Preparation,microstructure and ion-irradiation damage behavior of Al4SiC4-added SiC ceramics

Single-phase Al₄SiC₄ powder with a low neutron absorption cross section was synthesized and mixed with SiC powder to fabricate highly densified SiC ceramics by hot pressing. The densification of SiC ceramics was greatly improved by the decomposition of Al₄SiC₄ and the formation of aluminosilicate liquid phase during the sintering process. The resulting SiC ceramics were composed of fine equiaxed grains with an average grain size of 2.0 μm and exhibited excellent mechanical properties in terms of a high flexure strength of 593 ± 55 MPa and a fracture toughness of 6.9 ± 0.2 MPa m^1/2. Furthermore, the ion-irradiation damage in SiC ceramics was investigated by irradiating with 1.2 MeV Si⁵⁺ ions at 650 °C using a fluence of 1.1 × 10¹⁶ ions/cm², which corresponds to 6.3 displacements per atom (dpa). The evolution of the microstructure was investigated by X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy. The breaking of Si–C bonds and the segregation of C elements on the irradiated surface was revealed by XPS, whereas the formation of Si–Si and C–C homonuclear bonds within the Si–C network of SiC grains was detected by Raman spectroscopy.     

中国抗中子辐照钢的抗辐照设计与验证

聚变堆等未来先进核能系统要求材料在强流高能中子辐照下长期保持良好的结构稳定性和机械性能。为适应未来先进核能技术发展的需要，中国科学院核能安全技术研究所•凤麟团队牵头研发了具有我国自主知识产权的中国抗中子辐照钢--CLAM钢。CLAM钢的设计考虑了未来核能清洁性的要求，以及苛刻服役环境中材料抗辐照、耐高温、耐腐蚀等性能要求。通过中子学计算分析设计了低活化成分范围，基于选择性纳米相析出进行了抗辐照、耐高温性能优化设计。针对材料的抗辐照性能，利用国内外中子、离子、电子及等离子体辐照设施开展了系列辐照考验研究，通过多角度表征辐照前后材料的微观结构和宏观性能，综合评估了材料的辐照性能，并与国际上同类材料在相近或相同条件下的辐照性能进行了对比分析，结果表明CLAM钢具有良好的抗辐照性能。     

瞬态闭锁试验在0.13 μm大规模集成电路中引起的潜在损伤

瞬态剂量率辐射试验会引起集成电路发生损伤或失效，其原因至少有两种：闭锁大电流引起的电路内部金属互连熔融；累积电离总剂量引起的氧化层电荷造成阈值电压偏移。本文以一种0.13 μm体硅CMOS处理器为对象，研究了瞬态剂量率和稳态电离总剂量辐射效应规律。结果表明：瞬态剂量率闭锁效应对处理器造成了显著的潜在损伤，导致其总剂量失效阈值从1 030 Gy(Si)降低至600 Gy(Si)。研究结论对于大规模集成电路的可靠性评估和指导辐射加固设计有重要参考意义。     

基于线状特性的大数据地铁用户识别与定位方法

从MR原始数据特性出发研究数据结构与字段,采用基于线状特性分析MR大数据的地铁用户识别过滤方法和位置定位方法,该方法经过验证能够有效输出地铁用户数据并定位在地图上,解决人工地铁测试效率低且测试片面的问题,实现基于大数据的自动化地铁网络评估,提供海量的有效定位数据用于地铁评估与分析。     

基于新型光纤的传输技术探讨

随着互联网的高速发展，引发了网络流量、电信骨干网流量急速增长，使得网络容量的提升迫在眉睫。目前，100G 系统已经商用，超100G系统能够更有效地解决流量和网络带宽持续增长带来的压力，本文主要介绍新型单模光纤的特性以及探讨基于新型单模光纤的传输技术。     

A robust locating multi-optima approach for damage identification of plate-like structures

