A precise method of color space conversion in the digital printing process based on PSO-DBN

2022 ◽  
pp. 004051752110672
Author(s):  
Zebin Su ◽  
Jinkai Yang ◽  
Pengfei Li ◽  
Junfeng Jing ◽  
Huanhuan Zhang
Keyword(s):  
Neural Network ◽  
Color Space ◽  
Back Propagation ◽  
Optimal Solution ◽  
B Value ◽  
Digital Printing ◽  
Generalized Regression Neural Network ◽  
Color Management ◽  
Printing Process ◽  
Color Space Conversion

Neural networks have been widely used in color space conversion in the digital printing process. The shallow neural network easily obtains the local optimal solution when establishing multi-dimensional nonlinear mapping. In this paper, an improved high-precision deep belief network (DBN) algorithm is proposed to achieve the color space conversion from CMYK to L*a*b*. First, the PANTONE TCX color card is used as sample data, in which the CMYK value of the color block is used as input and the L*a*b* value is used as output; then, the conversion model from CMYK to L*a*b* color space is established by using DBN. To obtain better weight and threshold, DBN is optimized by a particle swarm optimization algorithm. Experimental results show that the proposed method has the highest conversion accuracy compared with Back Propagation Neural Network, Generalized Regression Neural Network, and traditional DBN color space conversion methods. It can also adapt to the actual production demand of color management in digital printing.

Study on Color Space Conversion Based on RBF Neural Network

2010 ◽  
Vol 174 ◽  
pp. 28-31 ◽  
Author(s):  
Cong Jun Cao ◽  
Qiang Jun Liu
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Color Space ◽  
Color Management ◽  
Color Target ◽  
Color Space Conversion ◽  
Fast Training ◽  
Testing Data ◽  
Modeling Data ◽  
Target Data

The conversions of color spaces are core techniques of modern ICC color management and the study of color space conversion algorithm between L*a*b* and CMYK is valuable both in theory and in application. In this paper, firstly ECI2002 standard color target data are uniformly selected, including modeling data and testing data; secondly the models of color space conversions from CMYK to L*a*b* and from L*a*b* to CMYK are built based on Radial Basis Function (RBF) neural network; finally the precision of the models are evaluated. This research indicates that the RBF neural network is suitable for the color space conversions between CMYK and L*a*b*. The models’ building processes are simpler and more convenient; the network has fast training speed and good results. With the improvement of the modeling method, this method for color space conversion will have a broader application.

scholarly journals Dissolved Gases Forecasting Based on Wavelet Least Squares Support Vector Regression and Imperialist Competition Algorithm for Assessing Incipient Faults of Transformer Polymer Insulation

Polymers ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 85 ◽  
Author(s):  
Jiefeng Liu ◽  
Hanbo Zheng ◽  
Yiyi Zhang ◽  
Xin Li ◽  
Jiake Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Percentage Error ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Dissolved Gases ◽  
Dissolved Gas ◽  
Simulated Case

A solution for forecasting the dissolved gases in oil-immersed transformers has been proposed based on the wavelet technique and least squares support vector machine. In order to optimize the hyper-parameters of the constructed wavelet LS-SVM regression, the imperialist competition algorithm was then applied. In this study, the assessment of prediction performance is based on the squared correlation coefficient and mean absolute percentage error methods. According to the proposed method, this novel procedure was applied to a simulated case and the experimental results show that the dissolved gas contents could be accurately predicted using this method. Besides, the proposed approach was compared to other prediction methods such as the back propagation neural network, the radial basis function neural network, and generalized regression neural network. By comparison, it was inferred that this method is more effective than previous forecasting methods.

scholarly journals Climatic Characteristics and Modeling Evaluation of Pan Evapotranspiration over Henan Province, China

