No abstract

Cable force measurement is an essential step in bridge construction and structural health evaluation; it has developed rapidly in recent years and can be achieved using a wireless sensor network, which greatly reduces the monitoring cost and improves the efficiency of monitoring compared with traditional monitoring methods. However, expensive sensors, devices, transfer systems, and professionally trained technicians are needed in a wireless network system. Therefore, it is necessary to develop a portable and convenient method that requires less professional components. Smartphones with powerful operating systems are becoming popular; some have built-in highperformance sensors and a network that can transmit data. Based on these facts, a novel cable force measuring method using smartphone was proposed in this work. An iPhonebased cable force measurement software named OrionCloud Cell (Orion-CC) was developed and launched in the Apple App Store by our group. It has the advantages of being low cost, convenient, time saving, and easy-to-operate. First, a comparison test between an iPhone and a wireless monitoring method was conducted to verify the feasibility of a cable force measuring method using smartphones. Second, Orion-CC was applied to laboratory cable model tests. A comparison of the results from Orion-CC and MATLAB post-processing data proved the accuracy and operability of this method. Finally, this method was applied to the Dalian Xinghai Bay Cross-sea Bridge, which demonstrated its efficiency in an actual engineering application.

With significant development of sensors and Internet of things, researchers nowadays can easily know what happens in physical space by acquiring time-varying values of various factors. Essentially, growing data category and size greatly contribute to solve problems happened in physical space. In this paper, we aim to solve a complex problem that affects both cities and villages, i.e., flood. To reduce impacts induced by floods, hydrological factors acquired from physical space and data-driven models in cyber space have been adopted to accurately forecast floods. Considering the significance of modeling attention capability among hydrology factors, we believe extraction of discriminative hydrology factors not only reflect natural rules in physical space, but also optimally model iterations of factors to forecast run-off values in cyber space. Therefore, we propose a novel data-driven model named as STA-LSTM by integrating Long Short-Term Memory (LSTM) structure and spatiotemporal attention module, which is capable of forecasting floods for small- and medium-sized rivers. The proposed spatiotemporal attention module firstly explores spatial relationship between input hydrological factors from different locations and run-off outputs, which assigns time-varying weights to various factors. Afterwards, the proposed attention module allocates temporal-dependent weights to hidden output of each LSTM cell, which describes significance of state output for final forecasting results. Taking Lech and Changhua river basins as cases of physical space, several groups of comparative experiments show that STA-LSTM is capable to optimize complexity of mathematically modeling floods in cyber space.

To deeply investigate the nonlinear interaction between the sheet beam and the slow wave mode in the dielectric loaded rectangular Cerenkov maser, a third order differential equation of the field profile function is rigorously derived. By combining with the relativistic equation of motion and using the traveling-wave boundary condition, the nonlinear phenomena, which involve with the growth rate, the electron phase bunching, the saturated power and length, etc., can be predicted through numerical calculations. An illustrative example has been given to demonstrate the validation of this method. The results show that a beam with axial momentum spread will lower the saturated power, increase the saturated length, and decrease the working bandwidth.