Semantic Waveform Measurement Method of Kansei Transition for Time-series Media Contents

Saturation flow rate (SFR) is a fundamental parameter to the level of service evaluation, lane capacity calculation, and signal timing plan optimization at signalized intersections. It is affected by a variety of factors including weather conditions, lane width, and the type of the driver. How to accurately estimate the SFR remains one of the most important tasks in traffic engineering. Existing studies generally rely on the field measurement method which requires a large number of people collecting data at the intersection. As a result, the method incurs a high economic cost and cannot adapt to the dynamic change of SFR. In recent years, video detectors have been widely installed at intersections which are capable of recording the time each vehicle passes the stop line, the number plate of each vehicle, and the vehicle type. This paper therefore aims to propose an automatic estimation method for the SFR based on video detector data in order to overcome the limitation of the field measurement method. A prerequisite for estimating the SFR is to recognize the saturation headway. We consider the actual vehicle headway as time series and build an auxiliary regression equation whose parameters are estimated through the ordinary least squares method. We employ the Dickey-Fuller test to verify whether the headways in the time series are saturation headways. An iterative method using quantiles is proposed to filter out abnormal data. The SFR is finally calculated using the average value of saturation headways. To demonstrate the proposed method, we conduct a case study using data from an intersection with three entrance lanes in Qujing city, Yunnan Province, China. The overall estimation process is displayed and the impacts of quantile selection and data duration on the estimation accuracy are analyzed.

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The Development of Predictability Measurement Method for Winter Monsoon Forecasts in Thailand

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.866.160 ◽

2017 ◽

Vol 866 ◽

pp. 160-163

Author(s):

Sunisa Saiuparad

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Shallow Water ◽

Measurement Method ◽

Climate Model ◽

Global Climate Model ◽

Winter Monsoon ◽

Water Model ◽

Shallow Water Model ◽

Series Analysis

The forecasting method is important because it can predict phenomena in the future. The accuracy of the forecast depends on the model and the initial conditions. In addition, the predictability measurement method is important can be check the accurate of forecasts. In this research is winter monsoon forecasts in Thailand by the shallow water model. The data from The Bjerknes Centre for Climate Research (BCCR), University of Bergen, Norway. The global climate model is Bergen Climate Model (BCM) Version 2.0 (BCCR-BCM2.0) of the Intergovernmental Panel on Climate Change (IPCC) is used. The Lyapunov exponent (LE) is the predictability measurement method for verify the efficiency of model and establish the new predictability measurement method by the time series analysis. The result to show that the new predictability measurement method by the time series analysis can be measure the efficiency of the winter monsoon forecasts in Thailand by the shallow water model for December 2015 to December 2056 are suitable.

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A New Time Series Similarity Measurement Method Based on the Morphological Pattern and Symbolic Aggregate Approximation

IEEE Access ◽

10.1109/access.2019.2934109 ◽

2019 ◽

Vol 7 ◽

pp. 109751-109762 ◽

Cited By ~ 1

Author(s):

Jiancheng Yin ◽

Rixin Wang ◽

Huailiang Zheng ◽

Yuantao Yang ◽

Yuqing Li ◽

...

Keyword(s):

Time Series ◽

Measurement Method ◽

Similarity Measurement ◽

Morphological Pattern ◽

Symbolic Aggregate Approximation ◽

New Time

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The LDE-Type Flare Occurrence (1969-1993)

International Astronomical Union Colloquium ◽

10.1017/s0252921100025458 ◽

1994 ◽

Vol 144 ◽

pp. 279-282

Author(s):

A. Antalová

Keyword(s):

Time Series ◽

Fourier Analysis ◽

Sunspot Cycle ◽

List Type ◽

Type Number ◽

Solar Cycles ◽

Type Iv ◽

Long Duration ◽

Cycle Maximum ◽

Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

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