珠湖蓄滞洪区溃堤洪水模拟及损失评估

为有效评估蓄滞洪区溃堤洪水灾害风险,本文依托鄱阳湖区珠湖蓄滞洪区,建立了洪水演进数值模型,模拟了蓄滞洪区溃堤洪水演进过程,获得了历史最高洪水位下的蓄滞洪区淹没范围、淹没水深及流速等重要淹没信息,进而根据洪水淹没信息分别估算了溃堤洪水可能给蓄滞洪区造成的生命、经济和生态环境损失.研究结果可为溃堤洪水灾害风险评估、蓄滞洪区内灾害等级划分、人员和物资撤离及迁移方案制定等提供重要的理论依据.     

SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

(Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage, quick learning speed, and great generalization performances. The number of hidden neurons in ELM is set to 2000. Ten runs of 10-fold cross-validation are implemented to generate impartial results. (Result) For the 296-image dataset, our SNELM model attains a sensitivity of 96.35 ± 1.50%, a specificity of 96.08 ± 1.05%, a precision of 96.10 ± 1.00%, and an accuracy of 96.22 ± 0.94%. For the 640-image dataset, the SNELM attains a sensitivity of 96.00 ± 1.25%, a specificity of 96.28 ± 1.16%, a precision of 96.28 ± 1.13%, and an accuracy of 96.14 ± 0.96%. (Conclusion) The proposed SNELM model is successful in diagnosing COVID-19. The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.     

一种径向基函数神经网络的参数求解方法

为了求解径向基函数神经网络的权值,首先分析了传统基于训练误差的方法,发现该方法容易造成数据过拟合,原因在于训练误差是风险函数的下偏估计;因此,文中提出采用缺一交叉验证得分代替训练误差,来实现无偏估计风险函数;实验对摩托数据与玻璃数据进行拟合,证实了基于缺一交叉验证的方法优于传统基于训练误差的方法,且所得到的径向基函数网络能够较光滑地拟合数据,不会造成过拟合.     

中医针灸领域术语自动抽取研究

针对中医针灸领域术语的构成特点,该文建立了一种基于规则的领域术语抽取算法模型,该模型首先对中医针灸领域术语种子集进行有限次的迭代,生成中医针灸领域术语构件集;然后,以术语构件集为领域词典,采用最大向前匹配算法对中文针灸医学文献中的句子进行切分,并抽取候选术语;最后,利用语言规则对候选术语进行过滤处理,筛选出中医针灸领域专业术语。分别以关键字集和中医词典为种子集进行实验,开式测试的F值分别达到76.96%和35.59%。
     

基于几何推理的装配序列自动规划研究

文章以问题规约的求解策略,以分解法规划产生的装配序列,提出与或图表达装配体拆卸序列解空间。通过求解拆卸序列与或图的解空间得到装配体的拆卸序列,将拆卸序列反向得到装配序列。为降低装配体拓扑联接图的复杂度,对作业组件做出识别。另外提出了判断零件拆卸性的三个条件,大部分零件无需进行干涉检验,而白逻辑推理即可判断是否满足拆卸条件,避免了多次试凑,具有更高的求解效率,可更广泛地用于装配序列求解。     

一种描述特征交互作用的类属命名机制研究

文中描述了产成式设计中的几类特征编辑操作的结构,并设法将所有因特征发生交互作用而引起的面的拓扑变化(如融合、分割)引入一种类命名机制,它在基于特征造型系统中起到标识实体模型中的拓扑实体和检验拓扑实体存在的合法性的作用.     

冰箱压缩机活塞伸出量测微仪中几个频率的关系

１问题的提出 开发冰箱压缩机活塞伸出量自动测微仪,需要先确定信号频宽、载波频率和采样频率。当我们根据１／１０法则［１］确定信号的频宽,再按照推荐的载波频率和采样频率与信号频宽的关系式确定载波频率和采样频率时,发现它们并不能满足测量精度要求,因此需要对这些关系做出修正。２信号频宽 由图１可建立活塞上Ｂ点的运动方程：式中：ｒ为曲轴偏心距（曲柄半径）,ｍｍ;ｌ为连杆长度,ｍ;λ为曲柄半径与连杆长度之比ｒ／ｌ;为测量时曲轴的角频率,ｒａｄ／ｓ。 将ｘ（ｔ）展开为傅里叶级数后,其表达式可写作： 根据测量对象（…     

高精度齿轮综合误差检测仪的设计

讨论了设计高精度齿轮综合误差检测仪的必要性，介绍了齿轮综合误差的检测方法和高精度齿轮综合误差检测仪的工作原理、结构特点和测量电路框图。     

Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging

We propose the use of exponent of wavelet transform (EWT) coefficients as a sparse representation which is combined with the iterative shrinkage/threshold algorithm (ISTA) for the reconstruction of compressed sensing magnetic resonance imaging. In addition, random shifting (RS) is employed to guarantee the translation invariance property of discrete wavelet transform. The proposed method is termed the exponential wavelet iterative shrinkage/threshold algorithm with random shifting (EWISTARS), which takes advantages of the sparse representation of EWT, the simplicity of ISTA, and the translation invariance of RS. Simulation results on brain, vertebrae, and knee MR images demonstrate that EWISTARS is superior to existing algorithms with regard to reconstruction quality and computation time. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

石油醚提取褐煤蜡的条件选择及优化

以石油醚为萃取剂,对云南曲靖褐煤进行了褐煤蜡提取实验研究,采用单因素试验对褐煤蜡提取率影响因素进行选择,并用正交试验设计方法对提取条件进行了优化,对优化产物进行了酸值、皂化值测定。结果表明:对褐煤蜡提取率影响最大的因素是提取温度,其次是水分,提取时间影响最小。优化工艺为:含水量为6.32%,温度为45℃,时间为150min,料液比为1∶4。在此条件下提取率达到4.97%,产物酸值、皂化值分别为:46.95 mg KOH/g、62.03 mg KOH/g。