Texture Feature Extraction Method for Ground Nephogram Based on Contourlet and the Power Spectrum Analysis Algorithm

It is important to extract texture feature from the ground-base cloud image for cloud type automatic detection. In this paper, a new method is presented to capture the contour edge, texture and geometric structure of cloud images by using Contourlet and the power spectrum analysis algorithm. More abundant texture information is extracted. Cloud images can be obtained a multiscale and multidirection decomposition. The coefficient matrix from Contourlet transform of ground nephogram is calculated. The energy, mean and variance characteristics calculated from coefficient matrix are composed of the feature information. The frequency information of the data series from the feature vector values is obtained by the power spectrum analysis. Then Support Vector Machines (SVM) classifier is used to classify according to the frequency information of the trend graph of data series. It is shown that altocumulus and stratus with different texture frequencies can be effectively recognized and further subdivided the types of clouds.     

An inverse problem for non-selfadjoint Sturm–Liouville operator with discontinuity conditions inside a finite interval

This paper is concerned with the inverse problem for non-selfadjoint Sturm–Liouville operator with discontinuity conditions inside a finite interval. Firstly, we give the definitions of generalized weight numbers for this operator which may have the multiple spectrum and then investigate the connections between the generalized weight numbers and other spectral characteristics. Secondly, we obtain the generalized spectral data, which consists of the generalized weight numbers and the spectrum. Then the operator is determined uniquely by the method of spectral mappings. Finally, we give an algorithm for reconstructing the potential function and the coefficients of the boundary conditions and the coefficients of the discontinuity conditions.     

基于衰变链计算的裂变产物缓发γ能谱分析

在军控核查技术中，缓发γ能谱是核材料的“指纹”。为计算和分析铀裂变产物的缓发γ能谱，本文将各种类型的衰变链简化为基态线性链和激发态线性链，推导了零时前后各级核素数目的变化公式，建构了计算缓发γ射线能谱的C语言程序代码，并通过实验对理论推导进行了验证。通过分析几种核素的缓发γ射线计数发现，计算结果与实验数据吻合较好。     

抗新型冠状病毒2019-nCoV新药的研发

2019年底，湖北武汉暴发了2019新型冠状病毒感染疾病（COVID-19），并快速在全国蔓延，且在多个国家发现病例。其感染病例和死亡病例在短时间内快速超过重症急性呼吸综合征（SARS），给中国带来了不可估量的损失。中国科研人员在短时间内，快速锁定病原体为2019-nCoV（或hCoV-19或SARS-CoV-2)，并且在不同层面开展了相关抗病毒药物的研发工作。本文介绍了抗2019-nCoV新药研发的现状。同时，鉴于突发病原体的药物研发过程相对迟缓，我们建议对于潜在流行可能性的病原体，其药物研发要具有前瞻性，国家层面要推进广谱药物的研发和临床试验，以应对可能出现的疫情风险。     

基于自回归谱外推的全聚焦成像分辨力提升

针对相邻缺陷全聚焦超声成像混叠问题，结合低阶、宽有效频带自回归谱外推方法，压缩超声波时域脉冲宽度，实现亚波长级全聚焦（Total focusing method，TFM）成像分辨力。建立碳钢试块模型，设置两个中心间距1.8 mm，直径1.3 mm圆孔，选用中心频率2.25 MHz，32阵元相控阵探头采集全矩阵数据。针对全矩阵数据，选择自回归阶数为2，信号频谱最大幅值下降14 dB为有效频带，建立自回归模型并外推有效频带外的高频与低频成分，随后对全矩阵数据进行延迟叠加处理和TFM成像。仿真结果表明，低阶、宽有效频带自回归谱外推处理方法具有较高的鲁棒性和准确性，TFM成像后可有效分离中心间距0.7λ（λ为超声波长）圆孔，保留缺陷横向位置信息的同时，定位误差不超过0.73%。对碳钢试块中相同位置及尺寸的圆孔进行试验验证，定位误差不超过1.06%，有效地提高TFM成像分辨力。     

