基于密集神经网络的灰度图像着色算法

针对在灰度图像着色领域中，传统算法信息提取率不高、着色效果不理想的问题，提出了基于密集神经网络的灰度图像着色算法，以实现改善着色效果，让人眼更好地观察图片信息的目的。利用密集神经网络的信息提取高效性，构建并训练了一个端到端的深度学习模型，对图像中的各类信息及特征进行提取。训练网络时与原图像进行对比，以逐渐减小网络输出结果的信息、分类等各类型的损失。训练完成后，只需向网络输入一张灰度图片，即可生成一张颜色饱满、鲜明逼真的彩色图片。实验结果表明，引入密集网络后，可有效改善着色过程中的漏色、细节信息损失、对比度低等问题，所提算法着色效果较基于VGG网络及U-Net、双流网络结构、残差网络（ResNet）等性能优异的先进着色算法而言取得了显著的改进。     

基于均匀光纤光栅的DWDM系统PMD补偿方法

提出一种基于均匀光纤Bragg光栅(FBG)的透射型密集波分复用(DWDM)系统多信道偏振模色散(PMD)补偿方案。当FBG受到横向挤压时，会产生双折射现象。当一波长的光信号从光栅带隙附近透射时，就会在快轴和慢轴之间产生时延差(DGD)。通过改变外力的大小来调节DGD的大小可以实现对PMD的补偿。通过将多个补偿光栅级联，就可以实现对DWDM系统多信道PMD的补偿。在100N外力作用下，5cm长的光栅最大可以补偿121ps的PMD，而对相邻0．8nm的信道，只引入0．2ps的DGD。     

Influence of Missing Values on the Prediction of a Stationary Time Series

Abstract. The influence of missing observations on the linear prediction of a stationary time series is investigated. Simple bounds for the prediction error variance and asymptotic behaviours for short and long‐memory processes respectively are presented.     

高密度澄清池在邯钢第二污水处理厂的应用

高密度澄清池是一种新型高效的澄清池。介绍了高密度澄清池的工艺原理、运行条件和主要参数及在邯钢第二污水处理厂的应用情况。     

Analytic methods for modeling longitudinal data from rolling therapy groups with membership turnover.

Interventions for a variety of emotional and behavioral problems are commonly delivered in the context of treatment groups, with many using rolling admission to sustain membership (i.e., admission, dropout, and discharge from group are perpetual and ongoing). The authors present an overview of the analytic challenges inherent in rolling group data and outline commonly used (but flawed) analytic and design approaches to addressing (or sidestepping) these issues. Moreover, the authors propose use of latent class pattern mixture models (LCPMMs) as a statistically and conceptually defensible approach for modeling treatment data from rolling groups. The LCPMM approach is illustrated with rolling group data from a group-based alcoholism pilot treatment trial (N = 128). Different inferences were made with regard to treatment efficacy under LCPMM vs. the commonly used standard group-clustered latent growth model (LGM); coupled with other preliminary findings in this area, inferences from LGMs may be overly liberal when applied to data from rolling groups. Continued work on data analytic difficulties in groups with membership turnover is critical for furthering the ecological validity of research on behavioral treatments. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

光学薄膜在光通信中的应用

主要介绍了光学薄膜在光通信的无源型器件中的原理及应用,并与光纤光栅和平面光波导型的无源器件作简单比较。     

免振捣自密实混凝土技术在多层住宅中的应用

介绍了免振捣自密实混凝土的特点和质量控制措施，结合某小区自密实混凝土工程应用实例，阐述了该项施工技术，其经济和社会效益良好，提出了进一步发展该技术的若干设想。     

郑411块超稠油试油试采工艺技术应用

郑411块沙三段地层疏松出砂、原油粘度高，属中深层超稠油，常规试油试采方法难以突破出油关，采用APR全通径氮气垫测试、地层与井筒复合防砂、水平井配合直井蒸汽吞吐、氮气泡沫封堵调剖和井筒加热举升稠油等配套工艺技术，取得了较好的效果，为中深层超稠油油藏的开发提供了技术思路。     

Speech Enhancement via Residual Dense Generative Adversarial Network

Generative adversarial networks (GANs) are paid more attention to dealing with the end-to-end speech enhancement in recent years. Various GAN-based enhancement methods are presented to improve the quality of reconstructed speech. However, the performance of these GAN-based methods is worse than those of masking-based methods. To tackle this problem, we propose speech enhancement method with a residual dense generative adversarial network (RDGAN) contributing to map the log-power spectrum (LPS) of degraded speech to the clean one. In detail, a residual dense block (RDB) architecture is designed to better estimate the LPS of clean speech, which can extract rich local features of LPS through densely connected convolution layers. Meanwhile, sequential RDB connections are incorporated on various scales of LPS. It significantly increases the feature learning flexibility and robustness in the time-frequency domain. Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments. Specifically, in the untrained acoustic test with limited priors, e.g., unmatched signal-to-noise ratio (SNR) and unmatched noise category, RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes. It indicates that our method is more generalized in untrained conditions.     

智能力敏传感器的研制

针对传统的差压密度计和压力式液位计分别存在可靠性不高、不适用于介质密度经常变化的场合的问题,设计了一种基于Freescale MC9S08AW60微控制器的智能力敏传感器,详细介绍了该传感器主要电路的设计。该智能传感器实现了数据采集、信号处理、数据通信和电流环输出式数模转换功能。实际应用表明,在-20～+80℃的工作温度范围内,密度测量精度可达±5kg/m3,液位测量不受悬浮液回流和液位波动的影响,很好地满足了工业现场的应用。