The control of paracetamol particle size and surface morphology through crystallisation in a spray dryer

The parameters governing the crystallisation of paracetamol using various conventional techniques has been extensively studied, however the factors influencing the drug crystallisation using spray drying is not as well understood. The aim of this work was to investigate the crystallisation of an active pharmaceutical ingredient through evaporative crystallisation using a spray dryer to study the physicochemical properties of the drug and to use semi-empirical equations to gain insight into the morphology and particle size of the dried powder. Paracetamol solutions were spray dried at various inlet temperatures ranging from 60 °C to 120 °C and also from a series of inlet feed solvent compositions ranging from 50/50% v/v ethanol/water to 100% ethanol and solid-state characterisation was done. The size and morphology of the dried materials were altered with a change in spray drying parameters, with an increase in inlet temperature leading to an increase in particle Sauter mean diameter (from 3.0 to 4.4 µm) and a decrease in the particle size with an increase in ethanol concentration in the feed (from 4.6 to 4.4 µm) as a result of changes in particle density and atomised droplet size. The morphology of the dried particles consisted of agglomerates of individual crystallites bound together into larger semi-spherical agglomerates with a higher tendency for particles having crystalline ridges to form at higher ethanol concentrations of the feed.     

基于四旋翼无人机平台的实时多目标检测算法

随着无人机技术和深度学习技术的发展,基于深度学习的多目标检测算法在工业无人机中得到了广泛的应用。针对目前基于深度学习的多目标检测算法占用大量计算量资源,难以在算力有限的中小型无人机平台上实时运行的问题,分析了深度学习算法在低功耗CPU上的耗时,提出一种卷积神经网络计算优化方法。在机载计算机中进行仿真,结果表明在检测效果基本不变的条件下,算法帧率达到了56FPS,实现了无人机平台上的实时多目标检测。     

A survey on analysis and implementation of state-of-the-art haze removal techniques

Haze is a poor-quality state described by the opalescent appearance of the atmosphere which reduces the visibility. It is caused by high concentrations of atmospheric air pollutants, such as dust, smoke and other particles that scatter and absorb sunlight. The poor visibility can result in the failure of multiple computer vision applications such as smart transport systems, image processing, object detection, surveillance etc. One of the major issues in the field of image processing is the restoration of images that are corrupted due to different degradations. Typically, the images or videos captured in the outside environment have low contrast, colour fade and restricted visibility due to suspended particles of the atmosphere that directly influence the image quality. This can cause difficulty in identifying the objects in the captured hazy images or frames. To address this problem, several image dehazing techniques have been developed in the literature, each of which has its own advantages and limitations, but effective image restoration remains a challenging task. In recent times, various learning (Machine learning & Deep learning) based methods greatly condensed the drawbacks of manual design of haze related features and reduces the difficulty in efficient restoration of images with less computational time and cost. The current state-of-the-art methods for haze free images, mainly from the last decade, are thoroughly examined in this survey. Moreover, this paper systematically summarizes the hardware implementations of various haze removal methods in real time. It is with the hope that this current survey acts as a reference for researchers in this scientific area and to provide a direction for future improvements based on current achievements.     

Deep reinforcement learning for a color-batching resequencing problem

In automotive paint shops, changes of colors between consecutive production orders cause costs for cleaning the painting robots. It is a significant task to re-sequence orders and group orders with identical color as a color batch to minimize the color changeover costs. In this paper, a Color-batching Resequencing Problem (CRP) with mix bank buffer systems is considered. We propose a Color-Histogram (CH) model to describe the CRP as a Markov decision process and a Deep Q-Network (DQN) algorithm to solve the CRP integrated with the virtual car resequencing technique. The CH model significantly reduces the number of possible actions of the DQN agent, so that the DQN algorithm can be applied to the CRP at a practical scale. A DQN agent is trained in a deep reinforcement learning environment to minimize the costs of color changeovers for the CRP. Two experiments with different assumptions on the order attribute distributions and cost metrics were conducted and evaluated. Experimental results show that the proposed approach outperformed conventional algorithms under both conditions. The proposed agent can run in real time on a regular personal computer with a GPU. Hence, the proposed approach can be readily applied in the production control of automotive paint shops to resolve order-resequencing problems.     

Influences of flow rate and baffle spacing on hydraulic characteristics of a novel baffle dropshaft

Due to limited flow capacity and the instability of the asymmetric structure of traditional baffle dropshafts, a novel baffle dropshaft with a symmetric structure, adopting the construction shield well directly, is proposed for large-range flow discharge in deep tunnel drainage systems. In this study, a two-phase flow field of the novel baffle dropshaft with three different baffle spacings was simulated at seven different flow rates with a three-dimensional (3D) numerical model verified with experiments, to study hydraulic characteristics of this novel baffle dropshaft. The results show that the novel baffle dropshaft has a remarkable energy dissipation effect. Baffle spacing of the novel baffle dropshaft has a greater effect on flow patterns and baffle pressure distributions than the comprehensive energy dissipation rate. Flow rate is a critical issue for the selection of baffle spacing in the design. Some guidance on baffle spacing selection and structure optimization for the application of this novel baffle dropshaft in deep tunnel drainage systems is proposed.     

