scholarly journals An End-To-End Model for Pipe Crack Three-Dimensional Visualization Based on a Cascade Neural Network

2020 ◽  
Vol 10 (4) ◽  
pp. 1290
Author(s):  
Xia Fang ◽  
Yang Wang ◽  
Yong Li ◽  
Jie Wang ◽  
Libin Zhou
Keyword(s):  
Neural Network ◽  
Crack Detection ◽  
Three Dimensional ◽  
Side Wall ◽  
Long Time ◽  
End To End ◽  
Cascade Neural Network ◽  
Ambiguous Data ◽  
High Level ◽  
Navigation Equipment

With the continuous progress of machine vision technology, crack detection in pipelines has been greatly improved. For crack detection in deep holes, inner tubes, and other environments, it is not only necessary to detect the existence of cracks, but also to collect important information regarding the crack detection direction for further analysis. Because shooting with a frontal field of view causes the real side wall images to produce certain distortions, the detection and calibration of cracks requires a certain amount of professional technology and time. It usually takes a long time to collect the image to eliminate the distortion, and then to identify the crack and mark the direction according to the data line. Therefore, a simple and efficient end-to-end neural network model for crack recognition and three-dimensional visualization are proposed by using a cascade network and simple recognition technology in conjunction with inertial navigation equipment. In addition, we screen the crack data via pixel calibration and eliminate the ambiguous data to make the visualization more accurate. Experiments in pipelines and burrows show that the accuracy, performance, and efficiency of the proposed method reached a high level.

scholarly journals Towards High-Level Intrinsic Exploration in Reinforcement Learning

2020 ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Reinforcement Learning ◽  
Time Horizon ◽  
State Of The Art ◽  
Experimental Results ◽  
Prior Work ◽  
Extrinsic Rewards ◽  
Intrinsic Reward ◽  
Long Time ◽  
End To End ◽  
High Level

Deep reinforcement learning (DRL) methods traditionally struggle with tasks where environment rewards are sparse or delayed, which entails that exploration remains one of the key challenges of DRL. Instead of solely relying on extrinsic rewards, many state-of-the-art methods use intrinsic curiosity as exploration signal. While they hold promise of better local exploration, discovering global exploration strategies is beyond the reach of current methods. We propose a novel end-to-end intrinsic reward formulation that introduces high-level exploration in reinforcement learning. Our curiosity signal is driven by a fast reward that deals with local exploration and a slow reward that incentivizes long-time horizon exploration strategies. We formulate curiosity as the error in an agent’s ability to reconstruct the observations given their contexts. Experimental results show that this high-level exploration enables our agents to outperform prior work in several Atari games.

scholarly journals KeratoDetect: Keratoconus Detection Algorithm Using Convolutional Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Alexandru Lavric ◽  
Popa Valentin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Treatment Options ◽  
Diagnostic Errors ◽  
Detection Algorithm ◽  
Rapid Screening ◽  
Test Set ◽  
Corneal Deformation ◽  
Long Time ◽  
High Level

Keratoconus (KTC) is a noninflammatory disorder characterized by progressive thinning, corneal deformation, and scarring of the cornea. The pathological mechanisms of this condition have been investigated for a long time. In recent years, this disease has come to the attention of many research centers because the number of people diagnosed with keratoconus is on the rise. In this context, solutions that facilitate both the diagnostic and treatment options are quickly needed. The main contribution of this paper is the implementation of an algorithm that is able to determine whether an eye is affected or not by keratoconus. The KeratoDetect algorithm analyzes the corneal topography of the eye using a convolutional neural network (CNN) that is able to extract and learn the features of a keratoconus eye. The results show that the KeratoDetect algorithm ensures a high level of performance, obtaining an accuracy of 99.33% on the data test set. KeratoDetect can assist the ophthalmologist in rapid screening of its patients, thus reducing diagnostic errors and facilitating treatment.

scholarly journals Improved Crack Detection and Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Keqin Chen ◽  
Amit Yadav ◽  
Asif Khan ◽  
Yixin Meng ◽  
Kun Zhu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Crack Detection ◽  
Recognition Rate ◽  
Machine Learning Methods ◽  
Long Time ◽  
Detection And Recognition

