scholarly journals Enhancement of damaged-image prediction through Cahn–Hilliard image inpainting

2021 ◽  
Vol 8 (5) ◽  
pp. 201294
Author(s):  
José A. Carrillo ◽  
Serafim Kalliadasis ◽  
Fuyue Liang ◽  
Sergio P. Perez
Keyword(s):  
Neural Network ◽  
Computational Cost ◽  
Image Inpainting ◽  
Finite Volume Scheme ◽  
Binary Images ◽  
Hilliard Equation ◽  
The Neural Network ◽  
Image Prediction ◽  
Different Types ◽  
Cahn Hilliard Equation

We assess the benefit of including an image inpainting filter before passing damaged images into a classification neural network. We employ an appropriately modified Cahn–Hilliard equation as an image inpainting filter which is solved numerically with a finite-volume scheme exhibiting reduced computational cost and the properties of energy stability and boundedness. The benchmark dataset employed is Modified National Institute of Standards and Technology (MNIST) dataset, which consists of binary images of handwritten digits and is a standard dataset to validate image-processing methodologies. We train a neural network based on dense layers with MNIST, and subsequently we contaminate the test set with damages of different types and intensities. We then compare the prediction accuracy of the neural network with and without applying the Cahn–Hilliard filter to the damaged images test. Our results quantify the significant improvement of damaged-image prediction by applying the Cahn–Hilliard filter, which for specific damages can increase up to 50% and is advantageous for low to moderate damage.

scholarly journals Optimization of machining parameters during Drilling by Taguchi based Design of Experiments and Validation by Neural Network

2018 ◽  
Vol 15 (2) ◽  
pp. 294-301
Author(s):  
Reddy Sreenivasulu ◽  
Chalamalasetti SrinivasaRao
Keyword(s):  
Neural Network ◽  
Design Of Experiments ◽  
Computational Cost ◽  
Machining Parameters ◽  
Burr Size ◽  
Taguchi Design Of Experiments ◽  
The Neural Network ◽  
Input Parameters ◽  
Hole Making ◽  
The Cost

Drilling is a hole making process on machine components at the time of assembly work, which are identify everywhere. In precise applications, quality and accuracy play a wide role. Nowadays’ industries suffer due to the cost incurred during deburring, especially in precise assemblies such as aerospace/aircraft body structures, marine works and automobile industries. Burrs produced during drilling causes dimensional errors, jamming of parts and misalignment. Therefore, deburring operation after drilling is often required. Now, reducing burr size is a serious topic. In this study experiments are conducted by choosing various input parameters selected from previous researchers. The effect of alteration of drill geometry on thrust force and burr size of drilled hole was investigated by the Taguchi design of experiments and found an optimum combination of the most significant input parameters from ANOVA to get optimum reduction in terms of burr size by design expert software. Drill thrust influences more on burr size. The clearance angle of the drill bit causes variation in thrust. The burr height is observed in this study.  These output results are compared with the neural network software @easy NN plus. Finally, it is concluded that by increasing the number of nodes the computational cost increases and the error in nueral network decreases. Good agreement was shown between the predictive model results and the experimental responses.  

scholarly journals The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 691 ◽  
Author(s):  
Irina Popova ◽  
Alexandr Rozhnoi ◽  
Maria Solovieva ◽  
Danila Chebrov ◽  
Masashi Hayakawa
Keyword(s):  
Neural Network ◽  
Radio Wave ◽  
Seismic Activity ◽  
Low Frequency ◽  
Magnetic Storms ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Different Types ◽  
Parameters Of Seismicity

