An enhanced image binarization method incorporating with Monte-Carlo simulation

We proposed an enhanced image binarization method.The proposed solution incorporates Monte-Carlo simulation into the local thresholding method to address the essential issues with respect to complex background,spatially-changed illumination,and uncertainties of block size in traditional method.The proposed method first partitions the image into square blocks that reflect local characteristics of the image.After image partitioning,each block is binarized using Otsu’s thresholding method.To minimize the influence of the block size and the boundary effect,we incorporate Monte-Carlo simulation into the binarization algorithm.Iterative calculation with varying block sizes during Monte-Carlo simulation generates a probability map,which illustrates the probability of each pixel classified as foreground.By setting a probability threshold,and separating foreground and background of the source image,the final binary image can be obtained.The described method has been tested by benchmark tests.Results demonstrate that the proposed method performs well in dealing with the complex background and illumination condition.

HPDS133-bus:A Benchmark Test System for Distribution Systems with High Penetration of Distributed Generation

The benchmark test system for distribution systems is necessary and important for system analysis and result data checking.In order to meet the development demand of the distribution systems which integrate with a high penetration of distributed generation(DG),a benchmark test system for the distribution systems which adapts to current and future development trends is proposed.Based on the concentric zone theory and the typical regional settlement model in China,three typical distribution system scenarios corresponding to different system circle structures and regional development characteristics are designed in this paper.In order to adapt to the trend that the distribution systems will present the mixed state of AC and DC,a typical improvement scheme of distribution systems in urban scenario is presented.The benchmark test system of low-voltage distribution systems in a rural scenario is also designed because of the high penetration of DG.Moreover,the system structure,load parameters,integrated capacity and output characteristics of varies types of DG in different scenarios are described in detail.Finally,the simulation results,such as the time series power flow,reliability evaluation and hosting capacity of DGs are given.The benchmark test system proposed in this paper will lay a foundation for future research on distribution systems with a high proportion of DG and effectively support the distribution systems planning,design,operations and dispatches.

Adaptive mutation sparrow search algorithm-Elman-AdaBoost model for predicting the deformation of subway tunnels

A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm(AM-SSA),called AMSSAElman-AdaBoost,is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground.The novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and exploration.In AM-SSA,firstly,the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow,enhancing the global search ability.Then the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered,expanding the local search ability.Finally,the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local optimal.In addition,it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original SSA.To demonstrate the effectiveness and reliability of AM-SSA,23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions(CEC2005),are employed as the numerical examples and investigated in comparison with some wellknown optimization algorithms.The statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search spaces.By utilizing the AdaBoost algorithm,multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction output.Additionally,the on-site monitoring data acquired from a deep excavation project in Ningbo,China,were selected as the training and testing sample.Meanwhile,the predictive outcomes are compared with those of other different optimization and machine learning techniques.In the end,the obtained results in this real-world geotechnical engineering field reveal the feasibility of the proposed hybrid algorithm model,illustrating its power and superiority in terms of computational efficiency,accuracy,stability,and robustness.More critically,by observing data in real time on daily basis,the structural safety associated with metro tunnels could be supervised,which enables decision-makers to take concrete control and protection measures.

OBTAINING MULTI-AXIAL ELASTIC POTENTIALS FOR RUBBER-LIKE MATERIALS VIA AN EXPLICIT,EXACT APPROACH BASED ON SPLINE INTERPOLATION

An explicit, exact approach is proposed to obtain multi-axial elastic potentials for isotropic rubber-like materials undergoing large incompressible deformations. By means of two direct, explicit procedures, this approach reduces the problem of determining multi-axial poten- tials to that of determining one-dimensional elastic potentials. To this end, two one-dimensional potentials for uniaxial case and simple shear case are respectively determined via spline inter- polation and, then, the two potentials are extended to generate a multi-axial elastic potential using a novel method based on certain logarithmic invariants. Eventually, each of the multi-axial potentials will exactly match the finite strain data from four benchmark tests.