A Satellite Observation Data Transmission Scheduling Algorithm Oriented to Data Topics

Chen, Hao; Zhai, Baorong; Wu, Jiangjiang; Du, Chao; Li, Jun

doi:10.1155/2020/2180674

Cited by 8 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Users can build applications at this layer, use the middleware services and database services provided by the platform to develop and deploy their own applications, and expand data processing capabilities. Using the services provided by this layer does not need to manage the underlying resources; just upload and deploy personal programs and data using the tools provided by the platform [ 19 , 20 ]. Application Layer .…”

Section: Cloud Computingmentioning

confidence: 99%

Cloud Computing to Tourism Economic Data Scheduling Algorithm under the Background of Image and Video

Zhang

Feng

Wang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

With the rapid development of image video and tourism economy, tourism economic data are gradually becoming big data. Therefore, how to schedule between data has become a hot topic. This paper first summarizes the research results on image video, cloud computing, tourism economy, and data scheduling algorithms. Secondly, the origin, structure, development, and service types of cloud computing are expounded in detail. And in order to solve the problem of tourism economic data scheduling, this paper regards the completion time and cross-node transmission delay as the constraints of tourism economic data scheduling. The constraint model of data scheduling is established, the fitness function is improved on the basis of an artificial immune algorithm combined with the constraint model, and the directional recombination of excellent antibodies is carried out by using the advantages of gene recombination so as to obtain the optimal solution to the problem more appropriately. When the resource node scale is 100, the response time of EDSA is 107.92 seconds.

show abstract

Section: Cloud Computingmentioning

confidence: 99%

Cloud Computing to Tourism Economic Data Scheduling Algorithm under the Background of Image and Video

Zhang

Feng

Wang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…high priority 1 ≤ Priority ≤ 6, low priority (12) where equation ( 12) represents the calculation function of task priority. And it is divided into high priority (7-10 levels) and low priority (1-6 levels) to distinguish the importance of the target task.…”

Section: A the Msimp Unified Model Based On Task Prioritymentioning

confidence: 99%

“…Wu et al [11] proposed an improved constraint fulfillment model for NSGA-III MSIMP based on the imaging scheduling problem for large-scale satellite formation systems. Chen et al [12] studied a constraint satisfaction model-based MSIMP for data subject scheduling, which solves the challenge of scheduling large-scale satellite observation data using an enhanced non-dominated ranking genetic algorithm. Zhang et al [13] proposed a large-scale multi-satellite mission planning algorithm based on SVM+NSGA-II, which considers the periodicity of satellite resource conflict, the large-scale characteristics of multi-star missions, and the optimization objective constraint satisfaction model.…”

Section: Introductionmentioning

confidence: 99%

A Unified Model for Multi-Satellite Imaging Mission Planning in Various Scenarios

Yang

Zhang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Multi-satellite imaging mission planning (MSIMP) has been difficult in various scenarios due to the complex constraints of multi-satellite imaging, the wide area covered by target points, and the difficulty of achieving different mission requirements in a short period of time with limited satellite resources. In addressing this challenge, this work investigates multi-satellite imaging mission planning based on the Unified Plan Model and Improved Adaptive Differential Evolution algorithm (UPM-IADE). First, a unified model is built based on two scenarios: a large-scale imaging mission and an emergency support mission, and then a mission assignment framework is adaptively selected based on mission priority. Second, a monorail task synthesis method based on visible time windows is created to clarify the execution relationship between the satellite and the target point. Finally, an individual weight ranking rule is developed, and the weight is used to combine the fitness value ranking and diversity ranking into a final fitness value ranking, which is used to select individuals that satisfy the mutation requirements into the mutation strategy pool for adaptive mutation strategy selection. Experiments 1, 2, 3, and 4 have demonstrated that UPM-IADE can successfully resolve the imaging satellite mission planning for both scenarios while providing remarkable performance in terms of high mission benefit and rapid response.INDEX TERMS Multi-satellite imaging mission planning, global large-scale imaging mission, emergency support mission, adaptive differential evolution algorithm, individual weight ranking rule.

show abstract

“…The W learning algorithm is a self-organizing behavioral selection scheme applied to multiple parallel task target systems, which is developed on the basis of Q learning [24]. W learning can well realize the distributed learning function of multiagents, which overcomes the shortcomings of insufficient environmental information in complex heterogeneous networks.…”

Section: Artificial Intelligence-based Health Management Systemmentioning

confidence: 99%

National Sports AI Health Management Service System Based on Edge Computing

Hua-li

Han

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

With the development of the economy, people’s living standards continue to improve, and the management awareness of their own health and safety has gradually increased. The human body usually has a long process of transition from being healthy to the appearance of disease, and people’s lifestyle and daily behavior will affect the health of the body to a large extent. This paper studies the national sports AI health management service system based on edge computing. This paper organically combines edge computing, artificial intelligence, and health management service systems. Through detection devices with smart sensors and smart health systems, a comprehensive health management service platform for the masses is constructed to promote the development of sports health management services. Upgrade and transform to make it more in line with the modern intelligent environment. The advancement of modern technology has gradually transformed my country’s traditional health record management service to an intelligent electronic health record management system. From the experimental data, it can be seen that the filing rate and pass rate of residents’ electronic files have reached 88.275% and 87.435%, but the utilization rate of health files is only 31.685%. The establishment of a supporting health management service system is precisely to effectively increase the utilization rate of health files, so that users can truly enjoy intelligent, professional, and real-time health services.

show abstract

A Satellite Observation Data Transmission Scheduling Algorithm Oriented to Data Topics

Cited by 8 publications

References 30 publications

Cloud Computing to Tourism Economic Data Scheduling Algorithm under the Background of Image and Video

Cloud Computing to Tourism Economic Data Scheduling Algorithm under the Background of Image and Video

A Unified Model for Multi-Satellite Imaging Mission Planning in Various Scenarios

National Sports AI Health Management Service System Based on Edge Computing

Contact Info

Product

Resources

About