The Errors-in-Variables (EIV) model from statistics is often employed in computer vision though only rarely under this name. In an EIV model all the measurements are corrupted by noise while the a priori information is captured with a nonlinear constraint among the true (unknown) values of these measurements. To estimate the model parameters and the uncorrupted data, the constraint can be linea...

multiple traveling salesman problem (mtsp) is one of the most popular operation research problem and is known as combinatorial optimization problems. mtsp is an extension version of the famous and widely used problem named traveling salesman problem (tsp). because of its benefices in various domains, many researchers have tried to solve that, and many methods have proposed so far. mtsp is a np-...

cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. coronary artery disease (cad) is the most common type of these diseases worldwide and is the main reason of heart attacks. thus early diagnosis of cad is very essential and is an important field of medical studies. many methods are used to diagnose cad so far. these methods reduce cost and...

The ability to accurately track a ball is a critical issue in humanoid robot soccer, made difficult by processor limitations and resultant inability to process all available data from a high-definition image. This paper proposes a computationally efficient method of determining position and size of balls in a RoboCup environment, and compares the performance to two common methods: one utilising...

The aim of this paper is to construct Levenberg-Marquardt level set methods for inverse obstacle problems, and to discuss their numerical realization. Based on a recently developed framework for the construction of level set methods, we can define LevenbergMarquardt level set methods in a general way by varying the function space used for the normal velocity. In the typical case of a PDE-constr...

evolutionary algorithms are some of the most crucial random approaches tosolve the problems, but sometimes generate low quality solutions. on the otherhand, learning automata are adaptive decision-making devices, operating onunknown random environments, so it seems that if evolutionary and learningautomaton based algorithms are operated simultaneously, the quality of results willincrease sharpl...

determining the optimal location of wells with the aid of an automated search algorithm is a significant and difficult step in the reservoir development process. it is a computationally intensive task due to the large number of simulation runs required. therefore,the key issue to such automatic optimization is development of algorithms that can find acceptable solutions with a minimum number of...

microgrids are small-scale, low voltage (lv) power networks which employ renewable distribution energy resources (ders) with power electronic interfaces (peis). microgrids as single controlled units and active distribution networks require flexible control systems to ensure reliable and secure operation in different modes. these various operations of microgrid cause variations in voltage and fr...

we present an improved implementation of the wagner-whitin algorithm for economic lot-sizing problems based on the planning-horizon theorem and the economic- part-period concept. the proposed method of this paper reduces the burden of the computations significantly in two different cases. we first assume there is no backlogging and inventory holding and set-up costs are fixed. the second model ...

clustering is a widespread data analysis and data mining technique in many fields of study such as engineering, medicine, biology and the like. the aim of clustering is to collect data points. in this paper, a cultural algorithm (ca) is presented to optimize partition with n objects into k clusters. the ca is one of the effective methods for searching into the problem space in order to find a n...