• KIISE Transactions on Computing Practices
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• v.21 no.5
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• pp.379-386
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• 2015

We propose a novel user-perceived performance optimization framework for the Android platform that takes advantage of the user-centric response time analysis. To this end, we propose a new definition of response time, which we call the user-centric response time, as a metric for the quality of user-perceived performance of the smartphone application. In this paper, we describe the design and implementation of an on-line user-centric response time analyzer for Android-based smartphones, which provides smartphone application developers with valuable insight for user-perceived performance optimization. We implemented the user-centric response time analyzer on the Android platform, version 4.0.4 (ICS) running on a Galaxy Nexus smartphone. From our experimental results, the proposed user-centric response time analyzer accurately estimates user-centric response times with an accuracy of 92.0% compared to manually measured times with less than 1% performance penalty. In order to evaluate the efficiency of the proposed framework, we were able to reduce the user-centric response time of the target application by up to 16.4% based on the evaluation results by the proposed framework.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.51-59
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• 2019

In this paper, we analyzed the relationship between charging operation system and electricity charges connected with charging infrastructure among data of many demonstration projects focused on electric vehicles recently. At this point in time, due to the rapid increase in demand for the electric charging infrastructure that will take place in the future, we can prepare for an upcoming era in the sense of forecasting the demand value. At the same time, demonstrating and modeling optimized system modeling centering on sites is a prerequisite. The modeling based on the existing small - scale simulation and the design of the operating system are based on the data linkage analysis. In this paper, we implemented a new optimized system modeling and introduced it as a standard format to analyze time - dependent time - divisional data for each vehicle and user in each point and node. In order to verify the efficiency of the optimization based on the data linkage analysis for the actual implemented electric car charging infrastructure and operation system.