Research on Prediction Method of Ship Rolling Motion Based on Deep Learning

2020 ◽  
Author(s):  
Yuchao Wang ◽  
Mingyue Zhang ◽  
Huixuan Fu ◽  
Qiusu Wang
Keyword(s):  
Deep Learning ◽  
Prediction Method ◽  
Rolling Motion ◽  
Ship Rolling

The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

2020 ◽  
pp. 002072092093683
Author(s):  
Yu Zhu
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Social Network ◽  
Random Walk ◽  
Prediction Method ◽  
Random Walk Model ◽  
Personality Characteristics ◽  
User Personality ◽  
Network Platform ◽  
Personality Prediction

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

scholarly journals Stochastic Inverse Identification of Nonlinear Roll Damping Moment of a Ship Moving at Nonzero-Forward Speeds

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
S. L. Han ◽  
Takeshi Kinoshita
Keyword(s):  
Nonlinear Damping ◽  
Rolling Motion ◽  
Forward Speed ◽  
Roll Damping ◽  
Nonparametric Approach ◽  
Nonlinear Responses ◽  
Different Types ◽  
Experimental Trials ◽  
And Control ◽  
Ship Rolling

The nonlinear responses of ship rolling motion characterized by a roll damping moment are of great interest to naval architects and ocean engineers. Modeling and identification of the nonlinear damping moment are essential to incorporate the inherent nonlinearity in design, analysis, and control of a ship. A stochastic nonparametric approach for identification of nonlinear damping in the general mechanical system has been presented in the literature (Han and Kinoshits 2012). The method has been also applied to identification of the nonlinear damping moment of a ship at zero-forward speed (Han and Kinoshits 2013). In the presence of forward speed, however, the characteristic of roll damping moment of a ship is significantly changed due to the lift effect. In this paper, the stochastic inverse method is applied to identification of the nonlinear damping moment of a ship moving at nonzero-forward speed. The workability and validity of the method are verified with laboratory tests under controlled conditions. In experimental trials, two different types of ship rolling motion are considered: time-dependent transient motion and frequency-dependent periodic motion. It is shown that this method enables the inherent nonlinearity in damping moment to be estimated, including its reliability analysis.

A Stochastic Theory for Nonlinear Ship Rolling in irregular Seas

1982 ◽  
Vol 26 (04) ◽  
pp. 229-245 ◽  
Author(s):  
J. B. Roberts
Keyword(s):  
Markov Processes ◽  
Digital Simulation ◽  
Stochastic Theory ◽  
Simple Expression ◽  
Roll Angle ◽  
Rolling Motion ◽  
Approximate Theory ◽  
Averaging Techniques ◽  
Good Agreement ◽  
Ship Rolling

By a combination of averaging techniques with the theory of Markov processes, an approximate theory is developed for the rolling motion of a ship in beam waves. A simple expression is obtained for the distribution of the roll angle, and is tested by a comparison with a set of digital simulation estimates due to Dalzell. Good agreement is obtained over a realistic range of damping values.

scholarly journals A stock price prediction method based on deep learning technology

2021 ◽  
Vol 5 (1) ◽  
pp. 55-72
Author(s):  
Xuan Ji ◽  
Jiachen Wang ◽  
Zhijun Yan
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Stock Price ◽  
Prediction Method ◽  
Series Data ◽  
Learning Technology ◽  
Content Type ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Text Feature

Purpose Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data. Design/methodology/approach This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price. Findings The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price. Originality/value In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

scholarly journals Wavelet Analysis for Ship Rolling Motion of Purse Seiners in North Sulawesi, Indonesia

2012 ◽  
Vol 126 (0) ◽  
pp. 277-282
Author(s):  
Heffry Veibert DIEN ◽  
Hiroki YASUMA ◽  
Yasuzumi FUJIMORI ◽  
Nobuo KIMURA
Keyword(s):  
Wavelet Analysis ◽  
Rolling Motion ◽  
North Sulawesi ◽  
Ship Rolling

scholarly journals Research on prediction method of S-wave velocity based on deep learning

2021 ◽  
Vol 2083 (4) ◽  
pp. 042065
Author(s):  
Guojie Yang ◽  
Shuhua Wang
Keyword(s):  
Deep Learning ◽  
Wave Velocity ◽  
Prediction Method ◽  
P Wave ◽  
Learning Technology ◽  
S Wave ◽  
Advantages And Disadvantages ◽  
Wave Prediction ◽  
Wide Range ◽  
Velocity Prediction

Abstract Aiming at the s-wave velocity prediction problem, based on the analysis of the advantages and disadvantages of the empirical formula method and the rock physics modeling method, combined with the s-wave velocity prediction principle, the deep learning method is introduced, and a deep learning-based logging s-wave velocity prediction method is proposed. This method uses a deep neural network algorithm to establish a nonlinear mapping relationship between reservoir parameters (acoustic time difference, density, neutron porosity, shale content, porosity) and s-wave velocity, and then applies it to the s-wave velocity prediction at the well point. Starting from the relationship between p-wave and s-wave velocity, the study explained the feasibility of applying deep learning technology to s-wave prediction and the principle of sample selection, and finally established a reliable s-wave prediction model. The model was applied to s-wave velocity prediction in different research areas, and the results show that the s-wave velocity prediction technology based on deep learning can effectively improve the accuracy and efficiency of s-wave velocity prediction, and has the characteristics of a wide range of applications. It can provide reliable s-wave data for pre-stack AVO analysis and pre-stack inversion, so it has high practical application value and certain promotion significance.

scholarly journals Using Length of Bilge Keel to Length of Waterline Ratio to Reduce Ship Rolling Motion

2018 ◽  
Vol 8 (2) ◽  
pp. 2731-2734
Author(s):  
P. K. D. N. Y. Putra ◽  
B. H. Iskandar ◽  
Y. Novita
Keyword(s):  
Good Stability ◽  
Length Ratio ◽  
Rolling Motion ◽  
Fishing Vessels ◽  
Bilge Keel ◽  
Minimum Ratio ◽  
Ship Rolling

Fishing vessels must have good stability and manoeuvrability. Hull with round bottom shape has a relatively poor rolling duration compared to other forms. Rolling duration reduction will improve the quality of stability of the ship which can be obtained with bilge keel installation. The objectives of this research are 1) to compare each parameter value on model ship by using bilge keel and 2) to determine the minimum ratio of bilge keel's length toward waterline length on the model ship which still has the ability to reduce rolling motion on the ship. The method used was giving treatment to model ship and observing its rolling motion with different lengths of bilge keel. Based on the result of the research, it can be concluded that bilge keel installation with some length ratio toward length of waterline has different results significantly, and bilge keel with length ratio 0.2 still have effective capability in reducing the rolling motion of the model ship.

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