Concrete cracks are very serious and potentially dangerous. There are three obvious limitations existing in the present machine learning methods: low recognition rate, low accuracy, and long time. Improved crack detection based on convolutional neural networks can automatically detect whether an image contains cracks and mark the location of the cracks, which can greatly improve the monitoring efficiency. Experimental results show that the Adam optimization algorithm and batch normalization (BN) algorithm can make the model converge faster and achieve the maximum accuracy of 99.71%.

scholarly journals Pruned Cascade Neural Network Image Classification

2019 ◽  
Vol 8 (3) ◽  
pp. 6454-6457
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Learning Algorithm ◽  
Classification Problem ◽  
Training Algorithm ◽  
Training Time ◽  
Neural Network Learning ◽  
Filter Size ◽  
Long Time ◽  
Cascade Neural Network

In this paper we propose a new model of deep neural network to build in deeper network. The convoluational neural network is one of the leading Image classification problem. The vanishing gradient problem requires us to use small learning rate with gradient descent which needs many small steps to converge and its take long time to proceed . By using GPU we can process more than one dataset (CIFAR-100) in a particular session. To overcome vanishing gradient problem by using the prune cascade correlation neural network learning algorithm compared to the deep cascade learning in CNN architecture. We improve the filter size, to reduce to the problem by training algorithm that trains in the network from bottom to top approach and its performing attain the task for better image classification in Google Net. We reduce the time complexity (training time ), storage capacity can be used pre training algorithm.

Three-Dimensional Ultrasonic Crack Detection in Anisotropic Materials

1997 ◽  
Vol 9 (2) ◽  
pp. 59-79 ◽  
Author(s):  
J. Mattsson ◽  
A. J. Niklasson ◽  
A. Eriksson
Keyword(s):  
Crack Detection ◽  
Three Dimensional ◽  
Anisotropic Materials

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

Guided surgery with 3D printed device: a case report

2020 ◽  
Author(s):  
Michelle Carvalho de Sales ◽  
Rafael Maluza Flores ◽  
Julianny da Silva Guimaraes ◽  
Gustavo Vargas da Silva Salomao ◽  
Tamara Kerber Tedesco ◽  
...  
Keyword(s):  
Operating Time ◽  
Three Dimensional ◽  
Conventional Technique ◽  
Cervical Region ◽  
Mean Deviation ◽  
Apical Region ◽  
Virtual Planning ◽  
Surgical Guide ◽  
Surgical Guides ◽  
High Level

Dental surgeons need in-depth knowledge of the bone tissue status and gingival morphology of atrophic maxillae. The aim of this study is to describe preoperative virtual planning of placement of five implants and to compare the plan with the actual surgical results. Three-dimensional planning of rehabilitation using software programs enables surgical guides to be specially designed for the implant site and manufactured using 3D printing. A patient with five teeth missing was selected for this study. The patient’s maxillary region was scanned with CBCT and a cast model was produced. After virtual planning using ImplantViewer, five implants were placed using a printed surgical guide. Two weeks after the surgical procedure, the patient underwent another CBCT scan of the maxilla. Statistically significant differences were detected between the virtually planned positions and the actual positions of the implants, with a mean deviation of 0.36 mm in the cervical region and 0.7 mm in the apical region. The surgical technique used enables more accurate procedures when compared to the conventional technique. Implants can be better positioned, with a high level of predictability, reducing both operating time and patient discomfort.

To the memory of the geobotanist. Leonid E. Rodin (to the 100th anniversary of his birthday)

2007 ◽  
pp. 106-107
Author(s):  
B. K. Gannibal
Keyword(s):  
End Of Life ◽  
100Th Anniversary ◽  
State University ◽  
Botanical Institute ◽  
Komarov Botanical Institute ◽  
The Future ◽  
Long Time ◽  
High Level

Leonid Efimovich Rodin (1907-1990) was a graduate of Leningrad state University. To him, the future is known geobotanica, happened to a course in Botanical geography is still at the N. A. Bush. His teachers were also A. P. Shennikov and A. A. Korchagin, who subsequently headed related Department of geobotany and Botanical geography of Leningrad state University. This was the first school scientist. And since the beginning of the 30s of XX century and until the end of life L. E. was an employee of the Department of geobotany of the Komarov Botanical Institute (RAS), where long time worked together with E. M. Lavrenko, V. B. Sochava, B. A. Tikhomirov, V. D. Alexandrova and many other high-level professionals, first continuing to learn and gain experience, then defining the direction of development of geobotany in the Institute and the country as a whole.

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