An Improved Method for CT Image Coding Using PSNR Prediction Model Based on ELM

2015 ◽  
Vol 738-739 ◽  
pp. 598-601
Author(s):  
Han Yang Zhu ◽  
Xin Yu Jin ◽  
Jian Feng Shen
Keyword(s):  
Computational Complexity ◽  
Prediction Model ◽  
Image Coding ◽  
Region Of Interest ◽  
Image Encoding ◽  
Large Size ◽  
Learning Machine ◽  
Intractable Problem ◽  
Encoding Efficiency ◽  
Better Than

In telemedicine, medical images are always considered very important telemedicine diagnostic evidences. High transmission delay in a bandwidth limited network becomes an intractable problem because of its large size. It’s important to achieve a quality balance between Region of Interest (ROI) and Background Region (BR) when ROI-based image encoding is being used. In this paper, a research made on balancing method of LS-SVM based ROI/BR PSNR prediction model to optimize the ROI encoding shows it’s much better than conventional methods but with very high computational complexity. We propose a new method using extreme learning machine (ELM) with lower computational complexity to improve encoding efficiency compared to LS-SVM based model. Besides, it also achieves the same effect of balancing ROI and BR.

A Region-of-interest Image Coding Algorithm Based on EBCOT

2010 ◽  
Vol 36 (5) ◽  
pp. 650-654 ◽  
Author(s):  
Chao SUN ◽  
Shou-Da JIANG ◽  
Jian-Feng WANG
Keyword(s):  
Image Coding ◽  
Region Of Interest

scholarly journals Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 4896
Author(s):  
Jianguo Zhou ◽  
Dongfeng Chen
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Emission Reduction ◽  
Search Algorithm ◽  
Carbon Price ◽  
Integration Scheme ◽  
Combination Model ◽  
Price Prediction ◽  
Price Series ◽  
Learning Machine

Effective carbon pricing policies have become an effective tool for many countries to encourage emission reduction. An accurate carbon price prediction model is helpful for the implementation of energy conservation and emission reduction policies and the decision-making of governments and investors. However, it is difficult for a single prediction model to achieve high prediction accuracy because of the high complexity of the carbon price series. Many studies have proved the nonlinear characteristics of carbon trading prices, but there are very few studies on the chaotic nature of carbon price series. As a consequence, this paper proposes an innovative hybrid model for carbon price prediction. A decomposition-reconstruction-prediction-integration scheme is designed to predict carbon prices. Firstly, several intrinsic mode functions (IMFs) and one residue were obtained from the raw data decomposed by ICEEMDAN. Next, the decomposed subsection is reconstructed into a new sequence according to the calculation results by the Lempel-Ziv complexity algorithm. Then, considering the chaotic characteristics of sequence, the input variables of the models are determined through the phase space reconstruction (PSR) algorithm combined with the partial autocorrelation function (PACF). Finally, the Sparrow search algorithm (SSA) is introduced to optimize the extreme learning machine (ELM) model, which is applied in the carbon price prediction for the purpose of verifying the validity of the proposed combination model, which is applied to the pilots of Hubei, Beijing, and Guangdong. The empirical results show that the combination model outperformed the 13 other models in predicting accuracy, speed, and stability. The decomposition-reconstruction-prediction-integration strategy is a method for predicting the carbon price efficiently.

scholarly journals A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1328
Author(s):  
Jianguo Zhou ◽  
Shiguo Wang
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Influencing Factors ◽  
Search Algorithm ◽  
Carbon Price ◽  
Carbon Trading ◽  
Price Prediction ◽  
Model Based ◽  
Mode Decomposition ◽  
Learning Machine

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

SECURE FRACTAL IMAGE CODING BASED ON FRACTAL PARAMETER ENCRYPTION

Fractals ◽  
2009 ◽  
Vol 17 (02) ◽  
pp. 149-160 ◽  
Author(s):  
SHIGUO LIAN ◽  
XI CHEN ◽  
DENGPAN YE
Keyword(s):  
Image Coding ◽  
Parameter Distribution ◽  
Self Similarity ◽  
Image Encoding ◽  
Coding Scheme ◽  
Fractal Image ◽  
Fractal Image Coding ◽  
Fractal Parameters ◽  
Fractal Encoding ◽  
Computational Resources

In recent work, various fractal image coding methods are reported, which adopt the self-similarity of images to compress the size of images. However, till now, no solutions for the security of fractal encoded images have been provided. In this paper, a secure fractal image coding scheme is proposed and evaluated, which encrypts some of the fractal parameters during fractal encoding, and thus, produces the encrypted and encoded image. The encrypted image can only be recovered by the correct key. To maintain security and efficiency, only the suitable parameters are selected and encrypted through investigating the properties of various fractal parameters, including parameter space, parameter distribution and parameter sensitivity. The encryption process does not change the file format, keeps secure in perception, and costs little time or computational resources. These properties make it suitable for secure image encoding or transmission.

Research on thermal property and temperature rating prediction of Mongolian robe ensembles

2018 ◽  
Vol 30 (6) ◽  
pp. 747-756 ◽  
Author(s):  
Xiaofang Guo ◽  
Hui Shi ◽  
Chenglong Wei ◽  
Xiao Dong Chen
Keyword(s):  
Prediction Model ◽  
Thermal Property ◽  
Thermal Insulation ◽  
Design Methodology ◽  
Environmental Adaptation ◽  
Photographic Method ◽  
Mongolian Plateau ◽  
Content Type ◽  
Research Findings ◽  
Better Than

Purpose The purpose of this paper is to reveal the unique thermal property of Mongolian clothing from the current western clothing and explain their environmental adaptation to the climate of Mongolian plateau in China. Design/methodology/approach Thermal insulation and the temperature rating (TR) of eight Mongolian robe ensembles and two western clothing ensembles were investigated by manikin testing and wearing trials, respectively. The clothing area factor (fcl) of these Mongolian clothing was measured by photographic method and estimated equation from ISO 15831. Finally, the TR prediction model for Mongolian clothing was built and compared with current models for western clothing in ISO 7730 and for Tibetan clothing in previous article. Findings The results demonstrated that the total thermal insulation of Mongolian robe ensembles was much bigger than that of western clothing ensembles and ranged from 1.81clo to 3.11clo during the whole year. The fcl of the Mongolian clothing should be determined by photographic method because the differences between these two methods were much bigger from 0.6 to 13.9 percent; the TR prediction model for Mongolian robe ensembles is TR=25.57−7.13Icl, which revealed that the environmental adaptation of Mongolian clothing was much better than that of western clothing and similar to that of Tibetan clothing. Originality/value The research findings give a detailed information about the thermal property of China Mongolian clothing, and explain the environmental adaptation of Mongolian clothing to the cold and changing climate.

scholarly journals Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Xue-cun Yang ◽  
Xiao-ru Yan ◽  
Chun-feng Song
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Particle Swarm ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Coal Slurry ◽  
Swarm Optimization ◽  
Learning Machine

For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

2017 ◽  
Vol 8 (4) ◽  
pp. 58-83 ◽  
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Kapur’S Entropy ◽  
Moth Flame Optimization Algorithm ◽  
Popular Image ◽  
Better Than

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

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