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。     

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.     

一种新的修正恒模算法

阐述了恒模算法(CMA)、修正恒模算法(MCMA)和判决引导(DD)算法的基本原理。针对CMA和MCMA收敛速度慢,固定步长条件下收敛速度和剩余误差之间存在矛盾的缺陷,在分析CMA误差特性的基础上,利用DD算法误差函数的模值和判决器输出构造新的误差函数,提出了一种新的变步长MCMA。新算法在加快收敛速度的同时保持小的剩余误差。仿真结果表明,新算法比CMA和MCMA收敛速度快,均衡输出剩余码间干扰(ISI)小而且能克服相位偏移,具有很好的实用性。     

Aluminum. II. Derivation of C v0from C p and comparison to C v0 calculated from anharmonic models

The specific heat at constant pressure, C _p, of aluminum measured by Ditmars, Plint, and Shukla has been reduced to the volume V ₀ appropriate for 0 K employing the Murnaghan equation. The C _v0 thus obtained is compared with the theoretical C _v0 calculated in the harmonic and the lowest-order anharmonic approximation from three different pseudopotentials (Harrison, Ashcroft, and Dagens-Rasolt-Taylor) as well as a phenomenological Morse potential. The higher-order ( ⁴) anharmonic contributions are calculated from the same nearest-neighbor Morse potential as in the lowest-order anharmonic theory. The role of the vacancy and the higher-order anharmonic contributions to C _v0 has been examined and we conclude that the ⁴ contributions to C _v0 are much smaller than the vacancy contribution. After removal of the vacancy contribution, the reduced C _v0 is found to be in excellent agreement with the Ashcroft and Harrison pseudopotentials as well as the Morse potential including the ² and ⁴ contributions to C _v0.     

解析参数对活性焦再生过程及再生效果的影响

 活性焦的热解析参数对再生活性焦的脱硫脱硝性能和机械强度至关重要。为了明确解析参数对活性焦再生过程和再生效果的影响规律,通过热解析试验探究活性焦硫残余比例、CO₂和CO生成量及再生活性焦脱硫脱硝性能随解析温度和解析时间的变化规律,继而明确适宜的活性焦热解析参数。结果表明,活性焦升温解析过程中,脱硫产物在317 ℃左右迅速分解,随后分解速率下降;在进入恒温解析阶段后脱硫产物分解速率先快速下降,而后进入缓慢解析状态。硫残余比例随恒温解析温度的升高而下降,在530 ℃下解析3 h可使脱硫产物完全解析;解析温度高于430 ℃后,活性焦表面的酚基、醌基、内酯基等含氧官能团分解量明显增加,并随恒温解析温度的升高而持续增加,分解所生成的CO和CO₂也随之大幅增加,这将使活性焦的孔隙结构进一步发展,继而不利于活性焦机械强度的保持;解析温度低于530 ℃时,硫残余比例随解析温度的升高而持续降低,使再生活性焦的脱硫脱硝性能持续提高;解析温度高于530 ℃后,含氧官能团分解量随解析温度的升高而持续增加,这将有利于提高活性焦表面SO₂氧化反应速率,继而使再生活性焦的脱硫性能持续升高,但酚基、内酯基等酸性含氧官能团的分解使再生活性焦对NH₃的吸附性能降低,进而使其脱硝性能降低。在兼顾再生活性焦脱硫脱硝性能、机械强度和生产效率等多方面因素时,430 ℃恒温解析3 h是相对较优的解析参数。在此解析条件下,再生活性焦的硫残余比例仅为1.8%,含氧官能团尚未发生大量分解,脱硫脱硝性能相对较为优良。     

5%Si高硅奥氏体不锈钢元素偏析及均匀化处理

 高硅奥氏体不锈钢由于高含量硅元素的加入使其具有优异的耐高温腐蚀性能和较低的成本,在制酸行业有着潜在的应用价值。然而,该合金中高含量硅元素的加入会促进凝固过程中溶质再分配,进而造成显著的元素偏析,最终导致合金内部产生枝晶组织和大量的有害相。对铸锭组织进行均匀化处理能够有效消除枝晶与元素偏析,促进析出相回溶和枝晶消融,从而改善材料的热塑性,有效应对热变形开裂问题。因此,采用金相显微镜(OM)、扫描电镜能谱分析(SEM/EDS)、电子探针(EPMA)、JMatPro软件计算等方法,研究了实验室条件下制备的5%Si高硅奥氏体不锈钢铸锭的显微组织和元素分布状态,通过残余偏析指数、扩散动力学计算并结合均匀化处理试验验证,最终确定了5%Si高硅奥氏体不锈钢合理的均匀化处理工艺。结果表明,5%Si高硅奥氏体不锈钢凝固过程中钼元素偏析最为严重,通过残余偏析指数模型计算得到的均匀化动力学方程可用来指导该成分合金的均匀化处理工艺;5%Si高硅奥氏体不锈钢经过1 150 ℃×12 h均匀化处理后,铸锭内枝晶消融,元素偏析基本消除,析出相与铁素体回溶到基体中,合金转变为全奥氏体组织,热塑性得到改善;当加热温度达到1 250 ℃时,合金出现过烧现象,晶界开始熔化。     

Q390高强抗震钢板残余应力检测方法对比及分析

利用盲孔法、X-Ray法、中子衍射法和超声法对Q390高强抗震板及U形弯曲件进行残余应力检测,并对这4种方法测试得到的应力结果进行分析研究。结果表明,以上4种检测方法均要考虑其特定的检测应用范围,X-Ray法针对钢板表面下微米级深度的微米级晶格尺度范围局部点应力进行检测,中子衍射法可在毫米级尺度局部区域进行应力检测,盲孔法反映的是因毫米尺度钻孔导致的钢板表面孔周边的应力变化,超声法则可以通过连续扫描检测整个钢板残余应力的宏观分布变化。因此,对于工业钢板,超声法测试残余应力更加科学、合理、准确。同时,通过U形试样建立稳定的应力应变场,利用超声设备使用不同参考态对Q390高强抗震板弯曲件及退火弯曲件进行测试,结果表明,测试参考态的选取对应力测试结果影响很大,材料各向异性的影响对应力测试结果不可忽视。     

Characterization of blasted austenitic stainless steel and its corrosion resistance

It is known that the corrosion resistance of stainless steel is deteriorated by blasting, but the reason for this deterioration is not clear. A blasted austenitic stainless steel plate (JIS-SUS304) has been characterized with comparison to the scraped and non-blasted specimens. The surface roughness of the blasted specimen is larger than that of materials finished with #180 paper. A martensite phase is formed in the surface layer of both blasted and scraped specimens. Compressive residual stress is generated in the blasted specimen and the maximum residual stress is formed at 50–100 μm from the surface. The corrosion potentials of the blasted specimen and subsequently solution treated specimen are lower than that of the non-blasted specimen. The passivation current densities of the blasted specimens are higher those of the non-blasted specimen. The blasted specimen and the subsequently solution treated specimen exhibit rust in 5% sodium chloride (NaCl) solution, while the non-blasted specimen and ground specimen do not rust in the solution. It is concluded that the deterioration of corrosion resistance of austenitic stainless steel through blasting is caused by the roughed morphology of the surface.