A cluster analysis selection strategy for supersaturated designs

Zhao, Shengli; Zhang, Runchu

doi:10.1016/j.csda.2010.01.010

Cited by 23 publications

(11 citation statements)

References 25 publications

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“…Let r j denote the number of nodes that can perform sub-task, and r j ¼ P N i¼1 t ij . As stated above, in order to avoid some nodes joining in too many sub-tasks while others only join in few subtasks, we introduce the adjustment coefficient λi [26]. When a node has already been selected in the previous sub-task allocation rounds, this parameter may reduce the probability that a node being selected.…”

Section: Model Definitionmentioning

confidence: 99%

LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network

Zhou

Zhang²,

Wang³

et al. 2019

J Wireless Com Network

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Mobile crowd sensing (MCS) is a novel emerging paradigm that leverages sensor-equipped smart mobile terminals (e.g., smartphones, tablets, and intelligent wearable devices) to collect information. Compared with traditional data collection methods, such as construct wireless sensor network infrastructures, MCS has advantages of lower data collection costs, easier system maintenance, and better scalability. However, the limited capabilities make a mobile crowd terminal only support limited data types, which may result in a failure of supporting high-dimension data collection tasks. This paper proposed a task allocation algorithm to solve the problem of high-dimensional data collection in mobile crowd sensing network. The low-cost and balance-participating algorithm (LCBPA) aims to reduce the data collection cost and improve the equality of node participation by trading-off between them. The LCBPA performs in two stages: in the first stage, it divides the high-dimensional data into fine-grained and smaller dimensional data, that is, dividing an mdimension data collection task into k sub-task by K-means, where (k < m). In the second stage, it assigns different nodes with different sensing capability to perform sub-tasks. Simulation results show that the proposed method can improve the task completion ratio, minimizing the cost of data collection.

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Section: Model Definitionmentioning

confidence: 99%

LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network

Zhou

Zhang²,

Wang³

et al. 2019

J Wireless Com Network

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show abstract

“…It has been accepted that rolling bearings will generally experience several degradation conditions from normal to failure in degradation process. 31 In order to analyze the degradation principle and recognize degradation conditions effectively, BSE and GG fuzzy clustering method are combined and employed for solving the degradation condition recognition issue in this article, and the basic flow of the so-called BSE-GG method is shown in Figure 1.…”

Section: Degradation Condition Recognition Based On Bse-ggmentioning

confidence: 99%

Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering

Wang

Xiong

Sun

et al. 2018

Advances in Mechanical Engineering

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A method based on basic scale entropy and Gath-Geva fuzzy clustering is proposed in order to solve the issue of bearing degradation condition recognition. The evolution rule of basic scale entropy for bearing in performance degradation process is analyzed first, and the monotonicity and sensitivity of basic scale entropy are emphasized. Considering the continuity of the bearing degradation condition at the time scale, three-dimensional degradation eigenvectors are constructed including basic scale entropy, root mean square, and degradation time, and then, Gath-Geva fuzzy clustering method is used to divide different conditions in performance degradation process, thus realizing performance degradation recognition for bearing. Bearing whole lifetime data from IEEE PHM 2012 is adopted in application and discussion, and fuzzy c-means clustering and Gustafson-Kessel clustering algorithms are analyzed for comparison. The results show that the proposed basic scale entropy-Gath-Geva method has better clustering effect and higher time aggregation than the other two algorithms and is able to provide an effective way for mechanical equipment performance degradation recognition.

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“…If the statistic instant for spare space detection is 1.5, the identified occupation records within the statistic time are 3 , 4 , and 5 . Based on the analysis of the occupation records, the employed resource units at this moment are 1,1 , 1,2 , 1,3 , 1,4 , and 1,5 . Suppose the capacity of 1 is 6; then, the spare space of 1 at time instant 1.5 is 1.…”

Section: Inputmentioning

confidence: 99%

“…where is a flag to judge whether is a type of computing nodes, which is described in (5), and ( ) is used to judge whether is empty at , presented in (6). …”

Section: Load Balancing Model In Fog Environmentmentioning

confidence: 99%

Dynamic Resource Allocation for Load Balancing in Fog Environment

Qing

et al. 2018

Wireless Communications and Mobile Computing

162

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Fog computing is emerging as a powerful and popular computing paradigm to perform IoT (Internet of Things) applications, which is an extension to the cloud computing paradigm to make it possible to execute the IoT applications in the network of edge. The IoT applications could choose fog or cloud computing nodes for responding to the resource requirements, and load balancing is one of the key factors to achieve resource efficiency and avoid bottlenecks, overload, and low load. However, it is still a challenge to realize the load balance for the computing nodes in the fog environment during the execution of IoT applications. In view of this challenge, a dynamic resource allocation method, named DRAM, for load balancing in fog environment is proposed in this paper. Technically, a system framework for fog computing and the load-balance analysis for various types of computing nodes are presented first. Then, a corresponding resource allocation method in the fog environment is designed through static resource allocation and dynamic service migration to achieve the load balance for the fog computing systems. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of DRAM.

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A cluster analysis selection strategy for supersaturated designs

Cited by 23 publications

References 25 publications

LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network

LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network

Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering

Dynamic Resource Allocation for Load Balancing in Fog Environment

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