A Revised Deep Belief Network for Predicting the Slurry Concentration of a Cutter Suction Dredger

被引：0

作者：

Wei, Changyun ^{[1
]}

Ni, Fusheng ^{[1
]}

Yang, Jinbao ^{[1
]}

机构：

[1] Hohai Univ, Coll Mech Engn, Changzhou 213022, Peoples R China

来源：

PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING, INFORMATION SCIENCE & APPLICATION TECHNOLOGY (ICCIA 2017) | 2017年 / 74卷

关键词：

Cutter Suction Dredger; Slurry Concentration; Deep Belief Network; Classifier;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In order to predict the slurry concentration of a Cutter Suction Dredger (CSD), a revised Deep Belief Network (DBN) that contains two classifier models is proposed in this work. The two classifier models (i. e., a constant step model and a probability sampling model) are used to process the original data captured in a CSD during a dredging project. Then the classifier models are employed to build the revised DBN to predict the slurry concentration of a CSD. The simulated results show that the proposed approach can effectively extract the features of working data, and also predict the slurry concentration efficiently.

引用

页码：559 / 565

页数：7

共 38 条

[21] PM2.5 concentration modeling and prediction by using temperature-based deep belief network
Xing, Haixia
Wang, Gongming
Liu, Caixia
Suo, Minghe
NEURAL NETWORKS, 2021, 133 : 157 - 165
[22] PM2.5 CONCENTRATION PREDICTION METHOD BASED ON DEEP BELIEF NETWORK IN BIG DATA ENVIRONMENT
Liang, Nannan
Yang, Xiaoying
Lu, Biao
FRESENIUS ENVIRONMENTAL BULLETIN, 2021, 30 (6B): : 7444 - 7453
[23] Predicting Concentration of PM10 Using Optimal Parameters of Deep Neural Network
Oh, Byoung-Doo
Song, Hye-Jeong
Kim, Jong-Dae
Park, Chan-Young
Kim, Yu-Seop
INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2019, 25 (02): : 343 - 350
[24] BILSTM-BIGRU: A FUSION DEEP NEURAL NETWORK FOR PREDICTING AIR POLLUTANT CONCENTRATION
Dey, Prasanjit
Dev, Soumyabrata
Phelan, Bianca Schoen
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 5166 - 5169
[25] Predicting Cardiovascular and Cerebrovascular Events Based on Instantaneous High-Order Singular Entropy and Deep Belief Network
Shao, Shiliang
Wang, Ting
Mumtaz, Asad
Song, Chunhe
Yao, Chen
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2023, 27 (04) : 1670 - 1680
[26] Accuracy Analysis for Predicting Human Behaviour Using Deep Belief Network in Comparison with Support Vector Machine Algorithm
Ankita, D.
Khilar, Rashmita
Kumar, M. Naveen
2022 14TH INTERNATIONAL CONFERENCE ON MATHEMATICS, ACTUARIAL SCIENCE, COMPUTER SCIENCE AND STATISTICS (MACS), 2022,
[27] A Deep Belief Network Combined with Modified Grey Wolf Optimization Algorithm for PM2.5 Concentration Prediction
Xing, Yin
Yue, Jianping
Chen, Chuang
Xiang, Yunfei
Chen, Yang
Shi, Manxing
APPLIED SCIENCES-BASEL, 2019, 9 (18):
[28] Dioxin Emission Concentration Soft Measurement Model of MSWI Process Based on Unmarked Samples and Improved Deep Belief Network
Guo, Zihao
Tang, Jian
Qiao, Junfei
He, Haijun
PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE, 2020, : 5784 - 5789
[29] Prediction of nitrogen oxide emission concentration in cement production process: a method of deep belief network with clustering and time series
Hao, Xiaochen
Xu, Qingquan
Shi, Xin
Song, Zhixing
Ji, Yakun
Zhang, Zhipeng
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2021, 28 (24) : 31689 - 31703
[30] Prediction of nitrogen oxide emission concentration in cement production process: a method of deep belief network with clustering and time series
Xiaochen Hao
Qingquan Xu
Xin Shi
Zhixing Song
Yakun Ji
Zhipeng Zhang
Environmental Science and Pollution Research, 2021, 28 : 31689 - 31703

← 1 2 3 4 →