高超声速飞行器动力学建模与分析

吸气式高超声速飞行器本身具有复杂动力学特性,由于存在强烈的结构弹性/推力/气动的耦合以及其力学环境的诸多不确定因素,使得飞行器本身的动态特性相当复杂.经典牛顿力学方法建模难以清楚反映飞行器结构弹性/推力/气动耦合.为更为精确的分析高超声速飞行器复杂的动力学特性,本文针对吸气式高超声速飞行器采用拉格朗日法进行了建模与动态特性分析,选择了具有代表性的特征点,建立了小扰动模型,在不同特征点上采用拉格朗日模型和牛顿力学模型对比分析高超声速飞行器的动力学特性.结果表明,拉格朗日方法所建立的动力学模型能够更清楚地符合高超声速飞行器结构弹性和气动特性耦合以及发动机尾流和气动特性之间的耦合特性.     

SERT结晶器液位控制系统

介绍一种梅山冶金公司板坯连同上应用的新型的结晶器液位控制系统，它由浮式式液位检测装置，ＲＳＥ１０００控制器和电动塞棒执行机构所组成，由法国ＳＥＲＴ公司成套制造。     

PETG间歇装置的优化改造

结合公司PETG间歇装置实际生产情况,简单介绍了装置存在的问题,并进行了分析,提出了针对性的优化改造方案及落实情况,并对优化措施进行了总结和探讨.     

基于改进U-net和CNN的绝缘子自爆检测方法研究

针对绝缘子自爆故障人工检测效率低,成本高的问题,基于改进U-net和卷积神经网络（CNN）模型,提出一种可有效识别绝缘子自爆故障的双阶段目标检测算法。首先,在语义分割阶段使用改进U-net模型,通过翻倍提高图像分辨率的方法有效提高图像分割精度。其次,在图像分类阶段提出更适合所提问题且有效提高分类准确度的新型CNN模型。最后,使用无人机拍摄的绝缘子图片为实验数据进行实验。实验结果表明所提算法识别精度较高。     

硝基联苯、氨基联苯及啶酰菌胺的高效液相色谱分析方法

建立了一种高效液相色谱定量方法,测定以4-氯-2'-硝基联苯制备4-氯-2'-氨基联苯合成啶酰菌胺路线各步反应中的有效成分含量.采用InertSustain ODS-C18不锈钢柱(250 mm×4.6 mm i.d.×5μm)和二极管阵列检测器,以乙腈和0.02％磷酸水为流动相,流速为1.0 mL/min,在220 nm波长下对中间体4-氯-2'-硝基联苯进行分离和定量分析;在254 nm波长下对中间体4-氯-2'-氨基联苯进行分离和定量分析;在254 nm波长下对啶酰菌胺进行分离和定量.分析结果表明,4-氯-2'-硝基联苯保留时间约24.3 min,线性相关系数为1.0000,标准偏差为0.04,变异系数为0.34％,平均回收率为100.04％;4-氯-2'-氨基联苯保留时间约18.0 min,线性相关系数为1.0000,标准偏差为0.05,变异系数为0.33％,平均回收率为99.94％;啶酰菌胺保留时间约11.9 min,线性相关系数为1.0000,标准偏差为0.21,变异系数为0.32％,平均回收率为100.09％.方法 操作简便,精密度与准确度较高,满足定量分析要求.     

Incorporating Machine Learning into Established Bioinformatics Frameworks

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.     

The GNAQ T96S Mutation Affects Cell Signaling and Enhances the Oncogenic Properties of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the most common malignant tumor in the liver, grows and metastasizes rapidly. Despite advances in treatment modalities, the five-year survival rate of HCC remains less than 30%. We sought genetic mutations that may affect the oncogenic properties of HCC, using The Cancer Genome Atlas (TCGA) data analysis. We found that the GNAQ T96S mutation (threonine 96 to serine alteration of the Gαq protein) was present in 12 out of 373 HCC patients (3.2%). To examine the effect of the GNAQ T96S mutation on HCC, we transfected the SK-Hep-1 cell line with the wild-type or the mutant GNAQ T96S expression vector. Transfection with the wild-type GNAQ expression vector enhanced anchorage-independent growth, migration, and the MAPK pathways in the SK-Hep-1 cells compared to control vector transfection. Moreover, cell proliferation, anchorage-independent growth, migration, and the MAPK pathways were further enhanced in the SK-Hep-1 cells transfected with the GNAQ T96S expression vector compared to the wild-type GNAQ-transfected cells. In silico structural analysis shows that the substitution of the GNAQ amino acid threonine 96 with a serine may destabilize the interaction between the regulator of G protein signaling (RGS) protein and GNAQ. This may reduce the inhibitory effect of RGS on GNAQ signaling, enhancing the GNAQ signaling pathway. Single nucleotide polymorphism (SNP) genotyping analysis for Korean HCC patients shows that the GNAQ T96S mutation was found in only one of the 456 patients (0.22%). Our data suggest that the GNAQ T96S hotspot mutation may play an oncogenic role in HCC by potentiating the GNAQ signal transduction pathway.     

基于改进FCM聚类的窃电行为检测

窃电行为检测的主要目的 在于发现窃电用户,降低电力系统运营成本.在此背景下,提出基于改进模糊C均值聚类的窃电行为检测模型,包括因子分析、基于改进模糊C均值聚类的局部离群因子计算、ROC曲线模型评价与调参及最佳检测阈值选取等模块,适用于无大量已知窃电用户样本的情况.首先,通过因子分析对用户用电特征(包括用电负荷数据和电能表异常事件)进行维度规约,提升模型检测效率.再利用遗传模拟退火算法对模糊C均值聚类算法进行改进,对用户用电特征进行检测.最后与现有成熟算法进行比较,验证该模型对窃电行为具有较高的检测准确度.检测模型可输出所有被测用户用电行为离群度得分和窃电概率排序,利用该文检测模型的输出,能够以较高精度检测出窃电行为用户,根据结果进行现场稽查,可提升反窃电工作效率.     

预测帧编码中基于场景信息的宏块模式跳过算法*

在对场景信息进行分析的基础上,提出了H.264/AVC中预测帧的一种快速算法.对于未发生场景切换的预测帧,采用INTRA模式跳过算法(IMSA),跳过该帧中所有的INTRA模式的决策过程.对于发生场景切换的预测帧,跳过INTER模式的决策过程(PMSA).仿真结果显示,与采用完整的率失真优化(RDO)决策过程相比,未发生场景切换时,所提方法能减少30%～60%的编码时间;发生场景切换时,能减少60%～70%的编码时间,并且未导致明显的图像质量下降和码率的增加.