Introduction: Among different renewable energy sources, ocean is considered as one of the renewable energy sources that has a wide geographical range. Marine currents have different categories that tidal currents are among these currents. Near Japan, there is a current More
Introduction: Among different renewable energy sources, ocean is considered as one of the renewable energy sources that has a wide geographical range. Marine currents have different categories that tidal currents are among these currents. Near Japan, there is a current in the ocean known as the Kuroshio Current, which has a high potential for generating electricity. These currents have speed and consequently kinetic energy and can generate electricity by using turbines installed deep in the ocean. One of the problems that these currents have is that they change over time and therefore the power generation of energy converters of Kuroshio currents also varies. Therefore, the effect of these changes on different aspects of these converters such as reliability should be investigated.
Materials and Methods: In the reliability model of the current converter, both the component failure and output power variations, which are caused by the change in the speed of ocean currents are considered.
Results and Discussion: In this part, a Kuroshio Current energy converter that includes a turbine with a diameter of 2 meters is considered. In this part, the reliability indices of a sample test system are obtained, and then the effect of Kuroshio Current power plants on these indices is evaluated. The results show that as the peak load of the system increases, the reliability of the system deteriorates.
Conclusion: In this paper, the reliability model of Kuroshio Current converters is obtained. Numerical results conclude that the Kuroshio Current converters can improve reliability indices of power system.
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In the division of remote sensing image pixels using Watershed segmentation, the boundaries of the image are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that firs More
In the division of remote sensing image pixels using Watershed segmentation, the boundaries of the image are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that first the Watershed algorithm is used to segment the image obtained from the sum of the image derivative with the original image. Image derivation makes the borders of the image well defined and does not overlap between the borders. After segmentation, Fuzzy C-Means clustering is used to combine similar regions. Finally, in order to improve the clustering results, a new segmentation matrix is calculated for each area of the image, according to the characteristics of its neighboring areas. Due to the fact that remote sensing images contain a high level of noise, the proposed algorithm is more capable of dealing with noise compared to the conventional Watershed algorithm, and the edges of the image appear better. The test results of the proposed method on a sample of remote sensing image show the practicality and efficiency of the proposed algorithm.
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In the division of remote sensing image pixels using Watershed segmentation, the image boundaries are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that first the W More
In the division of remote sensing image pixels using Watershed segmentation, the image boundaries are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that first the Watershed algorithm is used to segment the image obtained from the sum of the image derivative with the original image. Image derivation makes the borders of the image well-defined and does not overlap between borders. After segmentation, Fuzzy C-Means clustering is used to combine similar regions. Finally, in order to improve the clustering results, a new segmentation matrix is calculated for each area of the image, according to the characteristics of its neighboring areas. Due to the fact that remote sensing images contain a high level of noise, the proposed algorithm is more capable of dealing with noise compared to the conventional Watershed algorithm, and the edges of the image appear better. The test results of the proposed method on a sample of remote sensing image show the practicality and efficiency of the proposed algorithm.
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People often change their minds at different times and at different places. It is important and valuable to indicate concept-drift patterns in unexpected ways for shopping behaviours for commercial applications. Research about concept drift has been growing in recent ye More
People often change their minds at different times and at different places. It is important and valuable to indicate concept-drift patterns in unexpected ways for shopping behaviours for commercial applications. Research about concept drift has been growing in recent years. Many algorithms dealt with concept-drift information and detected new market trends. This paper proposes an approach based on fuzzy c-means (FCM) to mine the concept drift of fuzzy membership functions. The proposed algorithm is subdivided into two stages. In the first stage, individual fuzzy membership functions are generated from different training databases by the proposed FCM-based approach. Then, the proposed algorithm will mine the concept-drift patterns from the sets of fuzzy membership functions in the second stage. Experiments on simulated datasets were also conducted to show the effectiveness of the approach.
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