基于条件深度卷积生成对抗网络的视网膜血管分割

视网膜血管的分割帮助医生对眼底疾病进行诊断有着重要的意义.但现有方法对视网膜血管的分割存在着各种问题, 例如对血管分割不足, 抗噪声干扰能力弱, 对病灶敏感等.针对现有血管分割方法的缺陷, 本文提出使用条件深度卷积生成对抗网络的方法对视网膜血管进行分割.我们主要对生成器的网络结构进行了改进,在卷积层引入残差模块进行差值学习使得网络结构对输出的改变变得敏感, 从而更好地对生成器的权重进行调整.为了降低参数数目和计算, 在使用大卷积核之前使用小卷积核对输入特征图的通道数进行减半处理.通过使用U型网络的思想将卷积层的输出与反卷积层的输出进行连接从而避免低级信息共享.通过在DRIVE和STARE数据集上对本文的方法进行了验证, 其分割准确率分别为96.08 %、97.71 %, 灵敏性分别达到了82.74 %、85.34 %, $F$度量分别达到了82.08 %和85.02 %, 灵敏度比R2U-Net的灵敏度分别高了4.82 %, 2.4 %.     

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。     

Role of HMOX1 Promoter Genetic Variants in Chemoresistance and Chemotherapy Induced Neutropenia in Children with Acute Lymphoblastic Leukemia

Whilst the survival rates of childhood acute lymphoblastic leukemia (ALL) have increased remarkably over the last decades, the therapy resistance and toxicity are still the major causes of treatment failure. It was shown that overexpression of heme oxygenase-1 (HO-1) promotes proliferation and chemoresistance of cancer cells. In humans, the HO-1 gene (HMOX1) expression is modulated by two polymorphisms in the promoter region: (GT)n-length polymorphism and single-nucleotide polymorphism (SNP) A(−413)T, with short GT repeat sequences and 413-A variants linked to an increased HO-1 inducibility. We found that the short alleles are significantly more frequent in ALL patients in comparison to the control group, and that their presence may be associated with a higher risk of treatment failure, reflecting the role of HO-1 in chemoresistance. We also observed that the presence of short alleles may predispose to develop chemotherapy-induced neutropenia. In case of SNP, the 413-T variant co-segregated with short or long alleles, while 413-A almost selectively co-segregated with long alleles, hence it is not possible to determine if SNPs are actually of phenotypic significance. Our results suggest that HO-1 can be a potential target to overcome the treatment failure in ALL patients.     

一种消除小直径管道焊接应力技术的数值分析

小直径管道的应力腐蚀问题一直是工程界较难解决的问题，作者针对这一难题，提出了一种新型的消除应力方法——温差形变处理技术。利用MSC．Marc大型通用非线性有限元分析软件，对小直径管道环缝焊接接头的原始残余应力及温差形变法消除残余应力进行了有限元模拟，得到了一系列关于加热位置、加热宽度和加热持续时间的数据和规律。结果表明，采用温差形变处理技术，在内外表面的焊缝处都可以得到零值左右的残余应力，从而为进一步的试验分析工作和工程应用奠定了基础。     

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。     

残留CO2对石灰在转炉初渣中溶解的影响

 在转炉炼钢过程中,石灰快速溶解对转炉高效脱磷具有十分重要的意义,石灰溶解过程中熔渣/石灰界面处形成的2CaO·SiO₂产物层被认为是阻碍石灰溶解的关键因素。制备了具有两种不同CO₂含量的部分煅烧石灰石,采用浸泡法研究了部分煅烧石灰石在转炉初渣中的溶解行为,并与纯石灰、石灰石的溶解行为进行比较。结果表明,石灰石溶解时在液态熔渣中CaO的传质系数为石灰的2.1倍,残留CO₂质量分数为10%的部分煅烧石灰石的传质系数高达石灰石的6.7倍。在CO₂质量分数为0～43.5%时,石灰的溶解速率先增大后减小。石灰溶解过程中形成的2CaO·SiO₂层严重阻碍了FeO_x的扩散,从而减缓了石灰的溶解。与石灰不同,石灰石分解产生的CO₂能够破坏2CaO·SiO₂层并破坏自身结构,有利于熔渣的渗透,这也适用于残留CO₂的部分煅烧石灰石。制备纯石灰的过程中为了确保石灰芯部完全煅烧,因此极易导致石灰外表面发生过烧,而制备部分煅烧石灰石能在一定程度上解决表面过烧的问题。此外,与石灰石相比,部分煅烧石灰石由于表面是石灰外壳,溶解初期其表面附近的炉渣温降相对更低,能够避免溶解初期出现停滞阶段。在转炉富余热量有限的情况下,部分煅烧石灰石的石灰替换比高于石灰石,这取决于部分煅烧石灰石中的CO₂残留量。     

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。