Highly Differentiated Target Detection under Extremely Low-Light Conditions Based on Improved YOLOX Model

This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy,specifically tailored for environments characterized by markedly low luminance levels.Conventional methodologies struggle with the challenges posed by luminosity fluctuations,especially in settings characterized by diminished radiance,further exacerbated by the utilization of suboptimal imaging instrumentation.The envisioned approach mandates a departure from the conventional YOLOX model,which exhibits inadequacies in mitigating these challenges.To enhance the efficacy of this approach in low-light conditions,the dehazing algorithm undergoes refinement,effecting a discerning regulation of the transmission rate at the pixel level,reducing it to values below 0.5,thereby resulting in an augmentation of image contrast.Subsequently,the coiflet wavelet transform is employed to discern and isolate high-discriminatory attributes by dismantling low-frequency image attributes and extracting high-frequency attributes across divergent axes.The utilization of CycleGAN serves to elevate the features of low-light imagery across an array of stylistic variances.Advanced computational methodologies are then employed to amalgamate and conflate intricate attributes originating from images characterized by distinct stylistic orientations,thereby augmenting the model’s erudition potential.Empirical validation conducted on the PASCAL VOC and MS COCO 2017 datasets substantiates pronounced advancements.The refined low-light enhancement algorithm yields a discernible 5.9%augmentation in the target detection evaluation index when compared to the original imagery.Mean Average Precision(mAP)undergoes enhancements of 9.45%and 0.052%in low-light visual renditions relative to conventional YOLOX outcomes.The envisaged approach presents a myriad of advantages over prevailing benchmark methodologies in the realm of target detection within environments marked by an acute scarcity of luminosity.

Deformation mechanism and forming properties of 6061Al alloys during compression in semi-solid state

The 6061 semi-solid aluminium alloy feedstocks prepared by near-liquidus casting were compressed in semi-solid state by means of Gleeble-3500 thermal-mechanical simulator.The relationship between the true stress and the true strain at different temperatures and strain rates was studied with the deformation degree of 70%.The microstructures during the deformation process were characterized.The deformation mechanism and thixo-forming properties of the semi-solid alloys were analyzed.The results show that the homogeneous and non-dendrite microstructures of semi-solid 6061Al alloy manufactured by near-liquidus casting technology could be transformed into semi-solid state with the microstructure suitable for thixo-forming which are composed of near-spherical grains and liquid phase with eutectic composition through reheating process.The deformation temperature and strain rate affect the peak stress significantly rather than steady flow stress.The resistance to deformation in semi-solid state decreases with the increase of the deformation temperature and decrease of the strain rate.At steady thixotropic deformation stage, the thixotropic property is uniform, and the main deformation mechanism is the rotating or sliding between the solid particles and the plastic deformation of the solid particles.

A study on light-aging resistance of instrument panel materials and bumper materials on the automobile

The light-aging test method commonly used in the automotive industry is utilized to carry out light- aging research on automotive instrument panel （IP） materials and bumper materials. On one hand, the impacts of common light-aging test methods on aging degree of automotive component materials are reviewed; on the other hand, the light-aging resistances of different component materials are compared. The results show that, for light-aging behavior of IP materials, the aging degree of the third test method is not severer than that of the second method, but it is severer than that of the first method. The light-aging resistance of IP material A is al- most the same as that of IP material B. With reference to light-aging behavior of bumper materials, the aging de- gree of three common test methods indicates that the aging degree of the sixth test method is not severer than that of the fourth method, but it is severer than that of the fifth method. The light-aging resistance of bumper material D is superior to that of bumper material C.

Carbody Structural Lightweighting Based on Implicit Parameterized Model

Most of recent research on carbody lightweighting has focused on substitute material and new processing technologies rather than structures. However, new materials and processing techniques inevitably lead to higher costs. Also, material substitution and processing lightweighting have to be realized through body structural profiles and locations. In the huge conventional workload of lightweight optimization, model modifications involve heavy manual work, and it always leads to a large number of iteration calculations. As a new technique in carbody lightweighting, the implicit parameterization is used to optimize the carbody structure to improve the materials utilization rate in this paper. The implicit parameterized structural modeling enables the use of automatic modification and rapid multidisciplinary design optimization （MDO） in carbody structure, which is impossible in the traditional structure finite element method （FEM） without parameterization. The structural SFE parameterized model is built in accordance with the car structural FE model in concept development stage, and it is validated by some structural performance data. The validated SFE structural parameterized model can be used to generate rapidly and automatically FE model and evaluate different design variables group in the integrated MDO loop. The lightweighting result of body-in-white （BIW） after the optimization rounds reveals that the implicit parameterized model makes automatic MDO feasible and can significantly improve the computational efficiency of carbody structural lightweighting. This paper proposes the integrated method of implicit parameterized model and MDO, which has the obvious practical advantage and industrial significance in the carbody structural lightweighting design.