A Study for Development of Route Prediction Method for Vessel using SVR

2020 ◽  
Vol 10 (1) ◽  
pp. 1-9
Author(s):  
Kotetsu KAWAKAMI ◽  
Jumpei Matsue ◽  
Ryota Isozaki ◽  
Shunsuke Inada ◽  
Masashi Sugimoto ◽  
...  
Keyword(s):  
Prediction Method ◽  
Route Prediction

A Study for Development of Route Prediction Method for Vessel using Machine Learning

2019 ◽  
Vol 9 (4) ◽  
pp. 203-207
Author(s):  
Jumpei MATSUE ◽  
Kotetsu KAWAKAMI ◽  
Ryota ISOZAKI ◽  
Shunsuke INADA ◽  
Masashi SUGIMOTO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Method ◽  
Route Prediction

Time Series Prediction of Tropical Storm Trajectory Using Self-Organizing Incremental Neural Networks and Error Evaluation

2018 ◽  
Vol 22 (4) ◽  
pp. 465-474 ◽  
Author(s):  
Wonjik Kim ◽  
Osamu Hasegawa ◽  
◽  
◽  
Keyword(s):  
Neural Network ◽  
Atmospheric Pressure ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Tropical Storm ◽  
Route Prediction ◽  
Wide Range ◽  
Location Parameters ◽  
Global Problems ◽  
Self Organizing

This study proposes a route prediction method using a self-organizing incremental neural network. The route trajectory is predicted from two location parameters (the latitude and longitude of the middle of a tropical storm) and the meteorological information (the atmospheric pressure). The method accurately predicted the normalized atmospheric pressure data of East Asia in the topological space of latitude and longitude, with low calculation cost. This paper explains the algorithms for training the self-organizing incremental neural network, the procedure for refining the datasets and the method for predicting the storm trajectory. The effectiveness of the proposed method was confirmed in experiments. With the results of experiments, possibility of prediction model improvement is discussed. Additionally, this paper explains the limitations of proposed method and brief solution to resolve. Although the proposed method was applied only to typhoon phenomena in the present study, it is potentially applicable to a wide range of global problems.

scholarly journals A Method for Driving Route Predictions Based on Hidden Markov Model

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ning Ye ◽  
Zhong-qin Wang ◽  
Reza Malekian ◽  
Qiaomin Lin ◽  
Ru-chuan Wang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Recommendation System ◽  
Hidden Markov ◽  
Prediction Method ◽  
Prediction Algorithm ◽  
Training Set ◽  
Rectangular Coordinate System ◽  
Route Prediction ◽  
Route Recommendation

We present a driving route prediction method that is based on Hidden Markov Model (HMM). This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based onK-means++ and the add-one (Laplace) smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown.

Selection of a leading edge noise prediction method for PNoise

2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Prediction Method ◽  
Leading Edge ◽  
Wind Farms ◽  
Broadband Noise ◽  
Turbulence Energy ◽  
Noise Prediction ◽  
Migration Routes ◽  
Turbulent Inflow

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.

Prediction Method of a System with Continuous Change Parameters Considering SNR Fluctuation

2018 ◽  
Vol 138 (9) ◽  
pp. 1075-1081
Author(s):  
Yasuhide Kobayashi ◽  
Mitsuyuki Saito ◽  
Yuki Amimoto ◽  
Wataru Wakita
Keyword(s):  
Prediction Method ◽  
Continuous Change

Validation of a Belt Model for Prediction of Hub Forces from a Rolling Tire4

2009 ◽  
Vol 37 (2) ◽  
pp. 62-102 ◽  
Author(s):  
C. Lecomte ◽  
W. R. Graham ◽  
D. J. O’Boy
Keyword(s):  
Internal Pressure ◽  
Equations Of Motion ◽  
Three Dimensional ◽  
Prediction Method ◽  
Analytical Models ◽  
Beam Model ◽  
Impedance Boundary ◽  
Bending Waves ◽  
Tire Rotation ◽  
Three Dimensional Models

Abstract An integrated model is under development which will be able to predict the interior noise due to the vibrations of a rolling tire structurally transmitted to the hub of a vehicle. Here, the tire belt model used as part of this prediction method is first briefly presented and discussed, and it is then compared to other models available in the literature. This component will be linked to the tread blocks through normal and tangential forces and to the sidewalls through impedance boundary conditions. The tire belt is modeled as an orthotropic cylindrical ring of negligible thickness with rotational effects, internal pressure, and prestresses included. The associated equations of motion are derived by a variational approach and are investigated for both unforced and forced motions. The model supports extensional and bending waves, which are believed to be the important features to correctly predict the hub forces in the midfrequency (50–500 Hz) range of interest. The predicted waves and forced responses of a benchmark structure are compared to the predictions of several alternative analytical models: two three dimensional models that can support multiple isotropic layers, one of these models include curvature and the other one is flat; a one-dimensional beam model which does not consider axial variations; and several shell models. Finally, the effects of internal pressure, prestress, curvature, and tire rotation on free waves are discussed.

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