Adding spatial penalty to fuzzy C-means (FCM) model is an important way to reduce the influence of noise in image segmentation. However, these improved algorithms easily cause segmentation failures when the image has ...
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Adding spatial penalty to fuzzy C-means (FCM) model is an important way to reduce the influence of noise in image segmentation. However, these improved algorithms easily cause segmentation failures when the image has the characteristics of unequal cluster sizes. Besides, they often fall into local optimal solutions if the initial cluster centers are improper. This paper presents a noise robust hybrid algorithm for segmenting image with unequal cluster sizes based on chaotic crow search algorithm and improved fuzzy c-means to overcome the above defects. Firstly, each size of clusters is integrated into the objective function of noise detecting fuzzy c-means algorithm (NDFCM), which can reduces the contribution of larger clusters to objective function and then the new membership degree and cluster centers are deduced. Secondly, a new expression called compactness, representing the pixel distribution of each cluster, is introduced into the iteration process of clustering. Thirdly, we use two- paths to seek the optimal solutions in each step of iteration: one path is produced by the chaotic crow search algorithm and the other is originated by gradient method. Furthermore, the better solutions of the two-paths go to next generation until the end of the iteration. Finally, the experiments on the synthetic and non-destructive testing (NDT) images show that the proposed algorithm behaves well in noise robustness and segmentation performance.
Diagnosis of Parkinson's disease at its early stage is important in proper treatment of the patients so they can lead productive lives for as long as possible. Although many techniques have been proposed to diagno...
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Diagnosis of Parkinson's disease at its early stage is important in proper treatment of the patients so they can lead productive lives for as long as possible. Although many techniques have been proposed to diagnose the Parkinson's disease at an early stage but none of them are efficient. In this work, to improve the diagnosis of Parkinson's disease, we have introduced a novel improved and optimized version of crowsearchalgorithm(OCSA). The proposed OCSA can be used in predicting the Parkinson's disease with an accuracy of 100% and help individual to have proper treatment at early stage. The performance of OCSA has been measured for 20 benchmark datasets and the results have been compared with the original chaotic crow search algorithm(CCSA). The experimental result reveals that the proposed nature-inspired algorithm finds an optimal subset of features, maximizing the accuracy and minimizing a number of features selected and is more stable.
Uncertainties related to the power output from the renewable energy sources and low inertia of a standalone microgrid (SMG) demand a robust control strategy for continuous frequency control of the SMG. Consequently, t...
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Uncertainties related to the power output from the renewable energy sources and low inertia of a standalone microgrid (SMG) demand a robust control strategy for continuous frequency control of the SMG. Consequently, this paper proposes a novel hybrid fuzzy proportional derivative-tilt integral derivative (FPD-TID) controller for the load frequency control (LFC) analysis of a SMG. Inspiration for the proposed controller comes from combining the advantages of both the FPD and the TID controllers. Gains of the proposed controller are optimized using a robust chaotic crow search algorithm (CCSA). In order to validate the proposed control scheme, comparative frequency deviation responses of the SMG are presented considering multiple disturbances. Also, the proposed controller is put to test for its sensitivity and robustness subject to a +/- 30% variation in the SMG parameters and disconnection of various SMG subsystems, respectively. Since operational stability of the SMG is highly desirable under such circumstances, the proposed control scheme aims to achieve a trade-off between its performance and the operational stability of the SMG. The operational stability of the SMG is established through eigenvalue and root locus analysis.
The existing models implemented to coordinate the plug-in electric vehicles (PEV) based on the price of electricity almost exclusively use the uniform price methods. These methods are based on price-taker mechanisms w...
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The existing models implemented to coordinate the plug-in electric vehicles (PEV) based on the price of electricity almost exclusively use the uniform price methods. These methods are based on price-taker mechanisms where prices are exogenous and unaffected by the PEVs activity. This standpoint, however, suffers from two major issues. First, the important indexes of network operation are neglected, e.g., power losses, network reliability, cost/benefit of PEVs operation. Second, coordination with distributed generations (DG) is not possible, efficiently. In this paper, a new viewpoint based on locational marginal price (LMP) is formulated for coordination of PEVs and DGs in electricity day-ahead markets. The LMPs at fleet and DG connected busses are used as a signal to motivate PEV and DG owners to participate in coordination problem. Also, coordination problem is inserted in the optimal operation problem of the network and an efficient algorithm based on chaotic crow search algorithm (CCSA) is used to meet the optimal operation of the network. To make a bidirectional power flow, the vehicle-to-grid (V2G) is used as the PEVs;and, for modeling random behavior of loads, market price, and PEVs, a stochastic modeling framework based on PEM is implemented. The feasibility and satisfying performance of the proposed method has been tested on an 84-bus test system, and simulation results reveal promising results.
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