基于特征选择的集成分类器抗噪性能分析

特征选择有助于增强集成分类器成员间的随机差异性,从而提高泛化精度。研究了随机子空间法（RandomSub-space）和旋转森林法（RotationForest）两种基于特征选择的集成分类器构造算法,分析讨论了两算法特征选择的方式与随机差异程度之间的关系。通过对UCI数据集引入噪声,比较两者在噪声环境下的分类精度。实验结果表明：当噪声增加及特征关联度下降时,基本学习算法及噪声程度对集成效果均有影响,当噪声增强到一定程度后。集成效果和单分类器的性能趋于一致。     

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...     

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.     

一种新的基于梯度方向直方图的图像配准方法*

构建了一种新的局部梯度方向直方图,同时定义了特征点的主方向,从而提出了一种具有旋转不变性的图像配准算法.首先采用高斯加权求模技术对特征点邻域内的像素的梯度作直方图统计,确定出具有旋转不变性和抗噪性的特征点主方向;然后用主方向作角度直方图统计,确定待配准图像之间的旋转角度.根据得到的特征点信息及旋转角度定义了特征点对互信息匹配准则,这样使得新配准算法对于图像间旋转角度的范围没有限制,获得了良好的配准效果.     

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。