凸轮的表面裂纹分析

本文对一凸轮的表面裂纹进行了分析。渗碳后表层残余奥氏体含量过高级网状碳化物导致该凸轮在精磨过程中出现了磨削裂纹，本文还提出了二次回火或冰冷处理等改进措施来防止磨削裂纹的产生。     

振动沉管挤密碎石桩在闸基处理中的应用

振动沉管挤密碎石桩是近十几年来在挤密砂桩和振冲桩的基础上发展起来的地基处理新技术,具有设备简单、操作方便、成本低、施工快、无污染等特点，被工程界广泛使用，积累了一定的工程经验。其在共产主义闸地基处理中的应用即为一例。     

一种改进的灰度预测模型

灰色预测模型通常是GM模型,但预测精度有时不令人满意。在对模型GM做了进一步研究的基础上,提出了一个预测精度较高的新灰色预测模型,并从理论上证明了这种模型可以有效提高建模数据序列的光滑度。最后把此方法应用于东部某镇GDP的建模中,试验结果表明该方法是可行且有效的,所建模型的精度优于传统GM（1,1）模型的精度。     

烟草制丝线上调节阀流量特性的选择

介绍调节阀的控制原理,分析调节阀流量特性对控制系统的影响,结合烟草设备的工艺特性,提出烟草制丝设备上调节阀流量特性选择的原则。     

基于最短路径树的优化生存时间路由算法

为提高无线传感网的生存时间,提出基于最短路径树的优化生存时间路由算法(LORA_SPT).该算法引入节点分类概念,构造基于链路能耗因子、自身节点剩余能量因子、邻居节点剩余能量因子和类型权重因子等多个因子的权值函数.针对不同类型的节点采用不同的权重因子,最后利用dijkstra算法完成最短路径树,所有节点沿着最短路径树将...     

PTN网络的二次断纤链路规划算法

在PTN（PacketTransportNetwork）网络规划建设中,需要对光纤链路留出备份带宽,以保证部分光纤断开时,受影响业务有足够的容量进行路由重组。这也是提高网络生存性的有效方法之一。文中首先遍历网络双链路的失效状态,然后断开网络中任意两条链路,通过逐次增加链路容量来保证失效业务能够重组路由;最后提出二次断纤链路容量规划算法并进行试验仿真。结果表明,该算法在节约网络带宽、降低建造成本以及故障容错方面有着良好性能,能够很好应用于传送网络的链路规划中。     

华北中部平原地表水入渗试验 ——以南宫溹泸河为例

华北平原中部地区含水砂层颗粒细且薄,对地下水储蓄和地表水入渗不利。选择河道带进行地表水入渗试验,以获取最大地下储蓄空间,为该区水资源合理利用提供良策。试验区选在南宫溹泸河,人工引水1.5个月,获取大量监测数据,据此对地下水位和水量的变化特征、入渗量和地下水库调蓄能力进行研究,结果表明：干涸河道引水入渗,入渗比例可达80.54%;溹泸河上下游2个断面Ⅰ-Ⅰ′和Ⅱ-Ⅱ′河道垂向渗透速度为10.81 m/d和10.72 m/d;水平扩散速度为1.69 m/h和2.38 m/h。由于Ⅰ-Ⅰ′断面垂向渗透速度大于Ⅱ-Ⅱ′断面,而水平扩散速度小于Ⅱ-Ⅱ′断面,所以Ⅰ-Ⅰ′断面形成的水丘相比Ⅱ-Ⅱ′断面较为明显,且不易消散。计算结果显示,试验区调蓄库容为1.87亿m3。     

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.     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.