Deep learning-based axial capacity prediction for cold-formed steel channel sections using Deep Belief Network

Structures ◽  
2021 ◽  
Vol 33 ◽  
pp. 2792-2802
Author(s):  
Zhiyuan Fang ◽  
Krishanu Roy ◽  
Jiri Mares ◽  
Chiu-Wing Sham ◽  
Boshan Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Deep Belief Network ◽  
Belief Network ◽  
Axial Capacity ◽  
Cold Formed Steel ◽  
Capacity Prediction

Deep Learning-based Framework for Smart Sustainable Cities

2019 ◽  
Vol 15 (4) ◽  
pp. 76-107
Author(s):  
Nagarathna Ravi ◽  
Vimala Rani P ◽  
Rajesh Alias Harinarayan R ◽  
Mercy Shalinie S ◽  
Karthick Seshadri ◽  
...  
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Deep Learning ◽  
Air Pollutants ◽  
Human Life ◽  
Deep Belief Network ◽  
Motor Vehicles ◽  
Human Beings ◽  
Long Term Effects ◽  
Belief Network

Pure air is vital for sustaining human life. Air pollution causes long-term effects on people. There is an urgent need for protecting people from its profound effects. In general, people are unaware of the levels to which they are exposed to air pollutants. Vehicles, burning various kinds of waste, and industrial gases are the top three onset agents of air pollution. Of these three top agents, human beings are exposed frequently to the pollutants due to motor vehicles. To aid in protecting people from vehicular air pollutants, this article proposes a framework that utilizes deep learning models. The framework utilizes a deep belief network to predict the levels of air pollutants along the paths people travel and also a comparison with the predictions made by a feed forward neural network and an extreme learning machine. When evaluating the deep belief neural network for the case study undertaken, a deep belief network was able to achieve a higher index of agreement and lower RMSE values.

scholarly journals A Novel Deep Belief Network Model Constructed by Improved Conditional RBMs and its Application in RUL Prediction for Hydraulic Pumps

2020 ◽  
Vol 25 (3) ◽  
pp. 373-382
Author(s):  
He Yu ◽  
Zaike Tian ◽  
Hongru Li ◽  
Baohua Xu ◽  
Guoqing An
Keyword(s):  
Deep Learning ◽  
Graph Model ◽  
Parameter Tuning ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Deep Feature ◽  
Proposed Model ◽  
Vector Machines ◽  
Deep Feature Extraction

Residual Useful Life (RUL) prediction is a key step of Condition-Based Maintenance (CBM). Deep learning-based techniques have shown wonderful prospects on RUL prediction, although their performances depend on heavy structures and parameter tuning strategies of these deep-learning models. In this paper, we propose a novel Deep Belief Network (DBN) model constructed by improved conditional Restrict Boltzmann Machines (RBMs) and apply it in RUL prediction for hydraulic pumps. DBN is a deep probabilistic digraph neural network that consists of multiple layers of RBMs. Since RBM is an undirected graph model and there is no communication among the nodes of the same layer, the deep feature extraction capability of the original DBN model can hardly ensure the accuracy of modeling continuous data. To address this issue, the DBN model is improved by replacing RBM with the Improved Conditional RBM (ICRBM) that adds timing linkage factors and constraint variables among the nodes of the same layers on the basis of RBM. The proposed model is applied to RUL prediction of hydraulic pumps, and the results show that the prediction model proposed in this paper has higher prediction accuracy compared with traditional DBNs, BP networks, support vector machines and modified DBNs such as DEBN and GC-DBN.

scholarly journals Mining and Reuse on Design Knowledge of Non-standard Special Tool Based on Deep Learning

2020 ◽  
Author(s):  
Mingwei Wang ◽  
Jingtao Zhou ◽  
Xiaoying Chen ◽  
Zeyu Li
Keyword(s):  
Deep Learning ◽  
Association Rule ◽  
Deep Belief Network ◽  
Design Knowledge ◽  
Machining Features ◽  
Belief Network ◽  
Dual Channel ◽  
Engineering Drawings ◽  
Low Efficiency

Abstract Aiming at the problems of design difficulty, low efficiency and unstable quality of non-standard special tools, facing the strong correlation between part machining features and tools, this article takes the two-dimensional engineering drawings of tools and parts as research objects, proposes the research on mining and reuse on design knowledge of non-standard special tool based on deep learning. Firstly, a dual-channel deep belief network is established to complete the feature modeling of machining features and tool features; secondly, the deep belief network is used to realize the association relationship mining between the machining features and tool features; thirdly, both the key local features of the tool and the overall similar design case of the tool are reused through association rule reasoning; finally, the non-standard special turning tool is used as an example to verify the effectiveness of the proposed method.

scholarly journals Segmentação de Vértebras e Diagnóstico de Fraturas em Imagens de Ressonância Magnética Utilizando U-Net 3D e Deep Belief Network

2019 ◽  
Author(s):  
Anderson Matheus Passos Paiva ◽  
João Otávio Bandeira Diniz ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso Paiva
Keyword(s):  
Deep Learning ◽  
Deep Belief Network ◽  
Belief Network

A dor lombar é uma razão comum para visitas clı́nicas e o exame de ressonância magnética é frequentemente utilizado em sistemas de apoio a di- agnóstico de patologias na coluna. Visando aprimorar e automatizar esse pro- cesso, este estudo propõe o uso de técnicas computacionais para a segmentação de vértebras em imagens de ressonância magnética, com o objetivo de realizar posteriores análises acerca de patologias na coluna. Para este fim, são utili- zadas duas arquiteturas de Deep Learning: a U-Net para a segmentação em 3D e a Deep Belief Network para a classificação de vértebras que apresen- tam ruptura ou não. Os resultados obtidos mostram que a U-Net é promissora em localizar a região da vértebra, obtendo um valor de Coeficiente de Dice médio de 89,51%, superando assim vários trabalhos importantes focados no problema. A classificação também se mostrou eficiente, com valores de 94,38% para acurácia e 88,8% de sensibilidade.

