基于改进粗糙集理论的SAR图像目标增强

图像增强在提高SAR图像舰船目标检测精度方面具有十分重要的意义。由于传统算法不能很好地对SAR图像进行目标增强，提出了基于改进粗糙集理论和引力场强度的目标增强算法。通过借鉴引力场相关理论知识，将粗糙集条件属性集中的梯度属性改进为引力场强度属性，从而实现对原图像的目标增强。与其他算法进行了实验比较，结果表明提出的改进算法相比于其他算法更适用于SAR图像特性，能更好地对舰船目标像素进行针对性增强，具备一定的工程应用价值。     

在线粒度分析仪的工业应用

催化裂化催化剂应具有良好的颗粒分布,以保证良好的流化状态。一般要求催化剂颗粒<40μm的不大于25%,40～111μm不小于50%,>111μm的不大于30%。目前催化裂化催化剂生产过程中通常采用压力式喷雾干燥成型工艺,用高压泵把制备好的胶体在与热空气的接触中,迅速脱去水分汽化,即得到粉状或颗粒状干燥产品。现有生产装置在生产过程中,因分析结果滞后,无法及时指导装置调整催化剂成品的粒度分布,特引进使用了在线粒度分析仪。通过对该分析仪的简单研究,介绍了该分析仪的使用现状,并对使用中出现的问题进行了简单总结。     

Low-light image enhancement via deep Retinex decomposition and bilateral learning

Low-light images enhancement is a challenging task because enhancing image brightness and reducing image degradation should be considered simultaneously. Although existing deep learning-based methods improve the visibility of low-light images, many of them tend to lose details or sacrifice naturalness. To address these issues, we present a multi-stage network for low-light image enhancement, which consists of three sub-networks. More specifically, inspired by the Retinex theory and the bilateral grid technique, we first design a reflectance and illumination decomposition network to decompose an image into reflectance and illumination maps efficiently. To increase the brightness while preserving edge information, we then devise an attention-guided illumination adjustment network. The reflectance and the adjusted illumination maps are fused and refined by adversarial learning to reduce image degradation and improve image naturalness. Experiments are conducted on our rebuilt SICE low-light image dataset, which consists of 1380 real paired images and a public dataset LOL, which has 500 real paired images and 1000 synthetic paired images. Experimental results show that the proposed method outperforms state-of-the-art methods quantitatively and qualitatively.     

Photoluminescence enhancement in a Na5Y(MoO4)4:Dy3+ white-emitting phosphor by partial replacement of MoO42− with WO42− or VO43−

A series of Na₅Y(MoO₄)_4-yA_y:Dy³⁺ (A = WO₄^2?, VO₄^3?; y = 0–0.05) phosphors were synthesized by the combustion method. Some of the MoO₄^2? sites were occupied by WO₄^2? and VO₄^3? anions, which enhanced the luminescence property of Dy³⁺-doped Na₅Y(MoO₄)₄. XRD results show that the crystal structures of the samples were consistent with the standard Na₅Y(MoO₄)₄ phase. Under excitation at 352 nm, the Na₅Y(MoO₄)_4-yA_y:Dy³⁺ phosphors exhibited a characteristic blue emission at 485 nm and a yellow emission at 577 nm, which originated from the ⁴F_9/2 → ⁶H_15/2 and ⁴F_9/2 → ⁶H_13/2 transitions of Dy³⁺ ions, respectively. White light can be achieved by combining these blue and yellow emissions. After replacing MoO₄^2? with WO₄^2? and VO₄^3? anions in Na₅Y(MoO₄)₄:Dy³⁺, the luminescence intensity of Dy³⁺ was significantly improved due to the crystal field effect. The results indicate that Na₅Y(MoO₄)_3.97(WO₄)_0.03:Dy³⁺ and Na₅Y(MoO₄)_3.97(VO₄)_0.03:Dy³⁺ phosphors have good prospects for application in n-UV-excited w-LEDs.     