Land ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 229
Author(s):  
Miao Zhang ◽  
Bo Su ◽  
Majid Nazeer ◽  
Muhammad Bilal ◽  
Pengcheng Qi ◽  
...  
Keyword(s):  
Neural Network ◽  
Empirical Model ◽  
Hydrological Modeling ◽  
Climatic Factors ◽  
Back Propagation ◽  
Henan Province ◽  
Generalized Regression Neural Network ◽  
Climatic Data ◽  
Ann Models ◽  
Agricultural Water Resources

Pan evapotranspiration (E) is an important physical parameter in agricultural water resources research. Many climatic factors affect E, and one of the essential challenges is to model or predict E utilizing limited climatic parameters. In this study, the performance of four different artificial neural network (ANN) algorithms i.e., multiple hidden layer back propagation (MBP), generalized regression neural network (GRNN), probabilistic neural networks (PNN), and wavelet neural network (WNN) and one empirical model namely Stephens–Stewart (SS) were employed to predict monthly E. Long-term climatic data (i.e., 1961–2013) was used for the validation of the proposed model in the Henan province of China. It was found that different models had diverse prediction accuracies in various geographical locations, MBP model outperformed other models over almost all stations (maximum R2 = 0.96), and the WNN model was the best over two sites, the accuracies of the five models ranked as MBP, WNN, GRNN, PNN, and SS. The performances of WNN and GRNN were almost the same, five-input ANN models provided better accuracy than the two-input (solar radiation (Ro) and air temperature (T)) SS empirical model (R2 = 0.80). Similarly. the two-input ANN models (maximum R2 = 0.83) also generally performed better than the two-input (Ro and T) SS empirical model. The study could reveal that the above ANN models can be used to predict E successfully in hydrological modeling over Henan Province.

Detection of Organic Pollutions Using Artificial Neural Networks and Domain Transform Techniques

2014 ◽  
Vol 610 ◽  
pp. 279-282
Author(s):  
Ling Gao ◽  
Shou Xin Ren
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Noise Removal ◽  
Generalized Regression Neural Network ◽  
Mathematical Methods ◽  
Artificial Neural ◽  
Generalized Regression

This paper presented a novel method for detection of organic pollutions based on artificial neural networks combining domain transform techniques. Domain transform techniques are mathematical methods that allow the direct mapping of information from one domain to another. The most effectively used domain transform technique is wavelet packet transform (WPT). Wavelet packet representations of signals provided a local timefrequency description and separation ability between information and noise. The quality of the noise removal can be further improved by using best-basis algorithm and thresholding operation. Artificial neural network (ANN) is a form of artificial intelligence that mathematically simulates biological nervous system. Generalized regression neural network (GRNN) is a kind of ANN and is applied for overcoming the convergence problem met in back propagation training and facilitating nonlinear calculation. In the case a method named WPT-based generalized regression neural network (WPTGRNN) was used for analyzing overlapping spectra.

scholarly journals Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1631 ◽  
Author(s):  
Zhuo Zhang ◽  
Fayu Sun ◽  
Qingling Li ◽  
Weiqiang Wang ◽  
Dedong Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Generalized Regression Neural Network ◽  
Data Sets ◽  
Generalized Regression ◽  
Supercritical Carbon

With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.

scholarly journals Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3261 ◽  
Author(s):  
Solomon Asante-Okyere ◽  
Chuanbo Shen ◽  
Yao Yevenyo Ziggah ◽  
Mercy Moses Rulegeya ◽  
Xiangfeng Zhu
Keyword(s):  
Neural Network ◽  
Gaussian Process ◽  
Covariance Function ◽  
Back Propagation ◽  
Gaussian Process Regression ◽  
Kernel Functions ◽  
Generalized Regression Neural Network ◽  
Model Parameters ◽  
Porosity And Permeability ◽  
Reservoir Porosity