Optimal weighted feature vector and deep belief network for medical data classification

2019 ◽  
Vol 18 (02) ◽  
pp. 2050006 ◽  
Author(s):  
Vinod Jagannath Kadam ◽  
Shivajirao Manikrao Jadhav
Keyword(s):  
Deep Learning ◽  
Data Classification ◽  
Medical Data ◽  
Deep Belief Network ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Belief Network ◽  
Medical Diagnoses ◽  
Whale Optimization ◽  
Medical Data Classification

Medical data classification is the process of transforming descriptions of medical diagnoses and procedures into universal medical code numbers. The diagnoses and procedures are usually taken from a variety of sources within the healthcare record, such as the transcription of the physician’s notes, laboratory results, radiologic results and other sources. However, there exist many frequency distribution problems in these domains. Hence, this paper intends to develop an advanced and precise medical data classification approach for diabetes and breast cancer dataset. With the knowledge of the features and challenges persisting with the state-of-the-art classification methods, deep learning-based medical data classification methodology is proposed here. It is well known that deep learning networks learn directly from the data. In this paper, the medical data is dimensionally reduced using Principle Component Analysis (PCA). The dimensionally reduced data are transformed by multiplying by a weighting factor, which is optimized using Whale Optimization Algorithm (WOA), to obtain the maximum distance between the features. As a result, the data are transformed into a label-distinguishable plane under which the Deep Belief Network (DBN) is adopted to perform the deep learning process, and the data classification is performed. Further, the proposed WOA-based DBN (WOADBN) method is compared with the Neural Network (NN), DBN, Generic Algorithm-based NN (GANN), GADBN, Particle Swarm Optimization (PSONN), PSO-based DBN (PSODBN), WOA-based NN (WOANN) techniques and the results are obtained, which shows the superiority of proposed algorithm over conventional methods.

Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

2017 ◽  
Vol 26 (2) ◽  
pp. 023005 ◽  
Author(s):  
Fei He ◽  
Ye Han ◽  
Han Wang ◽  
Jinchao Ji ◽  
Yuanning Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Iris Recognition ◽  
Gabor Filters ◽  
Deep Belief Network ◽  
Belief Network

Combined deep belief network in deep learning with affinity propagation clustering algorithm for roller bearings fault diagnosis without data label

2018 ◽  
Vol 25 (2) ◽  
pp. 473-482 ◽  
Author(s):  
Fan Xu ◽  
Peter W. Tse
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Deep Belief Network ◽  
Affinity Propagation ◽  
Cluster Center ◽  
Support Vector ◽  
Belief Network ◽  
Roller Bearings ◽  
Clustering Model

Unlike many traditional feature extraction methods of vibration signal such as ensemble empirical mode decomposition (EEMD), deep belief network (DBN) in deep learning can extract the useful information automatically and reduce the reliance on experts, with signal processing technology, and troubleshooting experience. In conventional fault diagnosis, data labels are required for classifiers such as support vector machine, random forest, and artificial neural networks. These are usually based on expert knowledge, for training and testing. But the process is usually tedious. The clustering model, on the other hand, can finish the roller bearings fault diagnosis without data labels, which is more efficient. There are some common clustering models which include fuzzy C-means (FCM), Gustafson–Kessel (GK), Gath–Geva (GG) models, and affinity propagation (AP). Unlike FCM, GK, and GG, which require knowledge or experience to pre-set the number of cluster center points, AP clustering algorithm can obtain the cluster center point according to the responsibility and availability calculations for all data points automatically. To the best of the authors’ knowledge, AP is rarely used for fault diagnosis. In this paper, a method which combines DBN, with several hidden layers, and AP for roller bearings fault diagnosis is proposed. For data visualization, the principal component analysis (PCA) is deployed to reduce the dimension of the extracted feature. The first two principal components are employed as the input of the FCM, GK, GG, and AP models for roller bearings faults diagnosis. Compared with other combination models such as EEMD–PCA–FCM/GK/GG and DBN–PCA–FCM/GK/GG, the proposed method, from the experimental results, is superior to the aforementioned combination models.

scholarly journals A Novel Method of Hyperspectral Data Classification Based on Transfer Learning and Deep Belief Network

2019 ◽  
Vol 9 (7) ◽  
pp. 1379 ◽  
Author(s):  
Ke Li ◽  
Mingju Wang ◽  
Yixin Liu ◽  
Nan Yu ◽  
Wei Lan
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Deep Belief Network ◽  
Learning Method ◽  
Target Domain ◽  
Data Set ◽  
Belief Network ◽  
Target Data

The classification of hyperspectral data using deep learning methods can obtain better results than the previous shallow classifiers, but deep learning algorithms have some limitations. These algorithms require a large amount of data to train the network, while also needing a certain amount of labeled data to fine-tune the network. In this paper, we propose a new hyperspectral data processing method based on transfer learning and the deep learning method. First, we use a hyperspectral data set that is similar to the target data set to pre-train the deep learning network. Then, we use the transfer learning method to find the common features of the source domain data and target domain data. Second, we propose a model structure that combines the deep transfer learning model to utilize a combination of spatial information and spectral information. Using transfer learning, we can obtain the spectral features. Then, we obtain several principal components of the target data. These will be regarded as the spatial features of the target domain data, and we use the joint features for the classifier. The data are obtained from a hyperspectral public database. Using the same amount of data, our method based on transfer learning and deep belief network obtains better classification accuracy in a shorter amount of time.

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