初始啁啾对飞秒脉冲在光子晶体光纤中超连续谱产生的影响

采用分步傅里叶方法模拟了初始啁啾对光子晶体光纤中超连续谱产生的影响.根据光纤长度，将光子晶体光纤中脉冲的演化分成初始展宽、剧烈展宽和饱和展宽三个阶段.通过讨论啁啾脉冲和无初始啁啾脉冲在各阶段演化的区别，发现啁啾只在初始展宽和剧烈展宽阶段对光谱有影响，当β₂C<0时啁啾有利于光谱的展宽，当β₂C>0时则刚好相反，在饱和展宽阶段啁啾不再对光谱产生影响.要想利用啁啾脉冲来获得较宽的光谱，必须选择合适的光纤长度，使输出的脉冲处于剧烈展宽阶段.这为利用啁啾脉冲在光子晶体光 关键词： 超连续谱 光子晶体光纤 分步傅里叶法 啁啾     

3004铝合金“反常”锯齿屈服现象的研究

在应变速率为5.56×10^-5s^-1—5.56×10^-3s^-1的范围内，在不同温度下(从223K至773K)，对3004铝合金进行系列拉伸试验，探索其锯齿屈服规律；通过激活能的计算、内耗研究、微观组织观察和能谱分析，探讨锯齿屈服的机理与物理本质.结果表明，3004铝合金在形变过程中会出现动态应变时效现象；发现了一种“反常”的锯齿屈服现象：在出现锯齿屈服的温区内，存在锯齿屈服临界应变量转变温度T_t， 关键词： 动态应变时效 锯齿屈服 铝合金 内耗     

微纳米加工技术在纳米物理与器件研究中的应用

物质在纳米尺度下可能呈现出与体材料不同的物理特件，这正是纳米科技发展的基础之一。要想探索在纳米尺度下材料物埋性质的变化规律及可能的应用领域，离不开相应的技术手段，微纳米加工技术作为当今高技术发展的重要技术领域之一，是实现功能人工纳米结构与器件微纳米化的基础。本文根据几个不同的应用领域，介绍了微纳米加工技术在纳米物理与器件研究领域的应用。     

虚光场在原子的周期衰变和回复效应中的影响

本文利用非相对论量子电动力学理论, 研究了在旋波近似(R w A ) 和非旋波近似下与双光子场作用的二能级原子算符的时间演化规律, 通过数值法和解析法论证了虚光场是原子的周期衰变和回复呈现量子噪声的根源, 并且说明了可忽略虚光场对回复时间影响的原因. 关键词：     

双齿配体—1,2—C_6H_4SX(X=O,S)的钴化合物~1H NMR研究

以~1H NMR谱学和磁化率对一些含邻苯二硫酚或巯基苯酚配体的钴化合物的磁性作了研究,并讨论了它们的电子结构。     

交叉验证多路径匹配追踪水声矢量阵稀疏方位估计

水下运动目标的高分辨DOA估计和目标的左右舷分辨问题一直是水声阵列信号处理中的一个核心问题。矢量阵相比于声压阵具有天然的左右舷分辨能力和更高的处理增益,近年来得到了广泛关注。Capon等一些传统高分辨处理方法存在不能解相干源、需要多快拍处理以及对阵列流行误差敏感等多种问题。针对水声阵列信号处理领域面临的以上问题,利用声呐工作场景中空间目标的稀疏性,本文提出了一种基于交叉验证技术的多路径匹配追踪（Multiplepath Matching Pursuit with Cross Validation,CV-MMP）声矢量阵稀疏DOA估计算法。该算法采用交叉验证技术可以在未知场景中目标个数的条件下实现稀疏DOA的估计,相比于常规的声矢量阵Capon算法而言,可以在小快拍数甚至单快拍数条件下实现多目标的稀疏DOA估计以及高分辨能力。仿真和海试试验数据处理验证了提出的算法的有效性。     

Multi-Sensor Vibration Signal Based Three-Stage Fault Prediction for Rotating Mechanical Equipment