This paper proposes a robust optimization approach for multiple damage identification of plate-like structures. Different from traditional particle swarm optimizations (PSOs), a combined PSO and niche technique (NPSO) is proposed to solve multimodal optimization problems, with the full consideration of subswarm creation, merging and absorbing mechanism. As a hypersensitive parameter to damage, the curvature mode shape is adopted to construct the objective function. Case studies are conducted to investigate the effectiveness and robustness of the algorithm on multi-damage identification. Simulation results show that the proposed algorithm exhibits robust search performance on identifying damage locations accurately with good convergence behavior. It is hoped that this study can provide guidance on robust damage detection, especially when the structure is subject to multiple damages and external disturbances.     

深井超深井钻井堵漏材料高温老化性能评价

深井超深井钻进裂缝性油气层极易发生频繁的钻井液漏失,造成严重储层损害和重大经济损失。储层段钻进过程中经常发生重复性漏失,意味着仅用酸溶率、粒度分布等常规堵漏材料评价指标已不能满足钻井液漏失控制工程需要。笔者以塔里木盆地克深气田钻井常用的核桃壳、毫米级碳酸钙为研究对象,开展了堵漏材料高温老化评价实验。实验评价结果表明,在180℃的柴油中老化24 h后,核桃壳的颜色由黄色变成黑色,质量损失率为25.16 % ,摩擦系数下降28.24 % ,抗压强度下降21.21 % ;毫米级碳酸钙的颜色由白色变成淡黄色,质量损失率为2.47 % ,摩擦系数下降1.33 % ,抗压强度基本不变;由高温老化后的核桃壳和毫米级碳酸钙所形成的封堵层,其承压能力下降了48.84 % 。分析指出,堵漏材料高温老化失效是深井超深井裂缝封堵层结构破坏并在储层段发生重复性漏失的一个重要因素。     

Tibetan Multi-Dialect Speech and Dialect Identity Recognition

Tibetan language has very limited resource for conventional automatic speech recognition so far. It lacks of enough data, sub-word unit, lexicons and word inventories for some dialects. And speech content recognition and dialect classification have been treated as two independent tasks and modeled respectively in most prior works. But the two tasks are highly correlated. In this paper, we present a multi-task WaveNet model to perform simultaneous Tibetan multi-dialect speech recognition and dialect identification. It avoids processing the pronunciation dictionary and word segmentation for new dialects, while, in the meantime, allows training speech recognition and dialect identification in a single model. The experimental results show our method can simultaneously recognize speech content for different Tibetan dialects and identify the dialect with high accuracy using a unified model. The dialect information used in output for training can improve multi-dialect speech recognition accuracy, and the low-resource dialects got higher speech content recognition rate and dialect classification accuracy by multi-dialect and multi-task recognition model than task-specific models.     

Specific emitter identification for satellite communication using probabilistic neural networks

Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military.     

Vibration analysis and identification of breathing cracks in beams subjected to single or multiple moving mass using online sequential extreme learning machine

In this paper, a novel approach was presented to vibration analysis and identification of breathing cracks in Timoshenko beam under single or multiple moving mass. Dynamic strain energies (DSEs) and translational accelerations in beam structures under moving mass were used as forward problem and application of an emergent learning algorithm called the online sequential extreme learning machine algorithm as inverse problem to predict crack depths and locations. To demonstrate the potential of the proposed vibration analysis over existing ones, two validation studies have been done. To evaluate the proposed method to identify breathing cracks, two examples, namely, clamped–clamped beam and two span continuous beams have been studied. Also, the effect of the discrepancy in stiffness between the finite-element model and the actual tested dynamic system has been investigated. Another examination has been performed in which moving mass with different speeds are utilized. Also, the effect of multi mass passing through the beam has been studied. The obtained results indicated that the proposed method could identify the breathing cracks existence and severity in the beam under moving mass using DSE and accelerations, which may be noisy or noise free.