarchical circular honeycomb

Abstract: This study proposed a new bio-inspired hierarchical circular honeycomb (BHCH), mimicking the hierarchical structures from wood. The mechanical properties and energy absorption characteristics of the proposed structures were investigated by means of quasi-static compression tests and finite element (FE) analysis. Subsequently, the influences of geometric parameters and material selections on the energy absorption capacity were investigated. The experimental results and parametric study indicated that the BHCH outperformed the conventional circular honeycomb (CH) in terms of energy absorption capacity. Specifically, the BHCH had a higher specific energy absorption (SEA) than the corresponding CH with the same wall thickness (i.e. 45.3%) and the same volume of honeycombs (i.e. 71.2%). Furthermore, the relative stiffness, relative strength, and relative energy absorption of the BHCH were significantly higher than other popular cellular structures. Finally, a theoretical model was developed to estimate the mean crushing stress (MCS) and interaction effect of the proposed structure, which further demonstrated its enhanced energy absorption mechanisms. A good correlation between the predicted values and the numerical results proved the reliability of the proposed analytical models. With its excellent performance, the proposed BHCH provided a promising solution for the enhanced energy absorption of the honeycomb structure used in a wide range of applications from structural components of defence structures to protection systems in vehicles.

Parameter estimation for nonlinear Volterra systems by using the multi-innovation identification theory and tensor decomposition

Abstract: The Volterra model can represent a wide range of nonlinear dynamical systems. However, its practical use in nonlinear system identification is limited due to the exponentially growing number of Volterra kernel coefficients as the degree increases. This paper considers the identification issue of discrete-time nonlinear Volterra systems and uses a tensorial decomposition called PARAFAC to represent the Volterra kernels which can provide a significant parametric reduction compared with the conventional Volterra model. Applying the multi-innovation identification theory, the recursive algorithm by combining the l2-norm is proposed for the PARAFAC-Volterra models with the Gaussian noises. In addition, the multi-innovation algorithm combining with the logarithmic p-norms is investigated for the nonlinear Volterra systems with the non-Gaussian noises. Finally, some simulation results illustrate the effectiveness of the proposed identification methods.

Rethinking Semantic Segmentation: A Prototype View

Abstract: Prevalent semantic segmentation solutions, despite their different network designs (FCN based or attention based) and mask decoding strategies (parametric softmax based or pixel-query based), can be placed in one category, by considering the softmax weights or query vectors as learnable class prototypes. In light of this prototype view, this study uncovers several limitations of such parametric segmentation regime, and proposes a nonparametric alternative based on non-learnable prototypes. Instead of prior methods learning a single weight/query vector for each class in a fully parametric manner, our model represents each class as a set of non-learnable prototypes, relying solely on the mean fea-tures of several training pixels within that class. The dense prediction is thus achieved by nonparametric nearest prototype retrieving. This allows our model to directly shape the pixel embedding space, by optimizing the arrangement between embedded pixels and anchored prototypes. It is able to handle arbitrary number of classes with a constant amount of learnable parameters. We empirically show that, with FCN based and attention based segmentation models (i.e., HR-Net, Swin, SegFormer) and backbones (i.e., ResNet, HRNet, Swin, MiT), our nonparametric framework yields compel-ling results over several datasets (i.e., ADE20K, Cityscapes, COCO-Stuff), and performs well in the large-vocabulary situation. We expect this work will provoke a rethink of the current de facto semantic segmentation model design.

PaMIR: Parametric Model-Conditioned Implicit Representation for Image-Based Human Reconstruction

Abstract: Modeling 3D humans accurately and robustly from a single image is very challenging, and the key for such an ill-posed problem is the 3D representation of the human models. To overcome the limitations of regular 3D representations, we propose Parametric Model-Conditioned Implicit Representation (PaMIR), which combines the parametric body model with the free-form deep implicit function. In our PaMIR-based reconstruction framework, a novel deep neural network is proposed to regularize the free-form deep implicit function using the semantic features of the parametric model, which improves the generalization ability under the scenarios of challenging poses and various clothing topologies. Moreover, a novel depth-ambiguity-aware training loss is further integrated to resolve depth ambiguities and enable successful surface detail reconstruction with imperfect body reference. Finally, we propose a body reference optimization method to improve the parametric model estimation accuracy and to enhance the consistency between the parametric model and the implicit function. With the PaMIR representation, our framework can be easily extended to multi-image input scenarios without the need of multi-camera calibration and pose synchronization. Experimental results demonstrate that our method achieves state-of-the-art performance for image-based 3D human reconstruction in the cases of challenging poses and clothing types.