提高新疆准东露天煤成浆性的实验研究

为提高准东露天煤的成浆性能,利用宽沟煤、乌东煤、北山煤、黑山煤、红沙泉煤5种煤与准东露天煤进行了混合制浆实验,考察了各单种煤的成浆性能以及单种煤最高成浆浓度条件下混合制浆的煤浆性能。单一准东露天煤浆的成浆性研究表明:准东露天煤单独制浆时,在添加剂添加率3‰下,煤浆最高质量分数为47%,黏度650 mPa·s,但煤浆流动性和稳定性一般。与不同种煤混合制浆研究表明:准东露天煤与宽沟煤、黑山煤容易混合成浆,煤浆的浓度、流动性、稳定性均有明显提高;在准东露天煤与宽沟煤、黑山煤质量比5∶5时,煤浆质量分数可达57%,可满足水煤浆气化炉生产对煤浆的要求。     

Development of mobile miniature natural gas liquefiers

With increasing consumption of natural gas (NG), small NG reservoirs, such as coalbed methane and oil field associated gas, have recently drawn significant attention. Owing to their special characteristics (e.g., scattered distribution and small output), small-scale NG liquefiers are highly required. Similarly, the mixed refrigerant cycle (MRC) is suitable for small-scale liquefaction systems due to its moderate complexity and power consumption. In consideration of the above, this paper reviews the development of mobile miniature NG liquefiers in Technical Institute of Physics and Chemistry (TIPC), China. To effectively liquefy the scattered NG and overcome the drawbacks of existing technologies, three main improvements, i.e., low-pressure MRC process driven by oil-lubricated screw compressor, compact cold box with the new designed heat exchangers, and standardized equipment manufacturing and integrated process technology have been made. The development pattern of “rapid cluster application and flexible liquefaction center” has been eventually proposed. The small-scale NG liquefier developed by TIPC has reached a minimum liquefaction power consumption of about 0.35 kW·h/Nm³. It is suitable to exploit small remote gas reserves which can also be used in boil-off gas reliquefaction and distributed peak-shaving of pipe networks.     

Zeolite imidazolate framework (ZIF)-based mixed matrix membranes for CO2 separation: A review

Mixed matrix membranes (MMMs), which combine the good separation performance of inorganic materials with the low cost of polymers, have emerged as a research hotspot for gas separation membranes. Zeolite imidazolate frameworks (ZIFs) are widely used as fillers to prepare MMMs owing to their advantageous characteristics, such as adjustable pore channels, unsaturated sites, and easy functionalization. For MMMs, three directions can be employed as criteria for improvement compared with pristine polymeric membranes. In this article, the progress of ZIF-based MMMs is reviewed from the aspects of sole-ZIF-based MMMs and modified ZIF-based MMMs. Both strategies improve the separation performance through different improvement directions and mechanisms. Our analysis shows that the synergistic effect of the modified filler can change the structure of the membranes, such as by improving the filler–polymer interface voids, which provides a foundation to overcome the trade-off effect to a certain extent. © 2020 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48968.     

基于降噪时序深度学习网络的风电功率短期预测方法

利用风电场历史功率数据预测未来一段时间内的风功率,对保障电网安全稳定运行具有重要的意义。本文提出一种基于奇异谱分析SSA(singular spectrum analysis)和长短时记忆LSTM(long-short term memory net⁃work)网络的时序特征预测框架用于短期风功率的预测。首先通过SSA对历史风功率原始数据进行降噪处理,然后经过数据转换之后,以LSTM网络为基础进行预测模型的训练,最后通过某风电场提供的两个风机的历史功率数据进行验证。实验结果表明,奇异谱分析对风电场的历史数据具有良好的降噪性,SSA+LSTM模型在测试数据上取得了较好的预测性能,能够有效进行短期风功率的预测。     

基于光谱分类模型的保险杠物证无损研究

采用中红外光谱结合化学计量学的方法对车用保险杠碎片进行鉴别，分别对52个车用保险杠碎片样本的全波段光谱数据、指纹区光谱数据和主成分分析降维后的光谱数据建立Fisher判别分析和K近邻算法2种分类模型，并对分类结果进行比较。结果表明，主成分分析提取特征变量后构建的分类模型，分类的准确率更高，对聚丙烯（PP）、PP/滑石粉、PP/滑石粉/碳酸钙（CaCO₃）3种类型的样本分类准确率达到92.3 %，对PP/滑石粉类型中的10种品牌样本分类准确率达到88.9 %，分类结果理想；在构建的2种分类模型中，Fisher判别分析模型的分类率远高于K近邻算法模型，分析认为K近邻算法模型受到样本不均衡的影响；中红外光谱结合化学计量学可以实现对车用保险杠碎片的准确区分，且满足快速、无损的检验要求。