Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO

Abstract The 2-D maximum entropy method not only considers the distribution of the gray information, but also takes advantage of the spatial neighbor information with using the 2-D histogram of the image. As a global threshold method, it often gets ideal segmentation results even when the imageÕs signal noise ratio (SNR) is low. However, its time-consuming computation is often an obstacle in real time application systems. In this paper, the image thresholding approach based on the index of entropy maximization of the 2-D grayscale histogram is proposed to deal with infrared image. The threshold vector (t, s), where t is a threshold for pixel intensity and s is another threshold for the local average intensity of pixels, is obtained through a new optimization algorithm, namely, the particle swarm optimization (PSO) algorithm. PSO algorithm is realized successfully in the process of solving the 2-D maximum entropy problem. The experiments of segmenting the infrared images are illustrated to show that the proposed method can get ideal segmentation result with less computation cost

Reaction model and thermodynamic properties between sulfur-containing active groups and oxygen during coal self-heating

To further study the mechanism of coal self-heating, the reaction sequences and thermodynamic properties between sulfur-containing groups and oxygen during coal self-heating were analyzed. The benzyl mercaptan and diphenyl sulfide were selected as typical sulfur-containing structures existing in coal. Their structural parameters, frontier orbital characteristics, and thermodynamic parameters were analyzed through quantum chemistry calculation and their detailed reaction sequences with oxygen were proposed. The results indicate that the thiol structure in coal can easily react with oxygen at low temperatures and release large amounts of heat (146.70 kJ/mol) during coal self-heating, providing active free radicals and energy for subsequent chain reactions of coal spontaneous combustion. The oxidation reaction between the thioether structure and oxygen cannot occur at room temperature. With the accumulation of heat, thioether gradually becomes active and reacts with oxygen to form sulfoxide and release an enormous amount of heat (248.09 kJ/mol), which can be further oxidized to sulfone with an increase in temperature. The reaction models of thiol and thioether groups during coal self-heating were proposed, which involves eight main reaction sequences (R1∼R8). It indicates that the reactions of thiol and thioether groups play crucial roles during the evolution of coal self-heating, with a slow oxidation stage at low temperatures and an accelerated oxidation stage at high temperatures.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Prediction of Surface Topography at the End of Sliding Running-In Wear Based on Areal Surface Parameters

Running in is a complex process, and it significantly influences the performance and service life of wear components as the initial phase of the entire wear process. Surface topography is an important feature of wear components. Therefore, it is reasonable to investigate the running-in process with the help of surface topography for improvement. Because the surface roughness after running in is independent of the nature of initial roughness, it is difficult to predict the surface topography after running in based on unworn surface topography. Aiming to build a connection of surface topographies before and after the running-in process, a black-box model predicting surface topography after the running-in process was established based on least-squares support vector machine (LS-SVM), and the areal surface evaluation parameters were adopted as model variables. To increase the adaptability of the predictive model, the main factors of the work condition were also taken into consideration. The prediction effect and sensitivity of the model were tested and analyzed. The analysis indicates that the hybrid property of surface topographies before and after running in is closely related. Moreover, the surface topography after running in is influenced more by the initial surface topography than by the work condition