Hybrid many-objective particle swarm optimization algorithm for green coal production problem

Summary Historic landscape islands are a unique landscape phenomenon in the urbanization and modernization process in Shanghai. Shanghai has been fragmented and isolated at the landscape space level because of islanding, and its historic and cultural values have been broken and weakened. Therefore, it is urgent to implement fusion protection planning. Considering the islanding of urban historic landscape, cellular automata (CA) and neural network intelligent method were used to establish the intelligent CA model of Shanghai historic landscape islands and carry out computer simulation, expecting to provide a basis for studying urban historic landscapes in China via the intelligent evaluation of urban historic landscape islands and the establishment of an intelligent planning model. A technical path to alleviate landscape fragmentation and cultural value fracture of isolated islands was explored, so as to provide the latest theoretical basis for decision makers to manage historic landscape islands in Shanghai and offer reference for more scientific, intelligent, and efficient decision‐making.

Section: Research Backgroundmentioning

confidence: 99%

Construction of an intelligent cellular automata model for historic landscape islands in Shanghai and fusion simulation

Wei

Zhang

2020

“…With the coming of the 21st century, people have entered the information age, and computers have been more widely used in various fields including Cyber Physical Systems, 6 Public Opinion Polarization Process, 7 Blockchain, 8,9 Emergency Logistics Network 10 and multiobjective optimization problem, 11,12 and so on. Meanwhile, artificial algorithm has been widely used to all kind of fields including network structure optimization, 13,14 Malicious code detection, 15 Green Coal Production Problem, 16 mixed traffic flow model, 17 Road Network, 18 and so on. People are eager to use computers instead of humans to do some repetitive and simple work, hoping that computers can learn like humans.…”

Section: Introductionmentioning

confidence: 99%

Welding flame detection based on color recognition and progressive probabilistic Hough transform

Chen

Guo

et al. 2020

Summary Welders are widely used in machinery manufacturing and machining industry. In the process of electric welding, accidents are often caused by some unsafe behaviors. This paper focuses on the intelligent recognition method of welding flame, which can reduce the accidents of welding flame to a certain extent. Due to the difficulties in observing workers on job sites, developing an algorithm for intelligent identification of unsafe factors is significant. Firstly, Hue,Saturation and Value (HSV) color space with strong robustness is selected to segment the mark image with threshold value, and the region of interest in which the welding fame is located is segment; secondly, the Progressive Probabilistic Hough transform is used to detect the region of interest, judge the number of existing lines, and finally determine whether there is welding flame in this region. In this way, the welding flame can be better detected, which can reduce the workload of managers and is beneficial to the supervision of the management personnel.

“…However, the analysis is subjective and heavily dependent on personal experience and lacks reliable data support and persuasion. The second is the quantitative analysis of the phenomenon of group polarization by using the dynamic models of public opinion and optimization algorithm, which is mainly based on the classical D‐W model, JA model and HK model . Although some research studies in this area have analyzed the mechanism of group polarization to a certain extent, most of them are based on static networks and a single dimension.…”

Section: Introductionmentioning

confidence: 99%

Modeling and simulation analysis of public opinion polarization in a dynamic network environment

Yuan

Shi

Che

et al. 2020

Summary At present, there are many group incidents on the Internet, which has caused a high level of public opinion. Hot issues on the Internet can often trigger intense discussions among netizens and finally evolve toward bipolarization or multipolarization. This article focuses on the phenomenon of public opinion polarization under the dynamic network. First, the single‐dimensional evaluation system for the JA model was expanded to multidimensional and analyzes the main factors that cause the polarization of public opinion of network groups from multiple dimensions of hot events. Second, the strength of the relationship between individuals and its dynamic changes were taken into account in the model. The strength of the relationship between individuals and its changes will affect the interaction of opinions, and thus affect the degree and speed of group polarization. Third, combining the changing process of the strength of relationship, the dynamic connection mechanism of network nodes was set to make the network structure more consistent with the social network in real life. Finally, we verify the reliability and scientificity of the model by combining practical cases.