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When establishing a mathematical model to simulate solid mechanics, considering realistic geometries, special tools are needed to translate measured data, possibly with noise, into idealized geometrical entities. As an engineering application, wheel-rail contact interactions are fundamental in the dynamic modeling of railway vehicles. Many approaches used to solve the contact problem require a continuous parametric description of the geometries involved. However, measured wheel and rail profiles are often available as sets of discrete points. A reconstruction method is needed to transform discrete data into a continuous geometry.

The authors present an approximation method based on optimization to solve the problem of fitting a set of points with an arc spline. It consists of an initial guess based on a curvature function estimated from the data, followed by a least-squares optimization to improve the approximation. The authors also present a segmentation scheme that allows the method to increment the number of segments of the spline, trying to keep it at a minimal value, to satisfy a given error tolerance.

The paper provides a better understanding of arc splines and how they can be deformed. Examples with parametric curves and slightly noisy data from realistic wheel and rail profiles show that the approach is successful.

The developed methods have theoretical value. Furthermore, they have practical value since the approximation approach is better suited to deal with the reconstruction of wheel/rail profiles than interpolation, which most methods use to some degree.

The purpose of this paper is to exploit a new and robust method to forecast the long-term extreme dynamic responses for wave energy converters (WECs).

A new adaptive binned kernel density estimation (KDE) methodology is first proposed in this paper.

By examining the calculation results the authors has found that in the tail region the proposed new adaptive binned KDE distribution curve becomes very smooth and fits quite well with the histogram of the measured ocean wave dataset at the National Data Buoy Center (NDBC) station 46,059. Carefully studying the calculation results also reveals that the 50-year extreme power-take-off heaving force value forecasted based on the environmental contour derived using the new method is 3572600N, which is much larger than the value 2709100N forecasted via the Rosenblatt-inverse second-order reliability method (ISORM) contour method.

The proposed method overcomes the disadvantages of all the existing nonparametric and parametric methods for predicting the tail region probability density values of the sea state parameters.

It is concluded that the proposed new adaptive binned KDE method is robust and can forecast well the 50-year extreme dynamic responses for WECs.

This study aims to automate the process of converting grading patterns into parametric patterns using artificial intelligence and to objectively evaluate the fitness of the converted patterns.

The developed system consists of a user interface that defines input data by importing multi-size grading patterns, an artificial neural network that learns the relationship between human body size and pattern geometry, and a module that converts training results into parametric patterns. In order to evaluate the fitness of the generated pattern, an objective fitting evaluation method using drape simulation was developed.

The body sizes of the wearer were input to the converted parametric pattern to generate a customized pattern. Resulting pattern showed a better fit than the grading pattern on the off-average body model.

In this study, a method has been developed that enables the users with minimal pattern drafting knowledge to convert grading patterns into parametric patterns using artificial intelligence and drape simulation. The human body's symmetry and the physical properties of fabric were not considered.

The system developed in this study requires less data compared to existing methods that attempt to design clothing patterns with machine learning. In addition, it was possible to evaluate pattern fitness on various body models through drape simulation based fit evaluation process for the first time.

The main issue in the mass customization of apparel products is how to efficiently produce products of various sizes. A parametric pattern-making system is one of the notable ways to rectify this issue, but there is a lack of information on the parametric design itself and its application to the apparel industry. This study compares and analyzes three types of parametric clothing pattern CAD (P-CAD) software currently in use to identify the characteristics of each, and suggest a basic guideline for efficient and adaptable P-CAD software in the apparel industry.

This study compared three different types of P-CAD software with different characteristics: SuperALPHA: PLUS(as known as YUKA), GRAFIS and Seamly2D. The authors analyzed the types and management methodologies of each software, according to the three essential components that refer to previous studies about parametric design systems: entities, constraints and parameters.

The results demonstrated the advantages and disadvantages of methodology in terms of three essential components of each software. Based on the results, the authors proposed five strategies for P-CAD development that can be applied to the mass customization of clothing.

This study is meaningful in that it consolidates and organizes information about P-CAD software that has previously been scattered. The framework used in this study has an academic value suggesting guidelines to analyze P-CAD systems.

