Research of NiMH Battery Modeling and Simulation Based on Linear Regression Analysis Method

 The battery state-of-charge estimation was one of core issues in the development of electric vehicles battery management system, and higher accurate model was needed in state-of-charge estimation correctly. Therefore, accurate battery modeling and simulation was researched here. The Thevenin equivalent circuit model of NiMH battery was established for the poor accuracy of traditional model. Based on the data which were brought from the 6V 6Ah NiMH battery hybrid pulse cycling test experiments, Thevenin model parameters were identified by means of the linear regression analysis method. Then, the battery equivalent circuit simulating model was built in the MATLAB/Simulink environment. The simulation and experimental results showed that the model has better accuracy and can be used to guide the battery state-of-charge estimation

Stability analysis and evaluation of Zengziyan in Jinfo Mountain under seismic (vibration) action

The dynamics analysis of dangerous rock mass simplified into improved shear-beam model is progressed based on the engineering example of Zenziyan in Jinfo Mountain, and the definite problem of seismic response on stratified rock mass is solved to obtain the maximum response rules of absolute acceleration, shear stress and relative displacement of rock strata, then the practical monitoring data are taken to make contrastive analysis of the above calculation results and verify the rationality of stability evaluation using this analytic method which can complement the quantitative calculation means of dangerous rock mass and provide reference for the similar engineering construction designs

Generative Steganography Diffusion

Generative steganography (GS) is an emerging technique that generates stego images directly from secret data. Various GS methods based on GANs or Flow have been developed recently. However, existing GAN-based GS methods cannot completely recover the hidden secret data due to the lack of network invertibility, while Flow-based methods produce poor image quality due to the stringent reversibility restriction in each module. To address this issue, we propose a novel GS scheme called "Generative Steganography Diffusion" (GSD) by devising an invertible diffusion model named "StegoDiffusion". It not only generates realistic stego images but also allows for 100\% recovery of the hidden secret data. The proposed StegoDiffusion model leverages a non-Markov chain with a fast sampling technique to achieve efficient stego image generation. By constructing an ordinary differential equation (ODE) based on the transition probability of the generation process in StegoDiffusion, secret data and stego images can be converted to each other through the approximate solver of ODE -- Euler iteration formula, enabling the use of irreversible but more expressive network structures to achieve model invertibility. Our proposed GSD has the advantages of both reversibility and high performance, significantly outperforming existing GS methods in all metrics.Comment: Draft for ACM-mm 2023.Shall not be reproduced without permission, rights reserved