Peng Geng

To enhance the rationality of the layout of electric vehicle charging stations, meet the actual needs of users, and optimise the service range and coverage efficiency of charging stations, this paper proposes an optimisation ... | Find, read and cite all the research you need on Tech Science Press

With the rapid development of electric vehicles, the requirements for charging stations are getting higher and higher. In this study, we constructed a charging station topology network in Nanjing through the Space-L method, mapping charging stations as network nodes and constructing edges through road relationships. The experiment introduced five EV charging recommendation strategies (based on distance, number of fast charging piles, user preference, price, and overall rating) used to simulate disordered charging caused by different user preferences, and the impact of the network dynamic robustness in case of node failure is explored by simulating the load-capacity cascade failure model. In this paper, two important metrics for evaluating network robustness are selected: the relative size of the maximum connected subgraph and the network efficiency. The experimental results point out that in the price recommendation strategy, the network stability significantly decreases when the node failure ratio reaches 75.4%, while the fast charging quantity recommendation strategy significantly decreases when the node failure ratio is 62.3%. Therefore, the robustness of the charging station network is best under the price recommendation, while the network robustness is poor under the fast charging quantity recommendation.While the network robustness is poor under preference recommendation. Based on this finding, this study particularly emphasizes that in the process of improving the robustness of the charging station network, it is necessary to comprehensively consider the market demand and guide users to charge in an orderly manner by reasonably adjusting the price strategy. This strategy not only effectively prevents network stability problems that may result from disorderly charging behavior, but also enhances the ability of the charging network to resist node failure and improves the overall dynamic robustness of the network.

This article presents a real-time localization method for Unmanned Aerial Vehicles (UAVs) based on continuous image processing. The proposed method employs the Scale Invariant Feature Transform (SIFT) algorithm to identify key points in multi-scale space and generate descriptor vectors to match identical objects across multiple images. These corresponding points in the image provide pixel positions, which can be combined with transformation equations, allow for the calculation of the UAV’s actual ground position. Additionally, the physical coordinates of matching points in the image can be obtained, corresponding to the UAV’s physical coordinates. The method achieves real-time positioning and tracking during UAV flight, with experimental results demonstrating that within an acceptable error range, the UAV coordinates calculated using the proposed algorithm are consistent with the actual coordinates. The maximum error along the x-axis, y-axis, and z-axis is 4.501 cm, with the horizontal error exhibiting high stationarity and the vertical error having a low average value of 0.041 cm. The real-time positioning algorithm presented in this article possesses characteristics such as simplicity, ease of implementation, and low error, making it suitable for UAVs with limited computational processing power.

In the understanding of important edges in complex networks, the edges with larger degree are naturally considered more important, and they will cause greater destructiveness when attacked. However, through simulation analysis, we conclude that this understanding needs to be based on certain preconditions. In this article, the robustness of BA scale-free network and WS small-world network is studied based on the edge-removal attack strategy considering the edge load and cascading failure. Specific attack methods include: High Load Edge-removal Attacks (HLEA) and Low Load Edge-removal Attacks (LLEA). The simulation results show that the importance of edges is closely related to the load parameter δ. When 0<δ1, attacking the edge with larger degree will cause greater cascading failure. Therefore, the edge with larger degree is more important for this condition.

This article aims to address the clustering effect caused by unorganized charging of electric vehicles by adopting a two-tier recommendation method. The electric vehicles (EVs) are classified into high-level alerts and general alerts based on their state of charge (SOC). EVs with high-level alerts have the most urgent charging needs, so the distance to charging stations is set as the highest priority for recommendations. For users with general alerts, a comprehensive EV charging station recommendation model is proposed, taking into account factors such as charging price, charging time, charging station preference, and distance to the charging station. Using real data from EV charging stations and ride-hailing vehicles in Xiamen City, Fujian Province, simulation analyses are conducted using Python for different periods of the day. The research results show that the stability of the multi-factor recommendation model in terms of service density variance, coverage rate, price cost, and distance cost outperform single-factor models. This indicates that our composite multi-factor recommendation model has significant practical value in resolving the clustering phenomenon caused by unorganized EV charging, optimizing the EV charging service system, and improving user satisfaction.

Platform monopoly has attracted wide attention from politicians and the public.. The European Commission has made unremitting efforts in platform antitrust enforcement in the last decade, but together with antitrust investigations, the stock prices of platform giants like Google and Facebook keep breaking their highest points. At the end of 2020, the Chinese government also started antitrust investigations towards platform companies like Alibaba. In contrast, the stock price of Alibaba crashed and lost more than half of its market value. By analyzing their CAR, we proved that the stock performance of Alibaba is significantly worse than Google after their most serious antitrust investigations. The difference reflects investors' different expectations of the European Commission and China's antitrust enforcement. A noteworthy problem then comes out: while the Chinese government is seriously strengthening platform antitrust and putting forward reforms in platform regulation, is there any authority that is able to effectively regulate the international platform giants and maximize the welfare of their users worldwide?

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Wireless sensor networks are often used in harsh environments, such as military fields, the node energy is difficult to be added. How to design an energy efficient network model to achieve the balanced utilization of node energy and prolong the lifetime is an important challenge for the research of wireless sensor networks. Small world networks have short average path length and large clustering coefficient, which present the characteristics of high global data transmission efficiency and good local node connectivity. So the characteristics of small world networks provide a theoretical basis for building an energy efficient network. In this paper, the small world construction methods for wireless sensor networks are summarized. Wireless sensor networks with small world characteristics are constructed based on the hybrid mode, which can be divided into the stage of topology optimization, small world model construction and energy dynamic balance. The MSW-WSNs-A3 and MSW-WSNs-A3 Cov are formed based on hybrid model of small world wireless sensor networks. Based on Java platform, the new network modes are simulated and analyzed. The results show that MSW-WSNs-A3 and MSW-WSNs-A3 Cov have longer lifetime and better effectiveness.