The existence of the regional Kuznets curve, i.e. an inverted U-shaped relationship between regional disparity and economic development is widely debated and discussed. The bell-shaped curve of the spatial growth process where during the initial phase inequality increases and then reduces is theoretically supported by Myrdal (1957), Hirschman (1958), and Williamson (1965). It becomes important to understand regional Kuznets curve globally. Understanding the relationship between regional disparity and economic development becomes essential for public policy for balanced regional growth.

Regional Kuznets Curve which is an inverted U-shaped relationship between regional disparity and economic development is not a new phenomenon. Theoretical framework by Myrdal (1957), Hirschman (1958), and Williamson (1965) support the an inverted U-shaped relationship. To understand the relationship between regional disparity and economic development, the authors investigate the regional Kuznets curve by using data for 184 countries and 1765 subnational regions. Using parametric, semi-parametric, and non-parametric, it is found that there exists an inverted U-shaped relationship between regional disparity and economic development. The presence of the regional Kuznets curve is observed. As the theoretical framework suggests, regional inequality increases with income initially and decreases after attaining a certain level of income. This study identifies two stages of divergence-convergence where in the first stage, divergence across regions in a country happens with increasing income and in the later stage, convergence across regions in a country occurs with increasing income.

Using the parametric approach (panel data analysis), semi-parametric and non-parametric approaches, it is found that there exists a regional Kuznets curve. It is found that there exists an inverted-U relationship between regional inequality and per capita GNI. This suggests that the divergence-convergence passes through two stages. In the first stage, divergence across regions in a country happens with increasing income while in the later stage convergence occurs.

This research work has done three important things which fill the research gap that exists in the literature: (1) constructing the Gini coefficient to measure the regional inequality for 184 countries using 1765 subnational regional data; (2) using a parametric approach (panel data analysis) to understand the regional Kuznets phenomenon and (3) using a semi-parametric approach and non-parametric approach to understand the regional Kuznets phenomenon.

The heat transfer properties play significant roles in the thermal comfort of the clothing products. The purpose of this paper is to find the relationship between heat transfer properties and fabrics' structure, yarn properties and predict the effective thermal conductivity of single layer woven fabrics by a parametric mathematical model.

First, the weave unit was divided into four types of element regions, including yarn overlap regions, yarn crossing regions, yarn floating regions and pore regions. Second, the number and area proportion of each region were calculated respectively. Some formulas were created to calculate the effective thermal conductivity of each element region based on serial model, parallel model or series–parallel mixing model. Finally, according to the number and area proportion of each region in weave unit, the formulas were established to calculate the fabric overall effective thermal conductivity in thickness direction based on the parallel models.

The influences of yarn spacing, yarn width, fabric thickness, the compressing coefficients of air layers and weave type on the effective thermal conductivity were further discussed respectively. In this model, the relationships between the effective thermal conductivity and each parameter are some polynomial fitting curves with different orders. Weave type affects the change of effective thermal conductivity mainly through the numbers of different elements and their area ratios.

In this model, the formulas were created respectively to calculate the effective thermal conductivity of each element region and whole weave unit. The serial–parallel mixing characteristics of yarn and surrounding air are considered, as well as the compression coefficients of air layers. The results of this study can be further applied to the optimal design of mixture fabrics with different warp and filling yarn densities or different yarn thermal properties.

Considering only two-dimensional (2D) ease allowance cannot fully reflect the three-dimensional (3D) relationship between the position of clothing and the human body. The purpose of this paper is to propose a method with a 3D space vector and corresponding distance ease to characterize fitting garments and then used to construct personalized clothing for similar shape body.

Firstly, a 3D scanner was used to obtain mannequin and fitted garment data, and 17 layers of cross-sections of the upper body were extracted. Then, 37 space vectors and corresponding space angles on each cross-section were obtained with the original point. Secondly, the detailed distance ease between the mannequin and garment was constructed due to the difference between garment vectors and body vectors. Thirdly, the distance ease mathematical models were achieved and used to calculate distance ease on a similar shape body. Additionally, the fit garment is constructed, and the garment pattern is altered by the geometric pattern alteration method.

The results show that 3D space vectors can explain the relationship between body skin and garment surface of the upper body properly. The distance ease is modeled by mathematic expressions and successfully used to make a new garment to fit a similar shape body.

