Predicting Quality of Service via Leveraging Location Information

Chen, Liang; Xie, Fei; Zheng, Zibin; Wu, Yaoming

doi:10.1155/2019/4932030

Cited by 19 publications

(9 citation statements)

References 36 publications

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“…Since PE was first suggested, numerous modifications of PE have been developed [9][10][11][12][13]. Among them, the improved permutation entropy (IPE) might be closest to LPE in our study [13] since it is a kind of coarse-grained PE splitting the quantity space of a time series with fixed bins. IPE should use large bins due to a convergence problem, which allows a detection of the change only in a large scale.…”

Section: Lpe Reflecting the Depth Of Anesthesiamentioning

confidence: 96%

“…For example, the permutation entropy (PE), based on the quantification of pattern motifs, is one of the most widely used, providing a simple, yet powerful way to extract a measure of symbolic complexity from a given time series [9][10][11][12][13]. Most variations of PE-type algorithms, however, experience a common difficulty in tackling with empirical time series contaminated by a various form of noise and artifacts, in particular, noisy physiological signals such as EEG data during general anesthesia.…”

Section: Introductionmentioning

confidence: 99%

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A robust complexity measure for noisy EEG time series under dynamic transitions during anesthesia

Joo

Kim

Noh

et al. 2021

J. Korean Phys. Soc.

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Measuring complexity from noisy time series provides a crucial insight into the understanding and monitoring of pattern dynamics of complex dynamical systems, including physiological systems such as complex electroencephalogram (EEG) times series observed during anesthesia. We introduce a simple, yet noble complexity measure, called the lumped permutation entropy (LPE) based on the permutation entropy (PE), which allows a tie rank on the pattern formation and shows the robustness under the influence of strong noise, overcoming some limitations of PE and its variants in noisy signals.The robustness of LPE is demonstrated for complex time series from a typical chaotic dynamical system and is applied to empirical electroencephalographic (EEG) data obtained from subjects anesthetized by propofol. In particular, we found that the entropic complexity of EEG time series based on LPE is inversely correlated with plasma concentration of propofol and shows better performance and more robustness than PE and other types of entropic algorithms in indicating the anesthetic depth during the progress of general anesthesia. LPE can be used as complexity measure for real-time monitoring of anesthetic depth during anesthesia.

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Section: Lpe Reflecting the Depth Of Anesthesiamentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

A robust complexity measure for noisy EEG time series under dynamic transitions during anesthesia

Joo

Kim

Noh

et al. 2021

J. Korean Phys. Soc.

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“…Fuzzy comprehensive evaluation method is a mathematical model that uses specific methods of fuzzy measurement, fuzzy statistics, and fuzzy evaluation in the process of calculation. It can be used to solve the evaluation problem of a fuzzy phenomenon [8]. e theoretical methods of sets and fuzzy mathematics can be used to digitize the fuzzy quantity in practice for quantitative evaluation.…”

Section: Fuzzy Comprehensive Evaluationmentioning

confidence: 99%

Community Public Safety Evaluation System Based on Location Information Service Architecture

Zhao

2021

Mobile Information Systems

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The establishment of communities is for the better operation and management of cities, and community safety has gradually become a topic of increasing concern. It is not only an important part of the community public safety system, but also the foundation of social and economic development. With the improvement of the overall economic level of society, wireless network coverage has been greatly improved, smart devices have also been rapidly developed and popularized, and location-based information service systems have gradually entered the field of community public safety. At present, my country still lacks experience in community safety management. At the same time, there is no complete set of standards and evaluation system for the public safety evaluation system of urban communities. However, community public safety involves people’s livelihood issues and the stability and development of the city. Therefore, it is urgent to build a community public safety evaluation system based on the location information service architecture. Based on the safety community research and location service system, this paper uses the combination of theory and practice, quantitative and qualitative methods, and tentatively explored and constructed a community public safety evaluation system based on the location information service architecture. This article integrates location information service technology into the community public safety evaluation system, selects three indicator factors, investigates a more comprehensive evaluation subject, and combines the evaluation subject’s qualitative evaluation of community public safety with quantitative evaluation through fuzzy comprehensive evaluation. This paper conducts an empirical analysis of a certain community and finds that its public safety evaluation score is 62.6232 points, which is in the “general” ranks, and it needs to be combined with the location information service system to improve the community safety issues. This study has guiding significance for ensuring community public safety and has theoretical and practical significance for community construction and development.

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“…Zheng et al [22] proposes a modified collaborative filter so that the prediction considers measurements by both other similar clients and other nearby ones (in the topological sense). Chen et al [25] uses the same dataset as [12], complementing QoS information with coarse-level location data, like country or Internet Autonomous System (AS) Number. While it is interesting to select a web service provider at a worldwide scale, this information is not helpful in a community network in which all nodes will likely belong to the same country and a very few, closely located ASs.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Web Proxy Response Times in Community Networks Using Matrix Factorization Algorithms

et al. 2020

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In community networks, users access the web using a proxy selected from a list, normally without regard to its performance. Knowing which proxies offer good response times for each client would improve the user experience when navigating, but would involve intensive probing that would in turn cause performance degradation of both proxies and the network. This paper explores the feasibility of estimating the response times for each client/proxy pair by probing only a few of the existing pairs and then using matrix factorization. To do so, response times are collected in a community network emulated on a testbed platform, then a small part of these measurements are used to estimate the remaining ones through matrix factorization. Several algorithms are tested; one of them achieves estimation accuracy with low computational cost, which renders its use feasible in real networks.

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Predicting Quality of Service via Leveraging Location Information

Cited by 19 publications

References 36 publications

A robust complexity measure for noisy EEG time series under dynamic transitions during anesthesia

A robust complexity measure for noisy EEG time series under dynamic transitions during anesthesia

Community Public Safety Evaluation System Based on Location Information Service Architecture

Estimation of Web Proxy Response Times in Community Networks Using Matrix Factorization Algorithms

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