scholarly journals Correction: Pham et al. Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model. Sensors 2020, 20, 3382

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5280
Author(s):  
Hai Chien Pham ◽  
Quoc-Bao Ta ◽  
Jeong-Tae Kim ◽  
Duc-Duy Ho ◽  
Xuan-Linh Tran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Graphical Model ◽  
Monitoring Framework ◽  
Bolt Loosening

The authors wish to make the following correction to this paper [...]

scholarly journals Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3382 ◽  
Author(s):  
Hai Chien Pham ◽  
Quoc-Bao Ta ◽  
Jeong-Tae Kim ◽  
Duc-Duy Ho ◽  
Xuan-Linh Tran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Graphical Model ◽  
Synthetic Data ◽  
Learning Model ◽  
Training Data ◽  
Bolted Joint ◽  
Bolt Loosening ◽  
Truss Bridge ◽  
Deep Learning Model ◽  
Real Scale

In this study, we investigate a novel idea of using synthetic images of bolts which are generated from a graphical model to train a deep learning model for loosened bolt detection. Firstly, a framework for bolt-loosening detection using image-based deep learning and computer graphics is proposed. Next, the feasibility of the proposed framework is demonstrated through the bolt-loosening monitoring of a lab-scaled bolted joint model. For practicality, the proposed idea is evaluated on the real-scale bolted connections of a historical truss bridge in Danang, Vietnam. The results show that the deep learning model trained by the synthesized images can achieve accurate bolt recognitions and looseness detections. The proposed methodology could help to reduce the time and cost associated with the collection of high-quality training data and further accelerate the applicability of vision-based deep learning models trained on synthetic data in practice.

scholarly journals Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6888
Author(s):  
Quoc-Bao Ta ◽  
Jeong-Tae Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Steel Structures ◽  
Image Processing Algorithm ◽  
Detection Scheme ◽  
Perspective Distortion ◽  
Light Intensities ◽  
Hough Transforms ◽  
Bolt Loosening ◽  
Perspective Distortions

In this study, a regional convolutional neural network (RCNN)-based deep learning and Hough line transform (HLT) algorithm are applied to monitor corroded and loosened bolts in steel structures. The monitoring goals are to detect rusted bolts distinguished from non-corroded ones and also to estimate bolt-loosening angles of the identified bolts. The following approaches are performed to achieve the goals. Firstly, a RCNN-based autonomous bolt detection scheme is designed to identify corroded and clean bolts in a captured image. Secondly, a HLT-based image processing algorithm is designed to estimate rotational angles (i.e., bolt-loosening) of cropped bolts. Finally, the accuracy of the proposed framework is experimentally evaluated under various capture distances, perspective distortions, and light intensities. The lab-scale monitoring results indicate that the suggested method accurately acquires rusted bolts for images captured under perspective distortion angles less than 15° and light intensities larger than 63 lux.

scholarly journals A Study on the Real-time Fire Classification Algorithm Based on Stacked LSTM

2021 ◽  
Vol 21 (3) ◽  
pp. 93-104
Author(s):  
Yoseob Heo ◽  
Seongho Seo ◽  
We Shim ◽  
Jongseok Kang
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Dependence ◽  
Real Time ◽  
Sensor Data ◽  
Fire Monitoring ◽  
Monitoring Framework ◽  
Sensor Signals ◽  
Fire Detector ◽  
Using Data

Several researchers have been drawn to the development of fire detector in recent years, to protect people and property from the catastrophic disaster of fire. However, studies related to fire monitoring are affected by some unique characteristics of fire sensor signals, such as time dependence and the complexity of the signal pattern based on the variety of fire types,. In this study, a new deep learning-based approach that accurately classifies various types of fire situations in real-time using data obtained from multidimensional channel fire sensor signals was proposed. The contribution of this study is to develop a stacked-LSTM model that considers the time-series characteristics of sensor data and the complexity of multidimensional channel sensing data to develop a new fire monitoring framework for fire identification based on improving existing fire detectors.

scholarly journals A deep learning-based social distance monitoring framework for COVID-19

2020 ◽  
pp. 102571 ◽  
Author(s):  
Imran Ahmed ◽  
Misbah Ahmad ◽  
Joel J.P.C. Rodrigues ◽  
Gwanggil Jeon ◽  
Sadia Din
Keyword(s):  
Deep Learning ◽  
Social Distance ◽  
Monitoring Framework

Quasi-autonomous bolt-loosening detection method using vision-based deep learning and image processing

2019 ◽  
Vol 105 ◽  
pp. 102844 ◽  
Author(s):  
Thanh-Canh Huynh ◽  
Jae-Hyung Park ◽  
Hyung-Jo Jung ◽  
Jeong-Tae Kim
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Detection Method ◽  
Bolt Loosening

scholarly journals Quantum-assisted associative adversarial network: applying quantum annealing in deep learning

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Max Wilson ◽  
Thomas Vandal ◽  
Tad Hogg ◽  
Eleanor G. Rieffel
Keyword(s):  
Deep Learning ◽  
Latent Variable ◽  
Graphical Model ◽  
Generative Models ◽  
Generative Model ◽  
Feature Representation ◽  
Boltzmann Machine ◽  
Adversarial Network ◽  
Uniform Noise ◽  
Quantum Processor

AbstractGenerative models have the capacity to model and generate new examples from a dataset and have an increasingly diverse set of applications driven by commercial and academic interest. In this work, we present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model. This graphical model is learned by a Boltzmann machine which learns low-dimensional feature representation of data extracted by the discriminator. A quantum processor can be used to sample from the model to train the Boltzmann machine. This novel hybrid quantum-classical algorithm joins a growing family of algorithms that use a quantum processor sampling subroutine in deep learning, and provides a scalable framework to test the advantages of quantum-assisted learning. For the latent space model, fully connected, symmetric bipartite and Chimera graph topologies are compared on a reduced stochastically binarized MNIST dataset, for both classical and quantum sampling methods. The quantum-assisted associative adversarial network successfully learns a generative model of the MNIST dataset for all topologies. Evaluated using the Fréchet inception distance and inception score, the quantum and classical versions of the algorithm are found to have equivalent performance for learning an implicit generative model of the MNIST dataset. Classical sampling is used to demonstrate the algorithm on the LSUN bedrooms dataset, indicating scalability to larger and color datasets. Though the quantum processor used here is a quantum annealer, the algorithm is general enough such that any quantum processor, such as gate model quantum computers, may be substituted as a sampler.

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