scholarly journals Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network

2017 ◽  
Vol 232 (20) ◽  
pp. 3767-3780 ◽  
Author(s):  
Van Tung Tran ◽  
Faisal AlThobiani ◽  
Tiedo Tinga ◽  
Andrew Ball ◽  
Gang Niu
Keyword(s):  
Receptive Fields ◽  
Deep Belief Network ◽  
Restricted Boltzmann Machine ◽  
Fault Classification ◽  
Fuzzy Artmap ◽  
Training Procedure ◽  
Reciprocating Compressor ◽  
Boltzmann Machine ◽  
Restricted Boltzmann Machines ◽  
Belief Network

In this paper, a hybrid deep belief network is proposed to diagnose single and combined faults of suction and discharge valves in a reciprocating compressor. This hybrid integrates the deep belief network structured by multiple stacked restricted Boltzmann machines for pre-training and simplified fuzzy ARTMAP (SFAM) for fault classification. In the pre-training procedure, an algorithm for selecting local receptive fields is used to group the most similar features into the receptive fields of which top values are the units of each layer, and then restricted Boltzmann machine is applied to these units to construct a network. Unsupervised learning is also carried out for each restricted Boltzmann machine layer in this procedure to compute the network weights and biases. Finally, the network output is fed into SFAM to perform fault classification. In order to diagnose the valve faults, three signal types of vibration, pressure, and current are acquired from a two-stage reciprocating air compressor under different valve conditions such as suction leakages, discharge leakages, spring deterioration, and their combination. These signals are subsequently processed so that the useful fault information from the signals can be revealed; next, statistical features in the time and frequency domains are extracted from the signals and used as the inputs for hybrid deep belief network. Performance of hybrid deep belief network in fault classification is compared with that of the original deep belief network and the deep belief network combined with generalized discriminant analysis, where softmax regression is used as a classifier for the latter two models. The results indicate that hybrid deep belief network is more capable of improving the diagnosis accuracy and is feasible in industrial applications.

scholarly journals Deep Belief Network for Predicting the Predisposition to Lung Cancer in TP53 Gene

2020 ◽  
pp. 171-177 ◽  
Author(s):  
Zahraa Naser Shahweli
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Smoking Habit ◽  
Back Propagation ◽  
Tp53 Gene ◽  
Back Propagation Neural Network ◽  
Biological Data ◽  
Deep Belief Network ◽  
Restricted Boltzmann Machines ◽  
Belief Network

Lung cancer, similar to other cancer types, results from genetic changes. However, it is considered as more threatening due to the spread of the smoking habit, a major risk factor of the disease. Scientists have been collecting and analyzing the biological data for a long time, in attempts to find methods to predict cancer before it occurs. Analysis of these data requires the use of artificial intelligence algorithms and neural network approaches. In this paper, one of the deep neural networks was used, that is the enhancer Deep Belief Network (DBN), which is constructed from two Restricted Boltzmann Machines (RBM). The visible nodes for the first RBM are 13 nodes and 8 nodes in each hidden layer for the two RBMs. The enhancer DBN was trained by Back Propagation Neural Network (BPNN), where the data sets were divided into 6 folds, each is split into three partitions representing the training, validation, and testing. It is worthy to note that the proposed enhancer DBN predicted lung cancer in an acceptable manner, with an average F-measure value of  0. 96 and an average Matthews Correlation Coefficient (MCC) value of 0. 47 for 6 folds.

Deep-belief network for predicting potential miRNA-disease associations

2020 ◽  
Author(s):  
Xing Chen ◽  
Tian-Hao Li ◽  
Yan Zhao ◽  
Chun-Chun Wang ◽  
Chi-Chi Zhu
Keyword(s):  
Computational Models ◽  
Cross Validation ◽  
Biological Data ◽  
Deep Belief Network ◽  
Computational Method ◽  
Restricted Boltzmann Machines ◽  
Belief Network ◽  
Disease Associations ◽  
Leave One Out ◽  
The Impact

Abstract MicroRNA (miRNA) plays an important role in the occurrence, development, diagnosis and treatment of diseases. More and more researchers begin to pay attention to the relationship between miRNA and disease. Compared with traditional biological experiments, computational method of integrating heterogeneous biological data to predict potential associations can effectively save time and cost. Considering the limitations of the previous computational models, we developed the model of deep-belief network for miRNA-disease association prediction (DBNMDA). We constructed feature vectors to pre-train restricted Boltzmann machines for all miRNA-disease pairs and applied positive samples and the same number of selected negative samples to fine-tune DBN to obtain the final predicted scores. Compared with the previous supervised models that only use pairs with known label for training, DBNMDA innovatively utilizes the information of all miRNA-disease pairs during the pre-training process. This step could reduce the impact of too few known associations on prediction accuracy to some extent. DBNMDA achieves the AUC of 0.9104 based on global leave-one-out cross validation (LOOCV), the AUC of 0.8232 based on local LOOCV and the average AUC of 0.9048 ± 0.0026 based on 5-fold cross validation. These AUCs are better than other previous models. In addition, three different types of case studies for three diseases were implemented to demonstrate the accuracy of DBNMDA. As a result, 84% (breast neoplasms), 100% (lung neoplasms) and 88% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by recent literature. Therefore, we could conclude that DBNMDA is an effective method to predict potential miRNA-disease associations.

