Minimum Cross Entropy Research Articles

Prompt gamma-based neutron dosimetry for Am-Be and other workplace neutron spectra

A new field-deployable technique for estimating the neutron ambient dose equivalent H *(10) by using the measured prompt gamma intensities emitted from borated high-density polyethylene (BHDPE) and the combination of normal HDPE and BHDPE with different configurations have been evaluated in this work. Monte Carlo simulations using the FLUKA code has been employed to calculate the responses from the prompt gammas emitted due to the monoenergetic neutrons interacting with boron, hydrogen, and carbon nuclei. A suitable linear combination of these prompt gamma responses (dose conversion coefficient [DCC]-estimated) is generated to approximate the International Commission on Radiological Protection provided DCC using the cross-entropy minimization technique. In addition, the shape and configurations of the HDPE and BHDPE combined system are optimized using the FLUKA code simulation results. The proposed method is validated experimentally, as well as theoretically, using different workplace neutron spectra with a satisfactory outcome.

PAPR reduction for pilot-aided OFDM systems with the parametric minimum cross-entropy method

This letter considers the selection of the optimal pilot symbol to decrease the peak-to-average power ratio (PAPR) in pilot-aided orthogonal frequency division multiplexing systems. The conventional pilot-aided PAPR reduction technique necessitates an exhaustive search of all combinations of possible pilot symbol configurations, resulting in high computational complexity. To reduce computational complexity while maintaining PAPR reduction performance, we propose the parametric minimum cross-entropy (PMCE) method for determining near-optimal pilot symbols. Simulation results show that performance of the PMCE-based PAPR reduction is very close to that obtained using the exhaustive search algorithm, with low computational complexity. Compared with the standard cross-entropy method, PMCE has the advantages of better PAPR reduction performance at the same computational complexity.

New quantum inspired meta-heuristic techniques for multi-level colour image thresholding

The efficient meta-heuristic techniques, called ant colony optimization, differential evolution and particle swarm optimization, inspired by the fundamental features of quantum systems, are presented in this paper. The proposed techniques are Quantum Inspired Ant Colony Optimization, Quantum Inspired Differential Evolution and Quantum Inspired Particle Swarm Optimization for Multi-level Colour Image Thresholding. These techniques find optimal threshold values at different levels of thresholding for colour images. A minimum cross entropy based thresholding method, called Li's method is employed as an objective (fitness) function for this purpose. The efficiency of the proposed techniques is exhibited computationally and visually on ten real life true colour images. Experiments with the composite DE (CoDE) method, the backtracking search optimization algorithm (BSA), the classical ant colony optimization (ACO), the classical differential evolution (DE) and the classical particle swarm optimization (PSO), have also been conducted subsequently along with the proposed techniques. Experimental results are described in terms of the best threshold value, fitness measure and the computational time (in seconds) for each technique at various levels. Thereafter, the accuracy and stability of the proposed techniques are established by computing the mean and standard deviation of fitness values for each technique. Moreover, the quality of thresholding for each technique is determined by computing the peak signal-to-noise ratio (PSNR) values at different levels. Afterwards, the statistical superiority of the proposed techniques is proved by incorporating Friedman test (statistical test) among different techniques. Finally, convergence curves for different techniques are presented for all test images to show the visual representation of results, which proves that the proposed Quantum Inspired Ant Colony Optimization technique outperforms all the other techniques.

Estimating attributes importance for container shipping industry by closing the listening gap with maximum convergent validity

This paper estimates the relationship of attributes importance (AI) to prospect purchase intention by closing the listening gap between customers and managers of container shipping companies. A proposition of minimum cross entropy is proposed to find a solution to the problem with maximum convergent validity, and this proposition is used to estimate AI, in which both opinions about AI of customers and managers of container shipping companies are included. Results indicate that price, discount, personal selling, and word of mouth, are the most important attributes to prospect purchase intention, within the industry. In addition, managerial implications are also discussed.

Cross-entropy minimization estimation for two-phase sampling and non-response

This paper considers the problem of estimating the finite population total in two-phase sampling when some information on auxiliary variable is available. The authors employ an information-theoretic approach which makes use of effective distance between the estimated probabilities and the empirical frequencies. It is shown that the proposed cross-entropy minimization estimator is more efficient than the usual estimator and has some desirable large sample properties. With some necessary modifications, the method can be applied to two-phase sampling for stratification and non-response. A simulation study is presented to assess the finite sample performance of the proposed estimator.

