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Accurate mapping is crucial for the positioning and navigation of mobile robots. Recent advancements in algorithms and the accuracy of LiDAR sensors have led to a gradual improvement in map quality. However, challenges such as lag in closing loops and vignetting at map boundaries persist due to the discrete and sparse nature of raster map data. The purpose of this study is to reduce the error of map construction and improve the timeliness of closed loop.

In this letter, the authors introduce a method for dynamically adjusting point cloud distance constraints to optimize data association (ODA-d), effectively addressing these issues. The authors propose a dynamic threshold optimization method for matching point clouds to submaps during scan matching.

Large deviations in LiDAR sensor point cloud data, when incorporated into the submap, can result in irreparable errors in correlation matching and loop closure optimization. By implementing a data association framework with double constraints and dynamically adjusting the matching threshold, the authors significantly enhance submap quality. In addition, the authors introduce a dynamic fusion method that accounts for both submap size and the distance between submaps during the mapping process. ODA-d reduces errors between submaps and facilitates timely loop closure optimization.

The authors validate the localization accuracy of ODA-d by examining translation and rotation errors across three open data sets. Moreover, the authors compare the quality of map construction in a real-world environment, demonstrating the effectiveness of ODA-d.

The purpose of this paper is to propose a relatively simple and rapid method to create a digital human model (DHM) to serve clothing industry.

Human body’s point cloud is divided into hands, foots, head and torso. Then forward modeling method is used to model hands and foots, photo modeling method is used to model head and reverse modeling method is used to model torso. After that, hands, foots, head and torso are integrated together to get a static avatar. Next, virtual skeleton is bound to the avatar. Finally, a lifelike digital human body model is created by the mixed modeling method (MMM).

In allusion to the defect of the three-dimension original data of human body, this paper presented an MMM, with which we can get a realistic digital human body model with accurate body dimensions. The DHM can well meet the needs of fashion industry.

The DHM, which is got by the MMM, can be well applied in the field of virtual try on, virtual fashion design, virtual fashion show and so on.

The originality of the paper lies in the integration of forward modeling, reverse modeling and photo modeling to present a novel method of human body modeling.

The purpose of this paper is to discuss the relationship between bra's visual impression and bra parts, and then to explore the application of visual impression in bra design.

Firstly, 82 female undergraduates are asked to answered this questionnaire online, which is about the importance of parts in bra design. In the part of data analysis, the method of principal part analysis (PCA) are used to get the relationship between bra's parts, and reduce dimension of factors that influence bra design. After that, those group of features are further discussed from the perspective of visual design. Finally, design an application based on conclusion.

To get the influence features of bra appearance and improve the visual design effect, this paper matches the bra parts with visual features (color, texture, shape and space) and presents four main features of bra design: “color,” “visual texture,” “design shape” and “spatial expression” together with corresponding bra parts and technique of expression. Moreover, user interface in bra cloud customization is designed.

The conclusion, which shows the corresponding relationship between bra visual effect and its basic parts, has an important role in bra visual design. First, it can be useful for design idea with different technique of expression, which may supply a theoretical basis for design. Secondly, the combination of bra parts and visual features can be used to evaluate the appearance.

Discussing the bra visual impression based on bra's basic parts and visual features provides a theoretical method for bra design and its appearance evaluation.

The purpose of this paper is to find out the main factors that influence wearing comfort and how they influence garment-wearing comfort.

Overall, 120 postures were extracted from the activities of daily life and work. Then, the numerical values of clothing pressure of these postures were measured using three-dimension virtual-reality technology. Finally, the data mining technology was applied to analyze the collected data.

The wearing comfort of pants is mainly influenced by four factors – waist-hip factor, knee-shank factor, crotch factor and thigh-calf factor – and their contributions account for 39.17, 16.4, 13.96 and 6.95 percent, respectively. Hip, waist, crotch and knee influence wearing comfort significantly, and the part below the knee and the part of back thigh have no obvious effect on wearing comfort. Furthermore, the wearing comfort is acceptable if the numerical clothing pressures are below 20 kPa at the parts of hip, waist and crotch and below 10 kPa at the parts of back thigh, knee and shank.

The paper demonstrates how different human body parts influence garment-wearing comfort. All of the results in this research facilitate pattern design of pants and quantitative evaluation of garment-wearing comfort.

The purpose of this paper is to design and fabricate a three-dimensional (3D) bionic airflow sensing array made of two multi-electrode piezoelectric metal-core fibers (MPMFs), inspired by the structure of a cricket’s highly sensitive airflow receptor (consisting of two cerci).

A metal core was positioned at the center of an MPMF and surrounded by a hollow piezoceramic cylinder. Four thin metal films were spray-coated symmetrically on the surface of the fiber that could be used as two pairs of sensor electrodes.

In 3D space, four output signals of the two MPMFs arrays can form three “8”-shaped spheres. Similarly, the sensing signals for the same airflow are located on a spherical surface.

Two MPMF arrays are sufficient to detect the speed and direction of airflow in all three dimensions.

This paper aims to analyse the roles of relational capital (RC) and knowledge management (KM) during the COVID-19 in Italian public and private hospitals, considering that intangible elements are essential during periods of uncertainty.

Authors used a qualitative design in a case study on two Italian hospitals that have different ownership structures, which are located in the epicentre of the pandemic in Lombardy. The study was carried out using the CAOS (“caratteristiche personali”, “ambiente”, “organizzazione” and “start-up”) model (Paoloni, 2021), which allows for comprehending and commenting on RC because of the connections between typical factors that influence an organisation. The model also allows for discussion of the use of a network and how it supports organisations.

Findings of the analysis showed that during the management of the COVID-19 health emergency, ownership structure was not a discriminating factor, the created relationships were similar and they were considered in the same way. The relationships were mainly formal (except for contributions by associations or individuals) and temporary. The RC's reactive role in overcoming crises was confirmed, and the findings indicated that this result was possible also, thanks to the KM's role played within the organisation.

Theoretical implications of the work are that it contributes to the sparse healthcare literature on intellectual capital (IC) and on RC and its relationships with KM. The practical implications are related to the creation of new relationships during the healthcare emergency between hospitals and the central government, which can be considered a useful lesson for the future. The theoretical implications derived from the analysis are generalisable to all organisations regardless of their type and location, as well as the practical implications are applicable to the entire national territory.

This study aims to propose an efficient method for solving reliability-based design optimization (RBDO) problems.

In the proposed algorithm, genetic algorithm (GA) is employed to search the global optimal solution of design parameters satisfying the reliability and deterministic constraints. The Kriging model based on U learning function is used as a classification tool to accurately and efficiently judge whether an individual solution in GA belongs to feasible region.

Compared with existing methods, the proposed method has two major advantages. The first one is that the GA is employed to construct the optimization framework, which is helpful to search the global optimum solutions of the RBDO problems. The other one is that the use of Kriging model is helpful to improve the computational efficiency in solving the RBDO problems.

Since the boundaries are concerned in two Kriging models, the size of the training set for constructing the convergent Kriging model is small, and the corresponding efficiency is high.