General Features of the q-XY Opinion Model

Prenga, Dodë

doi:10.28991/hef-2020-01-02-05

Cited by 6 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The energy of photon and neutron sources, irradiation angle, beam type, and atomic densities of materials required for the simulation were obtained from the literature, considering the ALARA principle [4,13,[22][23][24][25][26][27][28].…”

Section: Resultsmentioning

confidence: 99%

Numerical Analysis of Backscatter Radiography for Prediction of Pipelines Situation: Their Bursting and Casing Failure Consideration from inside

2022

IJE

View full text Add to dashboard Cite

Backscatter radiography as a technique can successfully be applied for predicting the pipeline bursting and casing failure. A valid numerical technique will allow predicting these issues without needing to access the outside of the pipelines. Furthermore, this technique has the ability to estimate the shape and depth of damages. It is normally preferred to apply non-destructive testing (NDT) methods which can monitor the status of a pipeline from the internal surface of it without needing to access both sides of it depending on various locations of pipe such as underground or submarine. In the current study, backscatter radiography as an applicable and NDT method to detect locations with potential for pipeline bursting or casing failure was carefully investigated using the Monte Carlo simulation tool. T he data obtained by the simulation process showed that backscatter radiography could detect deformations, corroded areas, depositions, creation of load on the casing, lacking proper cementation, excessive pressure inside the pipelines, and other factors which may increase the risk of pipelines bursting or casing failure (in-situ and online) with an acceptable accuracy.

show abstract

Section: Resultsmentioning

confidence: 99%

Numerical Analysis of Backscatter Radiography for Prediction of Pipelines Situation: Their Bursting and Casing Failure Consideration from inside

2022

IJE

View full text Add to dashboard Cite

show abstract

“…Generally, when using deep networks for lane detection, a deep convolutional neural network is used as the base method in adaptation for specific operations. Convolutional neural networks are very popular because of their compatibility with a variety of feature extraction methods in signals, including various machine learning models or even group models of conflict algorithms such as q_xy [26], which determine and extract twodimensional features. However, the research works mentioned so far all are supervised types.…”

Section: Literature Reviewmentioning

confidence: 99%

A Method for Automatic Lane Detection using a Deep Network

2022

IJE

View full text Add to dashboard Cite

Lane detection is amongst the most important operations during the automatic driving process. This process aims to detect lane lines to control the vehicle's direction in a specific lane on the road and it can be effective in preventing accidents. Besides being online, the most requirement for lane detection methods in automatic driving is their high accuracy. The use of deep learning to create fully automated systems in lane detection has been done extensively. Automated learning-based methods used for road lane detection are often of supervised type. One of the disadvantages of these methods, despite their excellent accuracy, is that they need a set of labeled data, which limits the development procedure of the lane detection system, and most importantly establishing a standard set of labeled data is very timedemanding. The recommended solution is to use appropriate learning approaches that can be used to achieve the relative accuracy of the supervised approaches and to improve their speed. It also enables us to use different datasets without a constraint label that the tagged dataset would create in the algorithm development relative to the new dataset. In this research, we present an automatic semi-supervised learning method using deep neural networks to extract the data of lane lines, using labeled (mask for detected lines) and unlabeled datasets. The results demonstrate the suitable accuracy of the adopted method according to the proposed approach and also improves its computational complexity due to the significant reduction in the number of teachable network parameters.

show abstract

“…Their model demonstrated improved feature extraction capability and produced better results than other models [14]. Because CNN can be combined with a variety of other models or algorithms such as q-xy [15], studies have also involved diagnoses using CNN combined with various classifiers. Yuan et al…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Bearings With the Common-Domain Data

Kim

Chai

2022

IEEE Access

View full text Add to dashboard Cite

Rolling element bearings are one of the important components in rotating machines. Therefore, many studies on bearing diagnosis have been conducted with artificial intelligence (AI) to do maintenance on the machines on time. In general, AI successfully diagnoses the defects of bearing when it is trained with the sufficient data of a specific machine, but it hardly provides reasonable results when it is untrained or insufficiently trained. However, it is hard to obtain sufficient data even for a specific machine in practice. In this paper, a new method was developed to increase training data by transferring the cross-domain data into the common-domain data. Therefore, all the data from different kinds of machines with various bearings can be combined as a big training data. Bearings under consideration in this paper have different specifications and characteristics. In transferring into the common-domain, it is important to get rid of structural and environmental noise by signal processing, which makes it plausible to extract common features. With the common-domain data, one-dimensional convolutional neural network (1D-CNN) with feature domain adaptation is applied and successfully classifies the defects of each bearing. Moreover, 1D-CNN combined with support vector machine (SVM) can also classify defects successfully without feature domain adaptation, which makes it possible to train the model only with normal data of the machine in concern. To verify the proposed method, not only the bearing data from Case Western Reserve University and Paderborn University but also the bearing data with flow noise of Ajou University are used.INDEX TERMS Bearing fault diagnosis, common-domain data, convolutional neural network, cross-domain fault diagnosis, domain adaptation, signal processing, support vector machine.

show abstract

General Features of the q-XY Opinion Model

Cited by 6 publications

References 23 publications

Numerical Analysis of Backscatter Radiography for Prediction of Pipelines Situation: Their Bursting and Casing Failure Consideration from inside

Numerical Analysis of Backscatter Radiography for Prediction of Pipelines Situation: Their Bursting and Casing Failure Consideration from inside

A Method for Automatic Lane Detection using a Deep Network

Fault Diagnosis of Bearings With the Common-Domain Data

Contact Info

Product

Resources

About