Study of anisotropic diffusion model based on pulse coupled neural network and image entropy

In image processing, most of the anisotropic diffusion models based on partial differential equation use gradient information to detect image edge. If the image edge is seriously polluted by noise, these methods would not be able to detect image edge, so the edge features cannot be retained. Pulse coupled neural network (PCNN) has the property that similar input neurons can generate pulse at the same time; this property is used to process the noisy image, and we can get an image entropy sequence. The image entropy sequence which will be used as an edge detecting operator is introduced into the diffusion equation, and this will not only reduce the defects produced when the gradient is used as an edge detecting operator so it is easily affected by the noise, but the area image information can also retain more completely. Then, we will use the rule of minimum cross entropy to search for a minimum threshold, which would satisfy the condition that the information difference between noisy image and denoised image is the minimum. The optimal threshold designed will control diffusion intensity reasonably, and the anisotropic diffusion model based on pulse coupled neural network and image entropy (PCNN-IEAD) can be established. Analysis and simulation results show that the proposed model preserves more image information than the classical ones. It removes the image noise and at the same time protects the edge texture details of the image; the proposed model retains the area image information more completely, the performance indexes can also confirm the superiority of the new model. In addition, the operating time of the proposed model is shorter than that of the classical models, therefore, the proposed model may be the ideal one.

A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution

We propose a novel multi-level thresholding method for unsupervised separation between objects and background from a natural color image using the concept of the minimum cross entropy (MCE). MCE based thresholding techniques are widely popular for segmenting grayscale images. Color image segmentation is still a challenging field as it involves 3-D histogram unlike the 1-D histogram of grayscale images. Effectiveness of entropy based multi-level thresholding for color image is yet to be explored and this paper presents a humble contribution in this context. We have used differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, to improve the computation time and robustness of the proposed algorithm. The performance of DE is also investigated extensively through comparison with other well-known nature inspired global optimization techniques like genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The proposed method is evaluated by comparing it with seven other prominent algorithms both qualitatively and quantitatively using a well known benchmark suite – the Barkley Segmentation Dataset (BSDS300) with 300 distinct images. Such comparison reflects the efficiency of our algorithm

Multispectral image unsupervised segmentation using watershed transformation and cross-entropy minimization in different land use

A general-purpose unsupervised segmentation algorithm based on cross-entropy minimization by pixel was developed; this algorithm, known as the SCEMA (Segmentation Cross-Entropy Minimization Algorithm), starts from an initial segmentation and iteratively searches the best statistical model, estimating the probability density of the image to reduce the cross-entropy with respect to the previous iteration. The SCEMA was tested using satellite images from the Landsat Thematic Mapper sensor of Landsat 5 for the Amazon region (12 images for testing and 15 for validation). Theme classes identified in the image were (1) water, (2) vegetation, and (3) agriculture. Using the Kappa index and other statistics parameters, the comparison of classifications is made with the following segmentation methods: (1) cross-entropy minimization by pixel, (2) cross-entropy minimization by region, (3) K-means, and (4) maximum likelihood. The results indicate that cross-entropy minimization by pixel results in a consistent segmentation of images. The algorithm also compares favorably to other well-known image segmentation methods, and the numerical test results illustrate the efficiency of our approach for image segmentation.

GPCEMA.GMS: A General Purpose Cross-Entropy Matrix Adjustment Program (Quick User Guide)

This note briefly presents the GAMS matrix adjustment program GPCEMA.gms (GPCEMA stands for General Purpose Cross-Entropy Matrix Adjustment). The matrix adjustment problem tackled in GPCEMA.gms is to adjust an existing matrix (called the prior matrix) so that the adjusted matrix (the posterior matrix) has row and column sums that conform to known marginal totals. GPCEMA.gms is based on the minimum information-gain principle (also known as minimum cross-entropy), as it has been extended to deal with negative entries by Junius and Oosterhaven (2003). The program automatically deals with situations where row sums, column sums, or both are constrained to zero.