Robust image retrieval by cascading a deep quality assessment network

The performance of computer vision algorithms can severely degrade in the presence of a variety of distortions. While image enhancement algorithms have evolved to optimize image quality as measured according to human visual perception, their relevance in maximizing the success of computer vision algorithms operating on the enhanced image has been much less investigated. We consider the problem of image enhancement to combat Gaussian noise and low resolution with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising or super resolution, allowing for optimization of the enhancement CNN to maximize retrieval performance. This framework allows us to couple enhancement to the retrieval problem. We also consider the problem of adapting image features for robust retrieval performance in the presence of distortions. We show through experiments on distorted images of the Oxford and Paris buildings datasets that our algorithms yield improved mean average precision when compared to using enhancement methods that are oblivious to the task of image retrieval. ¹     

Research on the parallelization of image quality analysis algorithm based on deep learning

Image quality assessment is an indispensable in computer vision applications, such as image classification, image parsing. With the development of Internet, image data acquisition becomes more conveniently. However, image distortion is inevitable due to imperfect image acquisition system, image transmission medium and image recording equipment. Traditional image quality assessment algorithms only focus on low-level visual features such as color or texture, which could not encode high-level features effectively. CNN-based methods have shown satisfactory results in image quality assessment. However, existing methods have problems such as incomplete feature extraction, partial image block distortion, and inability to determine scores. So in this paper, we propose a novel framework for image quality assessment based on deep learning. We incorporate both low-level visual features and high-level semantic features to better describe images. And image quality is analyzed in a parallel processing mode. Experiments are conducted on LIVE and TID2008 datasets demonstrate the proposed model can predict the quality of the distorted image well, and both SROCC and PLCC can reach 0.92 or higher.     

深度学习技术在火灾图像识别中的应用

火灾事故频发严重威胁着社会公共安全和人们的生命财产安全。火灾发生的不可预见性增加了火灾防控的难度。传统温感、烟感火灾探测设备对室内空间火灾探测效率较高;以人工选择特征为依据的火灾图像识别技术受限于实际火灾场景特征复杂多变,存在误报情况;深度学习技术通过海量火灾场景图片训练和网络参数优化,自动提取火灾图像深度抽象特征,以达到对火灾的精准识别和预警判断。本文就火灾图像识别及深度学习技术在该领域中的应用进行分析,对影响深度学习技术在火灾图像识别应用中的瓶颈问题进行探讨,并展望了该技术的未来发展。     

Yb和Al-Ti-B复合变质对过共晶Al-Si铸造合金组织和性能的影响

采用光学显微镜、扫描电子显微镜、电子探针,研究了不同比例的稀土Yb和Al-5Ti-B复合变质剂对过共晶Al-20Si合金显微组织与性能的影响。结果表明,Al-20Si合金中添加0.5% Yb和0.3% Al-5Ti-1B复合变质剂使得粗大的多边形状、块状和五瓣星状初生Si变质为细小块状,共晶Si由粗大的片状或针状结构变质为细小颗粒或者纤维结构,而且粗大的α-Al枝晶被细化为等轴枝晶。然而,当Al-5Ti-B变质剂含量达到0.4%时,初生Si和共晶Si出现粗化现象。力学性能测试结果表明,合金的抗拉强度和延伸率分别增加了83.7%和92.1%。     

Relationship between amounts of low-melting-point eutectics and hot tearing susceptibility of ternary Al−Cu−Mg alloys during solidification

A systematical study on the relationship between the amounts of different eutectic phases especially the low-melting-point (LMP) eutectics and the hot tearing susceptibility of ternary Al−Cu−Mg alloys during solidification was performed. By controlling the concentrations of major alloying elements (Cu, Mg), the amounts of LMP eutectics at the final stages of solidification were varied and the corresponding hot tearing susceptibility (HTS) was determined. The results showed that the Al−4.6Cu−0.4Mg (wt.%) alloy, which contained the smallest fraction of LMP eutectics among the investigated alloys, was observed to be the most susceptible to hot tearing. With the amount of total residual liquid being approximately the same in the alloys, the hot tearing resistance is considered to be closely related to the amounts of LMP eutectics. Specifically, the higher the amount of LMP eutectics was, the lower the HTS of the alloy was. Further, the potential mechanism of low HTS for alloys with high amounts of LMP eutectics among ternary Al−Cu−Mg alloys was discussed in terms of feeding ability and permeability as well as total viscosity evolution during solidification.