This paper proposes a new clustering algorithm which integrates Fuzzy C-means clustering with Markov random field (FCM). The density function of the first principal component which sufficiently reflects the class differences and is applied in determining of initial labels for FCM algorithm. Thus, the sensitivity to the random initial values can be avoided. Meanwhile, this algorithm takes into account the spatial correlation information of pixels. The experiments on the synthetic and QuickBird images show that the proposed method can achieve better classification accuracy and visual qualities than the general FCM algorithm.
Small-diameter turbodrills have great potential for use in slim boreholes because of their lower cost and higher efficiency when used in geothermal energy and other underground resource applications. Multistage hydraulic components consisting of stators and rotors are key aspects of turbodrills. This study aimed to develop a suitable blade that can be used under high temperature in granite formations. First, prediction models for single-and multi-stage blades were established based on Bernoulli's Equation. The design requirement of the blade for high-temperature geothermal drilling in granite was proposed. A Φ89 blade was developed based on the dimensionless parameter method and Bezier curve; the parameters of the blade, including its radial size, symotric parameters, and blade profiles, were input into ANASYS and CFX to establish a calculation model of the single-stage blade. The optimization of the blade structure of the small-diameter turbodrill enabled a multistage turbodrill model to be established and the turbodrill's overall output performance to be predicted. The results demonstrate that the design can meet the turbodrill's performance requirements and that the multistage model can effectively improve the accuracy of the prediction.
The use of a general EM (expectation-maximization) algorithm in multi-spectral image classification is known to cause two problems: singularity of the variance-covariance matrix and sensitivity of randomly selected initial values. The former causes computation failure; the latter produces unstable classification results. This paper proposes a modified approach to resolve these defects. First, a modification is proposed to determine reliable parameters for the EM algorithm based on a k-means algorithm with initial centers obtained from the density function of the first principal component, which avoids the selection of initial centers at random. A second modification uses the principal component transformation of the image to obtain a set of uncorrelated data. The number of principal components as the input of the EM algorithm is determined by the principal contribution rate. In this way, the modification can not only remove singularity but also weaken noise. Experimental results obtained from two sets of remote sensing images acquired by two different sensors confirm the validity of the proposed approach. . The Gaussian mixture model is a weighted sum of Gaussian probability density functions (referred to as Gaussian components) of the mixture model describing a class, and it has been widely used in pattern recognition and classification [15][16][17][18]. In remote sensing classification, a unimodal assumption for class conditional distribution is unsuitable for remote sensing images, particularly for high spatial resolution images. It is appropriate to describe the class conditional distribution as a Gaussian mixture model. Without a model or a classification label, it would be difficult to determine the parameters of a Gaussian mixture model in classification applications. Newton-Raphson and scoring algorithms in parameter solutions are very complex and difficult [19,20]. The EM algorithm-constructed maximum likelihood has good properties because of effective model labels [1,21]. However, the EM algorithm is very sensitive to initializations and easily gets trapped in local minima. In practice, the algorithm runs many times with different initial parameters, and various local search heuristics are used to find better parameters near convergences. Moreover, significant difficulty is encountered in estimating parameters of the Gaussian mixture models with creasing dimensions. Multi-spectral and hyper-spectral images with high correlations can lead to a singular variance-covariance matrix that terminates in an iteration of the EM algorithm without a reliable result [8,22,23]. Furthermore, in the case of a large component overlap, the EM algorithm suffers from slow convergence [24]. Finally, the EM algorithm fails when the covariance matrix corresponding to one or more components becomes ill-conditioned (singular or nearly singular). Gaussian mixture model, EM algorithm, Kernel density estimation, principal component transformation
Abstract:The accurate control of the wellbore pressure not only prevents lost circulation/blowout and fracturing formation by managing the density of the drilling fluid, but also improves productivity by mitigating reservoir damage. Calculating the geothermal pressure of a geothermal well by constant parameters would easily bring big errors, as the changes of physical, rheological and thermal properties of drilling fluids with temperature are neglected. This paper researched the wellbore pressure coupling by calculating the temperature distribution with the existing model, fitting the rule of density of the drilling fluid with the temperature and establishing mathematical models to simulate the wellbore pressures, which are expressed as the variation of Equivalent Circulating Density (ECD) under different conditions. With this method, the temperature and ECDs in the wellbore of the first medium-deep geothermal well, ZK212 Yangyi Geothermal Field in Tibet, were determined, and the sensitivity analysis was simulated by assumed parameters, i.e., the circulating time, flow rate, geothermal gradient, diameters of the wellbore, rheological models and regimes. The results indicated that the geothermal gradient and flow rate were the most influential parameters on the temperature and ECD distribution, and additives added in the drilling fluid should be added carefully as they change the properties of the drilling fluid and induce the redistribution of temperature. To ensure the safe drilling and velocity of pipes tripping into the hole, the depth and diameter of the wellbore are considered to control the surge pressure.
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