In order to reduce maintenance costs and avoid safety accidents, it is of great significance to carry out fault prediction to reasonably arrange maintenance plans for rotating mechanical equipment. At present, the relevant research mainly focuses on fault diagnosis and remaining useful life (RUL) predictions, which cannot provide information on the specific health condition and fault types of rotating mechanical equipment in advance. In this paper, a novel three-stage fault prediction method is presented to realize the identification of the degradation period and the type of failure simultaneously. Firstly, based on the vibration signals from multiple sensors, a convolutional neural network (CNN) and long short-term memory (LSTM) network are combined to extract the spatiotemporal features of the degradation period and fault type by means of the cross-entropy loss function. Then, to predict the degradation trend and the type of failure, the attention-bidirectional (Bi)-LSTM network is used as the regression model to predict the future trend of features. Furthermore, the predicted features are given to the support vector classification (SVC) model to identify the specific degradation period and fault type, which can eventually realize a comprehensive fault prediction. Finally, the NSF I/UCR Center for Intelligent Maintenance Systems (IMS) dataset is used to verify the feasibility and efficiency of the proposed fault prediction method.     

Breather molecules and localized interaction solutions in the (2+1)-dimensional BLMP equation

The (2+1)-dimensional Boiti–Leon–Manna–Pempinelli (BLMP) equation is an important integrable model. In this paper, we obtain the breather molecule, the breather-soliton molecule and some localized interaction solutions to the BLMP equation. In particular, by employing a compound method consisting of the velocity resonance, partial module resonance and degeneration of the breather techniques, we derive some interesting hybrid solutions mixed by a breather-soliton molecule/breather molecule and a lump, as well as a bell-shaped soliton and lump. Due to the lack of the long wave limit, it is the first time using the compound degeneration method to construct the hybrid solutions involving a lump. The dynamical behaviors and mathematical features of the solutions are analyzed theoretically and graphically. The method introduced can be effectively used to study the wave solutions of other nonlinear partial differential equations.     

时域压缩合成孔径超分辨水声成像算法

提出一种针对水下稀疏目标的时域压缩合成孔径声呐成像方法(TC-SAS),实现了水声目标高分辨实时成像。通过多子阵的孔径合成,在时域上构造出成像网格格点到有效孔径内逐帧阵列的格林函数,并给出成像区域散射强度到数据域的映射矩阵;然后利用该区域空域稀疏的先验知识,通过正交匹配追踪的稀疏重构方式,解算出成像区域散射系数矩阵,实现了稀疏目标高分辨成像.同时,针对线性调频信号提出数据缩减的方法,通过对观测数据和字典矩阵同时脉压后截取,减小了数据规模;进一步结合二维矩阵数表查表的方法,以空间换时间,实现了区块实时成像。数值仿真以及湖试试验表明,所提算法能分辨出传统的时延求和算法难以分辨的目标,并且在图像清晰度指标上平均提升4.9 dB.改善了合成孔径声呐的成像质量.     

Assessing Earthquake Forecast Performance Based on b Value in Yunnan Province,China

Many studies have shown that b values tend to decrease prior to large earthquakes. To evaluate the forecast information in b value variations, we conduct a systematic assessment in Yunnan Province, China, where the seismicity is intense and moderate–large earthquakes occur frequently. The catalog in the past two decades is divided into four time periods (January 2000–December 2004, January 2005–December 2009, January 2010–December 2014, and January 2015–December 2019). The spatial b values are calculated for each 5-year span and then are used to forecast moderate-large earthquakes (M ≥ 5.0) in the subsequent period. As the fault systems in Yunnan Province are complex, to avoid possible biases in b value computation caused by different faulting regimes when using the grid search, the hierarchical space–time point-process models (HIST-PPM) proposed by Ogata are utilized to estimate spatial b values in this study. The forecast performance is tested by Molchan error diagram (MED) and the efficiency is quantified by probability gain (PG) and probability difference (PD). It is found that moderate–large earthquakes are more likely to occur in low b regions. The MED analysis shows that there is considerable precursory information in spatial b values and the forecast efficiency increases with magnitude in the Yunnan Province. These results suggest that the b value might be useful in middle- and long-term earthquake forecasts in the study area.