一种低损耗声表面波滤波器

本文从声表面波滤波器的基本原理出发，介绍了我们研制的低损耗声表面波滤波器，给出了实验结果，经实验证实有较好的频率特性。     

一个用于VHSIC连接线瞬态分析的电路模拟器

本文介绍了一个可对含连接线的电路进行瞬态分析的电路模拟器,该模拟器是在SPICE基础上经修改扩充而成的,连接线的处理采用了文献[1]的方法.文中介绍了方法的基本原理,讨论了数值Laplace反变换时参数的选择与误差的控制.实际VHSIC电路的试算结果表明,该模拟器是相当有效的.     

Tributary use and large-scale movements of grass carp in Lake Erie

Infrequent captures of invasive, non-native grass carp (Ctenopharyngodon idella) have occurred in Lake Erie over the last 30+ years, with recent evidence suggesting wild reproduction in the lake’s western basin (WB) is occurring. Information on grass carp movements in the Laurentian Great Lakes is lacking, but an improved understanding of large-scale movements and potential areas of aggregation will help inform control strategies and risk assessment if grass carp spread to other parts of Lake Erie and other Great Lakes. Twenty-three grass carp captured in Lake Erie’s WB were implanted with acoustic transmitters and released. Movements were monitored with acoustic receivers deployed throughout Lake Erie and elsewhere in the Great Lakes. Grass carp dispersed up to 236 km, with approximately 25% of fish dispersing greater than 100 km from their release location. Mean daily movements ranged from <0.01 to 2.49 km/day, with the highest daily averages occurring in the spring and summer. The Sandusky, Detroit, and Maumee Rivers, and Plum Creek were the most heavily used WB tributaries. Seventeen percent of grass carp moved into Lake Erie’s central or eastern basins, although all fish eventually returned to the WB. One fish emigrated from Lake Erie through the Huron-Erie Corridor and into Lake Huron. Based on our results, past assessments may have underestimated the potential for grass carp to spread in the Great Lakes. We recommend focusing grass carp control efforts on Sandusky River and Plum Creek given their high use by tagged fish, and secondarily on Maumee and Detroit Rivers.     

Evidence of successful river spawning by lake trout (Salvelinus namaycush) in the Lower Niagara River,Lake Ontario

Restoration of a wild-produced lake trout Salvelinus namaycush population in Lake Ontario has not been successful despite the adult population often meeting or exceeding restoration targets. Lack of high-quality spawning habitat in Lake Ontario is suggested as one impediment to recruitment of wild lake trout, although the quantity and location of spawning habitat is poorly understood. If high-quality spawning habitat is limited in Lake Ontario, lake trout may be using uncommon spawning locations such as rivers. Anecdotal angler accounts point to the Niagara River as a lake trout spawning location. To better understand the potential of the Niagara River as a spawning location, egg and juvenile fish collections were conducted 12–14 river kilometers from the mouth of the Niagara River from 2010 to 2012; and mature female lake trout with surgically implanted acoustic tags were monitored from 2015 to 2019. Genetic analyses confirmed 60% of collected eggs and 93% of collected post-hatch juvenile fish in the Niagara River were lake trout. Tagged female lake trout returned to the Niagara River over consecutive years during the spawning season. The short duration of lake trout presence in the river (mean = 56 days/year) suggests female lake trout use the Niagara River primarily for spawning. Diversity in spawning locations may provide lake trout population’s resilience against environmental variability through a portfolio effect. Improved identification of riverine spawning locations, including their overall contribution to wild recruitment, may be a useful tool for managers to restore a wild-produced population of lake trout in Lake Ontario.     

LearningADD: Machine learning based acoustic defect detection in factory automation

Defect inspection of glass bottles in the beverage industrial is of significance to prevent unexpected losses caused by the damage of bottles during manufacturing and transporting. The commonly used manual methods suffer from inefficiency, excessive space consumption, and beverage wastes after filling. To replace the manual operations in the pre-filling detection with improved efficiency and reduced costs, this paper proposes a machine learning based Acoustic Defect Detection (LearningADD) system. Moreover, to realize scalable deployment on edge and cloud computing platforms, deployment strategies especially partitioning and allocation of functionalities need to be compared and optimized under realistic constraints such as latency, complexity, and capacity of the platforms. In particular, to distinguish the defects in glass bottles efficiently, the improved Hilbert-Huang transform (HHT) is employed to extend the extracted feature sets, and then Shuffled Frog Leaping Algorithm (SFLA) based feature selection is applied to optimize the feature sets. Five deployment strategies are quantitatively compared to optimize real-time performances based on the constraints measured from a real edge and cloud environment. The LearningADD algorithms are validated by the datasets from a real-life beverage factory, and the F-measure of the system reaches 98.48 %. The proposed deployment strategies are verified by experiments on private cloud platforms, which shows that the Distributed Heavy Edge deployment outperforms other strategies, benefited from the parallel computing and edge computing, where the Defect Detection Time for one bottle is less than 2.061 s in 99 % probability.     

Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks

A hybrid model incorporating wavelet and radial basis function neural network is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of international electrotechnical commission 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for surface discharge classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, by “marrying” the wavelet to radial basis function neural network very high levels of classification are achieved. Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. A radial basis function neural network classifier was used to classify the surface discharge and assess the suitability of this feature vector in classification. This learning method is proved to be effective by applying the wavelet radial basis function neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and reliable.     

An enhanced,acoustic emission-based,single-fiber-composite test

In the single-fiber-composite (SFC) test, a fiber imbedded in a matrix is loaded in tension, resulting in a fragmentation of the fiber. In the conventional version of this test, the final fiber fragmentation length distribution is used with a micro-mechanical model to determine the average fiber/matrix interfacial shear stress. In the enhanced version of this test, one also determines the applied stress at each fiber fracture, and from this, one can evaluate the strength of the fiber at short gage lengths. In our measurement system, we utilize an acoustic emission (AE) technique to detect the fiber fractures and to locate the fiber breaks and so determine both the fiber failure stresses as well as the fiber fragmentation lengths while the test is in progress. Critical to the success of this test is a broadband AE system that utilizes point-like AE sensors, procedures for evaluatingin situ, the wavespeed of the first wave arrival and signal processing techniques for determining the arrival time of this signal as precisely as possible for a broad range of wave shapes. Here we describe the application of such an enhanced SFC test procedure to investigate the failure of a Nicalon fiber in an epoxy matrix.     

Microwave surface resistance of epitaxial YBa2Cu3O7 thin films at 18.7 GHz measured by a dielectric resonator technique

We used a dielectric resonator technique for highly sensitive measurements of the temperature dependence of the microwave surface resistanceR _s of 1×1 cm² superconducting films at 18.7 GHz. It consists of a sapphire disc positioned on the film under investigation within a copper cavity which is acting as a radiation shield. In the TE₀₁ oscillation mode the highly reproducible quality factor of about 10⁵ results in a sensitivity of ±50 forR _s measurements. The temperature dependence ofR _s can be measured up to values as high as 1 . We have investigated several YBa₂Cu₃O₇ thin films prepared by high oxygen pressure d.c. sputtering on LaAlO₃ and NdGaO₃. Our best films exhibit a pronounced nonlinear behavior of the d.c. resistivity(T) with(300K)/(100K) values of about 3.7. Those films show, besides the initial fall-off just belowT _c , a further strong decrease ofR _s at low temperatures. This was observed both at 18.7 GHz and 87 GHz, as measured by a conventional cavity end plate replacement technique. ForTT_c/2 these films exhibit an exp (–T _c/T) dependence ofR _s with-values around 0.4. These observations may be explained by a superconducting energy gap with 2/kT _c0.8 for charge carriers localized in the CuO chains for YBa₂Cu₃O₇.     

基于AE的砂轮智能修整技术研究

本文基于声发射(AE,Acoustic Emission)研究了高精度磨削过程中砂轮的在线智能修整技术。在分析砂轮磨损和修整机理的基础上探讨了声发射技术在砂轮磨损检测中的应用,着重研究了砂轮修整过程中砂轮修整进给量的控制、运动的控制、AE在线智能修整系统的流程和砂轮在线智能补偿的分析。     

全封闭大功率永磁牵引电机的温度场数值计算

为研究全封闭大功率永磁牵引电机额定工况下的稳态温度场,以一台额定功率为815 kW的永磁同步牵引电机为研究对象,依据成型绕组槽部导热模型,建立了整机三维温度场共轭传热计算模型。考虑旋转磁化、冲压过程和磁密高阶谐波对定子铁耗的影响,以及水套-定子铁心间接触热阻抗对电机温升的影响,采用Fluent软件对电机额定工况下的稳态流场与温度场求解,使用电阻法和埋置检温计法（ETD）测得电机在额定工况下绕组的平均温升、铁心测点和绕组端部测点局部温升,绝对误差分别为-3.7 K、-0.3 K和7.6 K。进一步分析了电机水路和内风路流场分布特性、水套-定子铁心间接触间隙对绕组平均温升的影响以及槽内绕组附近的温升分布情况,最后提出了该类电机冷却系统设计优化方向。