The proposed method of constructing garments based on distance ease and 3D space vectors can create a fitted garment for a similar shape body effectively and accurately. It is useful for the personalized garment design and suitable for the manufacturing process.

In order to evaluate the rheological properties of asphalt more comprehensively and effectively, and to explore and discuss the practicability of relevant models in the evaluation of the rheological properties of asphalt.

Based on the rheological and viscoelastic theories, temperature scanning, frequency scanning and multiple stress creep recovery (MSCR) tests of different modified asphalt were carried out by dynamic shear rheometer (DSR) to obtain relevant viscoelastic parameters and evaluate the high temperature properties of different modified asphalt. Based on the time-temperature equivalence principle, the main curve was constructed to study the viscoelastic properties of asphalt in a wider frequency domain. The main curve was fitted with the CAM model, and the rheological properties of different modified asphalt were evaluated through the analysis of model parameters. The creep stiffness and creep velocity of different modified asphalt were obtained through the rheological test of bending beam (BBR), and the low-temperature performance of different modified asphalt was analyzed by using Burgers model to fit the creep compliance.

The results show that the high temperature rheological properties of several modified asphalt studied in the test are ranked from best to worst as follows: PE modified asphalt > SBS modified asphalt > SBR modified asphalt. Short-term aging can improve the high temperature performance of asphalt, and different types of modifiers can promote or inhibit this improvement effect. Based on BBR test and Burgers model fitting analysis, SBR modified asphalt has the best low temperature performance, followed by SBS modified asphalt, while PE modified asphalt has poor low temperature performance, so it is not suitable to be used as road material in low temperature area.

Combined with effective evaluation methods, the rheological properties of asphalt at different temperatures and angles were systematically evaluated, and the evolution of rheological properties of asphalt characterized by model parameters was further analyzed by advanced model simulation.

Material extrusion additive manufacturing processes inevitably produce bead-shaped surface patterns on the walls of parts, which create stress concentrations under load. This study aims to investigate the influence of such stress concentrations on the strength along the build direction (“Z-strength”).

This work consists of two main parts – an experimental demonstration to show the significance of stress concentrations on the Z-strength, followed by numerical modeling to evaluate the theoretical stress concentration factors (k_t) for such shapes. Meso-scale finite element analysis (FEA) was performed to evaluate k_t at the roots of the intersecting bead shapes. The critical bead shape parameters influencing k_t were identified, and parametric FEA studies were performed on different bead shapes by varying the normalized parameters.

The experimental results showed that up to a 40% reduction in the effective Z-strength could be attributed only to the presence of surface bead shapes. Bead overhang and root radius were identified as critical shape parameters influencing k_t. The results of the parametric FEA studies were used to generate a single empirical equation to determine k_t for any bead shape.

Predictive models for Z-strength often focus on crystallization kinetics and polymer chain interdiffusion to predict interlayer adhesion strength. The authors propose that the results of such studies must be combined with surface bead-shape induced stress concentration factors to obtain the combined, “effective” Z-strength.

Accurate and automatic clothing pattern making is very important in personalized clothing customization and virtual fitting room applications. Clothing pattern generating as well as virtual clothing simulation is an attractive research issue both in clothing industry and computer graphics.

Physics-based method is an effective way to model dynamic process and generate realistic clothing animation. Due to conceptual simplicity and computational speed, mass-spring model is frequently used to simulate deformable and soft objects follow the natural physical rules. We present a physics-based clothing pattern generating framework by using scanned human body model. After giving a scanned human body model, first, we extract feature points, planes and curves on the 3D model by geometric analysis, and then, we construct a remeshed surface which has been formatted to connected quad meshes. Second, for each clothing piece in 3D, we construct a mass-spring model with same topological structures, and conduct a typical time integration algorithm to the mass-spring model. Finally, we get the convergent clothing pieces in 2D of all clothing parts, and we reconnected parts which are adjacent on 3D model to generate the basic clothing pattern.

The results show that the presented method is a feasible way for clothing pattern generating by use of scanned human body model.

The main contribution of this work is twofold: one is the geometric algorithm to scanned human body model, which is specially conducted for clothing pattern design to extract feature points, planes and curves. This is the crucial base for suit clothing pattern generating. Another is the physics-based pattern generating algorithm which flattens the 3D shape to 2D shape of cloth pattern pieces.