Entropy-based Algorithm Research Articles

An entropy-based online multi-model identification algorithm and generalized predictive control

An entropy-based online multi-model identification algorithm and generalized predictive control

Efficient ISAR autofocus via minimization of Tsallis Entropy

Inverse synthetic aperture radar (ISAR) is a coherent imaging system formed by conducting signal processing on the received data consisting of radar cross section reflected from a maneuvering target. Autofocus is an essential step of ISAR imaging, whose performance has a great influence on the quality of the radar image. Minimum Shannon entropy phase adjustment (MSEPA) and minimum Renyi entropy-based algorithm (MREA) have been widely used to compensate for the phase error in ISAR autofocus. However, MSEPA and MREA have some drawbacks in terms of the noise sensitivity and computational efficiency. Tsallis entropy (nonextensive entropy) is a useful measure to describe the thermostatistical properties of physical systems. This paper concentrates on the performance of minimum Tsallis entropy phase adjustment (MTEPA) instead of the Shannon entropy. By minimizing the Tsallis entropy of an ISAR image, the MTEPA can significantly improve the computational efficiency, while retaining the image focal quality of the restored ISAR images, as compared to MSEPA and MREA. The order q of Tsallis entropy can be experimentally found by analyzing both an image quality metric and the computation time. In experimental results, the effectiveness of the MTEPA is illustrated and analyzed with simulated targets consisting of point scatterers and measured Boeing-747 data.

Maximum Non-Extensive Entropy Block Bootstrap for Non-stationary Processes

In this paper, we propose a novel entropy-based resampling scheme valid for non-stationary data. In particular, we identify the reason for the failure of the original entropy-based algorithm of Vinod and López-de Lacalle (2009) to be the perfect rank correlation between the actual and bootstrapped time series. We propose the Maximum Entropy Block Bootstrap which preserves the rank correlation locally. Further, we also introduce the Maximum non-extensive Entropy Block Bootstrap to allow for fat tail behaviour in time series. Finally, we show the optimal finite sample properties of the proposed methods via a Monte Carlo analysis where we bootstrap the distribution of the Dickey-Fuller test.

Applications of generalized rough set theory in evaluation index system of radar anti-jamming performance

Radar anti-jamming performance evaluation is a necessary link in the process of radar development, introduction and equipment. The applications of generalized rough set theory are proposed and discussed in this paper to address the problems of big data, incomplete data and redundant data in the construction of evaluation index system. Firstly, a mass of real-valued data is converted to some interval-valued data to avoid an unacceptable number of equivalence classes and classification rules, and the interval similarity relation is employed to make classifications of this interval-valued data. Meanwhile, incomplete data can be solved by a new definition of the connection degree tolerance relation for both interval-valued data and single-valued data, which makes a better description of rough set than the traditional limited tolerance relation. Then, E-condition entropy-based heuristic algorithm is applied to making attribute reduction to optimize the evaluation index system, and final decision rules can be extracted for system evaluation. Finally, the feasibility and advantage of the proposed methods are testified by a real example of radar anti-jamming performance evaluation.

SHEsisPlus, a toolset for genetic studies on polyploid species.

Currently, algorithms and softwares for genetic analysis of diploid organisms with bi-allelic markers are well-established, while those for polyploids are limited. Here, we present SHEsisPlus, the online algorithm toolset for both dichotomous and quantitative trait genetic analysis on polyploid species (compatible with haploids and diploids, too). SHEsisPlus is also optimized for handling multiple-allele datasets. It’s free, open source and also designed to perform a range of analyses, including haplotype inference, linkage disequilibrium analysis, epistasis detection, Hardy-Weinberg equilibrium and single locus association tests. Meanwhile, we developed an accurate and efficient haplotype inference algorithm for polyploids and proposed an entropy-based algorithm to detect epistasis in the context of quantitative traits. A study of both simulated and real datasets showed that our haplotype inference algorithm was much faster and more accurate than existing ones. Our epistasis detection algorithm was the first try to apply information theory to characterizing the gene interactions in quantitative trait datasets. Results showed that its statistical power was significantly higher than conventional approaches. SHEsisPlus is freely available on the web at http://shesisplus.bio-x.cn/. Source code is freely available for download at https://github.com/celaoforever/SHEsisPlus.

Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing

Monitoring front dynamics is essential for studying the ocean’s physical and biogeochemical processes. However, the diurnal displacement of fronts remains unclear because of limited in situ observations. Using the hourly satellite imageries from the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 500 m, we investigated the diurnal displacement of turbidity fronts in both the northern Jiangsu shoal water (NJSW) and the southwestern Korean coastal water (SKCW) in the Yellow Sea (YS). The hourly turbidity fronts were retrieved from the GOCI-derived total suspended matter using the entropy-based algorithm. The results showed that the entropy-based algorithm could provide fine structure and clearly temporal evolution of turbidity fronts. Moreover, the diurnal displacement of turbidity fronts in NJSW can be up to 10.3 km in response to the onshore-offshore movements of tidal currents, much larger than it is in SKCW (around 4.7 km). The discrepancy between NJSW and SKCW are mainly caused by tidal current direction relative to the coastlines. Our results revealed the significant diurnal displacement of turbidity fronts, and highlighted the feasibility of using geostationary ocean color remote sensing technique to monitor the short-term frontal variability, which may contribute to understanding of the sediment dynamics and the coupling physical-biogeochemical processes.

An entropy-based clustering algorithm for load balancing in WSN

Energy efficiency and traffic load balancing are the important factors for wireless sensor network WSN designer. To achieve the efficient energy consumption and well-distributed load balancing, the clustering techniques can be an efficient solution. Furthermore, the several applications like real-time surveillance require the guaranteed delay in order to satisfy their delay requirements. The guaranteed delay can be one of the critical design issues for these WSN applications as well. In this paper, we propose a novel clustering algorithm to solve the energy and delay issues in WSN. Our algorithm uses an entropy-based cluster cost function, which takes into account the traffic load and the traffic variation. The simulation results show that our algorithm has longer lifetime and lower delay than low-energy adaptive clustering hierarchy LEACH and hybrid energy-efficient distributed clustering HEED.

Plant Breeding Evaluation with Rank Entropy-based Decision Tree

Abstract: With the rapid development of breeding equipments, large scale breeding becomes possible, massive data evaluation has to be done by software automatically. In this circumstance, this paper tries to evaluate variety with the collected informative data through data mining technologies. According to the characteristics, plant breeding evaluation is considered as an ordinal classification task, and a rank entropy-based decision tree algorithm is proposed to do this classification. The algorithm uses historical trait phenotype and evaluation data to construct decision trees, which are then used to generate evaluations for future cultivars with their trait phenotype. To demonstrate the effectiveness, experiments are carried out on three groups of soybean variety comparison tests (early-maturity, medium-maturity, and green soybean). This work can free breeders from a large number of basic work while increase the efficiency of plant breeding.

Optimization of cable forces on concrete cable-stayed bridges including geometrical nonlinearities

This paper presents a numerical method to compute the cable stays prestressing forces on concrete cable-stayed bridges to achieve the desired final geometry. The procedure includes a discrete direct sensitivity analysis module and an entropy-based algorithm for structural optimization. The structural analysis includes the most relevant effects namely the concrete time-dependent effects, the construction sequence and the geometrical nonlinearities. Numerical examples are presented and the influence of these effects in the cable forces is analyzed. The results show the relevance of considering these effects to find adequate values of the cable forces.

Classification Rules for Hotspot Occurrences Using Spatial Entropy-based Decision Tree Algorithm

Forest fire is a state where forest affected by fire that led to forest damage and may cause disadvantages in human life. Forest fire event can be monitored using satellite by detecting hotspots as fire indicators at certain times and locations. The purpose of this work is to develop a decision tree to predict hotspot occurrences in Bengkalis district, Riau province Indonesia using the spatial entropy-based decision tree algorithm. The data used are forest fire data in Bengkalis area. The data include city centre, river, road, income source, land cover, population, precipitation, school, temperature, and wind speed. The results of this work using the 5-fold cross validation test are decision trees with the average accuracy of 89.04% on the training set and 52.05% on the testing set. The tree has 560 nodes with the land cover layer as the root node. From the decision tree, as many 255 rules were obtained to classify hotspot occurrences.

Using Bayesian posterior probability in confidence of attributes for feature selection

Rough set theory is an efficient reduction technique to deal with vagueness and uncertainty. Many studies have been accomplished for the feature selection while they have been carried out to trade off the sophisticated process of feature selection algorithm against the robustness and accuracy of reducts. In this paper, a new Bayesian posterior probability-based QuickReduct BPPQR measure is introduced to determine the optimal attributes with the accurate strength of the association among the indiscernible subsets. Therefore, a new rough entropy-based QuickReduct algorithm which focuses on the reduction of redundant attributes is proposed in order to extract the optimal reduct and the core. The performance of the system is evaluated in MATLAB on several benchmark datasets with resides in UCI machine learning repository. The proposed heuristic approach can cope with the drawbacks of the conventional one, and the satisfying performances have been carried out in the process of feature selection in decision systems.

