Social learning discrete Particle Swarm Optimization based two-stage X-routing for IC design under Intelligent Edge Computing architecture

Liu, Genggeng; Chen, Xiaohua; Zhou, Ruping; Xu, Saijuan; Chen, Yeh-Cheng; Chen, Guolong

doi:10.1016/j.asoc.2021.107215

Cited by 41 publications

(20 citation statements)

References 34 publications

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“…From a standalone model, the cell phone uses Markov's method to determine whether to work internally or via a MEC server. To solve this problem, we provide single-bit research to solve the problem of power limit latency [ 5 ]. Lin et al have been pursuing a goal of efficiency, providing a self-saving energy MEC system and a strategy for online accounting to get a good solution [ 6 ].…”

Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

Zhao

Liu

2022

Journal of Environmental and Public Health

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In order to explore the current income gap, a method based on the PSO algorithm in the edge computing environment is proposed. PSO calculates and simulates bird flock foraging activities, the Frank Heppner biological group model, and the three rules in bird activities. After studying the activities of these natural creatures, abstract problems are quantified and similar models established. The Gini coefficient is calculated by using grouped data, and the grouping basis is also innovative. The quantile grouping method is adopted, which can effectively solve the difference between the concentration index and the Gini coefficient, and the Gini coefficients of each year can be added up to finally get the Gini coefficient of the stock income. Experimental results show that the Gini coefficient of traffic income in 2017 and 2018 had dropped significantly, but the variation of the Gini coefficient of stock income (Delta CG) was still greater than 0. Obviously, the adjustment speed of the Gini coefficient of stock income was lagging behind, as was the Gini coefficient of traffic income. We found that after 1986, the facilitation effect was greater than the dilution effect, and the facilitation effect continued to push up the stock income gap, which indicated more income flow to the high-income group, with the income flow gap showing an upward trend and the upward trend becoming more and more obvious. It has been proved that the PSO algorithm can effectively identify the income gap in the edge computing environment, and the corresponding policy suggestions are given.

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Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

Zhao

Liu

2022

Journal of Environmental and Public Health

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“…Pooling layer: Retains the important information left after the convolutional layer. Its advantages are that it reduces parameters to speed up calculations (Liu et al, 2020a , b , 2021 ). If there is a slight change between adjacent pixels, it will have little effect on the output result of the pooling layer and reduce overfitting.…”

Section: Relative Workmentioning

confidence: 99%

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

Liu

Chen²,

Yao³

2022

Front. Psychol.

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With the emergence of big data, cloud computing, and other technologies, artificial intelligence (AI) technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, development of abilities, and emotional experiences. It also used comparative analysis of pre-tests and post-tests through online questionnaires to test the results. The impact of technology on teachers' teaching and students' learning processes, identified the problems in the teaching and learning processes in the context of the application of AI technology, and proposed strategies for reforming and optimizing teaching and learning. It recommends the application of software and platforms, such as Waston and Knewton, under the orientation of AI technology to improve efficiency in teaching and learning, optimize course design, and engage students in deep learning. The contribution of this research is that the teaching and learning processes will be enhanced by the use of intelligent and efficient teaching models on the teachers' side and personalized and in-depth learning on the students' side. On the one hand, the findings are helpful for teachers to better grasp the actual conditions of in-class teaching in real time, carry out intelligent lesson preparations, enrich teaching methods, improve teaching efficiency, and achieve personalized and precision teaching. On the other hand, it also provides a space of intelligent support for students with different traits in terms of learning and effectively improves students' innovation ability, ultimately achieving the purpose of “artificial intelligence + education.”

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“…In the relevant literature, compared to the existing algorithms such as ACO and GA, PSO is favoured to generate the shortest distance with enhanced collision avoiding capability [60,61]. Moreover, it is the best possible approach to significantly find the shortest distance in optimum time [62,63]. In the current study, the PSO optimization algorithm used in UAV Bushfire Application is inspired by "Seyedali Mirjalili (2021).…”

Section: Pso For Uavs Path Planning In Bushfires Monitoringmentioning

confidence: 99%

UAV Based Spatiotemporal Analysis of the 2019–2020 New South Wales Bushfires

et al. 2021

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Bushfires have been a key concern for countries such as Australia for a long time. These must be mitigated to eradicate the associated harmful effects on the climate and to have a sustainable and healthy environment for wildlife. The current study investigates the 2019–2020 bushfires in New South Wales (NSW) Australia. The bush fires are mapped using Geographical Information Systems (GIS) and remote sensing, the hotpots are monitored, and damage is assessed. Further, an Unmanned Aerial Vehicles (UAV)-based bushfire mitigation framework is presented where the bushfires can be mapped and monitored instantly using UAV swarms. For the GIS and remote sensing, datasets of the Australian Bureau of Meteorology and VIIRS fire data products are used, whereas the paths of UAVs are optimized using the Particle Swarm Optimization (PSO) algorithm. The mapping results of 2019–2020 NSW bushfires show that 50% of the national parks of NSW were impacted by the fires, resulting in damage to 2.5 million hectares of land. The fires are highly clustered towards the north and southeastern cities of NSW and its border region with Victoria. The hotspots are in the Deua, Kosciu Sako, Wollemi, and Yengo National Parks. The current study is the first step towards addressing a key issue of bushfire disasters, in the Australian context, that can be adopted by its Rural Fire Service (RFS), before the next fire season, to instantly map, assess, and subsequently mitigate the bushfire disasters. This will help move towards a smart and sustainable environment.

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Social learning discrete Particle Swarm Optimization based two-stage X-routing for IC design under Intelligent Edge Computing architecture

Cited by 41 publications

References 34 publications

[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

UAV Based Spatiotemporal Analysis of the 2019–2020 New South Wales Bushfires

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