Retailoring Box Splines to Lattices for Highly Isotropic Volume Representations

3D box splines are defined by convolving a 1D box function with itself along different directions. In volume visualization, box splines are mainly used as reconstruction kernels that are easy to adapt to various sampling lattices, such as the Cartesian Cubic (CC), Body‐Centered Cubic (BCC), and Face‐Centered Cubic (FCC) lattices. The usual way of tailoring a box spline to a specific lattice is to span the box spline by exactly those principal directions that span the lattice itself. However, in this case, the preferred directions of the box spline and the lattice are the same, amplifying the anisotropic effects of each other. This leads to an anisotropic volume representation with strongly preferred directions. Therefore, in this paper, we retailor box splines to lattices such that the sets of vectors that span the box spline and the lattice are disjoint sets. As the preferred directions of the box spline and the lattice compensate each other, a more isotropic volume representation can be achieved. We demonstrate this by comparing different combinations of box splines and lattices concerning their anisotropic behavior in tomographic reconstruction and volume visualization.     

Scale‐aware Structure‐Preserving Texture Filtering

This paper presents a novel method to enhance the performance of structure‐preserving image and texture filtering. With conventional edge‐aware filters, it is often challenging to handle images of high complexity where features of multiple scales coexist. In particular, it is not always easy to find the right balance between removing unimportant details and protecting important features when they come in multiple sizes, shapes, and contrasts. Unlike previous approaches, we address this issue from the perspective of adaptive kernel scales. Relying on patch‐based statistics, our method identifies texture from structure and also finds an optimal per‐pixel smoothing scale. We show that the proposed mechanism helps achieve enhanced image/texture filtering performance in terms of protecting the prominent geometric structures in the image, such as edges and corners, and keeping them sharp even after significant smoothing of the original signal.     

基于Isight与Tosca的抗侧滚扭杆装置优化分析

基于Isight和Tosca软件对抗侧滚扭杆装置轻量化设计问题进行优化分析.采用Isight软件集成Abaqus软件对扭杆轴进行结构参数优化分析;采用Tosca软件集成Abaqus软件和fe-safe软件对扭转臂进行结构拓扑优化分析,研究扭杆轴与扭转臂的轻量化设计方法.     

灰色DNGM(1,1)预测模型及其优化

传统的灰色GM(1,1)预测模型是针对近似齐次指数序列建立的预测模型。为了拓广灰色预测模型的适用范围,建立了近似非齐次指数序列的灰色DNGM(1,1)预测模型。研究了这种灰色预测模型的性质,证明了这种模型都具有线性不变性,也能够完全拟合非齐次指数序列。考虑到初值条件对灰色模型的影响,对该模型进行了参数优化。数据仿真和实例分析表明,灰色DNGM(1,1)预测模型具有较高的预测精度。     

Multi-camera calibration based on an invariant pattern

In this paper we present a method for the calibration of multiple cameras based on the extraction and use of the physical characteristics of a one-dimensional invariant pattern which is defined by four collinear markers. The advantages of this kind of pattern stand out in two key steps of the calibration process. In the initial step of camera calibration methods, related to sample points capture, the proposed method takes advantage of using a new technique for the capture and recognition of a robust sample of projective invariant patterns, which allows to capture simultaneously more than one invariant pattern in the tracking area and recognize each pattern individually as well as each marker that composes them. This process is executed in real time while capturing our sample of calibration points in the cameras of our system. This new feature allows to capture a more numerous and robust set of sample points than other patterns used for multi-camera calibration methods. In the last step of the calibration process, related to camera parameters' optimization, we explore the collinearity feature of the invariant pattern and add this feature in the camera parameters optimization model. This approach obtains better results in the computation of camera parameters. We present the results obtained with the calibration of two multi-camera systems using the proposed method and compare them with other methods from the literature.     

Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model

A generalized dynamic fuzzy neural network (GDFNN) was created to estimate heavy metal concentrations in rice by integrating spectral indices and environmental parameters. Hyperspectral data, environmental parameters, and heavy metal content were collected from field experiments with different levels of heavy metal pollution (Cu and Cd). Input variables used in the GDFNN model were derived from 10 variables acquired by gray relational analysis. The assessment models for Cd and Cu concentration employed five and six input variables, respectively. The results showed that the GDFNN for estimating Cu and Cd concentrations in rice performed well at prediction with a compact network structure using the training, validation, and testing sets (for Cu, fuzzy rules=9, R² greater than 0.75, and RMSE less than 2.5; for Cd, fuzzy rules=9, R² greater than 0.75, and RMSE less than 1.0). The final GDFNN model was then compared with a back-propagation (BP) neural network model, adaptive-network-based fuzzy interference systems (ANFIS), and a regression model. The accuracies of GDFNN model prediction were usually slightly better than those of the other three models. This demonstrates that the GDFNN model is more suitable for predicting heavy metal concentrations in rice.     

Rational method for 3D manufacturing tolerancing synthesis based on the TTRS approach “R3DMTSyn”

During the production of new part in an industrial environment, it results in a high percentage of scrap if manufacturing planning is not carried out properly. One of the major factors responsible for this phenomenon is tolerance synthesis. In this paper, we deal with tolerance synthesis, and especially tolerance type identified after transfer. An algorithmic Rational method for 3D Manufacturing Tolerancing Synthesis (R3DMTSyn), which is based on the use of the Technologically and Topologically Related Surface (TTRS) rules, is developed. The TTRS concept helps to generate only the necessary manufacturing specification needed to guarantee the respect of the functional specification studied.The manufacturing project is modeled by a graphical representation called the SPIDER GRAPH. With the SPIDER GRAPH, all active surfaces can be detected (machined and positioning surfaces), so it is possible to identify the location of the functional surfaces used in each functional specification. The construction or the determination of the tolerancing torsor, belonging to each active surface, contributes to the selection of the adequate case of associations. A semantic study is already done to identify all possible combinations or associations needed to locate surfaces during each phases.Finally, referring to the developed TTRS_Cars_Process and in every phase, one or more manufacturing specifications are generated until finishing the treatment of all surfaces (surface belonging to the functional condition, or intermediate surfaces).     

Segment inertial parameters of Korean adults estimated from three-dimensional body laser scan data

In this study, body segment parameters of Korean adults were estimated using the three-dimensional (3D) body scan data from the SizeKorea database. Mass-inertial parameters and segment dimensions of 40 male subjects and 40 female subjects (18-59 years old) were estimated using a 16-segment model under the assumption that each segment has a constant density distribution after reconstructing the scan data. Therefore, several sets of linear regression functions based on gender, stature, and weight were established, which provided a convenient method for estimating body segment parameters of Korean adults. The obtained mass ratios of body segments were compared with those for Russians reported by Zatsiorsky and Seluyanov (1983) and for those for Chinese and Germans reported by Shan and Bohn (2003), in which the same 16-segment model was used. In addition, the results were compared with the Korean data results reported by [Jung, 1993] and [Lim, 1994], and Park et al. (1999). These comparisons revealed that Asians have larger head mass ratios and smaller lower limb mass ratios than Caucasians, while the differences in the head mass ratios between males and females from the same ethnic groups were not significant. Females were found to have larger mass ratios for the lower torso and smaller mass ratios for the upper torso, middle torso, upper arm, forearm, foot, and hand, as compared to males from the same ethnic groups. In addition, the data obtained by different researchers were compared, thereby showing high reproducibility of our method.

Relevance to industry

The obtained segment parameters can be used to define digital human models and applied to the fields of ergonomics and biomechanics, such as for workspace design, furniture design, vehicle interior design, and human movement analysis.     

基于DNA遗传算法的CR多载波参数优化

提出了一种基于DNA计算的非支配排序多目标遗传算法(DNA-GA)来对CR多载波传输参数进行优化。该算法通过非支配排序计算个体适应度,结合克隆操作使算法收敛于全局最优,并引入DNA基因级操作,以提高算法的搜索性能,保持种群的多样性。通过在不同服务需求情况下得到的仿真参数结果,证明了DNA-GA可以有效地优化CR传输参数。     

中英文文本分类系统异同因素的探讨

论文介绍了文本分类技术的研究进展及发展方向,给出了中英文文本分类系统构建的一般模型和过程,分析总结了中英文文本分类过程中的异同,并介绍了针对这些异同因素常用的处理方法。