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.     

No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.     

微覆盖解决高低层混合居民楼深度覆盖的应用

城区高低层混合居民楼区域作为无线网络覆盖难点,传统宏站无法进行有效的覆盖。本文通过采用“微覆盖、微对打”的建设方式有效解决“高低层混合居民楼”无线网络信号的深度覆盖问题。     

多晶硅生产中残液搅拌罐密封结构的改造

在处理多晶硅生产中产生的残液时,残液搅拌罐填料密封出现泄漏,针对此问题提出了优化措施,达到了预期效果。     

鲸鱼优化支持向量机的短期风电功率预测

为提高风电预测的精度,提出一种鲸鱼优化支持向量机SVM(support vector machine)的组合预测模型。该模型针对风电序列的非平稳波动特性,首先应用集合经验模态分解技术EEMD(ensemble empirical mode de?composition)将原始风电序列分解为一系列不同特征尺度的子序列;并引入鲸鱼优化算法WOA(whales optimiza?tion algorithm)解决SVM中学习参数选择难的问题,进而对各子序列建立WOA_SVM预测模型;最后,叠加各子序列的预测值以得到最终预测值。仿真表明,所提EEMD_WOA_SVM模型具有较高的风电预测精度,显著优于其他基本模型。     

煤田地震勘探中多井组合爆炸法浅析

地震勘探中炸药作为激发震源的激发条件多样,采用有效的震源激发方式,才能取得高信噪比的地震原始数据。本文依据有关爆破原理,理论结合实践经验,提出在厚黄土区采用多井组合的激发方式,能有效压制噪声,增强性噪比,提高数据质量。     

矿用组合开关保护系统研究

简要介绍了本安先导电路的判定及继电器隔离方式的本安先导电路原理,运用3种方法分析了矿井低压电网漏电故障,最后在此基础上,从组合开关对电压保护、短路保护和过载保护三个方面,详细探讨了基于组合开关的矿井低压电网保护,相信可以通过多方面的综合分析研究出更可靠、保护更加全面和智能的组合开关,从而为中国井下煤矿的安全机械化开采提供更有保障的服务。     

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。