Sharif Digital Repository

Precipitation and scaling of calcium sulfate have been known as major problems facing process industries and oilfield operations. Most scale prediction models are based on aqueous thermodynamics and solubility behavior of salts in aqueous electrolyte solutions. There is yet a huge interest in developing reliable, simple, and accurate solubility prediction models. In this study, a comprehensive model based on least-squares support vector machine (LS-SVM) is presented, which is mainly devoted to calcium sulfate dihydrate (or gypsum) solubility in aqueous solutions of mixed electrolytes covering wide temperature ranges. In this respect, an aggregate of 880 experimental data were gathered from... 

In clustering spatial data, we are given a set of points in Rn and the objective is to find the clusters (representing spatial objects) in the set of points. Finding clusters with different shapes, sizes, and densities in data with noise and potentially outliers is a challenging task. This problem is especially studied in machine learning community and has lots of applications. We present a novel clustering technique, which can solve mentioned issues considerably. In the proposed algorithm, we let the structure of the data set itself find the clusters, this is done by having points actively send and receive feedbacks to each other. The idea of the proposed method is to transform the input... 

The goal of robust parameter estimation is developing a model which can properly fit to data. Parameter estimation of a geometric model, in presence of noise and error, is an important step in many image processing and computer vision applications. As the random sample consensus (RANSAC) algorithm is one of the most well-known algorithms in this field, there have been several attempts to improve its performance. In this paper, after giving a short review on existing methods, a robust and efficient method that detects the gross outliers to increase the inlier to outlier ratio in a reduced set of corresponding image points is proposed. It has a new hypothesis and verification scheme which... 

The goal of robust parameter estimation is developing a model which can properly fit to data. Parameter estimation of a geometric model, in presence of noise and error, is an important step in many image processing and computer vision applications. As the random sample consensus (RANSAC) algorithm is one of the most well-known algorithms in this field, there have been several attempts to improve its performance. In this paper, after giving a short review on existing methods, a robust and efficient method that detects the gross outliers to increase the inlier to outlier ratio in a reduced set of corresponding image points is proposed. It has a new hypothesis and verification scheme which... 

In the design of a carbon dioxide capture and storage (CCS) process, the thermal conductivity of carbon dioxide is of special concern. Hence, it is quite important to search for a quick and accurate determination of thermal conductivity of CO2 for precise modeling and evaluation of such a process. To achieve this aim, a robust computing methodology, entitled least square support vector machine (LSSVM) modeling, which is coupled with an optimization approach, was used to model this transport property. The model was constructed and evaluated employing a comprehensive data bank (more than 550 data series) covering wide ranges of pressures and temperatures. Before constructing the model, outlier... 

Similarity measure is an important key in image registration. Most traditional intensity-based similarity measures (e.g., SSD, CC, MI, and CR) assume stationary image and pixel by pixel independence. Hence, perfect image registration cannot be achieved especially in presence of spatially-varying intensity distortions and outlier objects that appear in one image but not in the other. Here, we suppose that non stationary intensity distortion (such as Bias field or Outlier) has sparse representation in transformation domain. Based on this as-sumption, the zero norm (ℓ0)of the residual image between two registered images in transform domain is introduced as a new similarity measure in presence... 

This paper addresses the problem of robust parameter estimation of simple linear profiles in multistage processes in the presence of outliers in Phase I. In this regard, two robust approaches, namely the Huber’s M-estimator and the MM estimator, are proposed to estimate the parameters of the process in Phase I in the presence of outliers in historical data. In addition, the U statistic is applied to the robust parameter estimates to remove the effect of the cascade property in multistage processes and as a result, to obtain adjusted robust estimates of the parameters of simple linear profiles. The performance of the proposed methods is evaluated under weak and strong autocorrelations... 

This paper addresses the problem of robust parameter estimation of simple linear profiles in multistage processes in the presence of outliers in Phase I. In this regard, two robust approaches, namely the Huber’s M-estimator and the MM estimator, are proposed to estimate the parameters of the process in Phase I in the presence of outliers in historical data. In addition, the U statistic is applied to the robust parameter estimates to remove the effect of the cascade property in multistage processes and as a result, to obtain adjusted robust estimates of the parameters of simple linear profiles. The performance of the proposed methods is evaluated under weak and strong autocorrelations... 

We propose a graph-based data clustering algorithm which is based on exact clustering of a minimum spanning tree in terms of a minimum isoperimetry criteria. We show that our basic clustering algorithm runs in O(nlogn) and with post-processing in almost O(nlogn) (average case) and O(n2) (worst case) time where n is the size of the data-set. It is also shown that our generalized graph model, which also allows the use of potentials at vertices, can be used to extract an extra piece of information related to anomalous data patterns and outliers. In this regard, we propose an algorithm that extracts outliers in parallel to data clustering. We also provide a comparative performance analysis of... 

Similarity measure is an important part of image registration. The main challenge of similarity measure is lack of robustness to different distortions. A well-known distortion is spatially-varying intensity distortion. Its main characteristic is correlation among pixels. Most traditional intensity based similarity measures (e.g., SSD, MI) assume stationary image and pixel to pixel independence. Hence, these similarity measures are not robust against spatially-varying intensity distortion. Here, we suppose that non-stationary intensity distortion has a sparse representation in transform domain, i.e. its distribution has high peak at origin and a long tail. We use two viewpoints of Maximum... 

We study the 2-center problem with outliers in high-dimensional data streams. Given a stream of points in arbitrary d dimensions, the goal is to find two congruent balls of minimum radius covering all but at most z points. We present a (1.8+ε)-approximation streaming algorithm, improving over the previous (4+ε)-approximation algorithm available for the problem. The space complexity and update time of our algorithm are poly(d,z,1/ε), independent of the size of the stream. © 2016 Elsevier B.V