scholarly journals I tornado negli USA: un'analisi dell'archivio dati (1950-2018)

2021 ◽  
pp. 3-9
Author(s):  
Sergio Pinna
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Economic Losses ◽  
Storm Prediction

A statistical analysis of the data contained in the NOAA’s Storm Prediction Center tornado archive (covering the period 1950-2018) has been carried out. The actual average values of the frequencies for the various intensity classes could be effectively provided for the period 1991-2018, because of some inhomogeneities of the archive due to variations in the methods and procedures of tornado reporting. The time series of the frequency of F2, F3, F4 and F5 events showed decreasing trends; this decline seems largely due to a significant reduction of the strongest events. This interpretation is supported by the decreasing trends of normalized economic losses and of number of victims.

scholarly journals Proposal for Tower Crane Productivity Indicators Based on Data Analysis in the Era of Construction 4.0

Buildings ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 21
Author(s):  
Thomas Danel ◽  
Zoubeir Lafhaj ◽  
Anand Puppala ◽  
Sophie Lienard ◽  
Philippe Richard
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Data Collection ◽  
Structural Phase ◽  
Sampling Technique ◽  
Construction Site ◽  
Data Logger ◽  
Tower Crane ◽  
Productivity Indicators

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.

scholarly journals Monitoring High Throughput Distributed System using Statistical Data Analysis

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.

LONG RANGE CORRELATION PROPERTIES OF AFTERSHOCK RELAXATION SIGNALS

Fractals ◽  
1995 ◽  
Vol 03 (04) ◽  
pp. 839-847 ◽  
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.

The Statistical Analysis of Time Series (T. W. Anderson)

SIAM Review ◽  
1976 ◽  
Vol 18 (2) ◽  
pp. 313-314
Author(s):  
Andrey Feuerverger
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Analysis Of Time Series

A Statistical Analysis of Cyclical Changes in the Time Series of Meteorological Parameters in the Southwest of the Valdai Hills

2018 ◽  
Vol 73 (1) ◽  
pp. 61-67
Author(s):  
V. A. Gazaryan ◽  
Yu. A. Kurbatova ◽  
T. A. Ovsyannikov ◽  
N. E. Shapkina
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Meteorological Parameters

Application of Kalman filter to reproduce displacement pattern along with the unknown soil properties of slow-moving landslides

2021 ◽  
Author(s):  
Mohit Mishra ◽  
Gildas Besançon ◽  
Guillaume Chambon ◽  
Laurent Baillet ◽  
Arnaud Watlet ◽  
...  
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Water Table ◽  
Field Data ◽  
Friction Angle ◽  
Early Warning Systems ◽  
Economic Losses ◽  
Slide Block ◽  
Slow Moving ◽  
Parameter Values

<p><span>Landslides display heterogeneity in movement types and rates, ranging from creeping motion to catastrophic acceleration. In most of the catastrophic events, rocks, debris, or soil can travel at several tens of meters per year speed, causing significant cost in life losses, infrastructure, economy, and ecosystem of the region. In contrast, slow-moving landslides display typical velocities scaling from few centimeters to several meters per year. Although slow-moving landslides rarely claim life losses, they can still cause considerable damage to public and private infrastructure. Sometimes these slow, persistent landslides eventually lead to catastrophic acceleration, e.g., clayey landslides are prone to these transitions. Such events need to be detected by Early Warning Systems (EWS) in advance to take timely actions to reduce life and economic losses. Several approaches are proposed to forecast the time of failure; still, there is a need to improve prediction strategies and EWS’s. </span></p><p><span>Here we present state and parameter estimation for a simplified viscoplastic sliding model of a landslide using a Kalman filter approach, which is termed as an observer problem in control theory. The model under investigation is based on underlying mechanics (physics-based model) that portray a landslide behavior. In this model, a slide block is assumed to be placed on an inclined surface, where landslide (slide block) motion is regulated by basal pore fluid pressure and opposed by sliding resistance governed by friction, cohesion, and viscosity. This model is described by an Ordinary Differential Equation (ODE) with displacement as a state and landslide material and geometrical properties as parameters. In this approach, known parameter values (landslide geometrical parameters and some material properties) and water table height time-series are provided as input. Finally, two illustrative examples validate the presented approach: i) a synthetic case study and ii) Hollin hill landslide (Uhlemann et al., 2016) field data. </span></p><p><span>In both examples, displacement, friction angle, and viscosity are well estimated from known parameter values, water table height time-series, and displacement measurements. In the simulation results for the Hollin Hill field data, it is observed that friction angle almost remains constant while viscosity varies significantly through time.</span></p><p> </p><p><span>Uhlemann, S., Smith, A., Chambers, J., Dixon, N., Dijkstra, T., Haslam, E., Meldrum P., Merritt, A., Gunn, D., and Mackay, J., (2016). Assessment of ground-based monitoring techniques applied to landslide investigations. </span><em><span>Geomorphology</span></em><span>, 253, 438-451. doi:10.1016/j.geomorph.2015.10.027.</span></p>

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