Intelligent Restoration of Historical Parametric Geometric Patterns by Zernike Moments and Neural Networks

2021 ◽  
Vol 14 (4) ◽  
pp. 1-27
Author(s):  
Bita Hajebi
Keyword(s):  
Mean Squared Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Zernike Moments ◽  
Individual Element ◽  
Periodic Patterns ◽  
Geometric Patterns ◽  
Efficient Performance ◽  
Mathematical Algorithms ◽  
Dominant Structure

Historical Islamic ornaments include a fantastic treasury of geometric and mathematical algorithms. Inevitably, restoration of these ornaments in periodic patterns consisting of repeated elements has been faced following and substituting the other available similar ingredients instead of vanished parts. Still, the prediction of parametric, quasi, or non-periodic patterns, where components are not identical, needs to be carried out in a more challenging process than the periodic ones due to shape, scale, or angle of rotation alteration. Intelligent restoration could facilitate the forecasting of damaged parts in such geometric patterns that an algorithm has changed their geometric characteristics. In some architectural heritage, geometric patterns include a parametric algorithm like parametric patterns in the ceiling of Sheikh Lotfollahmosque in Isfahan, Iran, and the dominant structure of Persian domes Karbandi. In this article, the aim is to propose a new method for the smart restoration of the parametric geometric patterns in which, by having access to the image of the existing patterns, the vanished parts could be reconstructed spontaneously. Our approach is based on image processing by detecting boundaries of deterioration, finding every individual element, and extracting features of detected individual patterns via Zernike moments. The order of individual patterns starts from the farthest pattern to detected deterioration. Then by creating a time series, the Back-propagation neural network would be trained by extracted features, and the vanished patterns’ features could be predicted and reconstructed. Eventually, the reconstructed and real patterns are compared to determine differences between them by mean-squared error and to evaluate the performance of our method. To validate the process, a parametric geometric pattern is designed by the assumption that some parts are disappeared. The proposed method’s results, in this case, hold an efficient performance with the accuracy of 92.99%. Furthermore, Sheikh Lotfollah’s patterns and Naseredin Mirza mansion’s patterns as two real cases are tested by the proposed method, representing reliable and suitable performance results.

Using BPNN to Predict Number of Low-Income Households in Taiwan Based on Seasonally Adjusted Annualized Rates for Real GDP

2014 ◽  
Vol 1044-1045 ◽  
pp. 1824-1827
Author(s):  
Yi Ti Tung ◽  
Tzu Yi Pai
Keyword(s):  
Neural Network ◽  
Low Income ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Percentage Error ◽  
Real Gdp ◽  
Squared Error ◽  
Input Variables ◽  
Low Income Households

In this study, the back-propagation neural network (BPNN) was used to predict the number of low-income households (NLIH) in Taiwan, taking the seasonally adjusted annualized rates (SAAR) for real gross domestic product (GDP) as input variables. The results indicated that the lowest mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and highest correlation coefficient (R) for training and testing were 4.759 % versus 19.343 %, 24429972.268 versus 781839890.859, 4942.669 versus 27961.400, and 0.945 versus 0.838, respectively.

scholarly journals Rainfall Forecasting Based on Surface Data of Chennai Region Using Artificial Neural Networks

2020 ◽  
pp. 26-36
Keyword(s):  
Mean Squared Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Training Data ◽  
Rainfall Forecasting ◽  
Data Set ◽  
Surface Data ◽  
Hourly Rainfall ◽  
Forecasting System ◽  
User Friendly

In this study, we developed user friendly rainfall forecasting system based on Back propagation Neural Network using MATLAB 7.10 to forecast Hourly rainfall in Chennai region. The dataset of 31488 samples has been collected from Nungambakkam Meteorological Station, Chennai for the period of 2005 to 2015. The data was organized into day-wise hourly recordings as well as day-wise, maximum, minimum, average data of Relative Humidity (RH), Temperature, Pressure and Wind Speed along with Rainfall data. The collected dataset has been used both for training and for testing the data. The developed system gives more accuracy of 94.8197% when the training data set is 55% and the testing data set is 45% with least Mean Squared Error (MSE) value 0.012437.

scholarly journals Groundwater Level Prediction based on Neural Networks: A case study in Linze, Northwestern China

2021 ◽  
Vol 266 ◽  
pp. 09005
Author(s):  
Hui Zhang ◽  
Jixuan Zhao ◽  
Chong Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Groundwater Level ◽  
Mean Squared Error ◽  
Rbf Neural Network ◽  
Groundwater Resources ◽  
Back Propagation ◽  
Theoretical Models ◽  
Back Propagation Neural Network ◽  
Coefficient Of Determination

Groundwater level is an important factor in evaluating groundwater resources. Due to numerous non-linear factors, establishing theoretical models is difficult.. Therefore, this paper proposesthe BP (Back Propagation) neural network and the Radial Basis Function (RBF) neural network. The study area is divided into two zones. The R2 (coefficient of determination) and RMSE (Root Mean Squared Error) are used to evaluate the performance. The BP neural network is used to predict groundwater level in the two zones with the R2of0.57 and 0.54, with the RMSE of 0.0804 meters and 0.1864 meters respectively. The RBF neural network is implemented with R2of 0.65 and 0.61, with RMSE of 0.0720 meters and 0.1519 meters, respectively. The results show the RBF neural network performs better than the BP neural network in the accuracy of predicting groundwater level. This study shows the feasibility and superiority of groundwater simulation using neural network.

ROOM MATERIAL IDENTIFICATION SYSTEM FROM PHOTO IMAGES USING GLCM, MODIFIED ZERNIKE MOMENTS, AND PSO-BP APPLICATION

2016 ◽  
Vol 78 (10) ◽  
Author(s):  
Fathin Liyana Zainudin ◽  
Abd Kadir Mahamad ◽  
Sharifah Saon ◽  
Musli Nizam Yahya
Keyword(s):  
Back Propagation ◽  
Image Data ◽  
Back Propagation Neural Network ◽  
Zernike Moments ◽  
Bp Algorithm ◽  
Material Surface ◽  
Identification System ◽  
Hybrid Particle Swarm Optimization ◽  
System Input ◽  
Occurrence Matrix

In acoustic engineering, the types of material used in a room are basically one of the fundamental features that are essential in some of room acoustic parameters computation. This paper proposed an improved system to identify room material type from its surface photographic image. Data images of several room surfaces were collected for the system input. This improved system implements Gray Level Co-occurrence Matrix (GLCM) and modified Zernike moments for image extraction and hybrid Particle Swarm Optimization and back-propagation (PSO-BP) algorithm for classification. For comparison purpose, experiments using variations combination of GLCM and modified Zernike moments extraction as well as Levenberg-Marquardt, back-propagation neural network (BPNN), and PSO-BP algorithm were executed. By applying the proposed methods, the system accuracy increased around 30% compared to previous research. Moreover, the convergence attained during training was three times faster compared to BP algorithm. Thus using the new methods in identifying material surface images had positively improved the system in becoming more efficient and reliable.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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