The neural network approach is proposed for studying very-low- and low-frequency (VLF and LF) subionospheric radio wave variations in the time vicinities of magnetic storms and earthquakes, with the purpose of recognizing anomalies of different types. We also examined the days with quiet geomagnetic conditions in the absence of seismic activity, in order to distinguish between the disturbed signals and the quiet ones. To this end, we trained the neural network (NN) on the examples of the representative database. The database included both the VLF/LF data that was measured during four-year monitoring at the station in Petropavlovsk-Kamchatsky, and the parameters of seismicity in the Kuril-Kamchatka and Japan regions. It was shown that the neural network can distinguish between the disturbed and undisturbed signals. Furthermore, the prognostic behavior of the VLF/LF variations indicative of magnetic and seismic activity has a different appearance in the time vicinity of the earthquakes and magnetic storms.

scholarly journals An adaptive Cahn-Hilliard equation for enhanced edges in binary image inpainting

2020 ◽  
Vol 14 ◽  
pp. 174830262094143
Author(s):  
Anis Theljani ◽  
Hamdi Houichet ◽  
Anis Mohamed
Keyword(s):  
Image Inpainting ◽  
Binary Image ◽  
Mesh Adaptation ◽  
Singular Set ◽  
Discrete Level ◽  
Diffusion Operator ◽  
Hilliard Equation ◽  
Proposed Model ◽  
Order Sets ◽  
Cahn Hilliard Equation

We consider the Cahn-Hilliard equation for solving the binary image inpainting problem with emphasis on the recovery of low-order sets (edges, corners) and enhanced edges. The model consists in solving a modified Cahn-Hilliard equation by weighting the diffusion operator with a function which will be selected locally and adaptively. The diffusivity selection is dynamically adopted at the discrete level using the residual error indicator. We combine the adaptive approach with a standard mesh adaptation technique in order to well approximate and recover the singular set of the solution. We give some numerical examples and comparisons with the classical Cahn-Hillard equation for different scenarios. The numerical results illustrate the effectiveness of the proposed model.

scholarly journals Finite-dimensional attractors for the Bertozzi--Esedoglu--Gillette--Cahn--Hilliard equation in image inpainting

2015 ◽  
Vol 9 (1) ◽  
pp. 105-125 ◽  
Author(s):  
Laurence Cherfils ◽  
◽  
Hussein Fakih ◽  
Alain Miranville ◽  
◽  
...  
Keyword(s):  
Image Inpainting ◽  
Hilliard Equation ◽  
Finite Dimensional ◽  
Cahn Hilliard Equation

scholarly journals Quantum Inspired Genetic Programming Model to Predict Toxicity Degree for Chemical Compounds

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Saad Mohamed Darwish
Keyword(s):  
Neural Network ◽  
Genetic Programming ◽  
Goodness Of Fit ◽  
Programming Model ◽  
Computational Cost ◽  
Chemical Compounds ◽  
Vital Role ◽  
Internal Validation ◽  
Fit Statistics ◽  
The Neural Network

Cheminformatics plays a vital role to maintain a large amount of chemical data. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug design, food safety, and manufacturing chemical compounds. Toxicity prediction topic requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; such techniques need more computational cost as the number of chemical compounds increases. State-of-the-art prediction methods such as neural network and multi-layer regression that requires either tuning parameters or complex transformations of predictor or outcome variables are not achieving high accuracy results.  This paper proposes a Quantum Inspired Genetic Programming “QIGP” model to improve the prediction accuracy. Genetic Programming is utilized to give a linear equation for calculating toxicity degree more accurately. Quantum computing is employed to improve the selection of the best-of-run individuals and handles parsimony pressure to reduce the complexity of the solutions. The results of the internal validation analysis indicated that the QIGP model has the better goodness of fit statistics and significantly outperforms the Neural Network model.

scholarly journals A Precision Agricultural Application: Manggis Fruit Classification Using Hybrid Deep Learning

2021 ◽  
Vol 35 (5) ◽  
pp. 375-381
Author(s):  
Putra Sumari ◽  
Wan Muhammad Azimuddin Wan Ahmad ◽  
Faris Hadi ◽  
Muhammad Mazlan ◽  
Nur Anis Liyana ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Stochastic Gradient Descent ◽  
Traditional Methods ◽  
High Performing ◽  
The Neural Network ◽  
Different Types ◽  
Filter Size ◽  
Fully Connected ◽  
Convolution Filters