Similarity quantification of soil parametric data and sites using confidence ellipses

Abstract: This paper presents a confidence ellipse-based method to evaluate the similarity of soil parametric data using the database from the site investigation reports. Then, the obtained similarity assessment results of parametric data are used to further estimate the site similarity via two proposed strategies, namely the mean and weighted mean approaches. The former referred to the average of parametric data similarity degrees, while the latter was the weighted average, and the weight was calculated using the coefficient of variation (COV) of each parameter. For illustration, the liquidity index (LI) dataset was firstly used to explore the performance of the presented method in the evaluation of parametric data similarity. Subsequently, the site similarity was assessed and the effects of numbers and weights of selected parameters for study were systematically studied. Lastly, the transformation models about the relationships between Cc and ω as well as between Cc and e0 were constructed to illustrate the application of the similarity analysis in reduction of transformation uncertainty. Results show that the greatest site similarity degree is at about 0.76 in this study, and the maximum decrease of transformation uncertainty can reach up to 18% and 25.5% as union parametric data similarity degree increases. Moreover, the site similarity degree represents the whole similarity between two different sites, and the presented union parameter similarity degree maintains a good agreement with transformation uncertainty.

Parametric investigation of the assembled bolt-connected buckling-restrained brace and performance evaluation of its application into structural retrofit

Abstract: In this paper, the hysteresis performance of an assembled bolt-connected buckling-restrained brace (AB-BRB) is experimentally discussed by a parametric study, and five changing factors are selected to analyze the corresponding influence in static behaviors. The deformation modes of core plates are compared, the critical points of hysteresis curves are analyzed, and six static indices for hysteresis responses are assessed. Afterwards, the seismic evaluations of AB-BRB applied into structural retrofit are conducted, based on an aged 3-span-3-story and 5-span-10-story reinforced concrete frame. The numerical model is established and verified for both local AB-BRB and integral frame structure, and the simulation details as well as selection strategies are suggested during the process. The nonlinear time history analyses, incremental dynamic analyses and seismic fragility analyses are all performed, and three dynamic indices, three limit states, three fractile probabilities, three comparing conditions and two intensity levels are well discussed for assessment, before and after retrofitting, respectively. In general, the influences of shell thickness are less than shell height, and a larger gap clearance may transfer the deformation pattern into end-wave modes. Less bolt number may affect the assembly operation and global buckling capacity, which proves the effectiveness of bolt design equation in a sense. Bolt number contributes greatly to the macro hysteresis trends, and less tightening bolts may weaken the retraining property, accompanied with the bolt-slippage occurrence under huge vertical thrusts transferred from core plates. The seismic indices for each ground motion are below limitations and are more uniform with less discreteness after retrofitting with AB-BRB. The structural capacity can be guaranteed safely, and the damage degree of the integrated system can be controlled with the application of AB-BRB, illustrating the apparent retrofitting superiority in performance enhancement and providing reference for the follow-up research of AB-BRB in the earthquake-prone areas.

Stability Indices to Deciphering the Genotype-by-Environment Interaction (GEI) Effect: An Applicable Review for Use in Plant Breeding Programs

Abstract: Experiments measuring the interaction between genotypes and environments measure the spatial (e.g., locations) and temporal (e.g., years) separation and/or combination of these factors. The genotype-by-environment interaction (GEI) is very important in plant breeding programs. Over the past six decades, the propensity to model the GEI led to the development of several models and mathematical methods for deciphering GEI in multi-environmental trials (METs) called “stability analyses”. However, its size is hidden by the contribution of improved management in the yield increase, and for this reason comparisons of new with old varieties in a single experiment could reveal its real size. Due to the existence of inherent differences among proposed methods and analytical models, it is necessary for researchers that calculate stability indices, and ultimately select the superior genotypes, to dissect their usefulness. Thus, we have collected statistics, as well as models and their equations, to explore these methods further. This review introduces a complete set of parametric and non-parametric methods and models with a selection pattern based on each of them. Furthermore, we have aligned each method or statistic with a matched software, macro codes, and/or scripts.

Non-standard computational analysis of the stochastic COVID-19 pandemic model: An application of computational biology

Abstract: The present study is conducted to analyse the computational dynamical analysis of the stochastic susceptible-infected-recovered pandemic model of the novel coronavirus. We adopted two ways for stochastic modelling like as transition probabilities and parametric perturbation techniques. We applied different and well-known computational methods like Euler Maruyama, stochastic Euler, and stochastic Runge Kutta to study the dynamics of the model mentioned above. Unfortunately, these computational methods do not restore the dynamical properties of the model like positivity, boundedness, consistency, and stability in the sense of biological reasoning, as desired. Then, for the given stochastic model, we developed a stochastic non-standard finite difference method. Following that, several theorems are presented to support the proposed method, which is shown to satisfy all of the model's dynamical properties. To that end, several simulations are presented to compare the proposed method's efficiency to that of existing stochastic methods.