Quantitative Analyses and Development of aq-Incrementation Algorithm for FCM with Tsallis Entropy Maximization

Tsallis entropy is aq-parameter extension of Shannon entropy. By extremizing the Tsallis entropy within the framework of fuzzyc-means clustering (FCM), a membership function similar to the statistical mechanical distribution function is obtained. The Tsallis entropy-based DA-FCM algorithm was developed by combining it with the deterministic annealing (DA) method. One of the challenges of this method is to determine an appropriate initial annealing temperature and aqvalue, according to the data distribution. This is complex, because the membership function changes its shape by decreasing the temperature or by increasingq. Quantitative relationships between the temperature andqare examined, and the results show that, in order to changeuikqequally, inverse changes must be made to the temperature andq. Accordingly, in this paper, we propose and investigate two kinds of combinatorial methods forq-incrementation and the reduction of temperature for use in the Tsallis entropy-based FCM. In the proposed methods,qis defined as a function of the temperature. Experiments are performed using Fisher’s iris dataset, and the proposed methods are confirmed to determine an appropriateqvalue in many cases.

The RBF Neural Network Learning Algorithm Based on Entropy Clustering and Competitive

Research and analysis of RBF neural network structure and characteristics. Find out its shortcomings and propose an improved method for the deficiencies, then created a neural network model for using entropy-based clustering and competitive learning algorithm. Using MATLAB simulation tools for model simulation, confirmed the entropy clustering and competitive learning algorithm of FBF prediction neural network have high precision and generalization ability of stronger character.

Optimization of a cylindrical shell housing a belt-conveyor bridge

A belt-conveyor bridge is built inside a ring-stiffened cylindrical shell, the belt-conveyor supports being independent of the stiffeners. Reliability-based and deterministic optimization results are compared. The design variables are the shell thickness as well as the thickness and the number of flat rings. Optimum solutions are evaluated for different bridge lengths in view of finding the most effective cost/span ratios. The design constraints relate to the local shell buckling strength, to the panel ring buckling and to the deflection of the bridge. The cost function includes the material and fabrication costs. A level II reliability method (FORM) is used to find the probability of failure. The overall structural reliability is obtained by using Ditlevsen method of conditional bounding. The costs of the plate designed to ensure a stipulated probability of failure will be compared with the solutions obtained for a code based method, which employs partial safety factors. A branch and bound strategy coupled with a entropy-based algorithm is used to provide discrete solutions.

Information Entropy- and Average-Based High-Resolution Digital Storage Oscilloscope

Vertical resolution is an essential indicator of digital storage oscilloscope (DSO) and the key to improving resolution is to increase digitalizing bits and lower noise. Averaging is a typical method to improve signal to noise ratio (SNR) and the effective number of bits (ENOB). The existing averaging algorithm is apt to be restricted by the repetitiveness of signal and be influenced by gross error in quantization, and therefore its effect on restricting noise and improving resolution is limited. An information entropy-based data fusion and average-based decimation filtering algorithm, proceeding from improving average algorithm and in combination with relevant theories of information entropy, are proposed in this paper to improve the resolution of oscilloscope. For single acquiring signal, resolution is improved through eliminating gross error in quantization by utilizing the maximum entropy of sample data with further noise filtering via average-based decimation after data fusion of efficient sample data under the premise of oversampling. No subjective assumptions and constraints are added to the signal under test in the whole process without any impact on the analog bandwidth of oscilloscope under actual sampling rate.

A comparison of satellite-derived sea surface temperature fronts using two edge detection algorithms

Satellite-derived sea surface temperature (SST) fronts provide a valuable resource for the study of oceanic fronts. Two edge detection algorithms designed specifically to detect fronts in satellite-derived SST fields are compared: the histogram-based algorithm of Cayula and Cornillon (1992, 1995) and the entropy-based algorithm of Shimada et al. (2005). The algorithms were applied to 4 months (July and August for both 1995 and 1996) of SST fields and the results are compared with SST data taken by the M.V. Oleander, a container ship that makes weekly transits between New York and Bermuda. There is no significant difference in front pixels found with the Cayula-Cornillon algorithm and those found in the in situ (Oleander) data. Furthermore, for strong fronts, with gradients greater than 0.2K/km, the distribution of fronts found with the Shimada et al. algorithm is quite similar to that of fronts found with the Cayula-Cornillon algorithm. However, there are significant differences in the number of weak fronts found. This is seen clearly in waters south of the Gulf Stream where the gradient magnitude of fronts found is less than 0.1K/km. In this region, the probability that the Shimada et al. algorithm detects a front rarely falls below 4% while neither the Cayula-Cornillon algorithm applied to the satellite-derived SST fields nor the gradient-based algorithm applied to the Oleander temperature time series find fronts more than 1% of the time. These results raise the question of exactly what qualifies as an SST front, a classic problem in edge detection.

