Hefei University of Technology

About: Hefei University of Technology is a education organization based out in Hefei, China. It is known for research contribution in the topics: Computer science & Microstructure. The organization has 28093 authors who have published 24935 publications receiving 324989 citations.

Robust sliding mode control for Steer-by-Wire systems with AC motors in road vehicles

Abstract: In this paper, the modeling of steer-by-wire (SbW) systems is further studied, and a sliding mode control scheme for the SbW systems with uncertain dynamics is developed. It is shown that an SbW system, from the steering motor to the steered front wheels, is equivalent to a second-order system. A sliding mode controller can then be designed based on the bound information of uncertain system parameters, uncertain self-aligning torque, and uncertain torque pulsation disturbances, in the sense that not only the strong robustness with respect to large and nonlinear system uncertainties can be obtained but also the front-wheel steering angle can converge to the handwheel reference angle asymptotically. Both the simulation and experimental results are presented in support of the excellent performance and effectiveness of the proposed scheme.

EEG-based Emotion Recognition via Channel-wise Attention and Self Attention

Abstract: Emotion recognition based on electroencephalography (EEG) is a significant task in the brain-computer interface field. Recently, many deep learning-based emotion recognition methods are demonstrated to outperform traditional methods. However, it remains challenging to extract discriminative features for EEG emotion recognition, and most methods ignore useful information in channel and time. This paper proposes an attention-based convolutional recurrent neural network (ACRNN) to extract more discriminative features from EEG signals and improve the accuracy of emotion recognition. First, the proposed ACRNN adopts a channel-wise attention mechanism to adaptively assign the weights of different channels, and a CNN is employed to extract the spatial information of encoded EEG signals. Then, to explore the temporal information of EEG signals, extended self-attention is integrated into an RNN to recode the importance based on intrinsic similarity in EEG signals. We conducted extensive experiments on the DEAP and DREAMER databases. The experimental results demonstrate that the proposed ACRNN outperforms state-of-the-art methods.

Uniform, Stable, and Efficient Planar-Heterojunction Perovskite Solar Cells by Facile Low-Pressure Chemical Vapor Deposition under Fully Open-Air Conditions

Abstract: Recently, hybrid perovskite solar cells (PSCs) have attracted extensive attention due to their high efficiency and simple preparing process. Herein, a facile low-pressure chemical vapor deposition (LPCVD) technology is first developed to fabricate PSCs, which can effectively reduce the over-rapid intercalating reaction rate and easily overcome this blocking issue during the solution process. As a result, the prepared uniform perovskite films exhibit good crystallization, strong absorption, and long carrier diffusion length. More strikingly, CH3NH3PbI3 absorbers by LPCVD demonstrate excellent moisture-resistant feature even under laser illumination and high-temperature conditions, which indicates that our proprietary method is very suitable for the future low-cost, nonvacuum production of the new generation photovoltaic devices. Finally, high efficiency of 12.73% is successfully achieved under fully open-air conditions. To the best of our knowledge, this is the first report of efficient PSCs with such a hi...