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。     

粗糙线性空间

将粗糙集思想引入到线性空间。提出线性空间中子集的上近似和下近似的概念,给出上、下粗线性子空间与上、下粗不变子空间的概念,讨论粗糙集线性空间的有关性质。     

信息时代的铌酸锂晶体:进展与展望

铌酸锂晶体具有非线性效应、电光效应、声光效应、光折变效应、压电效应与热释电效应等多种物理特性,在表面声波器件、光电器件、声光器件等方面获得广泛的应用.经历了六十多年的发展,铌酸锂晶体历久弥新,随着材料特性的不断开发,新功能、新器件、新应用层出不穷,尤其是铌酸锂单晶薄膜在薄膜滤波器、集成光电器件等领域的性能具有明显优势,...     

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.     

Integral Operators Between Fock Spaces∗

In this paper, the authors study the integral operator Sφf(z) = Z C φ(z, w)f(w)dλα(w) induced by a kernel function φ(z, ·) ∈ F ∞α between Fock spaces. For 1 ≤ p ≤ ∞, they prove that Sφ : F 1 α → F p α is bounded if and only if sup a∈C kSφkakp,α < ∞, (?) where ka is the normalized reproducing kernel of F 2 α; and, Sφ : F 1 α → F p α is compact if and only if lim |a|→∞ kSφkakp,α = 0. When 1 < q ≤ ∞, it is also proved that the condition (?) is not sufficient for boundedness of Sφ : F q α → F p α . In the particular case φ(z, w) = eαzw?(z ? w) with ? ∈ F 2 α, for 1 ≤ q < p < ∞, they show that Sφ : F p α → F q α is bounded if and only if ? = 0; for 1 < p ≤ q < ∞, they give sufficient conditions for the boundedness or compactness of the operator Sφ : F p α → F q α.     

Global Stability to Steady Supersonic Rayleigh Flows in One-Dimensional Duct∗

Heat exchange plays an important role in hydrodynamical systems, which is an interesting topic in theory and application. In this paper, the authors consider the global stability of steady supersonic Rayleigh flows for the one-dimensional compressible Euler equations with heat exchange, under the small perturbations of initial and boundary conditions in a finite rectilinear duct.     

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.     

油品对DMCC甲醛排放的比较研究

为了研究甲醇燃料发动机尾气中甲醛的排放特征,在一台经过改装的组合燃烧发动机上,采用标准测试柴油(TF)和普通市场柴油(MF)进行了实验,比较了燃用不同油料时的甲醛排放特征.实验结果表明;在相同甲醇掺烧比时,两种油料均是在低负荷时甲醛排放最高,在中负荷时最低,在高负荷时甲醛排放居中,三种负荷下MF甲醛排放均比TF高;此外,实验还对比了相同负荷、不同甲醇掺烧比时的甲醛排放,均表现出MF的甲醛排放比TF高,在高负荷下甚至达到2.5倍;实验还表明单纯的依靠氧化催化转化器不能有效降低甲醛排放.     

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.