scholarly journals Trustworthy Service Selection Integrating Cloud Model and Possibility Degree Ranking of Interval Numbers

2017 ◽  
Vol 26 (6) ◽  
pp. 1177-1183 ◽  
Author(s):  
Hua Ma ◽  
Zhigang Hu ◽  
Meiling Cai
Author(s):  
Shangguang Wang ◽  
Zibin Zheng ◽  
Qibo Sun ◽  
Hua Zou ◽  
Fangchun Yang

2019 ◽  
Vol 11 (8) ◽  
pp. 2268 ◽  
Author(s):  
Wang ◽  
Wang ◽  
Chen

To evaluate the ecological niche of photovoltaic agriculture in China, an evaluation index system was constructed. Based on the presentation form of interval numbers, we used the interval entropy weight method and interval cloud model to measure the niche state value and niche role value of photovoltaic agriculture. In this way, we determined the development trend of the ecological niche of photovoltaic agriculture. The results show that Chinese photovoltaic agriculture is in a good state and plays a good, but weak, role. The ecological niche of China’s photovoltaic agriculture will undergo a four-stage evolution process: positioning, integration, leap, and symbiosis. China has completed the positioning stage and entered the integration stage. Hence, it is important to constantly improve the level of industrial integration technology and to form a new photovoltaic agriculture recycling economic ecosystem.


2013 ◽  
Vol 336-338 ◽  
pp. 399-403
Author(s):  
Ying Wang ◽  
Da Yang ◽  
Yang Liu ◽  
Ce Chen

Considering the multi-valued mapping relationship of equipment response event to voltage sag, a lattice ordered evaluation method is proposed in this study. Each possible resulting state of equipment response event is described by an interval number. The interval numbers is with the characteristics of lattice order presented by the upper and lower probabilities. The possibility degree matrix is introduced to compare resulting states without satisfying the axioms of connectedness. Personal computer is simulated and compared testing results. The results have shown the validity and feasibility.


2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xu Wu

Mobile cloud computing (MCC) has attracted extensive attention in recent years. With the prevalence of MCC, how to select trustworthy and high quality mobile cloud services becomes one of the most urgent problems. Therefore, this paper focuses on the trustworthy service selection and recommendation in mobile cloud computing environments. We propose a novel service selection and recommendation model (SSRM), where user similarity is calculated based on user context information and interest. In addition, the relational degree among services is calculated based on PropFlow algorithm and we utilize it to improve the accuracy of ranking results. SSRM supports a personalized and trusted selection of cloud services through taking into account mobile user’s trust expectation. Simulation experiments are conducted on ns3 simulator to study the prediction performance of SSRM compared with other two traditional approaches. The experimental results show the effectiveness of SSRM.


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