Taking the urban rooftop photovoltaic project as the research object, considering its life cycle comprehensive cost, and aiming at maximizing the economic benefits of the project, the optimal inclination angle of photovoltaic (PV) is determined. Firstly, the design of PV scene includes the analysis of site resources, array inclination angle, array spacing, component selection and series-parallel number of components. Considering the time present value factor, the inclination angle is optimized as a variable to determine the comprehensive cost of the project and obtain the detailed economic benefits of the project. The results show that there is a difference between the maximum inclination angle of radiation and the maximum inclination angle of economic benefit. When the inclination angle gradually increases from 10° to the maximum inclination angle of radiation, the economic benefit increases first and then decreases. There is a PV inclination angle to maximize the economic benefits of the project

With the continuous growth of the number of motor vehicles in the country, traffic accidents occur frequently, and rear-end accidents occupy a large proportion, and rear-end accidents account for more than half of the traffic accidents occurring in intelligent vehicles. At present, the research on reducing the risk of rear-end collision mostly focuses on the risk of intelligent vehicle and the traffic in front of it, and less considers the risk of intelligent vehicle and the traffic in rear. Based on this, on the premise that there are traffic vehicles behind the intelligent vehicle, this paper analyzes the dangerous scene with traffic vehicles in front of the intelligent vehicle and the dangerous scene without traffic vehicles in front of the intelligent vehicle. For the non-existent dangerous scene of the traffic ahead, the dangerous situation is judged according to the minimum safe distance obtained from the braking process analysis. For the dangerous scene of the traffic in front of the car, the analysis is carried out according to the uniform speed, uniform acceleration and uniform deceleration of the traffic in front of the car, and the danger situation of the intelligent car and the traffic in the rear is judged by using the improved braking minimum safety distance. Finally, a total of 11 dangerous situations in the two dangerous scenarios are obtained. Then, MATLAB and PreScan were used to conduct joint simulation, and three dangerous situations were randomly selected for simulation experiment verification. The results show that at 0.921s, 2.433s and 2.438s before the collision, the intelligent car can predict the possible collision correctly according to the danger situation. Therefore, the dangerous situation obtained can effectively gain valuable time for avoiding the occurrence of rear-end collision accidents, and provide reference for the relevant research on reducing rear-end collision accidents.

For the task of multi-speaker dialog emotion recognition, we propose a Topic-Driven and Dialogue-Interactive Emotion Recognition model, TDCIER. The model contains two key modules: Topic Module and Dialogue-Interactive Module. The topic module first learns the word embeddings of the input text using the BERT model, and then combines the syntactic structure information and topic information to represent the sentences, to capture the semantic information and potential derivations. Meanwhile, the topic module also uses a variational autoencoder to extract the topic information of the text, which is used for dialog segmentation to solve the long-range dependency problem in multi-person dialogs. The dialog interaction module simulates the emotional interactions between speakers by constructing a dialog graph, in which nodes represent sentences and edges represent speaker relationships, and graph convolutional networks are used to update node features. Experiments on two datasets, MELD and EmoryNLP, show that the method in this paper achieves optimal results compared with several benchmark models, effectively simulates the dynamic changes of emotions in multi-person conversations, and proves the effectiveness of the model in the task of multi-person conversation emotion recognition

In order to further enhance the accuracy, timeliness, and adaptability of trajectory tracking for autonomous vehicles, an innovative Adaptive Model Predictive Control (AMPC) algorithm is proposed. Building upon the traditional Model Predictive Control (MPC) control algorithm, this approach takes the current vehicle speed and reference trajectory curvature as system inputs and utilizes a fuzzy control algorithm to achieve real-time adjustment of the prediction horizon for the MPC trajectory tracking controller. Through multiple simulation experiments and employing a third-degree polynomial fit, the control law for the control horizon is obtained, enabling adaptive adjustments. The reliability of the proposed Adaptive MPC trajectory tracking control method is validated through joint simulation in Matlab/Simulink and CarSim. The research results demonstrate that, compared to traditional MPC control, Adaptive MPC control significantly improves the accuracy of vehicle trajectory tracking, reduces average computation time, and provides a novel and effective approach for trajectory tracking control in autonomous vehicles.

In this paper, we generalize the notion of algebras in such a way that the main properties of algebras are preserved

This paper presents a current-mode universal biquad circuit using only positive current output current conveyors (CCs). The circuit enables low-pass, band-pass, high-pass, band-stop and all-pass responses by the selection and addition of the circuit currents with no component matching constraints. Moreover the circuit parameters ω₀ and Q can be set orthogonally adjusting the circuit components. The biquad circuit enjoys very low sensitivity with respect to the circuit components. The achievement example is given together with simulation results by PSPICE.