基于改进残差特征蒸馏的轻量级超分辨率网络

基于深度学习的图像超分辨率算法通常采用递归的方式或参数共享的策略来减少网络参数，这将增加网络的深度，使得运行网络花费大量的时间，从而很难将模型部署到现实生活中。为了解决上述问题，本文设计一种轻量级超分辨率网络，对中间特征的关联性及重要性进行学习，且在重建部分结合高分辨率图像的特征信息。首先，引入层间注意力模块，通过考虑层与层之间的相关性，自适应地分配重要层次特征的权重。其次，使用增强重建模块提取高分辨率图像中更精细的特征信息，以此得到更加清晰的重建图片。通过大量的对比实验表明，本文设计的网络与其他轻量级模型相比，有更小的网络参数量，并且在重建精度和视觉效果上都有一定的提升。     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

高职模具类人才培养新模式的研究与实践

为了解决传统教学无法全面培养人才的问题,采用"课程融合+时空耦合+过程结合"的方式开展人才培养研究。以注射模设计与制造生产过程为导向,构建大项目牵引下的跨越课程、跨越学期的课程融合新模式,利用信息通信技术及互联网平台,将互联网与传统教学进行深度融合,创造新的教学生态,构建多元多维互动的时空耦合教学新关系,以全方位评价为引导,构建课程多元多维的评价新机制。以3个班级为对象开展不同模式下的教学,对试验班级成绩进行分析,研究结果表明:课程融合+时空耦合+过程结合新模式培养的学生更好地完成了综合项目,获得了较好的教学效果。     

Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

极限工况下的管网“互联互通”改进方案

在全国天然气管道“主干互联、区域成网”(以下简称“互联互通”)基础格局逐渐形成的背景下,天然气管网规模日益扩大、管道分支和气源增加,并且分布不集中、输送方向可变,使得输气方案更加灵活,可以更好地解决某些地域的供气紧张问题;但受现有站场和设备的限制,暂不能满足某些多线组合极限工况,使得“互联互通”的初衷难以全部实现。为了使得现有的各输气干线在实现“互联互通”之后可以满足更多的多线组合工况,在分析“互联互通”背景下M管网工况变化的基础上,研发了可以进行水力仿真和压气站方案制订的计算软件,并对3种极限工况下的不同输气量情况进行了可行性试算,进而基于试算结果提出了相应的管网改进建议。研究结果表明:(1)经验证,软件计算误差满足要求;(2)在M管道某处增设压气站或在某些输气站场配置压缩机组;(3) M管网改进调整后,可以完成大部分的多线组合极限工况,真正实现“互联互通”的输气方案。结论认为,该研究成果有助于推进全国天然气管网早日实现“互联互通”。     

The use of seed texture features for discriminating different cultivars of stored apples

The aim of this study was to identify the textural features of apple seeds with the highest discriminatory power for distinguishing the seeds of different apple cultivars with the use of discriminative classifiers. The seeds of apple cvs. Gala, Jonagold and Idared were scanned with the use of a flatbed scanner, and the acquired images were processed to calculate textural features from color channels: L, a, b, R, G, B, Y, U, V, H, S, I, X, Y and Z. The selected textures were used to develop discriminative models and distinguish the seeds of the examined apple cultivars. The analyses were performed for color spaces and color channels. The seeds of apple cvs. Gala and Idared were discriminated with 100% accuracy in models based on the textures from Lab and YUV color spaces and color channel L for the Naive Bayes, Multilayer Perceptron and Multi Class classifiers. The discriminatory accuracies of the seeds of all analyzed apple cultivars (Gala, Idared and Jonagold) ranged from 72% to 85%. The discriminatory accuracy of the textures selected from Lab color space for the Naive Bayes classifier reached 85%. The seeds of apple cvs. Gala and Jonagold were discriminated with 78–90% accuracy, and the discriminatory accuracy of the textures from Lab color space and color channel b for the Naive Bayes classifier reached 90%. The seeds of apple cvs. Idared and Jonagold were distinguished with 80–94% accuracy. The models based on textures from Lab color space and color channel b for the Naive Bayes classifier were characterized by 94% discriminatory accuracy. The study demonstrated that textural features are useful for discriminating the seeds of different apple cultivars.     

Hollow mesoporous silica nanoparticles as nanocarriers employed in cancer therapy: A review

Hollow mesoporous silica nanoparticles (HMSNs) have become an attractive drug carrier because of their unique characteristics including stable physicochemical properties, large specific surface area and facile functionalization, especially made into intelligent drug delivery systems (DDSs) for cancer therapy. HMSNs are employed to transport traditional anti-tumor drugs, which can solve the problems of drugs with instability, poor solubility and lack of recognition, etc., while significantly improving the anti-tumor effect. And an unexpected good result will be obtained by combining functional molecules and metal species with HMSNs for cancer diagnosis and treatment. Actually, HMSNs-based DDSs have developed relatively mature in recent years. This review briefly describes how to successfully prepare an ordinary HMSNs-based DDS, as well as its degradation, different stimuli-responses, targets and combination therapy. These versatile intelligent nanoparticles show great potential in clinical aspects.     

铅锌多金属矿分段浓缩选别技术的研究与应用

通过多碎细磨、原矿分级溢流优先浓缩脱水,进行了高浓度选铅银、选锌,锌尾矿再浓缩脱水选硫、选锰,同时匹配合理的选矿药剂,原矿浓缩水回用于磨矿分级和选铅,锌尾浓缩水回用于选锌,尾矿浓缩水回用于选硫、选锰,剩余各种废水经适度处理回用于选铅和磨矿分级等,使一段磨矿细度-74μm从70%提高到80%,铅、锌、硫入选初始浓度分别提高到50%、40%、50%,选矿废水全部分质回用。铅锌硫银回收率分别提高了1.5、2.83、13.57、1.01个百分点,硫精矿主品位从38.9%提高到46.5%,设备减少40%、能耗降低25%,节约了选矿药剂消耗和废水处理费用,实现了铅锌多金属矿产资源的高效回收和节能环保。     

傣族织锦的艺术特征与传承

文章阐述了傣族织锦的艺术特征及传承发展现状,对其纹样、内涵及应用进行了分析,提出了傣锦传承和发展的措施与方法,为傣锦的保护和传承提供参考。