Uncertainty in Impact Identification Applied to a Commercial Wind Turbine Blade

This work evaluates the uncertainty of impact force and location estimates using an entropy-based impact identification algorithm applied to a commercial wind turbine blade. The effects of sensor placement, measurement directions and distance between impacts and sensor locations are studied. Results show that impacts to a 35m long wind turbine blade can be accurately located using a single tri-axial accelerometer regardless of sensor location. Uncertainties in impact force estimates are consistent across sensor locations. When omitting acceleration information in the spanwise direction, the bias and variance of force estimates is consistent, but when a single chan- nel of acceleration data is used, both increase somewhat. Impact force identification error was found to be uncorrelated with the distance between the impact and sensor location. The entropy of the estimated force time history, an indicator of the impulsivity of the estimate, was found to be a good indicator of the quality of force estimate. The bias and variance of impact force estimation error was found to be directly correlated with the entropy of the impact force estimate. When considering validation test data from all possible sensor configurations, the entropy of the recreated force estimates was a better indicator of the force magnitude prediction interval than was the specific sensor configuration. By classifying impact force estimates based upon entropy values, impact force prediction intervals were more precisely determined than when all validation impact data were considered at once.

Quick attribute reduction in inconsistent decision tables

This paper focuses on three types of attribute reducts in inconsistent decision tables: assignment reduct, distribution reduct, and maximum distribution reduct. It is quite inconvenient to judge these three types of reduct directly according to their definitions. This paper proposes judgment theorems for the assignment reduct, the distribution reduct and the maximum distribution reduct, which are expected to greatly simplify the judging of these three types of reducts. On this basis, we derive three new types of attribute significance measures and construct the Q-ARA (Quick Assignment Reduction Algorithm), the Q-DRA (Quick Distribution Reduction Algorithm), and the Q-MDRA (Quick Maximum Distribution Reduction Algorithm). These three algorithms correspond to the three types of reducts. We conduct a series of comparative experiments with twelve UCI (machine learning data repository, University of California at Irvine) data sets (including consistent and inconsistent decision tables) to evaluate the performance of the three reduction algorithms proposed with the relevant algorithm QuickReduct [9,34]. The experimental results show that QuickReduct possesses weak robustness because it cannot find the reduct even for consistent data sets, whereas our proposed three algorithms show strong robustness because they can find the reduct for each data set. In addition, we compare the Q-DRA (Quick Distribution Reduction Algorithm) with the CEBARKNC (conditional entropy-based algorithm for reduction of knowledge without a computing core) [43] because both find the distribution reduct by using a heuristic search. The experimental results demonstrate that Q-DRA runs faster than CEBARKNC does because the distribution function of Q-DRA has a lower calculation cost. Instructive conclusions for these reduction algorithms are drawn from the perspective of classification performance for the C4.5 and RBF-SVM classifiers. Last, we make a comparison between discernibility matrix-based methods and our algorithms. The experimental results indicate that our algorithms are efficient and feasible.

A comparison of period finding algorithms

This paper presents a comparison of popular period finding algorithms applied to the light curves of variable stars from the Catalina Real-time Transient Survey (CRTS), MACHO and ASAS data sets. We analyze the accuracy of the methods against magnitude, sampling rates, quoted period, quality measures (signal-to-noise and number of observations), variability, and object classes. We find that measure of dispersion-based techniques - analysis-of-variance with harmonics and conditional entropy - consistently give the best results but there are clear dependencies on object class and light curve quality. Period aliasing and identifying a period harmonic also remain significant issues. We consider the performance of the algorithms and show that a new conditional entropy-based algorithm is the most optimal in terms of completeness and speed. We also consider a simple ensemble approach and find that it performs no better than individual algorithms.

Study on Minimum Entropy-based Autofocus Algorithm Simulation for Spotlight SAR Imaging

Study on Minimum Entropy-based Autofocus Algorithm Simulation for Spotlight SAR Imaging