FRACTAL ANALYSIS OF TEMPERATURE TIME SERIES FROM BATCH SUGARCANE CRYSTALLIZATION

Batch crystallization is an important process in many industries, for example, fine chemicals, foods, and pharmaceuticals. Monitoring of the main process variables is essential for process understanding, diagnosis, and for product quality control. It is known that temperature has a critical effect on crystallization. Temperature measurements from crystallization systems display fluctuations with apparently random and complex behavior. Fractal analysis of complex time series has received significant attention in the last few years due to its capability for extraction of hidden useful information of the underlying phenomena behind the time-series complexity. In this work, the potential of fractal analysis of time series for diagnosis of industrial crystallization processes is investigated using temperature measurements from a typical batch sugarcane crystallization system. The crystallizer was operated at different cooling profiles, finding that fractal index is directly related to crystal mean diameter dynamics. Thus, we establish that fractal analysis is a simple and robust alternative for the characterization of batch crystallization.

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Representative Time Series Fractal Analysis of the Traffic Flows of a Dedicated High-Speed Link

10.31219/osf.io/svr6j ◽

2021 ◽

Author(s):

Ginno Millan ◽

manuel vargas ◽

Guillermo Fuertes

Keyword(s):

Time Series ◽

Traffic Flow ◽

Long Range ◽

Computer Networks ◽

Fractal Analysis ◽

High Speed ◽

Long Range Dependence ◽

Traffic Flows ◽

Speed Computer

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link exhibited fractal behavior since the Hurst exponent was in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between -0.5, 0. Based on these results, it is ideal to characterize both the singularities of the fractal traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyzes, the fact that the traffic flows of current computer networks exhibited fractal behavior with a long-range dependence was reaffirmed.

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Time Series Analysis of Computer Network Traffic in a Dedicated Link Aggregation

10.36227/techrxiv.14575941 ◽

2021 ◽

Author(s):

Ginno Millán ◽

Gastón Lefranc ◽

Román Osorio-Comparán ◽

Víctor Lomas-Berrie

Keyword(s):

Time Series ◽

Traffic Flow ◽

Long Range ◽

Computer Networks ◽

Fractal Analysis ◽

High Speed ◽

Computer Network ◽

Time Series Analyses ◽

Speed Computer

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the Internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link have fractal behavior when the Hurst exponent is in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between –0.5, 0. Based on these results, it is ideal to characterize both the singularities of the traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyses, the fact that the traffic flows of current computer networks exhibit fractal behavior with a long-range dependency is reaffirmed.

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Time series analysis of computer network traffic in a dedicated link aggregation

10.31219/osf.io/54jas ◽

2021 ◽

Author(s):

Ginno Millán

Keyword(s):

Time Series ◽

Traffic Flow ◽

Long Range ◽

Computer Networks ◽

Fractal Analysis ◽

High Speed ◽

Computer Network ◽

Time Series Analyses ◽

Speed Computer

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the Internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link have fractal behavior when the Hurst exponent is in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between –0.5, 0. Based on these results, it is ideal to characterize both the singularities of the traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyses, the fact that the traffic flows of current computer networks exhibit fractal behavior with a long-range dependency is reaffirmed.

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Time Series Analysis of Computer Network Traffic in a Dedicated Link Aggregation

10.36227/techrxiv.14575941.v1 ◽

2021 ◽

Author(s):

Ginno Millán ◽

Gastón Lefranc ◽

Román Osorio-Comparán ◽

Víctor Lomas-Berrie

Keyword(s):

Time Series ◽

Traffic Flow ◽

Long Range ◽

Computer Networks ◽

Fractal Analysis ◽

High Speed ◽

Computer Network ◽

Time Series Analyses ◽

Speed Computer

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the Internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link have fractal behavior when the Hurst exponent is in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between –0.5, 0. Based on these results, it is ideal to characterize both the singularities of the traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyses, the fact that the traffic flows of current computer networks exhibit fractal behavior with a long-range dependency is reaffirmed.

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Time Series Analysis of Computer Network Traffic in a Dedicated Link Aggregation

10.20944/preprints202105.0506.v1 ◽

2021 ◽

Author(s):

Ginno Millán ◽

Gastón Lefranc ◽

Román Osorio-Comparán ◽

Víctor Lomas-Barrie

Keyword(s):

Time Series ◽

Traffic Flow ◽

Long Range ◽

Computer Networks ◽

Fractal Analysis ◽

High Speed ◽

Computer Network ◽

Time Series Analyses ◽

Speed Computer

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the Internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link have fractal behavior when the Hurst exponent is in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between –0.5, 0. Based on these results, it is ideal to characterize both the singularities of the traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyses, the fact that the traffic flows of current computer networks exhibit fractal behavior with a long-range dependency is reaffirmed.

Download Full-text