Deep generative tread pattern design framework for efficient conceptual design

Tire tread patterns have played an important role in the automotive industry because they directly affect automobile performances. The conventional tread pattern development process has successfully produced and manufactured many tire tread patterns. However, a conceptual design process, which is a major part of the whole process, is still time-consuming due to repetitive manual interaction works between designers and engineers. In the worst case, the whole design process must be performed again from the beginning to obtain the required results. In this study, a deep generative tread pattern design framework is proposed to automatically generate various tread patterns satisfying the target tire performances in the conceptual design process. The main concept of the proposed method is that desired tread patterns are obtained through optimization based on integrated functions, which combine generative models and tire performance evaluation functions. To strengthen the effectiveness of the proposed framework, suitable image pre-processing, generative adversarial networks (GANs), 2D image-based tire performance evaluation functions, design generation, design exploration, and image post-processing methods are proposed with the help of domain knowledge of the tread pattern. The numerical results show that the proposed automatic design framework successfully creates various tread patterns satisfying the target tire performances such as summer, winter, or all-season patterns.

Periodic Pattern Detection of Printed Fabric Based on Deep Learning Algorithm

Printed fabric patterns contain multiple repeat pattern primitives, which have a significant impact on fabric pattern design in the textile industry. The pattern primitive is often composed of multiple elements, such as color, form, and texture structure. Therefore, the more pattern elements it contains, the more complex the primitive is. In order to segment fabric primitives, this paper proposes a novel convolutional neural network (CNN) method with spatial pyramid pooling module as a feature extractor, which enables to learn the pattern feature information and determine whether the printed fabric has periodic pattern primitives. Furthermore, by choosing pair of activation peaks in a filter, a set of displacement vectors can be calculated. The activation peaks that are most accordant with the optimum displacement vector contribute to pick out the final size of primitives. The results show that the method with the powerful feature extraction capabilities of the CNN can segment the periodic pattern primitives of complex printed fabrics. Compared with the traditional algorithm, the proposed method has higher segmentation accuracy and adaptability.

Electrochemical Sensor for Methamphetamine Detection Using Laser-Induced Porous Graphene Electrode

A 3D porous graphene structure was directly induced by CO2 laser from the surface of Kapton tape (carbon source) supported by polyethylene terephthalate (PET) laminating film. A highly flexible laser-induced porous graphene (LI-PGr) electrode was then fabricated via a facile one-step method without reagent and solvent in a procedure that required no stencil mask. The method makes pattern design easy, and production cost-effective and scalable. We investigated the performance of the LI-PGr electrode for the detection of methamphetamine (MA) on household surfaces and in biological fluids. The material properties and morphology of LI-PGr were analysed by scanning electron microscopy (SEM), energy dispersive x-ray (EDX) and Raman spectroscopy. The LI-PGr electrode was used as the detector in a portable electrochemical sensor, which exhibited a linear range from 1.00 to 30.0 µg mL−1 and a detection limit of 0.31 µg mL−1. Reproducibility was good (relative standard deviation of 2.50% at 10.0 µg mL−1; n = 10) and anti-interference was excellent. The sensor showed good precision and successfully determined MA on household surfaces and in saliva samples.

Intelligent Auxiliary Artificial Wood Plank Pattern Design Based on the Subject Search Algorithm of Multimedia Resources

Regarding the restriction of the wood processing enterprises in the market, intelligent artificial wood materials are mainly based on the demand for pattern quality levels, and the calculation method of multimedia resource theme search is used to achieve the pattern design of intelligent auxiliary artificial wood materials. First, analyze the pattern characteristics of intelligent auxiliary artificial wood materials. After analyzing the characteristics, use the multimedia resource subject search calculation method to carry out the binarization design. At the same time, use the self-learning method to optimize the convergence efficiency and reduce the design time. Finally, pass the softmax designer extracts design schemes for patterns and straight lines.

Turing pattern design principles and their robustness

Turing patterns have morphed from mathematical curiosities into highly desirable targets for synthetic biology. For a long time, their biological significance was sometimes disputed but there is now ample evidence for their involvement in processes ranging from skin pigmentation to digit and limb formation. While their role in developmental biology is now firmly established, their synthetic design has so far proved challenging. Here, we review recent large-scale mathematical analyses that have attempted to narrow down potential design principles. We consider different aspects of robustness of these models and outline why this perspective will be helpful in the search for synthetic Turing-patterning systems. We conclude by considering robustness in the context of developmental modelling more generally. This article is part of the theme issue ‘Recent progress and open frontiers in Turing’s theory of morphogenesis’.

Residential-type urban forest design in Buana Cicalengka Raya Estate, Cicalengka District, Bandung Regency

The existence of urban forests in the middle of residential areas will provide added value for beauty, comfort, and environmental health that could increase the value of land and buildings in the residential area. Residential-type urban forests need to be well designed so that they are compatible with the type of building and match the residential environment’s conditions. The purpose of this design is to create an appealing, multi-benefit urban forest design, which includes aspects of the urban forest theme concept, zoning arrangement, and vegetation arrangement, including species composition, density, and planting pattern. The outcome of the design resulted in a residential type, urban forest design by the theme of RINDU BCR Urban Forest (Relaxation, Inspiration, and Education for Buana Cicalengka Raya). The RINDU BCR Urban Forest functions as (1) a place of relaxation, (2) seeking positive inspiration, and (3) environmental education for the community. The planting pattern design consists of four types, namely: (1) opposite line pattern; (2) zigzag line pattern; (3) single line pattern, and (4) mixed pattern. The front-facing area of the RINDU BCR Urban Forest is designed by planting tree species that have the criteria of being ornamental, providing shade and safety, having a height of more than 300 cm, and a diameter of more than 10 cm.