涡轮增压器叶轮超速预过载处理技术的研究(1)——超速预过载叶轮残余变形场分析

利用循环对称条件，将增压器叶轮简化为由实体单元与平面壳体单元组合而成的有限元分析模型。用模塑性有限元法分析超速预过载处理后叶轮的残余位移，应变及应力场、给出相应的求解结果。将叶轮关键部位的实测值与计算结果比较，结果表明本文所采用的有限元分析模型是可行的，弹塑性有限元分析结果可作为叶轮设计及加工工艺的参考。     

直流系统接地故障的自动检测

在发电厂和变电所内，直流系统接地故障的检测，过去一直采用手动方式来完成。这样，既费事又危险。我们研制的直流系统接地自动检测装置不需停电，可自动检测直流系统接地故障。本文介绍了该装置的原理、组成和主要电路。     

Prognostics and Health Management of PEMFC – State of the art and remaining challenges

Fuel Cell systems (FC) represent a promising alternative energy source. However, even if this technology is close to being competitive, it is not ready for large scale industrial deployment: FC still must be optimized, particularly by increasing their limited lifespan. This involves a better understanding of wearing processes and requires emulating the behavior of the whole system. Furthermore, a new area of science and technology emerges: Prognostics and Health Management (PHM) appears to be of great interest to face the problems of health assessment and life prediction of FCs. According to this, the aim of this paper is to present the current state of the art on PHM of FCs, more precisely of Proton-Exchange Membrane Fuel Cells (PEMFC) stack. PHM discipline is described in order to depict the processing layers that allow early deviations detection, avoiding faults, deciding mitigation actions, and thereby increasing the useful life of FCs. On this basis, a taxonomy of existing works on PHM of PEMFC is given, highlighting open problems to be addressed. The whole enables getting a better understanding of remaining challenging issues in this area.     

Benchmarking of lightweight cryptographic algorithms for wireless IoT networks

Wireless Networks - Cryptographic algorithms that can provide both encryption and authentication are increasingly required in modern security architectures and protocols (e.g. TLS v1.3). Many...     

反转构造的反转程度及其与油气聚集的关系

结合典型反转构造地震剖面，分析了断层型和褶皱型两类反转构造的反转程度，上下皆正断层型和向形褶皱型反转构造的反转程度轻微；上逆下正断层型和透镜形褶皱型反转构造的反转程度中等；上下皆逆断层型和背形褶皱型反转构造的反转程度强烈。分析了反转构造的反转程度与油气聚集的关系，处于轻微反转程度的上下皆正断层型反转和向形褶皱型反转，以及处于强烈反转程度的上下皆逆断层型反转和背形褶皱型反转不利于油气聚集；处于中等反转程度的上逆下正断层型反转和透镜形褶皱型反转类型有利于油气聚集成藏。     

CORRELATION BETWEEN ASO AND AAO

Using a 34 years database consisting of sea level pressure and sea ice concentration, the relationship between Antarctic Oscillation (AAO) and Antarctic Sea-ice Oscillation (ASO) was investigated. Firstly, Empirical Orthogonal Function (EOF) analysis depicts the spatial variability of AAO and ASO and Antarctic Sea-ice Oscillation Index (ASOI) was defined for the first time. Secondly, power spectrum and headlag correlation analysis show that ASO and AAO have the same periods of quasi-three years, quasi-one year and quasihalf a year. Corresponding to AAO, ASO has an evident antioscillation after one and half a year.     

Estimation of Separation of Electrolytes and Organic Compounds by Nanofiltration Membranes Using an Irreversible Thermodynamic Modei

Nanofiltration separation has become a popular technique for removing large organic molecules and inorganic substances from water. It is achieved by a combination of three mechanisms: electrostatic repulsion, sieving and diffusion. In the present work, a modei based on irreversible thermodynamics is extended and used to estimate rejection of inorganic salts and organic substances. Binary systems are modeled, where the feed contains an ion that is much less permeable to the membrane as compared with the other ion. The two modei parameters are estimated by fitting the modei to the experimental data. Variation of these parameters with the composition of the feed is described by an empirical correlation. This work attempts to describe transport through the nanofiltration membranes by a simple model.     

基于单领航者相对位置测量的多AUV协同导航系统定位性能分析

自主水下航行器 (Autonomous underwater vehicle, AUV) 的协同导航是解决水下导航定位问题的重要方法, 其中导航系统的定位误差增长特性是衡量其定位性能的关键指标. 本文针对单领航者相对位置测量的多 AUV 协同导航系统, 利用扩展卡尔曼滤波方法建立了导航系统的整体定位误差关于相对位置量测误差的传递方程. 在此基础上, 通过求解系统定位误差随时间演化的代数黎卡提方程, 得到了其在稳态情形下的方差上界估计. 理论分析表明, 单领航 AUV 协同导航系统的整体定位误差有界收敛且与初始化滤波方差无关, 具有良好的综合性能. 最后, 仿真实例验证了文中理论分析结果的正确性.     

Adaptive partitioning schemes for bipartite ranking

Recursive partitioning methods are among the most popular techniques in machine learning. The purpose of this paper is to investigate how to adapt this methodology to the bipartite ranking problem. Following in the footsteps of the TreeRank approach developed in Clémençon and Vayatis (Proceedings of the 2008 Conference on Algorithmic Learning Theory, 2008 and IEEE Trans. Inf. Theory 55(9):4316–4336, 2009), we present tree-structured algorithms designed for learning to rank instances based on classification data. The main contributions of the present work are the following: the practical implementation of the TreeRank algorithm, well-founded solutions to the crucial issues related to the splitting rule and the choice of the “right” size for the ranking tree. From the angle embraced in this paper, splitting is viewed as a cost-sensitive classification task with data-dependent cost. Hence, up to straightforward modifications, any classification algorithm may serve as a splitting rule. Also, we propose to implement a cost-complexity pruning method after the growing stage in order to produce a “right-sized” ranking sub-tree with large AUC. In particular, performance bounds are established for pruning schemes inspired by recent work on nonparametric model selection. Eventually, we propose indicators for variable importance and variable dependence, plus various simulation studies illustrating the potential of our method.