Threshold Selection Method Based on Reciprocal Gray Entropy and Artificial Bee Colony Optimization

Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional(2-D)histogram region oblique division and artificial bee colony(ABC)optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional(1-D)method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization(NCPSO),the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.

An Effective Method of Threshold Selection for Small Object Image

The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.

Spatial pattern recognition for near-surface high temperature increases in mountain areas using MODIS and SRTM DEM

Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental issues.This paper develops a spatial pattern recognition method to measure the near-surface high temperature increase(NSHTI),one of the lesser-attended changes.First,raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM data.It fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell size.Second,a threshold selection was performed to identify the NSHTI cells using a threshold of-0.65℃/100 m.Then,the NSHTI strips were parameterized through raster vectorization and spatial analysis.Taking Yunnan,a mountainous province in southwestern China,as the study area,the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys,and the strips are almost parallel to the altitude contours with a slight northward uplift.Also,they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors,where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting depth.Additionally,the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend,and the horizontal width has a mean of about 1000 m and a maximum of over 5000 m.The result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains,providing support for the modeling of weather and climate systems and the development of mountain resources.

An Improved Double-Threshold Method Based on Gradient Histogram

This paper analyzes the characteristics of the output gradient histogram and shortages of several traditional automatic threshold methods in order to segment the gradient image better. Then an improved double-threshold method is proposed, which is combined with the method of maximum classes variance, estimating-area method and double-threshold method. This method can automatically select two different thresholds to segment gradient images. The computer simulation is performed on the traditional methods and this algorithm and proves that this method can get satisfying result. Key words gradient histogram image - threshold selection - double-threshold method - maximum classes variance method CLC number TP 391. 41 Foundation item: Supported by the National Nature Science Foundation of China (50099620) and the Project of Chenguang Plan in Wuhan (985003062)Biography: YANG Shen (1977-), female, Ph. D. candidate, research direction: multimedia information processing and network technology.

Intelligently Tuned Wavelet Parameters for GPS/INS Error Estimation

This paper presents a new algorithm for de-noising global positioning system （GPS） and inertial navigation system （INS） data and estimates the INS error using wavelet multi-resolution analysis algorithm （WMRA）-based genetic algorithm （GA） with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of the most exploited techniques that have been recently used to increase the precision and reliability of the integrated GPS/INS navigation system. To ameliorate the WMRA algorithm, GA was exploited to optimize the wavelet parameters so as to determine the best wavelet filter, thresholding selection rule （TSR）, and the optimum level of decomposition （LOD）. This results in increasing the robustness of the WMRA algorithm to estimate the INS error. The proposed intelligent technique has overcome the drawbacks of the tedious selection for WMRA algorithm parameters. Finally, the proposed method improved the stability and reliability of the estimated INS error using real field test data.

Automatic determination method of optimal threshold based on the bootstrapping technology

In order to predict the extreme load of the mechanical components during the entire life,an automatic method based on the bootstrapping technology(BT)is proposed to determine the most suitable threshold.Based on all the turning points of the load history and a series of thresholds estimated in advance,the generalized Pareto distribution is established to fit the exceedances.The corresponding distribution parameters are estimated with the maximum likelihood method.Then,BT is employed to calculate the mean squared error(MSE)of each estimated threshold based on the exceedances and the specific distribution parameters.Finally,the threshold with the smallest MSE will be the optimal one.Compared to the kurtosis method and the mean excess function method,the average deviation of the probability density function of exceedances determined by BT reduces by 38.52%and 29.25%,respectively.Moreover,the quantile-quantile plot of the exceedances determined by BT is closer to a straight line.The results suggest the improvement of the modeling flexibility and the determined threshold precision.If the exceedances are insufficient,BT will enlarge their amount by resampling to solve the instability problem of the original distribution parameters.

Image Thresholding Using Two-Dimensional Tsallis Cross Entropy Based on Either Chaotic Particle Swarm Optimization or Decomposition

The segmentation effect of Tsallis entropy method is superior to that of Shannon entropy method, and the computation speed of two-dimensional Shannon cross entropy method can be further improved by optimization. The existing two-dimensional Tsallis cross entropy method is not the strict two-dimensional extension. Thus two new methods of image thresholding using two-dimensional Tsallis cross entropy based on either Chaotic Particle Swarm Optimization (CPSO) or decomposition are proposed. The former uses CPSO to find the optimal threshold. The recursive algorithm is adopted to avoid the repetitive computation of fitness function in iterative procedure. The computing speed is improved greatly. The latter converts the two-dimensional computation into two one-dimensional spaces, which makes the computational complexity further reduced from O(L2) to O(L). The experimental results show that, compared with the proposed recently two-dimensional Shannon or Tsallis cross entropy method, the two new methods can achieve superior segmentation results and reduce running time greatly.

