Meshless acoustic analysis using a weakly singular Burton-Miller boundary integral formulation

The Burton-Miller boundary integral formulation is solved by a complex variable boundary element-free method (CVBEFM) for the boundary-only meshless analysis of acoustic problems with arbitrary wavenumbers. To regularize both strongly singular and hypersingular integrals and to avoid the computation of the solid angle and its normal derivative, a weakly singular Burton-Miller formulation is derived by considering the normal derivative of the solid angle and adopting the singularity subtraction procedures. To facilitate the implementation of the CVBEFM and the approximation of gradients of the boundary variables, a stabilized complex variable moving least-square approximation is selected in the meshless discretization procedure. The results show the accuracy and efficiency of the present CVBEFM and reveal that the method can produce satisfactory results for all wavenumbers, even for extremely large wavenumbers such as k = 10 000.     

卷积神经网络用于近红外光谱古筝面板木材分级

目前,我国乐器制作行业在古筝面板用木材等级的筛选上主要依赖于技师主观评判,但此法缺少科学理论的依据,效率低,客观性及出材率的提高等方面受到限制,无法满足乐器市场的大量需求。实现古筝面板用木材快速、智能化的分级工作是一个急需解决的课题。近红外光谱非常适用于测量含氢的有机物质。古筝面板木材主要化学成分的化学键均由含氢基团组成,不同等级板材的化学成分存在差异,这些差异反映在近红外光谱中,为判断木材等级提供了可能。同时卷积神经网络对非线性数据具有较强的特征提取能力,所以提出一种应用卷积神经网络模型对光谱数据进行分析的方法,进而判别木材的等级。应用了Savitzky Golay一阶、二阶微分两种预处理方法和核主成分分析、连续投影算法两种数据压缩方法,通过所设计的卷积神经网络模型以样本识别准确率和模型构建过程中的损失值作为判定指标选出最佳预处理和数据压缩方法。为了提高模型提取分析光谱数据的能力和避免过拟合现象,应用了多通道卷积核、批量归一化和early stopping策略,将通过两层卷积层提取的特征信息送入全连接层,从而充分提取剩余信息,通过Softmax函数获得板材的最终预测等级,从而确定了最终模型。最终Savitzky Golay一阶微分和核主成分分析为最佳数据处理方法,同时得出用于区分不同等级的古筝面板用木材的主要关键谱带,分别为1 163~1 243, 1 346~1 375和1 525~1 584 nm。将该模型应用于测试集样本,古筝面板用木材的等级识别准确率为95.5%。实验结果表明所提出的方法可以高效地处理光谱数据,有效识别区分不同等级的古筝面板用木材的关键特征,从而为广阔的乐器市场提供一定的技术支持。     

Path to achieving molecular dispersion in a dense reactive mixture

A uniform dispersion of reactants is necessary to achieve a complete reaction involving multicomponents. In this study, we have examined the role of plasticizer in the reaction of two seemingly unlikely reactants: a highly crystalline hexamethylenetetramine (HMTA) and a strongly hydrogen bonded phenol formaldehyde resin. By combining information from NMR, infrared spectroscopy and differential scanning calorimetry, we were able to determine the role of specific intermolecular interactions necessary for the plasticizer to dissolve the highly crystalline HMTA and to plasticize the phenol formaldehyde resin in this crosslinking reaction. The presence of the plasticizer increased the segmental mobility, disrupted the hydrogen bonded matrix, and freed the hydroxyl units, which further increased the solubility of the HMTA. Both the endothermic and exothermic transitions are accounted for in the calorimetric data obtained. For the first time, it is possible to obtain the effective molar ratio of each component needed to complete the crosslinking reaction efficiently. © 2015 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2015 , 53, 1519–1526     

非均匀波导中的声聚焦

理论研究了声波在非均匀波导中的空间聚焦问题,利用多模态导纳法构建波导内任意位置处声压与入射声压在模态域的映射关系,计算使声波聚焦于空间某位置时的最佳入射波,并画出了相应的聚焦声场.研究了三种非均匀情况:水平变截面波导、含散射体波导以及声速垂直变化波导.结果表明,当输入最佳入射波时,在非均匀波导中可以产生良好的单点或多点声聚焦效果,声波的聚焦过程充分地利用了波导结构及介质非均匀性对声波的散射作用.     

Eu:Lu2O3透明闪烁陶瓷的制备与性能

采用湿化学法合成了Eu原子掺量5%的Lu₂O₃陶瓷前驱体,通过SEM、XRD研究了煅烧前后前驱体和1 100 ℃煅烧4 h后粉体的形貌、结构以及物相。结果表明煅烧后的粉体为纳米类球形、高分散且结晶性良好的颗粒。颗粒尺寸为68.5 nm。使用煅烧后的粉体为原料,在1 650 ℃真空烧结30 h制备了高透过率的Eu:Lu₂O₃陶瓷,晶粒尺寸为46 μm,在611 nm处的直线透过率可以达到66.3%。此外对陶瓷的吸收曲线、光致激发和发射光谱特性以及X射线激发发射光谱进行研究。可观察到,Eu:Lu₂O₃陶瓷存在基质和激活离子两类吸收,光致发光光谱和X射线激发发射光谱均可以看出Eu:Lu₂O₃陶瓷存在极强的⁵D₀→⁷F₂跃迁发光,位于611 nm处。对比商业的BGO单晶的X射线发射光谱,可得本实验中制备的陶瓷的光输出为85 000 ph/MeV。Eu:Lu₂O₃陶瓷本身有着高X射线以及高能粒子的阻止能力,结合高光输出特性,表明Eu:Lu₂O₃陶瓷在X射线成像等领域具有巨大的潜在应用价值。     

