基于ASP.NET技术的新闻发布系统的设计与实现

完整描述了一种在部队师旅级单位通用的新闻发布系统。首先根据用户的特点,对系统进行了需求分析与功能模块划分,然后在此基础上从系统架构、系统流程和数据库设计等3个方面阐述了系统设计中的关键点,并以设计用户登录界面为例写出了部分功能的实现程序,最后对其在中小型企业建站中的应用进行了展望。     

高分辨率阵列感应测井仪在河南油田的应用

文章介绍了美国Baker-Atlas公司研制生产的高分辨率阵列感应成像测井仪（HDIL）,简述了该仪器的工作原理和技术性能指标及主要应用范围,并介绍了高分辨率阵列感应测井（HDIL）资料在河南南阳油田的具体应用实例.     

膨胀套管的弹塑性理论分析

分析认为，膨胀套管在膨胀过程中先进入弹性阶段，然后进入弹塑性阶段，最后进入塑性流动阶段。采用弹塑性分析方法，对膨胀套管膨胀过程中套管内壁的受力与变形进行了研究，模拟了其成形的过程，并建立了解析解，为膨胀套管膨胀操作过程中确定关键操作参数提供了理论支持。根据推导出的力学模型，计算出了外径107．9mm、钢级为J55的膨胀套管膨胀到直径139．7mm时膨胀芯头拉头处的轴向力为195．94kN。     

蒸汽减压站自控系统的巧设计

蒸汽是石油化工装置中最重要的物料。随着石油化工工业的迅猛发展 ,蒸汽在其中扮演着越来越重要的多重角色。因此 ,对蒸汽系统的平稳控制 ,对石油化工生产装置平稳、高效地运行是至关重要的。传统的蒸汽减压站自控系统基建投资大 ,紧急情况下靠人工手动操作稳定蒸汽压力系统 ,往往造成生产的波动或大面积的停车。针对传统蒸汽减压站自控系统的弱点 ,提出蒸汽减压站自控系统的优化设计方案 ,该方案得以在大型生产装置成功应用。新方案不但节省了大量的基建投资 ,而且取得了良好的生产效益     

长距离曲线顶管在杭州河坊街工程中的应用

对杭州市重点工程—杭州河坊街长距离曲线顶管进行了总结和介绍 ,可为同类工程借鉴和参考     

改进的第三代相干算法及应用

在讨论相干技术发展及三代相干算法优缺点的基础上，介绍了改进的第三代相干算法。应用自行开发的相干软件对多个油气勘探和开发区块的三维地震资料进行处理、分析，对相干时窗长度、相关道数等参数对算法效果的影响及相干体对断层、异常地质体的识别，以及解释方法进行了探讨。应用实例证明，改进的第三代相干算法抗干扰能力强，横向分辨率高，不仅适用于倾斜地层，而且对断层尤其对小断层、异常地质体具有较高的检测能力，是油气勘探和开发的一项有效和实用的技术。     

静态电子轨道衡组成部分解析及其合理选择

阐述静态电子轨道衡诸多优点;工作原理、允许误差及误差分配;轨道衡的结构组成部分、解析各部分的工作特性及技术指标要求;合理选配及推荐实例.     

建筑艺术--膜结构建筑

结合具体工程实例 ,介绍了膜结构建筑 ,从其类型、膜材料的种类、膜结构建筑的特点等方面进行了论述 ,展望了2 1世纪膜结构建筑的发展。     

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.     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.