MoO＿3／（TiO＿2－SiO＿2）催化剂的表面分散状态及催化性能的研究

用气相流动吸附法（ｇｒａｆｔｉｎｇ）制备复合载体，用浸渍法（ｉｍｐｒｅｇｎａｔｉｏｎ）制备ＭｏＯ３／（ＴｉＯ２－ＳｉＯ２）催化剂．应用ＬＲＳ和ＴＰＲ技术研究ＭｏＯ３在复合载体ＴｉＯ２－ＳｉＯ２表面的分散状态，发现ＴｉＯ２在ＳｉＯ２表面的分散可增强ＭｏＯ３与载体之间的相互作用，提高ＭｏＯ３在载体表面的分散阈值．催化剂的活性评价在固定床中压反应装置中进行，以６９％（ｗｔ）环己烷、２０％（ｗｔ）的环己烯、１０％（ｗｔ）的苯、１％（ｗｔ）的噻吩混合液为反应液，以噻吩、环己烯和苯的转化率作为催化剂的ＨＤＳ、ＨＹＤ、ＢＨＤ活性指标．结果表明，经ＴｉＯ２调变后，其ＨＤＳ、ＨＹＤ、ＢＨＤ活性都较原来高，对于不同ＭｏＯ３含量的ＭｏＯ３／（ＴｉＯ２－ＳｉＯ２），ＨＤＳ、ＨＹＤ催化性能测试发现，当ＭｏＯ３含量低于分散阈值时，其ＨＤＳ、ＨＹＤ活性随ＭｏＯ３含量线性上升，但在高于分散阈值后，几乎保持不变．该催化剂对苯几乎没有加氢活性，显示出很高的环己烯加氢选择性．通过分散阈值与其ＨＤＳ、ＨＹＤ活性的关系可知，分散阈值可作为优化加氢精制催化剂配比的一个重要参数，具有较强的实际意义     

A Spiking Neural Network Based on Retinal Ganglion Cells for Automatic Burn Image Segmentation

Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is essential to calculate the patient’s water requirement based on the percentage of the burn wound area in the total body surface area (TBSA%). However, burn wounds are so complex that there is observer variability by the clinicians, making it challenging to locate the burn wounds accurately. Therefore, an objective, accurate location method of burn wounds is very necessary and meaningful. Convolutional neural networks (CNNs) provide feasible means for this requirement. However, although the CNNs continue to improve the accuracy in the semantic segmentation task, they are often limited by the computing resources of edge hardware. For this purpose, a lightweight burn wounds segmentation model is required. In our work, we constructed a burn image dataset and proposed a U-type spiking neural networks (SNNs) based on retinal ganglion cells (RGC) for segmenting burn and non-burn areas. Moreover, a module with cross-layer skip concatenation structure was introduced. Experimental results showed that the pixel accuracy of the proposed reached 92.89%, and our network parameter only needed 16.6 Mbytes. The results showed our model achieved remarkable accuracy while achieving edge hardware affinity.     

DA-LSTM-VAE: Dual-Stage Attention-Based LSTM-VAE for KPI Anomaly Detection

To ensure the normal operation of the system, the enterprise’s operations engineer will monitor the system through the KPI (key performance indicator). For example, web page visits, server memory utilization, etc. KPI anomaly detection is a core technology, which is of great significance for rapid fault detection and repair. This paper proposes a novel dual-stage attention-based LSTM-VAE (DA-LSTM-VAE) model for KPI anomaly detection. Firstly, in order to capture time correlation in KPI data, long–short-term memory (LSTM) units are used to replace traditional neurons in the variational autoencoder (VAE). Then, in order to improve the effect of KPI anomaly detection, an attention mechanism is introduced into the input stage of the encoder and decoder, respectively. During the input stage of the encoder, a time attention mechanism is adopted to assign different weights to different time points, which can adaptively select important input sequences to avoid the influence of noise in the data. During the input stage of the decoder, a feature attention mechanism is adopted to adaptively select important latent variable representations, which can capture the long-term dependence of time series better. In addition, this paper proposes an adaptive threshold method based on anomaly scores measured by reconstruction probability, which can minimize false positives and false negatives and avoid adjustment of the threshold manually. Experimental results in a public dataset show that the proposed method in this paper outperforms other baseline methods.     

A Useful Criterion on Studying Consistent Estimation in Community Detection

In network analysis, developing a unified theoretical framework that can compare methods under different models is an interesting problem. This paper proposes a partial solution to this problem. We summarize the idea of using a separation condition for a standard network and sharp threshold of the Erdös–Rényi random graph to study consistent estimation, and compare theoretical error rates and requirements on the network sparsity of spectral methods under models that can degenerate to a stochastic block model as a four-step criterion SCSTC. Using SCSTC, we find some inconsistent phenomena on separation condition and sharp threshold in community detection. In particular, we find that the original theoretical results of the SPACL algorithm introduced to estimate network memberships under the mixed membership stochastic blockmodel are sub-optimal. To find the formation mechanism of inconsistencies, we re-establish the theoretical convergence rate of this algorithm by applying recent techniques on row-wise eigenvector deviation. The results are further extended to the degree-corrected mixed membership model. By comparison, our results enjoy smaller error rates, lesser dependence on the number of communities, weaker requirements on network sparsity, and so forth. The separation condition and sharp threshold obtained from our theoretical results match the classical results, so the usefulness of this criterion on studying consistent estimation is guaranteed. Numerical results for computer-generated networks support our finding that spectral methods considered in this paper achieve the threshold of separation condition.     

