scholarly journals Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3459 ◽  
Author(s):  
Shidrokh Goudarzi ◽  
Mohd Kama ◽  
Mohammad Anisi ◽  
Seyed Soleymani ◽  
Faiyaz Doctor
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Deep Belief Network ◽  
Fine Tuning ◽  
Superior Performance ◽  
Traffic Information ◽  
Series Data ◽  
Internet Of Vehicles ◽  
Belief Network ◽  
Self Organizing

To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators.

scholarly journals Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Xianming Lang ◽  
Zhiyong Hu ◽  
Ping Li ◽  
Yan Li ◽  
Jiangtao Cao ◽  
...  
Keyword(s):  
Sound Velocity ◽  
Wavelet Packet ◽  
Ultrasonic Signal ◽  
Deep Belief Network ◽  
Independent Component ◽  
Fine Tuning ◽  
Local Optimum ◽  
Wavelet Packet Analysis ◽  
Belief Network ◽  
Oil Pipeline

The leakage aperture cannot be easily identified, when an oil pipeline has small leaks. To address this issue, a leak aperture recognition method based on wavelet packet analysis (WPA) and a deep belief network (DBN) with independent component regression (ICR) is proposed. WPA is used to remove the noise in the collected sound velocity of the ultrasonic signal. Next, the denoised sound velocity of the ultrasonic signal is input into the deep belief network with independent component regression (DBNICR) to recognize different leak apertures. Because the optimization of the weights of the DBN with the gradient leads to a local optimum and a slow learning rate, ICR is used to replace the gradient fine-tuning method in conventional DBN for improving the classification accuracy, and a Lyapunov function is constructed to prove the convergence of the DBNICR learning process. By analyzing the acquired ultrasonic sound velocity of different leak apertures, the results show that the proposed method can quickly and effectively identify different leakage apertures.

Research on Internet of Vehicles and its Application in Intelligent Transportation

2013 ◽  
Vol 321-324 ◽  
pp. 2818-2821 ◽  
Author(s):  
Jian Ming Huang
Keyword(s):  
Internet Of Things ◽  
Radio Frequency Identification ◽  
Traffic Congestion ◽  
Intelligent Transportation ◽  
Traffic Information ◽  
Sensor Technology ◽  
Transportation Industry ◽  
Internet Of Vehicles ◽  
Frequency Identification ◽  
Internet Of Things Technology

At present, the transportation industry brings energy consumption, pollution and traffic congestion is becoming more and more serious, which has greatly restricted the economic and social development. This paper elaborates the concepts of Internet of things, intelligent transportation, and Internet of vehicles; Internet of vehicles based on Internet of Things technology is considered as an effective method to solve road congestion. The key technologies of the Internet of vehicles include traffic information perception technology, network communication technology, and cloud computing technologies etc. among them, radio frequency identification technology, sensor technology, floating car data technology, and GPS positioning technology are the focus. Application of Internet of vehicles is discussed, and its future development is prospected.

scholarly journals A Prediction Model Based on Deep Belief Network and Least Squares SVR Applied to Cross-Section Water Quality

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1929
Author(s):  
Jianzhuo Yan ◽  
Ya Gao ◽  
Yongchuan Yu ◽  
Hongxia Xu ◽  
Zongbao Xu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Prediction Model ◽  
Least Squares ◽  
Quality Parameters ◽  
Deep Belief Network ◽  
Water Quality Parameters ◽  
Series Data ◽  
Belief Network ◽  
The Mean

Recently, the quality of fresh water resources is threatened by numerous pollutants. Prediction of water quality is an important tool for controlling and reducing water pollution. By employing superior big data processing ability of deep learning it is possible to improve the accuracy of prediction. This paper proposes a method for predicting water quality based on the deep belief network (DBN) model. First, the particle swarm optimization (PSO) algorithm is used to optimize the network parameters of the deep belief network, which is to extract feature vectors of water quality time series data at multiple scales. Then, combined with the least squares support vector regression (LSSVR) machine which is taken as the top prediction layer of the model, a new water quality prediction model referred to as PSO-DBN-LSSVR is put forward. The developed model is valued in terms of the mean absolute error (MAE), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination ( R 2 ). Results illustrate that the model proposed in this paper can accurately predict water quality parameters and better robustness of water quality parameters compared with the traditional back propagation (BP) neural network, LSSVR, the DBN neural network, and the DBN-LSSVR combined model.

