Real-Time Illegal Parking Detection System Based on Deep Learning

The increasing illegal parking has become more and more serious. Nowadays the methods of detecting illegally parked vehicles are based on background segmentation. However, this method is weakly robust and sensitive to environment. Benefitting from deep learning, this paper proposes a novel illegal vehicle parking detection system. Illegal vehicles captured by camera are firstly located and classified by the famous Single Shot MultiBox Detector (SSD) algorithm. To improve the performance, we propose to optimize SSD by adjusting the aspect ratio of default box to accommodate with our dataset better. After that, a tracking and analysis of movement is adopted to judge the illegal vehicles in the region of interest (ROI). Experiments show that the system can achieve a 99% accuracy and real-time (25FPS) detection with strong robustness in complex environments.Comment: 5pages,6figure

How does hydrogen-based renewable energy change with economic development? Empirical evidence from 32 countries

The hydrogen-based renewable energy resource base is sufficient to meet several times the present world energy demand. This paper analyzes the drivers promoting hydrogen-based renewable energy utilization, focusing on a group of 32 countries by applying panel data techniques. The pooled ordinary least square estimator and fixed effect estimator are employed for comparison. Grey relational analysis is used to explore the relationships at a national level between renewable energy development and its influencing factors. The main results over our time span indicate that: (1) GDP per capita is a significantly positive contributor to renewable energy consumption, while oil price does not present a strong relationship in the use of renewables; (2) social awareness about climate change and concerns for energy security is not enough to motivate the switch from traditional to renewable energy sources; (3) the role of urbanization in renewable energy consumption relies on different stages of the urbanization process, resulting in opposite trends in renewable energy development between developing and developed countries. The results show that the market mechanism is not entirely responsible for encouraging the use of renewables and the role of climate change and energy security concerns in renewables use should be enhanced. By analyzing the results, policy implications are provided for the sustainable development of renewable energy

The effects of environmental inspection on air quality: Evidence from China

To address ecological and environmental issues, central environmental inspection (CEI) coordinated by the Chinese Ministry of Ecology and Environment has been implemented since 2016. This paper aims to comprehensively evaluate how and how much CEI affects air quality. The results of the difference-in-differences models show that CEI improved the air quality and reduced the concentrations of PM2.5, PM10, NO2, and SO2 by 8.8%, 8.1%, 7.9%, and 2.4%, respectively. Moreover, environmental effectiveness was strengthened over the course of four rounds of inspection. The mediating model results indicate that effectiveness was achieved through active public participation, administrative punishments from the central inspectors, and positive rectification actions from the local governments. The greatest improvement in air quality occurred during the on-site inspection period, after which the effects gradually weakened. A review inspection was carried out to supervise the rectification tasks. The adoption of review inspection made the effects on air quality improvement reappear, which verifies that CEI in China is not just a temporary campaign-style enforcement but a normalized and effective governance of air pollution

Secondary frequency control of islanded microgrid considering wind and solar stochastics

As the high penetration of wind and photovoltaic distributed generation (DG) in the microgrid, the stochastic and low inertia emerge, bringing more challenges especially when the microgrid operates in isolated islands. Nevertheless, the reserve power of DGs in deloading control mode can be utilized for frequency regulation and mitigating frequency excursion. This paper proposed a model predictive control (MPC) secondary frequency control method considering wind and solar power generation stochastics. The extended state-space matrix including unknown stochastic power disturbance is established, and a Kalman filter is used to observe the unknown disturbance. The maximum available power of wind and solar DGs is estimated for establishing real-time variable constraints that prevent DGs output power from exceeding the limits. Through setting proper weight coefficients, wind and photovoltaic DGs are given priority to participate in secondary frequency control. The distributed restorative power of each DG is obtained by solving the quadratic programming(QP) optimal problem with variable constraints. Finally, a microgrid simulation model including multiple PV and wind DGs is built and performed in various scenarios compared to the traditional secondary frequency control method. The simulation results validated that the proposed method can enhance the frequency recovery speed and reDGce the frequency deviation, especially in severe photovoltaic and wind fluctuations scenarios.Comment: Accepted by Acta energiae solaris sinica [In Chinese

Risk management of extreme events under climate change

Risk management is an effective way to mitigate the adverse consequences of extreme events, and plays an important role in climate change adaptation. On the basis of the literature, this paper presents a conceptual framework for managing the risk of extreme events under climate change, and accordingly summarizes the recent developments with a focus on several key topics. In terms of risk determinants, the impacts of climate variability on the frequency of extreme events are addressed, and the various meanings and measurements of specific vulnerability are compared. As for the process of risk management, the dynamic assessment approach regarding future climate condition is emphasized. Besides, in view of decision making the available means to enhance the effectiveness of adaptation and mitigation strategies are highlighted. Finally, uncertainty is discussed with respect to its sources and solution

Swarm splitting and multiple targets seeking in multi-agent dynamic systems

This paper presents an approach to swarm split control of a system of multi-agents with limited sensing capabilities. The control scheme utilizes the competition between the inter-agent repulsive and attractive interactions and can split one cohesive swarm into several clustered subswarms along the direction perpendicular to the common heading direction of agents. The cohesion and collision avoidance of agents are ensured by long-range attractive and short-range repulsive interactions between agents. The split of swarm is achieved via a Gaussian-like repulsive interaction between agents, whose magnitude affects the number of subswarm clusters and can be designed to control the swarm splitting/rejoining maneuver, and whose maximum location mainly affects the relative distance between clustered subswarms. The split control law is also applied to double targets seeking task in a swarm of 100 agents, and simulations are worked out. These results are of interest in understanding and utilizing the splitting dynamics in swarms of agents with local coupling interactions.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000295049105038&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Automation & Control SystemsEngineering, Electrical & ElectronicEICPCI-S(ISTP)