基于模糊理论的空间屏蔽滤波信号去噪

基于模糊理论提出了一种空间屏蔽滤波信号去噪方法.对信号进行二进小波变换后,对信号进行空间屏蔽滤波,对于未通过滤波器的系数进行模糊阈值处理实现去噪.通过对含噪HeaviSine和Doppler信号的去噪仿真,表明,该方法对噪音具有良好的抑制作用,能较好地保持信号在间断点处的信息.与传统空间屏蔽滤波相比,该方法对不同种类的含噪信号均具有良好的去噪效果.     

150 fs~10 ps脉宽下熔石英激光损伤的仿真与分析

基于约化电子数密度增长速率方程,建立了熔石英导带电子数密度随脉冲持续时间变化的模型。利用电子数临界密度这一概念,得到了150 fs~10 ps脉宽下,熔石英激光损伤阈值范围。分析表明,5~10 ps,雪崩电离仍然起主要作用,而光致电离提供的初始电子使雪崩电离不再依赖材料原有的初始电子;当脉宽减小到约为4 ps时,光致电离与雪崩电离作用相等;之后,光致电离起主要作用。通过仿真出的损伤阈值拟合,得到了该脉宽区间下新的脉宽定律：熔石英的损伤阈值正比于脉宽的0.38次方;考虑温度对熔石英损伤阈值的影响,熔石英的损伤阈值正比于脉宽的0.34次方。     

连续波多普勒引信雷电电磁脉冲辐照效应

为了研究雷电电磁脉冲场对连续波多普勒引信安全性能的影响,确定引信的能量耦合通道和效应规律,构建了引信雷电电磁脉冲辐照试验系统,并开展了该引信雷电电磁脉冲辐照效应试验。试验结果表明：受试引信最佳能量耦合姿态为引信竖直向上,弹体轴线与辐射场传播方向垂直,三角环天线平面垂直于辐射场传播方向; 主要能量耦合通道为弹体; 不同辐照波形下引信的临界干扰场强不同,在配用152 mm弹体时,1.2/50,5.4/70,0.25/100和10/350 s波形的临界干扰场强分别约为74,60,65和75 kV/m。雷电电磁脉冲对受试引信的作用机理为：辐射能量通过弹体耦合到执行电路输入端,由于感应信号脉冲宽度满足触发脉冲宽度的最低要求,从而推动引信执行电路误动作。     

铷蒸气激光器3D理论模型的建立及阈值特性模拟

基于三能级速率方程理论,结合惠更斯-菲涅尔衍射积分公式,利用分步光束传播方法建立了半导体泵浦碱金属蒸气激光器(DPAL)端面泵浦3D理论计算模型,用于模拟研究铷蒸气激光器的泵浦阈值特性。该模型将光束传播、光与物质相互作用独立考虑,利用迭代算法求解。考虑激光器腔内模式与泵浦光模式匹配对阈值特性的影响,模拟了单端泵浦铷蒸气激光器内部的三维动力学过程。在具体算例中研究了模式匹配最佳时,在阈值工作状态下铷蒸气室中的三维粒子数分布,以及沿不同轴线的增益分布。仿真了在最佳模式匹配位置附近,泵浦光模式变化对阈值特性的影响。根据蒸气池中粒子数分布和光场分布具体分析了模式匹配影响阈值特性的机理。同时还模拟了不同长度蒸气池对阈值特性的影响。结果表明,腔参数与泵浦光模式之间存在一定的关系,对于特定的泵浦光模式,存在恰当的腔参数,使得阈值达到最小。     

应用联合分析和混合回归模型进行市场细分

联合分析是一种有效地反映消费者需求差异的方法,所以被广泛地应用到市场细分的研究中。但是,传统的方法存在着一定的不足。本研究提出了一个残差分布假设不同的混合回归模型,模型估计的效率比较高,而且模型系数也必较可靠。所以不失为一个比较理想的市场细分分析工具。本文应用该模型方法对一个笔记本电脑联合分析案例进行了实证分析。     

高功率毫米波辐照鼠疼痛阈值及应激反应

 高功率8 mm波辐照时,大小鼠均产生热应激反应,依次经历平静期、警戒期、相持期和衰竭期。鼠的热痛阈值温度为(43.1±1.8) ℃,这一阈值与鼠的质量、初始温度、辐照功率密度等无关。 辐照3 s时：辐照功率密度为3.5 W/cm2时体表温升为(8.4±1.0) ℃;辐照功率密度为7.0 W/cm2时温升为(21.1±3.0) ℃。实验还测定了警戒期持续时间,结果表明动物应激反应的剧烈程度与警戒期持续时间相关。建立了小鼠警戒期持续时间与小鼠体表面积、有效辐照面积、辐照功率密度之间的关系,并将这一实验结果推广到大鼠。