In the complex and dynamic urban road environment, conflicts in time and space between different traffic participants are inevitable. In this paper, a model to predict the trajectories of oncoming straight vehicles is proposed based on the Gaussian Process Regression (GPR) model with the consideration of analysis of left-turn potential conflict behaviors. Then, a conflict resolution method for left-turn behavior of autonomous vehicles at urban intersections and a driving action selection method considering multiple factors, such as safety, efficiency, comfort and altruism, are proposed. Experiments show that the model can guide the unmanned vehicles to complete the task with a success rate of more than 90%. The research results can provide decision-making support for autonomous vehicles passing through urban intersections safely.
Transimpedance amplifier is an important part of the signal processing circuit in the fluorescent optical fiber temperature sensor to amplify the current signal and convert it into a voltage one. The two-stage CMOS operational amplifier, which can be designed to achieve high gain, high CMRR and low input offset voltage, can be used in transimpedance amplifier circuits. Based on 0.18 $\mu$m CMOS process, a two-stage differential operational amplifier is designed in this paper. The simulation results show that the gain of the reaches 85.5dB, the CMRR and the negative PSRR are both around 90dB. The Miller compensation circuit in the circuit increases the gain bandwidth of the two-stage CMOS operational amplifier and makes the phase margin reach 80°. This paper introduces the circuit principle of CMOS two-stage operational amplifier in detail, and according to the simulation result, this operational amplifier meets the index requirement.
This paper proposes a scheme for multiple source localization by using diffuseness estimation. According to the sparsity of the voice signal, single source time-frequency point (SSTP) always exists in the microphone recorded sound signal. The DOA estimates of these SSTPs have favorable localization statistical significance. Through analysis the characteristics of diffuseness measure, this paper gives an assumption that there is some correlation existed between diffuseness measure and SSTP. Through experiments, we found that the TF point with low diffuseness measure can be classified as SSTP. Therefore, a SSTP detection method based on diffuseness estimation is proposed. Meanwhile, the multiple source localization problem is converted to a single source localization among the detected SSTPs. The objective evaluations revealed that the proposed method achieved higher localization accuracy compared to some existing technique based on single sound source zone detection.
Aiming at the problems of VIENNA rectifier with traditional double closed-loop PI control strategy, such as large output voltage ripple, poor anti-interference ability and large THD content at AC terminal, etc. A double closed-loop sliding mode variable structure control strategy is proposed. The outer loop is based on sliding mode variable structure control, and the calculation of the input reference current of the inner loop is simplified by using the power balance principle; The current inner loop adopts the direct sliding mode variable structure control method, and outputs the command voltage to the SVPWM module to complete the system control. Matlab/Simulink software is used for simulation verification, and the results show that compared with the traditional double closed-loop PI control strategy, the double closed-loop sliding mode variable structure control strategy significantly improves the dynamic performance and robustness of the system.
With the development of new energy industry, it’s beneficial to analyze the characteristics of new energy which stabilize the operation of the power grid and economic dispatch. It’s more and more important to model the uncertainty of new energy. Although there are some probabilistic modeling methods based on real load data for wind power output, they are not accurate and have high computational complexity. In order to solve these problems, this paper proposes a source-charge probability distribution model based on variational autoencoder. The model uses deep learning techniques to perform variational autoencoder and probabilistic modeling for real-time wind power data. This unsupervised method can learn the characteristics of wind power data and generate new data based on the characteristics of the observation without the scene reduction process. It improves the efficiency and quality of scene generation.