基于斜分倒数交叉熵和蜂群优化的火焰图像阈值选取

A flame image segmentation method was proposed based on reciprocal cross entropy threshold selection and bee colony optimization to improve the segmented accuracy.By using the minimum reciprocal cross entropy as the threshold selection criteria,the drawback of an undefined value at zero in Shannon entropy definition was avoided.At the same time,the 2Dhistogram oblique segmentation was taken to partition the object and background precisely to improve the anti-noise performance.By which,only one threshold instead of two thresholds needs to be searched,and the running time is reduced.In addition,the bee colony optimization was applied to acceleration of the process to find the optimal threshold to further improve the real-time performance of this algorithm and increase the algorithmic speed by 80%-140%.Finally,a large number of experiments on flame images were processed and then the experimental results were compared with the maximum Shannon entropy method based on 2Dhistogram oblique segmentation and the maximum reciprocal entropy method based on 2D histogram oblique segmentation and Niche Chaotic Mutation Particle Swarm Optimization(NCPSO). The obtained results show that the proposed method has obvious advantages in segmentation effects and has better anti-noise ability and real-time performance for flame images.

Disaggregation of Statistical Livestock Data Using the Entropy Approach

A process of agricultural data disaggregation is developed to address the lack of updated disaggregated data concerning main livestock categories at subregional and county level in the Alentejo Region, southern Portugal. The model developed considers that the number of livestock units is a function of the agricultural and forest occupation, and data concerning the existing agricultural and forest occupation, as well as the conversion of livestock numbers into normal heads, are needed in order to find this relation. The weight of each livestock class is estimated using a dynamic process based on a generalized maximum entropy model and on a crossentropy minimization model, which comprises two stages. The model was applied to the county of Castelo de Vide and their results were validated in cross reference to real data from different sources.

Applications of Entropy in Finance: A Review

Although the concept of entropy is originated from thermodynamics, its concepts and relevant principles, especially the principles of maximum entropy and minimum cross-entropy, have been extensively applied in finance. In this paper, we review the concepts and principles of entropy, as well as their applications in the field of finance, especially in portfolio selection and asset pricing. Furthermore, we review the effects of the applications of entropy and compare them with other traditional and new methods.

A PAPR Reduction Method for Hexagonal Constellation-Based OFDM Signals

One of the major drawbacks of orthogonal frequency division multiplexing (OFDM) signals is the high peak to average power ratio (PAPR) of the transmitted signal. In this paper, a new method based on hexagonal constellation is proposed to reduce PAPR and to eliminate side information for OFDM signals. In order to get better PAPR reduction performance, we propose a new PAPR reduction algorithm based on parametric minimum cross entropy (PMCE). Simulation results show that PMCE algorithm is an efficient method to achieve significant PAPR reduction.

Multilevel Minimum Cross Entropy Image Thresholding using Artificial Bee Colony Algorithm

The minimum cross entropy thresholding (MCET) has been widely applied in image processing. In this paper, a new multilevel MCET algorithm based on the artificial bee colony (ABC) algorithm is proposed. The proposed thresholding algorithm is called ABC-based MCET algorithm. Four different methods including the exhaustive search, the honey bee mating optimization (HBMO), the particle swarm optimization (PSO) and the quantum particle swarm optimization (QPSO) methods are also implemented for comparison with the results of the proposed method. The experimental results demonstrate that the proposed ABC-based MCET algorithm can efficiently search for multiple thresholds that are very close to the optimal ones selected by using the exhaustive search method. Compared with the other three thresholding methods, the segmentation results using the ABC-based MCET algorithm is the best. It is promising to encourage further research for applying the HBMO algorithm to complex problems of image processing and pattern recognition. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3273

Unsupervised segmentation method for cuboidal cell nuclei in histological prostate images based on minimum cross entropy

This paper presents a novel segmentation method for cuboidal cell nuclei in images of prostate tissue stained with hematoxylin and eosin. The proposed method allows segmenting normal, hyperplastic and cancerous prostate images in three steps: pre-processing, segmentation of cuboidal cell nuclei and post-processing. The pre-processing step consists of applying contrast stretching to the red (R) channel to highlight the contrast of cuboidal cell nuclei. The aim of the second step is to apply global thresholding based on minimum cross entropy to generate a binary image with candidate regions for cuboidal cell nuclei. In the post-processing step, false positives are removed using the connected component method. The proposed segmentation method was applied to an image bank with 105 samples and measures of sensitivity, specificity and accuracy were compared with those provided by other segmentation approaches available in the specialized literature. The results are promising and demonstrate that the proposed method allows the segmentation of cuboidal cell nuclei with a mean accuracy of 97%.

