蝙蝠喉部发声组织建模及发声机理*

建立蝙蝠发声组织模型对超声机理研究及在智能设备的应用具有重要意义。根据蝙蝠喉部发声组织结构特点,通过有限元方法构建了蝙蝠的3种不同发声组织模型,分析了尺寸、材料力学参数、组织结构和张力4个因素对发声组织特征频率的影响。结果表明,如果用人类声带,按比例缩小构建蝙蝠喉部组织模型,蝙蝠无法发出超声波。构建组织结构含甲状软骨和声带的半鼓状模型和只含声带的条状模型,两种模型的特征频率相近且在合理的参数域内均无法达到超声范围。而含膜条状模型的特征频率可以通过张力进行超声频率的调节,这与文献的实验结果一致。因此,可基于含膜条状模型对蝙蝠喉管发声组织进行建模及其发声机理研究。     

Microwave Pyrolysis and Electrochemical Supercapacitor of S-doped g-C_3N_4 Nanoparticles

In this study, S-doped g-C_3N_4 nanoparticles were successfully prepared by one-step solid-state microwave synthesis. The detailed characterizations through XRD, FT-IR, SEM and XPS were studied. In addition, the electrochemical properties as supercapacitor of the sample were tested by cyclic voltammetry(CV), galvanostatic charge-discharge(GCD) and electrochemical impedance spectroscopy(EIS) techniques. The results showed a high specific capacitance of 691 F/g at current density of 4 A/g in 2 M KOH + 0.15 M K_3[Fe(CN)_6] electrolyte. This study shows that the microwave synthesis is a promising way to design carbon-based electrodes for supercapacitor.     

埋地热油管道正常运行的数值模拟研究

对高凝原油管道输送的水力热力问题进行分析研究,掌握管线运行规律,保证管线安全经济运行有着重要意义.本文建立了埋地热油管道正常运行的数学模型,采用非结构化网格和有限容积法对该问题进行了研究,计算结果与实验测量值吻合良好.     

Intelligent Fault Diagnosis of Industrial Robot Based on Multiclass Mahalanobis-Taguchi System for Imbalanced Data

One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach’s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms.     

碘化铅单晶体生长新方法探索

本文分析了用布里奇曼法生长碘化铅单晶体的结晶过程。发现坩埚下降法生长碘化铅单晶体过程中,碘的蒸发与凝结、铅的分凝与沉淀是影响晶体质量的两个重要因素。提出了采用U型安瓿提拉方式生长碘化铅单晶体的新方法,有效抑制了碘的蒸发并排除了分凝的铅,获得了桔红色半透明的碘化铅晶体。经测试,晶体的结晶性好,红外透过率达45%,电阻率为1.7×1012Ω.cm,为制备性能优异的碘化铅室温核辐射探测器奠定了基础。     

燃烧剂对低温燃烧合成Pr-CeO2粉体结构与性能的影响

以乙酸铵和柠檬酸为燃烧剂,Ce(NO3)3·6H2O和Pr6O11为主要原料,采用低温燃烧法(LCS)制备了Ce0.95Pr0.5O2纳米晶粉体.用DSC、XRD、SEM及色度测试等手段研究了Ce0.95Pr0.5O2纳米晶微粒前驱体的着火温度、产物晶体结构、晶体形貌及色度.结果表明:乙酸铵和柠檬酸作为燃烧剂的反应前驱体着火温度分别在250℃和300℃左右.两种燃烧产物均为单一的萤石型固溶体.与柠檬酸相比,乙酸铵作为燃烧剂得到的燃烧产物结晶程度更完善、Pr离子进入CeO2晶格的含量更多、呈色更好,且颗粒的团聚程度变小.根据Scherrer公式计算,用两种燃烧剂制备产物的平均晶粒尺寸分别为20～30 nm和10～15 nm,为纳米晶颗粒.最后得到Ce0.95Pr0.5O2粉体的颗粒尺寸则在200～300 nm之间.乙酸铵与硝酸铈的最佳摩尔配比为2:1,柠檬酸与硝酸铈的最佳摩尔配比为3:1.     

用化学溶液方法在Ni-5at%W基底上制备La2Zr2O7过渡层的研究

La2Zr2O7(LZO)过渡层以其独特的物理化学性质越来越受到人们的关注。本文以乙酰丙酮镧和乙酰丙酮锆为前驱盐,丙酸为溶剂配置前驱液,用化学溶液方法(CSD)在具有立方织构的Ni-5at%W基底上制备了LZO过渡层薄膜。研究了前驱液成分、性质以及退火温度对LZO成相以及取向的影响。用常规XRD和X射线四环衍射仪分析了LZO薄膜的相成分和织构。结果显示,在1050℃下退火可以获得强立方织构的LZO薄膜,其中(222)峰的Phi扫描半高宽值为8.95°;(400)峰的Chi扫描半高宽值为6.8°。用高分辨扫描电子显微镜(FE-SEM)观察到LZO薄膜表面均匀致密,没有裂纹和空洞。     

Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval

Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods.     

Tailoring Pore Structure and Morphologies in Covalent Organic Frameworks for Xe/Kr Capture and Separation

As a rising star among porous solid materials, covalent organic frameworks(COFs) with excellent properties including but not limit to facilely controllable structure, high porosity, and multi-chemical functionality represent significant potential for efficient ¹²⁷Xe/⁸⁵Kr capture and separation. In this study, through tuning the length of the organic ligands, two-dimensional(2D) COF mate-rials with identical connection group but different pore properties, denoted as ATFG-COF and TpPa-COF with AA-stacking model and TpBD-COF with AB-stacking model were synthesized and tested for Kr and Xe adsorption for the first time. Adsorption measurements indicate that the narrower pore apertures and higher porosity are conducive for COF materials to capture Xe and Kr. Furthermore, the Henry's constant, isosteric heat of adsorption(Q_st), and ideal adsorbed solution theory(IAST) selectivity of ATFG-COF, the pore size of which is closest to the kinetic diameter of the Xe atom(0.41 nm) among 2D COF materials, were carried out based on the single component sorption isotherms. The results illustrate that the high isosteric heat values of Xe/Kr adsorption on ATFG-COF are 25 and 16 kJ/mol at room temperature, respectively. Henry's law predicts that the selectivity factor of Xe to Kr is 6.07, consistent with the adsorption selectivity(ca. 6) calculated based on the IAST.     

耦合FE/WB法在声分析中的应用

简要描叙FE法(finite element method)和WB法(wave based method)的理论背景以及耦合FE/WB法的数学基础.耦合FE/WB法利用两者的优势——FE法的广泛应用和WB法的高收敛特性,将FE模型中较大且几何简单的部分采用WB法代替.耦合模型具有相对较少的自由度.对于较高的频率还可以进行细分得到更高的计算精度,并利用模态缩减法进一步减少自由度数.数值算例结果表明,该耦合方法有潜力覆盖中频段的声分析.