scholarly journals DEVELOPMENT AND IMPLEMENTATION OF REAL-TIME TSUNAMI INUNDATION PREDICTION METHOD

Author(s):  
Daisuke TATSUMI ◽  
Takashi TOMITA
2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.


2017 ◽  
Vol 12 (1) ◽  
pp. 87-96 ◽  
Author(s):  
J. S. Hyung ◽  
K. B. Kim ◽  
M. C. Kim ◽  
I. S. Lee ◽  
J. Y. Koo

Ozone dosage in most water treatment plants is operated by determining the ozone concentration with the experience of the operation. In this case, it is not economical. This study selected the factors affecting residual ozone concentration and attempted to estimate the optimum amount of hydrogen peroxide dosage for the control of the residual ozone concentration by developing a model for the prediction of the residual ozone concentration. The prediction formulas developed in this study can quickly respond to the environment of water quality and surrounding environmental factors, which change in real time, so it is judged that they could be used for the operation of the optimum ozone process, and the control of ozone dosage could be used as a new method in controlling the concentration of ozone dosage and the concentration of residual ozone.


2022 ◽  
Vol 64 (1) ◽  
pp. 38-44
Author(s):  
Maosheng Gao ◽  
Zhiwu Shang ◽  
Wanxiang Li ◽  
Shiqi Qian ◽  
Yan Yu

A sudden fault in a rolling bearing (RB) results in a large amount of downtime, which increases the cost of operation and maintenance. In this paper, a real-time diagnosis and trend prediction method for RBs is proposed. In this method, a novel resampling dynamic time warping (RDTW) algorithm is presented and two new time-domain indicators (NTDIRs) called TALAP and TRCKT are defined, which can describe the wear degree and trend of an RB inner ring wear fault (IRWF). TALAP and TRCKT are proposed by comprehensively considering the stability and sensitivity of existing time-domain indicators (TDIRs). First, RDTW is used to align the healthy vibration signal with the fault vibration signal. Then, the residual signal that can be used to monitor the running condition is obtained. TALAP and TRCKT of the residual signal are calculated to judge the degree of wear. When the wear limit is reached, a fault alarm is sent out and the downtime needed for replacement can be accurately indicated. The experimental results show that the method can perform accurate diagnosis and trend prediction of inner ring wear faults of RBs.


2013 ◽  
Vol 321-324 ◽  
pp. 757-761 ◽  
Author(s):  
Chen Liang Song ◽  
Zhen Liu ◽  
Bin Long ◽  
Cheng Lin Yang

According to the real-time prediction for performance degradation trend, the commonly used method is just based on field data. But this methods prediction result will not be so much ideal when the fitting of degradation trend of field data is not good. To solve the problem, the paper introduces a new method which is not only based on field method but also based on reliability experimental data coming from the history experiment. We use the relationship between the field data and reliability experimental data to get the result of the two kinds of data respectively and then get the weights according to the two prediction results. Finally, the final real-time prediction result for performance degradation tendency can obtain by allocating the weights to the two prediction results.


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