scholarly journals An Efficient Traffic Congestion Monitoring System on Internet of Vehicles

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Duc-Binh Nguyen ◽  
Chyi-Ren Dow ◽  
Shiow-Fen Hwang
Keyword(s):  
Traffic Flow ◽  
Monitoring System ◽  
Traffic Congestion ◽  
Macroscopic Model ◽  
Transport Systems ◽  
System Response ◽  
Internet Of Vehicles ◽  
Congestion Prediction ◽  
System Response Time ◽  
Verification Process

Existing intelligent transport systems (ITS) do not fully consider and resolve accuracy, instantaneity, and compatibility challenges while resolving traffic congestion in Internet of Vehicles (IoV) environments. This paper proposes a traffic congestion monitoring system, which includes data collection, segmented structure establishment, traffic-flow modelling, local segment traffic congestion prediction, and origin-destination traffic congestion service for drivers. Macroscopic model-based traffic-flow factors were formalized on the basis of the analysis results. Fuzzy rules-based local segment traffic congestion prediction was performed to determine the traffic congestion state. To enhance prediction efficiency, this paper presents a verification process for minimizing false predictions which is based on the Rankine-Hugoniot condition and an origin-destination traffic congestion service is also provided. To verify the feasibility of the proposed system, a prototype was implemented. The experimental results demonstrate that the proposed scheme can effectively monitor traffic congestion in terms of accuracy and system response time.

Coalbed methane content prediction using deep belief network

2020 ◽  
Vol 8 (2) ◽  
pp. T309-T321
Author(s):  
Fan Peng ◽  
Suping Peng ◽  
Wenfeng Du ◽  
Hongshuan Liu
Keyword(s):  
Machine Learning ◽  
Coal Seam ◽  
Coalbed Methane ◽  
Deep Belief Network ◽  
Training Data ◽  
Fine Tuning ◽  
Support Vector ◽  
Learning Models ◽  
Belief Network ◽  
Machine Learning Models

Accurate measurement of coalbed methane (CBM) content is the foundation for CBM resource exploration and development. Machine-learning techniques can help address CBM content prediction tasks. Due to the small amount of actual measurement data and the shallow model structure, however, the results from traditional machine-learning models have errors to some extent. We have developed a deep belief network (DBN)-based model with the input as continuous real values and the activation function as the rectified linear unit. We first calculated a variety of seismic attributes of the target coal seam to highlight the features of the coal seam, then we preprocessed the original attribute features, and finally developed the performance of the DBN model using the preprocessed features. We used 23,374 training data to train our model, 23,240 for pretraining, and 134 for fine-tuning. For the purpose of demonstrating the advantages of the DBN model, we compared it with two typical machine-learning models, including the multilayer perceptron model and the support vector regression model. These two models were trained based on the same labeled training data. The results, obtained from different models, indicated that the DBN model has the least error, which means that it is more accurate than the other two models when used to predict CBM content.

A self-organizing deep belief network for nonlinear system modeling

2018 ◽  
Vol 65 ◽  
pp. 170-183 ◽  
Author(s):  
Junfei Qiao ◽  
Gongming Wang ◽  
Xiaoli Li ◽  
Wenjing Li
Keyword(s):  
Nonlinear System ◽  
System Modeling ◽  
Deep Belief Network ◽  
Belief Network ◽  
Nonlinear System Modeling ◽  
Self Organizing

scholarly journals A Cycle Deep Belief Network Model for Multivariate Time Series Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Shuqin Wang ◽  
Gang Hua ◽  
Guosheng Hao ◽  
Chunli Xie
Keyword(s):  
Time Series ◽  
Network Model ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Deep Belief Network ◽  
Learning Ability ◽  
Series Data ◽  
Temporal Data ◽  
Belief Network ◽  
Data Objects

Multivariate time series (MTS) data is an important class of temporal data objects and it can be easily obtained. However, the MTS classification is a very difficult process because of the complexity of the data type. In this paper, we proposed a Cycle Deep Belief Network model to classify MTS and compared its performance with DBN and KNN. This model utilizes the presentation learning ability of DBN and the correlation between the time series data. The experimental results showed that this model outperforms other four algorithms: DBN, KNN_ED, KNN_DTW, and RNN.

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