scholarly journals Fast Convolution based on Winograd Minimum Filtering: Introduction and Development

2021 ◽  
Author(s):  
Gan Tong ◽  
Libo Huang
Keyword(s):  
Neural Network ◽  
Convolution Operators ◽  
Algorithm Optimization ◽  
Future Directions ◽  
Memory Space ◽  
Fast Convolution ◽  
Network Training ◽  
Convolution Algorithm ◽  
First Choice

Convolutional Neural Network (CNN) has been widely used in various fields and played an important role. Convolution operators are the fundamental component of convolutional neural networks, and it is also the most time-consuming part of network training and inference. In recent years, researchers have proposed several fast convolution algorithms including FFT and Winograd. Among them, Winograd convolution significantly reduces the multiplication operations in convolution, and it also takes up less memory space than FFT convolution. Therefore, Winograd convolution has quickly become the first choice for fast convolution implementation within a few years. At present, there is no systematic summary of the convolution algorithm. This article aims to fill this gap and provide detailed references for follow-up researchers. This article summarizes the development of Winograd convolution from the three aspects of algorithm expansion, algorithm optimization, implementation, and application, and finally makes a simple outlook on the possible future directions.

scholarly journals Klasifikasi Varietas Cabai Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network

2016 ◽  
Vol 10 (2) ◽  
pp. 161
Author(s):  
Kharis Syaban ◽  
Agus Harjoko
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Geographical Location ◽  
Moment Invariant ◽  
Network Training ◽  
The Neural Network ◽  
First Choice ◽  
Network Methods ◽  
Hidden Layer ◽  
Biological Methods

Compared with other methods of classifiers such as cellular and molecular biological methods, using the image of the leaves become the first choice in the classification of plants. The leaves can be characterized by shape, color, and texture; The leaves can have a color that varies depending on the season and geographical location. In addition, the same plant species also can have different leaf shapes. In this study, the morphological features of leaves used to identify varieties of pepper plants. The method used to perform feature extraction is a moment invariant and basic geometric features. For the process of recognition based on the features that have been extracted, used neural network methods with backpropagation learning algorithm. From the neural-network training, the best accuracy in classifying varieties of chili with minimum error 0.001 by providing learning rate 0.1, momentum of 0.7, and 15 neurons in the hidden layer foreach of various feature. To conduct cross-validation testing with k-fold tehcnique, obtained classification accuracy to be range of 80.75%±0.09% with k=4.

Nonsurgical Endodontik Retreatment of Maxillary First Premolar

DENTA ◽  
2017 ◽  
Vol 11 (1) ◽  
pp. 88
Author(s):  
Yongki Hadinata W ◽  
Karlina Samadi
Keyword(s):  
Root Canal ◽  
Radiographic Examination ◽  
Periapical Lesion ◽  
First Choice ◽  
Periapical Abscess ◽  
Dental Hospital ◽  
Conservative Dentistry ◽  
Previously Treated ◽  
Endodontic Retreatment

<p><strong><em>Background :</em></strong><em> There are some factors can cause endodontic failure such as inadequate in cleaning or shaping step, non hermetic obturation, or poor restoration, which can cause bacteria multiply. <strong>Purpose :</strong> To report the management of endodontic failure with nonsurgical treatment. <strong>Case :</strong> 46-year-old woman came to Airlangga Dental Hospital Conservative Dentistry Department to treat her upper right tooth which show symptomatic pain in the last 2 weeks. The tooth has been treated and crowned with porcelain fused to metal about 10 years ago. Clinical examination show the presence of fistula on premolar buccal gingiva, react to percussion.  Radiographic examination show not hermetic obturation in one root canal and radiolucency in the periapical area. The diagnosis for maxillary first premolar is previously treated tooth with chronic periapical abscess.. <strong>Treatment :</strong> Crown and post was removed from the tooth, and endodontic retreatment was done. Follow up 6 months after the retreatment show no reaction to percussion, and radiographic examination show no enlargement periapical lesion. <strong>Conclusion :</strong> Nonsurgical endodontic retreatment always become the first choice to resolve endodontic failure for previously treated tooth.</em></p><p><strong><em>Keywords :</em></strong><em> endodontic failure, maxillary first premolar, nonsurgical endodontic retreatment</em></p><p><strong><em>Correspondence:</em></strong><em> Yongki Hadinata W., drg. PPDGS Ilmu Konservasi Gigi Fakultas Kedokteran Gigi Universitas Airlangga, Surabaya. Jl. Mayjen. Prof. Dr. Moestopo No. 47, Surabaya.</em></p>

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

scholarly journals Immediate Post-Extraction Short Implant Placement with Immediate Loading and without Extraction of an Impacted Maxillary Canine: Two Case Reports

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2757
Author(s):  
José Antonio Moreno-Rodríguez ◽  
Julia Guerrero-Gironés ◽  
Francisco Javier Rodríguez-Lozano ◽  
Miguel Ramón Pecci-Lloret
Keyword(s):  
Dental Implant ◽  
Alveolar Bone ◽  
Case Reports ◽  
Young Patients ◽  
Aesthetic Result ◽  
First Choice ◽  
Surgical Extraction ◽  
Impacted Canine ◽  
Short Implant

For the treatment of impacted maxillary canines, traction associated with a complete orthodontic treatment is the first choice in young patients. However, in adults, this treatment has a worse prognosis. The surgical extraction of the impacted tooth can result in a series of complications and a compromised alveolar bone integrity, which may lead to the requirement of a bone regeneration/grafting procedure to replace the canine with a dental implant. These case reports aimed to describe an alternative treatment procedure to the surgical extraction of impacted maxillary canines in adults. Following clinical and computerized tomography-scan (CT-Scan) examination, the possibility of maintaining the impacted canine in its position and replacing the temporary canine present in its place with a dental implant was planned. A short dental implant with an immediate provisional crown was placed, without contacting the impacted canine. At 3 months follow-up, a definitive metal-ceramic restoration was placed. Follow-up visits were performed periodically. The implant site showed a physiological soft tissue color and firmness, no marginal bone loss, no infection or inflammation, and an adequate aesthetic result in all follow-up visits. These results suggest that the treatment carried out is a valid option to rehabilitate with an osseointegrated short implant area where a canine is included, as long as there is a sufficient amount of the remaining bone.

scholarly journals A Geometric Perspective on Information Plane Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 711
Author(s):  
Mina Basirat ◽  
Bernhard C. Geiger ◽  
Peter M. Roth
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Geometric Interpretation ◽  
Neural Network Training ◽  
Neural Network Learning ◽  
Network Learning ◽  
Plane Analysis ◽  
Network Training ◽  
Hidden Layer ◽  
The Impact

Information plane analysis, describing the mutual information between the input and a hidden layer and between a hidden layer and the target over time, has recently been proposed to analyze the training of neural networks. Since the activations of a hidden layer are typically continuous-valued, this mutual information cannot be computed analytically and must thus be estimated, resulting in apparently inconsistent or even contradicting results in the literature. The goal of this paper is to demonstrate how information plane analysis can still be a valuable tool for analyzing neural network training. To this end, we complement the prevailing binning estimator for mutual information with a geometric interpretation. With this geometric interpretation in mind, we evaluate the impact of regularization and interpret phenomena such as underfitting and overfitting. In addition, we investigate neural network learning in the presence of noisy data and noisy labels.

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