强度解调型光纤光栅温度传感实验

光纤光栅在工程上应用广泛,但由于存在解调系统复杂、成本高,尤其是需要使用光纤光谱仪等波长解调仪器,使得光纤光栅很难走入本科教学的实验课堂.本文提出了一种基于窄线宽DFB激光器的强度解调方案,极大地简化了光纤光栅传感器的解调系统,完全满足较高分辨率和实时检测的系统要求.合理安排的温度传感实验可以非常直观地展示光纤光栅的线性传感性能,使得工程化的光纤光栅传感技术轻松走进本科教学课堂,具有极高的推广和应用潜力.     

基于图像重组和比特置乱的多图像加密

针对现有多图像加密算法只能同时加密多张同类型同大小的图像,适用范围不广、实用性差等问题,提出一种基于图像重组和比特置乱的多图像加密算法.该算法通过将任意数量、不同大小和不同类型的图像重新组合成新多灰度图,一次完成同时加密,极大提高了加密效率和适用范围.首先,依次提取所有待加密图像像素值重新组合出N张m×n新灰度图,并将其转化成m×n×8N二进制矩阵.然后,采用3D比特置乱方式,对高位页进行行列比特置乱,低位页进行整页比特置乱.最后,进行异或扩散操作,得到密文图像.高低位分开置乱提高了算法的抗噪声能力,最终密文信息熵达到7.999以上,很好地掩盖了明文的统计特性.构造一种新型Logistic与广义三阶Fibonacci级联的混沌系统产生随机序列,增加了初值和控制参数范围,扩大了密钥空间,使其达到8×10^84以上,极大地提高了抗穷举攻击能力.既提高了序列随机性,又同时保留了低维混沌系统的快速性.结合明文哈希值(SHA-256)产生密钥,明文像素值发生微小改变后密文像素值变化率达到0.996以上,极大地提高了的明文敏感性和算法抗选择明文攻击的能力.实验分析表明,提出的多图像加密算法安全性高、实用性强.     

基于Retinex理论与概率非局部均值的红外图像增强方法

针对传统红外图像增强算法中细节模糊及过度增强的问题,提出了一种基于Retinex理论与概率非局部均值相结合的红外图像增强方法.首先通过单尺度Retinex方法调整图像中过暗与过亮部分的灰度级;然后利用概率非局部均值对图像进行分解处理得到基本层与细节层,对基本层采用直方图均衡化拉伸对比度,对细节层采用非线性函数进行增强;最后,将不同层次的结果融合得到对比度与细节增强的红外图像.用该方法对多组不同场景的红外图像进行仿真实验,并将其与多种增强方法进行主、客观对比分析,结果表明所提方法在红外图像的细节及对比度增强方面都获得了更好的效果.     

Measured and Predicted Solubility Phase Diagrams of Quaternary Systems LiBr-NaBr-MgBr2-H2O and LiBr-KBr-MgBr2-H2O at 298.15 K

The stable phase equilibria of quaternary systems LiBr-NaBr-MgBr₂-H₂O and LiBr-KBr-MgBr₂-H₂O at 298.15 K were studied by both experimental measurement(isothermal solution saturation method) and theoretical prediction(Pitzer model). The solubilities of the saturated solution have been determined experimentally and two stable phase diagrams and relevant water diagrams of the two quaternary systems were obtained. Results show that quaternary system LiBr-NaBr-MgBr₂-H₂O is hydrate II type as NaBr and NaBr·2H₂O coexistence. Its phase diagram consists of only one invariant point, four univariant curves, and five crystallization fields. The quaternary system LiBr-KBr-MgBr₂-H₂O is a complex type as the double salt KBr·MgBr₂·6H₂O formed. In addition to this double salt, the three single salts LiBr·2H₂O, KBr and MgBr₂·6H₂O also crystallize. In this paper, the solubilities of phase equilibria in above quaternary systems were also calculated by the Pitzer's electrolyte solution model. All the needed parameters can be obtained from the literature or be fitted by experimental data. On the Basis of the experimental and calculated results, the phase diagram of the quaternary system was plotted for comparison. It shows that the calculation results are consistent with the experimental ones.     

基于深度学习的ELM实时识别研究

基于深度学习的方法，在HL-2A装置上开发出了一套边缘局域模(ELM)实时识别算法。算法使用5200次放电数据(约24.19万数据切片)进行学习，得到一个深度为22层的卷积神经网络。为衡量算法的识别能力，识别了HL-2A装置自2009年实现稳定ELMy H模放电以来所有历史数据(约26000次放电数据)，共识别出1665次H模放电，其中误识别35次，误报率为2.10%。在实际的1634次H模放电中，漏识别4次，漏识别率为0.24%。该误报率和漏报率可以满足ELM实时识别的精度要求。识别算法在实时控制环境下，对单个时间点的平均计算时间为0.46ms，可以满足实时控制的计算速度要求。