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Research on the Intelligent Distribution System of College Dormitory Based on the Decision Tree Classification Algorithm 被引量:1
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作者 Huiping Han Beida Wang 《Journal of Contemporary Educational Research》 2023年第2期7-14,共8页
The trend toward designing an intelligent distribution system based on students’individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system,which neglects... The trend toward designing an intelligent distribution system based on students’individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system,which neglects the students’personality traits,causes dormitory disputes,and affects the students’quality of life and academic quality.This paper collects freshmen's data according to college students’personal preferences,conducts a classification comparison,uses the decision tree classification algorithm based on the information gain principle as the core algorithm of dormitory allocation,determines the description rules of students’personal preferences and decision tree classification preferences,completes the conceptual design of the database of entity relations and data dictionaries,meets students’personality classification requirements for the dormitory,and lays the foundation for the intelligent dormitory allocation system. 展开更多
关键词 intelligent allocation Personal preference Information gain Decision tree classification INDIVIDUALIZATION
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Intelligent Resources Management System Design in Information Centric Networking 被引量:2
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作者 Hengyang Zhang Shixiang Zhu +2 位作者 Renchao Xie Tao Huang Yunjie Liu 《China Communications》 SCIE CSCD 2017年第8期105-123,共19页
Information centric networking(ICN) is a new network architecture that is centred on accessing content. It aims to solve some of the problems associated with IP networks, increasing content distribution capability and... Information centric networking(ICN) is a new network architecture that is centred on accessing content. It aims to solve some of the problems associated with IP networks, increasing content distribution capability and improving users' experience. To analyse the requests' patterns and fully utilize the universal cached contents, a novel intelligent resources management system is proposed, which enables effi cient cache resource allocation in real time, based on changing user demand patterns. The system is composed of two parts. The fi rst part is a fi ne-grain traffi c estimation algorithm called Temporal Poisson traffi c prediction(TP2) that aims at analysing the traffi c pattern(or aggregated user requests' demands) for different contents. The second part is a collaborative cache placement algorithm that is based on traffic estimated by TP2. The experimental results show that TP2 has better performance than other comparable traffi c prediction algorithms and the proposed intelligent system can increase the utilization of cache resources and improve the network capacity. 展开更多
关键词 information centric networking traffi c estimation cache resources allocation time series analysis intelligent analysis
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Prediction of job characteristics for intelligent resource allocation in HPC systems:a survey and future directions
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作者 Zhengxiong HOU Hong SHEN +3 位作者 Xingshe ZHOU Jianhua GU Yunlan WANG Tianhai ZHAO 《Frontiers of Computer Science》 SCIE EI CSCD 2022年第5期17-33,共17页
Nowadays,high-performance computing(HPC)clusters are increasingly popular.Large volumes of job logs recording many years of operation traces have been accumulated.In the same time,the HPC cloud makes it possible to ac... Nowadays,high-performance computing(HPC)clusters are increasingly popular.Large volumes of job logs recording many years of operation traces have been accumulated.In the same time,the HPC cloud makes it possible to access HPC services remotely.For executing applications,both HPC end-users and cloud users need to request specific resources for different workloads by themselves.As users are usually not familiar with the hardware details and software layers,as well as the performance behavior of the underlying HPC systems.It is hard for them to select optimal resource configurations in terms of performance,cost,and energy efficiency.Hence,how to provide on-demand services with intelligent resource allocation is a critical issue in the HPC community.Prediction of job characteristics plays a key role for intelligent resource allocation.This paper presents a survey of the existing work and future directions for prediction of job characteristics for intelligent resource allocation in HPC systems.We first review the existing techniques in obtaining performance and energy consumption data of jobs.Then we survey the techniques for single-objective oriented predictions on runtime,queue time,power and energy consumption,cost and optimal resource configuration for input jobs,as well as multi-objective oriented predictions.We conclude after discussing future trends,research challenges and possible solutions towards intelligent resource allocation in HPC systems. 展开更多
关键词 high-performance computing performance prediction job characteristics intelligent resource allocation cloud computing machine learning
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Intelligent computing budget allocation for on-road tra jectory planning based on candidate curves
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作者 Xiao-xin FU Yong-heng JIANG +2 位作者 De-xian HUANG Jing-chun WANG Kai-sheng HUANG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2016年第6期553-565,共13页
In this paper, on-road trajectory planning is solved by introducing intelligent computing budget allocation(ICBA) into a candidate-curve-based planning algorithm, namely, ordinal-optimization-based differential evolut... In this paper, on-road trajectory planning is solved by introducing intelligent computing budget allocation(ICBA) into a candidate-curve-based planning algorithm, namely, ordinal-optimization-based differential evolution(OODE). The proposed algorithm is named IOODE with ‘I' representing ICBA. OODE plans the trajectory in two parts: trajectory curve and acceleration profile. The best trajectory curve is picked from a set of candidate curves, where each curve is evaluated by solving a subproblem with the differential evolution(DE) algorithm. The more iterations DE performs, the more accurate the evaluation will become. Thus, we intelligently allocate the iterations to individual curves so as to reduce the total number of iterations performed. Meanwhile, the selected best curve is ensured to be one of the truly top curves with a high enough probability. Simulation results show that IOODE is 20% faster than OODE while maintaining the same performance in terms of solution quality. The computing budget allocation framework presented in this paper can also be used to enhance the efficiency of other candidate-curve-based planning methods. 展开更多
关键词 intelligent computing budget allocation Trajectory planning On-road planning intelligent vehicles Ordinal optimization
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