An Adaptive Deep Belief Network With Sparse Restricted Boltzmann Machines

2020 ◽  
Vol 31 (10) ◽  
pp. 4217-4228 ◽  
Author(s):  
Gongming Wang ◽  
Junfei Qiao ◽  
Jing Bi ◽  
Qing-Shan Jia ◽  
MengChu Zhou
Keyword(s):  
Deep Belief Network ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Belief Network

scholarly journals Deep Belief Network Based 3D Models Classification in Building Information Modeling

2015 ◽  
Vol 11 (5) ◽  
pp. 57
Author(s):  
Li Wang ◽  
Zhi-kai Zhao ◽  
Na Xu
Keyword(s):  
3D Model ◽  
Building Information Modeling ◽  
3D Models ◽  
Deep Belief Network ◽  
Information Modeling ◽  
Layer By Layer ◽  
Restricted Boltzmann Machines ◽  
Belief Network ◽  
Model Library ◽  
Building Information

3D models classification is a critical process of Building Information Modeling (BIM). A Deep Learning Approach is proposed to classify 3D models in BIM environment. The ray based feature extraction algorithm is used to extract features of 3D models and form features matrix. The Deep Belief Network constructed by Restricted Boltzmann Machines applies the features matrix and classifies the models adopting the effective training process. The process of training DBN is layer by layer. Experiments were taken on the public 3D model library of PSB model database. The results show that compared with several commonly used classification method, the proposed method of this paper has achieved good results in the 3D model classification for efficiently BIM.

scholarly journals Deep Belief Network for Prediction of Rician Fading Channel

2019 ◽  
Vol 9 (2S) ◽  
pp. 195-199
Keyword(s):  
Fading Channel ◽  
Learning Algorithm ◽  
Deep Belief Network ◽  
Rician Fading ◽  
Restricted Boltzmann Machines ◽  
Belief Network ◽  
Channel Prediction ◽  
Echo State Networks ◽  
Rician Fading Channel ◽  
Predictive Approaches

In this paper a novel channel prediction scheme is presented for rician fading channel. The channel prediction is done by using a Deep Belief Network (DBN) which is composed of two Restricted Boltzmann Machines (RBMs), this deep learning algorithm can produce fewer predictive errors than echo state networks and other predictive approaches.. Simulation results shows that the DBN channel prediction system has a lower NMSE than the prediction of the echo state network and other conventional prediction methods and the obtained SER gap between the actual CSI and predicted CSI is small.

Sign Language Fingerspelling Recognition Using Depth Information and Deep Belief Networks

2018 ◽  
Vol 32 (06) ◽  
pp. 1850018 ◽  
Author(s):  
Yong Hu ◽  
Hai-Feng Zhao ◽  
Zhi-Gang Wang
Keyword(s):  
Sign Language ◽  
Deep Belief Network ◽  
Depth Information ◽  
Histogram Of Oriented Gradients ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Belief Network ◽  
Average Accuracy

In the sign language fingerspelling scheme, letters in the alphabet are presented by a distinctive finger shape or movement. The presented work is conducted for autokinetic translating fingerspelling signs to text. A recognition framework by using intensity and depth information is proposed and compared with some distinguished works. Histogram of Oriented Gradients (HOG) and Zernike moments are used as discriminative features due to their simplicity and good performance. A Deep Belief Network (DBN) composed of three Restricted Boltzmann Machines (RBMs) is used as a classifier. Experiments are executed on a challenging database, which consists of 120,000 pictures representing 24 alphabet letters over five different users. The proposed approach obtained higher average accuracy, outperforming all other methods. This indicates the effectiveness and the abilities of the proposed framework.

scholarly journals Learning Document Semantic Representation with Hybrid Deep Belief Network

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Yan ◽  
Xu-Cheng Yin ◽  
Sujian Li ◽  
Mingyuan Yang ◽  
Hong-Wei Hao
Keyword(s):  
Language Processing ◽  
Semantic Representation ◽  
Deep Belief Network ◽  
Boltzmann Machine ◽  
Document Representation ◽  
Single Word ◽  
Belief Network ◽  
Deep Boltzmann Machine ◽  
High Level ◽  
High Level Abstraction

High-level abstraction, for example, semantic representation, is vital for document classification and retrieval. However, how to learn document semantic representation is still a topic open for discussion in information retrieval and natural language processing. In this paper, we propose a new Hybrid Deep Belief Network (HDBN) which uses Deep Boltzmann Machine (DBM) on the lower layers together with Deep Belief Network (DBN) on the upper layers. The advantage of DBM is that it employs undirected connection when training weight parameters which can be used to sample the states of nodes on each layer more successfully and it is also an effective way to remove noise from the different document representation type; the DBN can enhance extract abstract of the document in depth, making the model learn sufficient semantic representation. At the same time, we explore different input strategies for semantic distributed representation. Experimental results show that our model using the word embedding instead of single word has better performance.

Sign in / Sign up

Export Citation Format

Share Document