Armature Structure Research of a Synchronous Induction Coil Launcher
Armature is very important for a synchronous induction coil launcher which can transform the electric energy to kinetic energy and push the load forward. The structure of the armature will influence the kinetic energy conversion efficiency directly. This paper analyzes the armature structure of a capacitor-driven synchronous induction coil launcher. Length and thickness of the armature are analyzed that how these parameters influence the maximum velocity and system efficiency of the launcher. Current filaments model is used to calculate the motion characteristics of a tubular linear induction motors, which is verified by an experiment and finite-element code. Genetic algorithm optimizer is used to calculate the best trigger times of the coils and the maximum velocity under the conditions of different length and thickness of the armature. It is shown that when the armature mass is fixed, length and thickness of the armature will greatly increase the velocity of the armature. The best length of the armature should be almost 2–2.5 times the length of the coil. The best thickness of the armature could be estimated by skin depth.
Current Sharing Analysis of Coil for Electromagnetic Launching
To deal with the uneven force on the inner and outer layers of the series coils as well as a large force on and temperature rise in its single layer under the influence of high pulse current during the electromagnetic launch (EML), this paper proposes a method of winding the wires of different diameters and turns by layers to make the multilayer coil parallel and the force on its layer uniform. But the prerequisite for the method is to ensure that the total current in the series coil and new-type coils keeps basically unchanged. The idea of designing the uniform distribution of coil current will provide a new approach to designing coils in the future.
Application of neural networks in bridge health prediction based on acceleration and displacement data domain
The health condition of the bridge can be predicted through sensors’ reading in bridge monitoring. The 
sensors measure the acceleration and displacement of bridge response. The data is sent to the local server through the data acquisition. Interpretation of the data applied neural network in the localized server system. This paper aims to define performance of the acceleration and displacement data domain as input in applied neural networks. The architecture of neural networks’ model used an input layer, one and two hidden layers with n neurons and an output layer. The input layer consists of time-acceleration domain and time-displacement domain of the 
bridge due to earthquake loads. Meanwhile, the output layer consists of bridge condition level which is determined using finite-element analysis software. The training activation used Gradient Descent Back-propagation and activation transfer function used Log Sigmoid function. The bridge condition is categorized in a range 0 to 3, which indicates the extent of bridge health condition ranging from safe to high-risk level. 
The case study is 3 spans of box-girder’s bridge subject to four earthquakes loads. The results showed that the prediction of bridge health condition based on displacement data domain with one hidden layer is more acceptable compared with based on acceleration. The comparison obtains the recommendation of the best of data reading from the sensors to predict the bridge health condition. The application neural networks in the bridge 
health prediction can help the authorities to know the condition of the bridge due to earthquake at monitoring time, as the repair and maintenance of bridges can be performed as early as possible before the bridge was damaged.