Classification and Statistical Analysis of Schizophrenic and Normal EEG Time Series

This article proposes a methodology to measure the productivity of a construction site through the analysis of tower crane data. These data were obtained from a data logger that records a time series of spatial and load data from the lifting machine during the structural phase of a construction project. The first step was data collection, followed by preparation, which consisted of formatting and cleaning the dataset. Then, a visualization step identified which data was the most meaningful for the practitioners. From that, the activity of the tower crane was measured by extracting effective lifting operations using the load signal essentially. Having used such a sampling technique allows statistical analysis on the duration, load, and curvilinear distance of every extracted lifting operation. The build statistical distribution and indicators were finally used to compare construction site productivity.

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The Statistical Analysis of Time Series.

Journal of the Royal Statistical Society Series A (Statistics in Society) ◽

10.2307/2983180 ◽

1996 ◽

Vol 159 (2) ◽

pp. 344

Author(s):

P. J. Young ◽

T. W. Anderson

Keyword(s):

Time Series ◽

Statistical Analysis ◽

Analysis Of Time Series

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Monitoring High Throughput Distributed System using Statistical Data Analysis

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9810.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 4590-4596

Keyword(s):

Time Series ◽

Statistical Analysis ◽

Distributed System ◽

High Throughput ◽

Statistical Data ◽

Time Interval ◽

Statistical Data Analysis ◽

Cloud Data ◽

Data Pipeline ◽

Cloud Data Centers

Monitoring high throughput distributed system by using a statistical analysis of the “historical time series” of an Instrumentation Data”. “The Pipeline has been made to process the information which can be otherwise called data pipeline, is a lot of information handling components associated in arrangement, where yield of one component is the contribution of the next one”. Several codes are giving different visualization for statistical analysis of data. “Network and Cloud Data Centers” generate a lot of data every second; this data can be gathered as period arrangement information. A timeseries is a grouping taken at progressive similarly dispersed focuses in time that implies at a particular time interval to a particular time, the estimations of explicit information that was taken is known as information of a time-series. “This time-series information can be gathered utilizing framework measurements like CPU, Memory, and Disk utilization”. The TICK and ELK Stack is abbreviation for a foundation of open source instruments worked “to make collection, storage, graphing, and alerting” on time arrangement data incredibly easy. As an information collector, using Telegraf, “for storing and analyzing” information and the time-series database InfluxDB and Elasticsearch. For plotting and visualizing used Grafana and Kibana. Watchman is utilized for alert refinement and once system metrics usage exceeds the specified threshold, the alert is generated and sends it to the Telegram.

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LONG RANGE CORRELATION PROPERTIES OF AFTERSHOCK RELAXATION SIGNALS

Fractals ◽

10.1142/s0218348x95000746 ◽

1995 ◽

Vol 03 (04) ◽

pp. 839-847 ◽

Cited By ~ 8

Author(s):

A. VESPIGNANI ◽

A. PETRI ◽

A. ALIPPI ◽

G. PAPARO ◽

M. COSTANTINI

Keyword(s):

Time Series ◽

Acoustic Emission ◽

Statistical Analysis ◽

Power Spectrum ◽

Power Law ◽

Amplitude Distribution ◽

Relaxation Processes ◽

Critical Dynamics ◽

Range Correlation ◽

Correlation Properties

Relaxation processes taking place after microfracturing of laboratory samples give rise to ultrasonic acoustic emission signals. Statistical analysis of the resulting time series has revealed many features which are characteristic of critical phenomena. In particular, the autocorrelation functions obey a power-law behavior, implying a power spectrum of the kind 1/f. Also the amplitude distribution N(V) of such signals follows a power law, and the obtained exponents are consistent with those found in other experiments: N(V) dV≃V–γ dV, with γ=1.7±0.2. We also analyzed the distribution N(τ) of the delay time τ between two consecutive acoustic emission events. We found that a N(τ) distribution rather close to a power law constitutes a common feature of all the recorded signals. These experimental results can be considered as a striking evidence for a critical dynamics underlying the microfracturing processes.

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