Search

In this paper, a study is conducted to solve voltage problems that may occur, when large-scale Distributed Generations (DGs) and Electric Vehicles (EVs) are connected to the distribution system, through coordinated control between DGs and EVs. Using the Graph Search Method (GSM), the voltage problem was solved through the reactive power control of EVs and DGs in the near area where the voltage problem occurred. As a result, it was possible to obtain a result with high robustness against the change of the topology and reduction of the total loss of distribution system. In addition, when the voltage problem cannot be solved by only reactive power control, the active power control was performed for EVs and DGs included in a specific divided system of the conventional distribution system using the GSM to maintain the voltage within the normal range. Finally, to verify the performance of the proposed method, the whole algorithm was implemented by linking the Open Source Distribution System Simulator (OpenDSS), and the MATLAB.

A bounded cost path planning method is developed for underwater vehicles assisted by a data-driven flow modeling method. The modeled flow field is partitioned as a set of cells of piece-wise constant flow speed. A flow partition algorithm and a parameter estimation algorithm are proposed to learn the flow field structure and parameters with justified convergence. A bounded cost path planning algorithm is developed taking advantage of the partitioned flow model. An extended potential search method is proposed to determine the sequence of partitions that the optimal path crosses. The optimal path within each partition is then determined by solving a constrained optimization problem. Theoretical justification is provided for the proposed extended potential search method generating the optimal solution. The path planned has the highest probability to satisfy the bounded cost constraint. The performance of the algorithms is demonstrated with experimental and simulation results, which show that the proposed method is more computationally efficient than some of the existing methods.

These Optical coherence tomography (OCT) is a non-invasive diagnostic technique first introduced in 1991. It uses low coherence light waves to render the crosssection view of the retina in micrometer resolution. In the analysis of a retinal anomaly, the sectional visualization proves to be an immensely powerful mechanism. For prior detection of retinal diseases, slicing of the required portion from the dataset images into definite layers is a necessary step. It helps to map and measure their thickness. These measurements help with the diagnosis and provide proper guidance for the treatment of diseases of the retina. In this work, we are taking an account of calculating the precise geodesic distance approach for the OCT segmentation of images in two-dimensional spaces. The technique we are using takes into account horizontal and vertical intensity variation and get onto a weighted geodesic distance. By applying the fast marching approach on the Eikonal equation we are calculating the shortest path. Although the proposed Geodesic distance method using the Eikonal equation is a bit complex method it outperforms graph as well as other techniques in portraying precisely the retinal layers. [ABSTRACT FROM AUTHOR]

The signed directed graph (SDG) model can be considered as a qualitative model to describe the variables and their cause–effect relations in a continuous process. Such models can allow one to obtain the fault propagation paths using the method of graph search. In this way, the authors can use SDGs to model and analyse the propagation of faults in large‐scale industrial systems. However, with increasing system scales, the time requirements of a graph search method would be too onerous. This can be alleviated by transforming a single‐layer SDG model into a hierarchical model to improve search efficiency. The hierarchical model would be composed of three layers: the top layer would consist of independent sub‐systems; the middle layer would have control systems configuration and the bottom layer would have all the variables. The possible root causes of faults can then be searched in this model, layer by layer according to the initial response of the system. The efficacy of the proposed approach is illustrated by application to a four‐tank system and a generator system in a power plant. The methodology presented here can also be used in process hazard analysis. [ABSTRACT FROM AUTHOR]