Estimation of statistical energy analysis loss factor for fiber reinforced plastics plate of yachts

Loss factor is one of the most significant parameters of Statistical Energy Analysis (SEA) which represents the damping loss characteristics of a system and indicates the ability of its vibration energy consumption. In order to estimate it, the power input method (PIM) and the impulse response decay method (IRDM) have become widely used especially when the object of study is made of Fiber Reinforced Plastics (FRP) of which dynamic interaction is really complicated. Numerical simulation is also applied to analyze the loss factor of the spring-damping-system with single degree of freedom (SDOF) using MATLAB to introduce the identification procedure of PIM and IRDM. With the comparison of the methods, the analytical study indicates these techniques are effective for the estimation of loss factor. This paper focuses on an experimental approach to get the SEA loss factor of FRP plate and the test investigations are performed in detail. The requirements and limitations of each method applied are discussed and PIM is a better solution dealing with this kind of the composite material. The loss factor of test specimen is obtained to provide a valuable reference for the prediction and control of vibration and noise of yachts with SEA

The Channel Compressive Sensing Estimation for Power Line Based on OMP Algorithm

Power line communication (PLC) can collect information by power line which increases the coverage and connectivity of the smart grid. In this paper, we analyze the transmission characteristics of the power line channel and model it with mathematics channel. The multipath effect of the power line channel is studied with a novel technology named compressive sensing herein. We also proposed a new method to the power line channel estimation based on compressive sensing. We can collect and extract the effective parameters of the power line channel to storage, which only take very little storage space. The simulation results show that the proposed approach can reduce the amount of processing data in the digital signal processing module and decrease the requirement for the hardware