Design of Millimeter-wave Detector for Gyrotron Power Monitoring

The real-time power monitoring of gyrotron is one of the key issues in the operation of electron cyclotron resonance heating system. The detector can be used for real-time power monitoring. We analyzed the principle of diode detection and designed a D-band wideband detector based on Schottky diode in this paper. The detector includes a waveguide-to-microstrip transition, a matching circuit, a diode, and a low pass filter. A novel waveguide-to-microstrip transition was developed based on probe coupling. A wideband lossy matching circuit was developed based on tapered-line and series matching resistor. The simulation results show that when the input power is -30dBm at 140 GHz, the detection sensitivity is about 1600V/W.Comment: 12 pages, 19 figure

Dynamic Relationships among Composite Property Prices of Major Chinese Cities: Contemporaneous Causality through Vector Error Corrections and Directed Acyclic Graphs

The present study is the first one that investigates dynamic relations among composite real estate price indices of ten different cities in China during the years from 2005 to 2021. Utilizing the data recorded on a monthly basis, we apply VECM (vector error-correction modeling) and DAGs (directed acyclic graphs) in order to characterize contemporaneous causal relations among the ten real estate price indices. We use the PC algorithm to identify a pattern with non-directed edges and the LiNGAM algorithm to determine the causal ordering, based on which we calculate the results of innovation accounting. The LiNGAM algorithm adopted here effectively utilizes non-normality for facilitating the arrival of complete causal orderings. Our results show that price dynamics revealed through processes of price adjustments due to shocks to prices are rather sophisticated and such dynamics are, in general, dominated by price indices of Shanghai and Shenzhen, which are two top-tier cities among the four top-tier cities in China. This indicates that policy design on composite property prices should be focusing on price indices of Shanghai and Shenzhen

Supported ITZ modification efficiencies via surface coating nanoparticles on aggregate and its influence on properties

In order to modify the porous interfacial transition zone (ITZ) microstructure of concrete more efficiently, a method of coating aggregate surfaces by using several nanoparticles was evaluated in this study. The compressive strength, chloride penetration of sound, and pre-loading samples were assessed in relation to the type of coating materials used (slag, nano-CaCO3, and nano-SiO2) and the designed coating thickness (5, 10, and 15 mu m). The ITZ microstructure was quantitatively determined via Backscattered electron (BSE) image analysis. Results showed that the overall performance of concrete is highly dependent on the coating materials and the designed coating thickness. Increasing the coating thickness of slag and nano-SiO2 could improve the chloride penetration resistance but decrease the compressive strength. Using nano-CaCO3 to coat the aggregate leads to a significant reduction in the properties of the so-prepared concrete. Though coating inert fine particles around aggregate could disturb the initial particle packing and modify the ITZ, it is not able to improve the overall concrete properties. Coating aggregate could determine the ITZ microstructure, especially within the region that is around 30 mu m away from aggregate surface

Deep Learning with S-shaped Rectified Linear Activation Units

Rectified linear activation units are important components for state-of-the-art deep convolutional networks. In this paper, we propose a novel S-shaped rectified linear activation unit (SReLU) to learn both convex and non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. Specifically, SReLU consists of three piecewise linear functions, which are formulated by four learnable parameters. The SReLU is learned jointly with the training of the whole deep network through back propagation. During the training phase, to initialize SReLU in different layers, we propose a "freezing" method to degenerate SReLU into a predefined leaky rectified linear unit in the initial several training epochs and then adaptively learn the good initial values. SReLU can be universally used in the existing deep networks with negligible additional parameters and computation cost. Experiments with two popular CNN architectures, Network in Network and GoogLeNet on scale-various benchmarks including CIFAR10, CIFAR100, MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement compared to other activation functions.Comment: Accepted by AAAI-1

BIOPHYSICAL STUDY OF TEAR FILM LIPID LAYER

Ph.D