scholarly journals ON THE DISTRIBUTION OF THE RANDOM ARRIVAL TIME

2020 ◽  
Author(s):  
BORIS DAVYDOV ◽  
VLADIMIR CHEBOTAREV ◽  
KSENIYA KABLUKOVA
Keyword(s):  
Arrival Time

An Arrival Time and Duration Estimation of a Signal

2000 ◽  
Vol 54 (8-9) ◽  
pp. 122-133
Author(s):  
Andrey Pavlovich Trifonov ◽  
Yurii Eduardovich Korchagin
Keyword(s):  
Arrival Time ◽  
Duration Estimation

The Prediction of Rainfall and the Direction of Rainfall by a Narrow Area Radar Rain Gauge

1993 ◽  
Vol 28 (11-12) ◽  
pp. 79-85
Author(s):  
Shinichi Kondo
Keyword(s):  
Small Area ◽  
Arrival Time ◽  
Rain Gauge ◽  
Rain Gauges ◽  
Sewage Plant ◽  
Future Prediction ◽  
Near Future ◽  
Narrow Area

Narrow area radar rain gauges are currently used for measuring rainfall. These radar gauges can measure rainfall accurately in a small area. In sewage plants it is important to predict stormwater. To calculate predicted stormwater the results of rainfall and a prediction of the near future are necessary. Recently urbanization has made the arrival time of flooding to the sewage plant much shorter. This paper deals with system technologies for the near future prediction of radar rain gauge rainfall. The method of prediction of rainfall, calculation of results and other considerations are described.

TSUNAMI ARRIVAL TIME CALCULATION IN CONSTRUCTION OF TSUNAMI SCENARIO BANK AND INFLUENCED BY TSUNAMI INUNDATION CALCULATION WITH DIFFERENCES OF PROTECTIVE FACILITY

2019 ◽  
Vol 75 (2) ◽  
pp. I_379-I_384
Author(s):  
Naotaka CHIKASADA ◽  
Makoto NEMOTO ◽  
Jumpei TAKAYAMA ◽  
Wataru SUZUKI ◽  
Takayuki MIYOSHI ◽  
...  
Keyword(s):  
Arrival Time ◽  
Tsunami Inundation ◽  
Time Calculation

On some characterizations of the poisson process

1989 ◽  
Vol 26 (01) ◽  
pp. 176-181
Author(s):  
Wen-Jang Huang
Keyword(s):  
Poisson Process ◽  
Renewal Process ◽  
Arrival Time ◽  
Random Variables ◽  
Poisson Processes

In this article we give some characterizations of Poisson processes, the model which we consider is inspired by Kimeldorf and Thall (1983) and we generalize the results of Chandramohan and Liang (1985). More precisely, we consider an arbitrarily delayed renewal process, at each arrival time we allow the number of arrivals to be i.i.d. random variables, also the mass of each unit atom can be split into k new atoms with the ith new atom assigned to the process Di, i = 1, ···, k. We shall show that the existence of a pair of uncorrelated processes Di, Dj, i ≠ j, implies the renewal process is Poisson. Some other related characterization results are also obtained.

Experimental Research on Damages Detection of Pipeline Structure by Using PZT-Based Waves

2011 ◽  
Author(s):  
Shi Yan ◽  
Binbin He ◽  
Naizhi Zhao
Keyword(s):  
Damage Detection ◽  
Lead Zirconate Titanate ◽  
Smart Materials ◽  
Arrival Time ◽  
Mode Conversion ◽  
Guided Wave ◽  
The Other ◽  
Damage Location ◽  
The Waves ◽  
Peak Value

Pipeline structure may generate damages during its service life due to the influence of environment or accidental loading. The damages need to be detected and repaired if they are severe enough to influence the transportation work. Non-destructive detection using smart materials combined with suitable diagonal algorithms are widely used in the field of structural health monitoring (SHM). Piezoelectric ceramics (such as Lead Zirconate Titanate, PZT) is one of the smart materials to be applied in the SHM due to the piezoelectric effect. So far, the PZT-based wave method is widely used for damage detection of structures, in particular, pipeline structures. A series of piezoelectric patches are bonded on the surface of the pipeline structure to monitor the damages such as local crack or effective area reduction due to corrosion by using diagonal waves. The damage of the pipeline structure can be detected by analysis of the received diagonal waves which peak value, phase, and arriving time can be deferent from the health ones. The response of the diagonal wave is not only correlated to the damage location through estimation of the arrival time of the wave peak, but also associated with the peak value of the wave for the reduction of wave energy as the guided wave passing through the damages. Therefore, the presence of damages in the pipeline structure can be detected by investigating the parameter change of the guided waves. The change of the wave parameters represents the attenuation, deflection and mode conversion of the waves due to the damages. In addition, the guided wave has the ability of quick detecting the damage of the pipeline structure and the simplicity of generating and receiving detection waves by using PZT patches. To verify the proposed method, an experiment is designed and tested by using a steel pipe bonded the PZT patches on the surface of it. The PZT patches consist of an array to estimate the location and level of the damage which is simulated by an artificial notch on the surface of the structure. The several locations and deep heights of the notches are considered during the test. A pair of the PZT patches are used at the same time as one is used as an actuator and the other as a sensor, respectively. A tone burst of 5 cycles of wave shape is used during the experiment. A wave generator is applied to create the proposed waves, and the waves are amplified by an amplifier to actuate the PZT patch to emit the diagonal waves with appropriately enough energy. Meanwhile, the other PZT patch is used as a sensor to receive the diagonal signals which contain the information of the damages for processing. For data processing, an index of root mean square deviation (RMSD) of the received data is used to estimate the damage level by compare of the data between the damaged and the health peak valves of the received signals. The time reversal method which aimed at increasing the efficiency of the detection is also used to detect the damage location by estimating the arrival time of the reflected wave passing with a certain velocity. The proposed method experimentally validates that it is effective for application in damage detection of pipeline structure.

Some Features of Formatting the Arrival Time Distribution

2019 ◽  
pp. 148-161
Author(s):  
Vladimir Chebotarev ◽  
Boris Davydov ◽  
Kseniya Kablukova ◽  
Vadim Gopkalo
Keyword(s):  
Time Distribution ◽  
Arrival Time ◽  
Arrival Time Distribution
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