Stability of three degrees-of-freedom auto-parametric system

Abstract: This paper focuses on studying the dynamical response of a vibrating three degrees-of-freedom (DOF)auto-parametric system near resonance. The structure of this system is composed of an attached damped oscillator with a damped spring pendulum. The governing equations of motion are derived using Lagrange’s equations of second kind. They are asymptotically solved using the multiple scales approach to obtain the analytic solutions up to the third approximations as new and accurate results. The resonance cases are classified and the effect of the different parameters of considered system is analysed. The stability and instability regions are examined in which the behavior of the system is found to be stable for a wide range of parameters. The achieved results reveal that we can use the pendulum as a dynamic absorber. The significance impact of this work is due to its great engineering applications in the high towers, buildings and bridges.

Non-standard Computational Analysis of the Stochastic COVID-19 Pandemic Model: An Application of Computational Biology

Abstract: The present study is conducted to analyse the computational dynamical analysis of the stochastic susceptible-infected-recovered pandemic model of the novel coronavirus. We adopted two ways for stochastic modelling like as transition probabilities and parametric perturbation techniques. We applied different and well-known computational methods like Euler Maruyama, stochastic Euler, and stochastic Runge Kutta to study the dynamics of the model mentioned above. Unfortunately, these computational methods do not restore the dynamical properties of the model like positivity, boundedness, consistency, and stability in the sense of biological reasoning, as desired. Then, for the given stochastic model, we developed a stochastic non-standard finite difference method. Following that, several theorems are presented to support the proposed method, which is shown to satisfy all of the model's dynamical properties. To that end, several simulations are presented to compare the proposed method's efficiency to that of existing stochastic methods.

Bayesian neural network-based uncertainty modelling: application to soil compressibility and undrained shear strength prediction

Abstract: This study adopts the Bayesian neural network (BNN) integrated with a strong non-linear fitting capability and uncertainty, which has not previously been used in geotechnical engineering, to propose a modelling strategy in developing prediction models for soil properties. The compression index C c and undrained shear strength s u of clays are selected as examples. Variational inference (VI) and Monte Carlo dropout (MCD), two theoretical frameworks for solving and approximating BNN, respectively, are employed and compared. The results indicate that the BNN focused on identifying patterns in datasets, and the predicted C c and s u show excellent agreement with the actual values. The reliability of the predicted results using BNN is high in the area of dense datasets. In contrast, the BNN demonstrates low reliability in the predicted result in the area of sparse datasets. Additionally, a novel parametric analysis method in combination with the cumulative distribution function is proposed. The analysis results indicate that the BNN-based models are capable of capturing the relationships of input parameters to the C c and s u . BNN, with its strong prediction capability and reliable evaluation, therefore, shows great potential to be applied in geotechnical design.

Open Vehicle Sketch Pad: An Open Source Parametric Geometry and Analysis Tool for Conceptual Aircraft Design

Abstract: OpenVSP is a parametric geometry tool for creating 3D models during the conceptual design process. It supports several engineering analyses and writes out files that can feed many other analysis tools. Over the years, OpenVSP and its predecessors have evolved from something useful for creating a model illustrating an aircraft concept to a geometry and analysis engine at the center of many aircraft design frameworks and workflows. OpenVSP has been widely adopted by established aerospace players including industry, government, and academia as well as innovative startups across the Mach-altitude envelope developing systems from UAV's, eVTOL, civil supersonics, hypersonics, space launch, and small satellites.

Lithium-ion battery State-of-Charge estimation based on an improved Coulomb-Counting algorithm and uncertainty evaluation

Abstract: An accurate estimation of the State-of-Charge (SoC) for a battery is the key to designing an efficient Battery Management System (BMS). This is due to the fact that SoC cannot be accessed directly. There are many factors leading to inaccurate estimation of SoC including battery model inaccuracies, parametric uncertainties, the nonlinearity of the battery system, battery capacity fade due to charge/discharge cycles, and temperature- and time-dependent characteristics. This paper presents a mathematical model to precisely estimate the SoC of a Lithium-ion battery based on an improved Coulomb-Counting (iCC) algorithm and uncertainty evaluation over a ten-year period. Experimental measurements using a 12V100Ah Lithium-ion battery are conducted to evaluate the performance and effectiveness of the proposed model. The obtained results indicate that the maximum estimation error using the proposed method is 0.3%, which verifies the high accuracy of SoC estimation compared to other analytical and heuristic approaches.