Threshold Selection and Resource Allocation for Quantized Identification

This paper is concerned with the optimal threshold selection and resource allocation problems of quantized identification, whose aims are improving identification efficiency under limited resources. Firstly, the first-order asymptotically optimal quantized identification theory is extended to the weak persistent excitation condition. Secondly, the characteristics of time and space complexities are established based on the Cramér-Rao lower bound of quantized systems. On these basis, the optimal selection methods of fixed thresholds and adaptive thresholds are established under aperiodic signals, which answer how to achieve the best efficiency of quantized identification under the same time and space complexity. In addition, based on the principle of maximizing the identification efficiency under a given resource, the optimal resource allocation methods of quantized identification are given for the cases of fixed thresholds and adaptive thresholds, respectively, which show how to balance time and space complexity to realize the best identification efficiency of quantized identification.

A method for threshold selection in load extrapolation based on multiple criteria decision-making technology

In order to establish an accurate peak over threshold(POT)model for reasonable load extrapolation,a new threshold selection method based on multiple criteria decision making(MCDM)technology is proposed.The fitting test criterion is taken into consideration in the method.For each candidate threshold,the fitting values of several fitting test criteria are integrated into a comprehensive evaluation value through entropy method and MCDM technology.The threshold corresponding to the minimum comprehensive evaluation value is assumed as the optimal threshold.A random simulation study is carried out to evaluate the performance of the method and to compare them with other literature methods.Genuine load data are applied the proposed method.Both the results shown that the proposed method could be seen as an additional method that complements existing threshold selection methods.

Temporal and spatial variations and statistical models of extreme runoff in Huaihe River Basin during 1956–2010

Based on the daily runoff data from 20 hydrological stations above the Bengbu Sluice in the Huaihe River Basin during 1956-2010, run test, trend test and Mann-Kendall test are used to analyze the variation trend of annual maximum runoff series. The annual maximum series （AM） and peaks over threshold series （POT） are selected to describe the extreme distributions of generalized extreme value distribution （GEV） and generalized Pareto distri- bution （GPD）. Temporal and spatial variations of extreme runoff in the Huaihe River Basin are analyzed. The results show that during the period 1956-2010 in the Huaihe River Basin, annual maximum runoff at 10 stations have a decreasing trend, while the other 10 stations have an unobvious increasing trend. The maximum runoff events almost occurred in the flood period during the 1960s and 1970s. The extreme runoff events in the Huaihe River Basin mainly occurred in the mainstream of the Huaihe River, Huainan mountainous areas, and Funiu mountainous areas. Through Kolmogorov-Smirnov test, GEV and GPD distributions can be well fitted with AM and POT series respectively. Percentile value method, mean ex- cess plot method and certain numbers of peaks over threshold method are used to select threshold, and it is found that percentile value method is the best of all for extreme runoff in the Huaihe River Basin.

Numerical assessment of the erosion risk for cavitating twisted hydrofoil by three methods

In this paper, the large eddy simulation (LES) method in conjunction with the Zwart cavitation model is adopted for the assessment of the erosion risk on a hydrofoil surface. The numerical results are in good agreement with the experiments. On this basis, three methods, namely the intensity function method (IFM), the time-averaged aggressiveness indicators (TAIs) and the gray level method (GLM), are applied for the assessment of the erosion risk. It is shown that the erosion intensity index of the IFM is extremely sensitive to the artificially selected thresholds, which greatly limits the application of the method. The erosion risk predicted by four indicators in the TAIs does not agree well with the experimental results. Further analysis demonstrates that the GLM using the instantaneous pressure field is relatively satisfactory, which can provide a reasonable assessment of the erosion and is not very sensitive to the artificially selected thresholds. To further improve the accuracy of the GLM for the erosion risk prediction, the time-average pressure field is adopted in the GLM for the erosion evaluation. It is suggested that the erosion assessment by using the time-averaged pressure field is in better agreement with the experimental results when compared with that by using the instantaneous pressure field.

Measured Impedance and Threshold Value of Electrode Line Impedance Supervision Based Protection in HVDC Transmission System

At present,electrode line impedance supervision(ELIS)based protection is widely used to detect faults on grounding electrode lines,which are indispensable elements of high-voltage direct current(HVDC)systems.The existing theoretical analysis of measured impedance is based on lumped line model and the threshold value is generally set according to engineering experience,which have caused the dead zone problem and even accidents.Therefore,a study on measured impedance of ELIS-based protection and its threshold value selection method is carried out to solve this problem.In this study,the expressions of measured impedance under normal operation and fault conditions are deduced based on rigorous and accurate line model.Based on the expressions,the characteristics of the measured impedance are calculated and analyzed.With the characteristics of the measured impedance,the applicability of the protection with the traditional threshold value is further discussed and the distribution of the dead zone can be located.Then,the method to calculate the threshold value of ELIS-based protection is proposed.With a proper threshold value selected by the proposed method,the dead zone of ELIS-based protection is effectively eliminated,and the protection can identify all types of faults even with large transition resistances.Case studies on PSCAD/EMTDC have been conducted to verify the conclusion.