Fruits come in different variants and subspecies. While some subspecies of fruits can be easily differentiated, others may require an expertness to differentiate them. Although farmers rely on the traditional methods to identify and classify fruit types, the methods are prone to so many challenges. Training a machine to identify and classify fruit types in place of traditional methods can ensure precision fruit classification. By taking advantage of the state-of-the-art image recognition techniques, we approach fruits classification from another perspective by proposing a high performing hybrid deep learning which could ensure precision mangosteen fruit classification. This involves a proposed optimized Convolutional Neural Network (CNN) model compared to other optimized models such as Xception, VGG16, and ResNet50 using Adam, RMSprop, Adagrad, and Stochastic Gradient Descent (SGD) optimizers on specified dense layers and filters numbers. The proposed CNN model has three types of layers that make up its model, they are: 1) the convolutional layers, 2) the pooling layers, and 3) the fully connected (FC) layers. The first convolution layer uses convolution filters with a filter size of 3x3 used for initializing the neural network with some weights prior to updating to a better value for each iteration. The CNN architecture is formed from stacking these layers. Our self-acquired dataset which is composed of four different types of Malaysian mangosteen fruit, namely Manggis Hutan, Manggis Mesta, Manggis Putih and Manggis Ungu was employed for the training and testing of the proposed CNN model. The proposed CNN model achieved 94.99% classification accuracy higher than the optimized Xception model which achieved 90.62% accuracy in the second position.

A Data-Driven Approach to Predict the Breakdown Pressure of the Tight and Unconventional Formation

2021 ◽  
Author(s):  
Zeeshan Tariq ◽  
Murtada Saleh Aljawad ◽  
Mobeen Murtaza ◽  
Mohamed Mahmoud ◽  
Dhafer Al-Shehri ◽  
...  
Keyword(s):  
Neural Network ◽  
Hydraulic Fracturing ◽  
Rock Properties ◽  
Breakdown Pressure ◽  
Rock Samples ◽  
In Situ Stresses ◽  
Fracturing Fluids ◽  
The Neural Network ◽  
Different Types

Abstract Unconventional reservoirs are characterized by their extremely low permeabilities surrounded by huge in-situ stresses. Hydraulic fracturing is a most commonly used stimulation technique to produce from such reservoirs. Due to high in situ stresses, breakdown pressure of the rock can be too difficult to achieve despite of reaching maximum pumping capacity. In this study, a new model is proposed to predict the breakdown pressures of the rock. An extensive experimental study was carried out on different cylindrical specimens and the hydraulic fracturing stimulation was performed with different fracturing fluids. Stimulation was carried out to record the rock breakdown pressure. Different types of fracturing fluids such as slick water, linear gel, cross-linked gels, guar gum, and heavy oil were tested. The experiments were carried out on different types of rock samples such as shales, sandstone, and tight carbonates. An extensive rock mechanical study was conducted to measure the elastic and failure parameters of the rock samples tested. An artificial neural network was used to correlate the breakdown pressure of the rock as a function of fracturing fluids, experimental conditions, and rock properties. Fracturing fluid properties included injection rate and fluid viscosity. Rock properties included were tensile strength, unconfined compressive strength, Young's Modulus, Poisson's ratio, porosity, permeability, and bulk density. In the process of data training, we analyzed and optimized the parameters of the neural network, including activation function, number of hidden layers, number of neurons in each layer, training times, data set division, and obtained the optimal model suitable for prediction of breakdown pressure. With the optimal setting of the neural network, we were successfully able to predict the breakdown pressure of the unconventional formation with an accuracy of 95%. The proposed method can greatly reduce the prediction cost of rock breakdown pressure before the fracturing operation of new wells and provides an optional method for the evaluation of tight oil reservoirs.

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