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Ship path planning is the core problem of autonomous driving of smart ships and the basis for avoiding obstacles and other ships reasonably. To achieve this goal, this study improved the traditional A* algorithm to propose a new method for ship collision avoidance path planning by combining the multi-target point artificial potential field algorithm (MPAPF). The global planning path was smoothed and segmented into multi-target sequence points with the help of an improved A* algorithm and fewer turning nodes. The improved APF algorithm was used to plan the path of multi-target points locally, and the ship motion constraints were considered to generate a path that was more in line with the ship kinematics. In addition, this method also considered the collision avoidance situation when ships meet, carried out collision avoidance operations according to the International Regulations for Preventing Collisions at Sea (COLREGs), and introduced the collision risk index (CRI) to evaluate the collision risk and obtain a safe and reliable path. Through the simulation of a static environment and ship encounter, the experimental results show that the proposed method not only has good performance in a static environment but can also generate a safe path to avoid collision in more complex encounter scenarios. [ABSTRACT FROM AUTHOR]

--The state of urban road traffic flow shows discontinuity and jumping phenomenon in the process of running. There was a data gap in the collected traffic flow data. Through the data analysis, it was found that the traffic flow state had the characteristics of multimode, mutation, inaccessibility, divergence and hysteresis, which were similar to the mutation characteristics of the basic model of catastrophe theory when the system state changed. The cusp catastrophe model of traffic flow based on traffic wave theory was established by analyzing the movement process of traffic flow. In this model, the traffic density was taken as the state variable, and traffic flow and wave speed were taken as the control variable. Referring to the basic idea of catastrophe theory, the solution method of the model was given, and the structural stability of the traffic flow state was analyzed. Through the critical equilibrium surface equation, the stability of the extreme value of the system potential function can be analyzed, and the bifurcation set equation when the traffic flow state changed can be obtained, which can be used to determine the critical range of the structural stability of the system. This paper discussed and analyzed the changing trend and constraint relationship among the wave speed, traffic density and traffic flow when the traffic flow state changed suddenly in different running environments. The analysis results were consistent with the actual road traffic flow state. A case was given, and the results showed that the cusp catastrophe model could describe the relationship among the three parameters of traffic flow from three-dimensional space, and could effectively analyze the internal relationship of the parameters when the traffic flow state changed. The validity of the model and analysis method was verified. The goal of this paper is to provide an analysis method for the judgment of urban road traffic state., Author(s): Yanguo Huang (corresponding author) [1]; Huimin Zhang [2]; Hongjun Liu [1]; Shengsheng Zhang [1] 1. Introduction The parameters reflecting urban road traffic flow mainly include flow, density, and speed. [...]

Summary: The output power of photovoltaic power has randomness and volatility, which poses new challenges to the peak shaving and dispatching of the power system. Therefore, the accurate prediction of photovoltaic power output is an effective way to maintain the security and stability of the power grid. In this article, a hybrid prediction model based on improved convolutional neural network and bidirectional gated recurrent unit is proposed to predict PV output power. Firstly, the concept of time relevance is introduced to fuse this feature with weather characteristics as the input of the prediction model. Secondly, the time series generated adversarial neural network is applied to the field of photovoltaic power generation prediction for the first time to enhance the dataset obtained. Thirdly, the model proposed in this article is trained and compared with other models under different characteristics, different weather conditions, and different forecast time steps. The experimental results show that the hybrid deep learning model proposed in this article has better prediction performance. Under different characteristics, the model proposed in this article has the highest prediction accuracy. After data enhancement, the prediction accuracy of each model has been improved by 1% to 3%. Among them, the prediction accuracy of the model proposed in this article is 0.977 and 0.960, respectively. Under different weather conditions and different time steps, the proposed model has the best stability. Under cloudy and rainy weather, the prediction accuracy of the proposed model only decreases by about 6%. In addition, the prediction accuracy of the proposed model only decreases by approximately 1.5% to 4% from 15 to 180 minutes. [ABSTRACT FROM AUTHOR]