Multilevel Image Thresholding with Glowworm Swam Optimization Algorithm based on the Minimum Cross Entropy

The measure of cross entropy is a widely used index applied to many areas of computer science. In this article, we proposed a new multilevel thresholding algorithm for image segmentation using the glowworm swarm optimization based on the criterion of minimum cross entropy (MCE). In this paper, a new multilevel MCE thresholding (MCET) algorithm using the glowworm swam optimization (GSO) algorithm is proposed. The proposed image thresholding algorithm is called the GSO-based MCET algorithm. The five different methods including the exhaustive search, the honey bee mating optimization (HBMO), the firefly (FF) algorithm, the artificial bee colony algorithm (ABC) and the particle swarm optimization (PSO) are also implemented for performance comparison. The experimental results demonstrate that the proposed GSO-based MCET algorithm can efficiently search for multiple thresholds that are very close to the optimal ones examined by the exhaustive search method. Compared with the other five thresholding methods, the needs of computation time using the FF-based MCET algorithm is the smallest. Furthermore, the segmentation performance of GSO-based MCET algorithm is better than the PSO-based MCET algorithm, while the results of GSO-based MCET algorithm are not significant different to the other three bio-inspired computing algorithms.

Peak Power Reduction of OFDM Systems Through Tone Injection via Parametric Minimum Cross-Entropy Method

It is well known that the major drawback of orthogonal frequency-division multiplexing (OFDM) is its high peak-to-mean envelope power ratio (PMEPR). One of the promising schemes proposed for alleviating the high PMEPR of OFDM signals is tone injection (TI). In this paper, we propose a novel low-complexity algorithm for PMEPR reduction using TI. This algorithm first formulates the TI scheme as a combinatorial optimization problem and then finds a near-optimal solution to this problem via parametric minimum cross-entropy (PMCE) method. We also demonstrate that by effectively exploiting TI schemes in conjunction with cross constellations, it is possible to reduce the peak power of the system by more than 2 dB compared with sign selection schemes over conventional quadrature-amplitude modulation (QAM) constellations. Simulation results show that our algorithm outperforms similar existing algorithms, including the cross-entropy (CE) TI algorithm, the greedy sign selection algorithm, and the derandomization sign selection algorithm, in terms of PMEPR. We also present a threshold-based PMCE algorithm for TI that results in significant complexity reduction. Numerical results show that to achieve a prescribed PMEPR threshold, this threshold-based PMCE algorithm requires less computational complexity than the CE TI algorithm.

Cloud Detection of Remote Sensing Image based on Minimum Cross-entropy

Cloud Detection of Remote Sensing Image based on Minimum Cross-entropy

交互熵法在洪水频率分布参数估计中的应用研究

本文研究基于分位数对约束下的交互熵进行洪水频率分布参数估计方法。以加拿大Feather河和陕北地区张村驿站年最大洪峰流量序列为例，选取Gumbel和Gamma分布，基于最小交互熵原理，进行年最大洪峰流量序列分布参数估计，并根据估计参数推求洪峰流量频率曲线图。与矩法和极大似然法所求熵值比较，结果表明：交互熵法获得最小熵值，频率点距拟合亦取得满意效果。因此，在考虑分位数对约束的条件下，交互熵法能有效的估计分布参数，且较矩法和极大似然法优越。 This paper studies on the application of fractile constrained cross-entropy to the estimation of dis- tribution parameters in flood frequency analysis. Based on the principle of minimum cross-entropy, two annual maximum flood peak series respectively in Feather River in Canada and Zhangcunyi Station in north- ern Shaanxi province with Gumbel distribution and Gamma distribution were employed to the parameter estimation of the four distribution functions. Four frequency curves with the estimated parameters were also plotted. Then, comparing the calculated cross-entropy values with those that are derived by traditional methods- MOM and MLM, it turned out that: by using cross entropy method, we got the minimum cross entropy values. The plotted theoretical frequency curves fit well with the empirical frequency curves. So, we can conclude that the quantile constrained cross-entropy method has the considerable merit in the flood frequency parameter estimation and is superior to the traditional methods-MOM and MLM.

Estimating Probability Distribution of Asset Value Based on Dual Effects of Deposit Insurance with the Minimum Cross-Entropy Principle

A model is proposed to estimate the probability distribution of asset value based on the benefit effects as well as the risk effects of deposit insurance with the minimum cross-entropy principle. Three scenarios are constructed to depict situations with different dual effects of deposit insurance. The corresponding assets distribution functions are obtained accordingly. The results show that it is positively correlated between the supervision level and the risk aversion effects of deposit insurance. The increase of the deposit insurance premium moves the bank’s assets distribution to the right side although not significantly. The asset probability distributions estimated in this paper can be taken as a reference for banks to choose the proper credit investment projects.