In this paper, a new predictive model based on Gaussian process regression (GPR) that does not require iterative tuning of user-defined model parameters has been proposed to determine reservoir porosity and permeability. For this purpose, the capability of GPR was appraised statistically for predicting porosity and permeability of the southern basin of the South Yellow Sea using petrophysical well log data. Generally, the performance of GPR is deeply reliant on the type covariance function utilized. Therefore, to obtain the optimal GPR model, five different kernel functions were tested. The resulting optimal GPR model consisted of the exponential covariance function, which produced the highest correlation coefficient (R) of 0.85 and the least root mean square error (RMSE) of 0.037 and 6.47 for porosity and permeability, respectively. Comparison was further made with benchmark methods involving a back propagation neural network (BPNN), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN). The statistical findings revealed that the proposed GPR is a powerful technique and can be used as a supplement to the widely used artificial neural network methods. In terms of computational speed, the GPR technique was computationally faster than the BPNN, GRNN, and RBFNN methods in estimating reservoir porosity and permeability.

scholarly journals Leaf recognition using BP-RBF hybrid neural network

2021 ◽  
Author(s):  
Xin Yang ◽  
Haiming Ni ◽  
Jingkui Li ◽  
Jialuo Lv ◽  
Hongbo Mu ◽  
...  
Keyword(s):  
Neural Network ◽  
Species Recognition ◽  
Color Space ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Classification Performance ◽  
Hybrid Neural Network ◽  
Plant Recognition ◽  
Image Pretreatment ◽  
Leaf Image

AbstractPlant recognition has great potential in forestry research and management. A new method combined back propagation neural network and radial basis function neural network to identify tree species using a few features and samples. The process was carried out in three steps: image pretreatment, feature extraction, and leaf recognition. In the image pretreatment processing, an image segmentation method based on hue, saturation and value color space and connected component labeling was presented, which can obtain the complete leaf image without veins and background. The BP-RBF hybrid neural network was used to test the influence of shape and texture on species recognition. The recognition accuracy of different classifiers was used to compare classification performance. The accuracy of the BP-RBF hybrid neural network using nine dimensional features was 96.2%, highest among all the classifiers.

scholarly journals Modify the Accuracy of MODIS PWV in China: A Performance Comparison Using Random Forest, Generalized Regression Neural Network and Back−Propagation Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 2215
Author(s):  
Zhaohui Xiong ◽  
Xiaogong Sun ◽  
Jizhang Sang ◽  
Xiaomin Wei
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Vapor ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Generalized Regression Neural Network ◽  
Learning Methods ◽  
Machine Learning Methods

Water vapor plays an important role in climate change and water cycling, but there are few water vapor products with both high spatial resolution and high accuracy that effectively monitor the change of water vapor. The high precision Global Navigation Satellite System (GNSS) Precipitable Water Vapor (PWV) is often used to calibrate the high spatial resolution Moderate−resolution Imaging Spectroradiometer (MODIS) PWV to produce new PWV product with high accuracy and high spatial resolution. In addition, the machine learning method has a good performance in modifying the accuracy of MODIS PWV. However, the accuracy improvement of different machine learning methods and different modeling timescale is different. In this article, we use three machine learning methods, namely, the Random Forest (RF), Generalized Regression Neural Network (GRNN), and Back−propagation Neural Network (BPNN) to calibrate MODIS PWV in 2019, at annual and monthly timescales. We also use the Multiple Linear Regression (MLR) method for comparison. The root mean squares (RMSs) at the annual timescale with the three machine learning methods are 4.1 mm (BPNN), 3.3 mm (RF), and 3.9 mm (GRNN), and the average RMSs become 2.9 mm (BPNN), 2.8 mm (RF), and 2.5 mm (GRNN) at the monthly timescale. Those results are all better than the MLR method (5.0 mm at the annual timescale and 4.6 mm at the monthly timescale). When there is an obvious variation pattern in the training sample, the RF method can capture the pattern to achieve the best results since the RF achieves the best performance at the annual timescale. Dividing such samples into several sub−samples each having higher internal consistency could further improve the performance of machine learning methods, especially for the GRNN, since GRNN achieves the best performance at the monthly timescale, and the performance of those three machine learning methods at the monthly timescale is better than that of annual timescale. The spatial and temporal variation patterns of the RMS values are significantly weakened after the modeling by machine learning methods for both three methods.

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