Learn from the past – sequentially one-to-one video deblurring network

With the growing availability of hand-held cameras in recent years, more and more images and videos are taken at any time and any place. However, they usually suffer from undesirable blur due to camera shake or object motion in the scene. In recent years, a few modern video deblurring methods are proposed and achieve impressive performance. However, they are still not suitable for practical applications as high computational cost or using future information as input. To address the issues, we propose a sequentially one-to-one video deblurring network (SOON) which can deblur effectively without any future information. It transfers both spatial and temporal information to the next frame by utilizing the recurrent architecture. In addition, we design a novel Spatio-Temporal Attention module to nudge the network to focus on the meaningful and essential features in the past. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art deblurring methods, both quantitatively and qualitatively, on various challenging real-world deblurring datasets. Moreover, as our method deblurs in an online manner and is potentially real-time, it is more suitable for practical applications.     

BE-ACGAN: Photo-realistic residual bit-depth enhancement by advanced conditional GAN

Since more demands for high quality visualization have been raised in various fields, monitors with higher bit-depth (HBD) become popular in recent years. However, most digital images are at low bit-depth (LBD) and usually of low visual quality with annoying false contours when displayed on HBD monitors directly. To reconstruct visually pleasant HBD images, many bit-depth enhancement (BE) algorithms have been proposed from various aspects, but the recovered HBD images are usually unsatisfactory with conspicuous false contours or over-blurred textures. Inspired by discriminative learning, we propose a residual BE algorithm based on advanced conditional generative adversarial network (BE-ACGAN), in which the discriminator adversarially helps assess image quality and train the generator to achieve more photo-realistic recovery performance. Besides, since it is hard to distinguish between the reconstructed and real HBD images with similar structures, the discriminator takes residual images as input and further takes LBD images as conditions to achieve more reliable performance. In addition, we present a novel loss function to deal with the difficulty of unstable adversarial training. The proposed algorithm outperforms the state-of-the-art methods on large-scale benchmark datasets. Source codes are available at https://github.com/TJUMMG/BE- ACGAN/.     

基于CFD⁃DEM的水下切粒装置水室内颗粒流动过程数值模拟

基于CFD?DEM耦合方法，研究了颗粒在水室内的流动状态，分析了不同刀盘转速、粒子水通入量和水室出口角度对造粒过程的影响，发现提高刀盘转速、增加粒子水通入量和水室出口倾斜一定的角度都有利于水室内颗粒的排出。进一步研究了颗粒与碎屑在水室内的流动，发现在水室出口处二者的流动基本呈现出一定的分离角度。     

An improved particle swarm optimisation for unit commitment in microgrids with battery energy storage systems considering battery degradation and uncertainties

Using electric storage systems (ESSs) is known as a viable strategy to mitigate the volatility and intermittency of renewable distributed generators (DGs) in microgrids (MGs). Among different electric storage technologies, battery energy storage (BES) is considered as the best option. In unit commitment (UC) module, the set of committed dispatchable DGs along with their power, power exported to/imported from macrogrid and status and power of ESS units are determined. In this paper, BES degradation is considered in UC formulation and an efficient particle swarm optimisation with quadratic transfer function is proposed for solving UC in BES‐integrated MGs, while the uncertainties of demand, renewable generation and market price are considered and dealt with robust optimisation. UC is formulated as a multi‐objective optimisation problem whose objectives are MG operation cost and BES degradation. The resultant multi‐objective optimisation problem is converted into a single‐objective optimisation problem and the effect of weight factors on MG operation cost and BES lifecycle are investigated. The results show that by consideration of BES degradation in objective function, BES lifecycle increases from 350 to 500 and the minimum depth of charge increases from 5.5% to 34%; however, MG operation cost increases from $8717 to $8910.2. The results also show that by consideration of uncertainties, MG's operation cost increases by 8.22%.