C-means clustering

Open Access Article

1 - Analysis of Kuroshio Current Converter as Renewable and Environmentally Friendly Power Plant
امیر قائدی

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. Manuscript profile

Open Access Article

2 - Using fuzzy c-means clustering algorithm for common lecturer timetabling among departments
hamed babaei Jaber Karimpour Sajjad Mavizi

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Open Access Article

3 - A method for segmenting remote sensing images using the Watershed algorithm and Fuzzy C-Means clustering
Mohsen Hamed Fatemeh Hajiani

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. Manuscript profile

Open Access Article

4 - A method for segmenting remote sensing images using the Watershed algorithm and Fuzzy C-Means clustering
Ebrahim Alibabaee Rouhollah Aghajani

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. Manuscript profile

Open Access Article

5 - Biogeography-based Optimized Adaptive Neuro-Fuzzy Control of a Nonlinear Active Suspension System
Ali Fayazi Hossein Ghayoumi Zadeh

10.52547/mjtd.11.1.43

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Open Access Article

6 - Presenting a model for Multi-layer Dynamic Social Networks to discover Influential Groups based on a combination of Developing Frog-Leaping Algorithm and C-means Clustering
lida naderloo Mohammad Tahghighi Sharabyan

Introduction: The current research examines a more complex social network called a multi-layered social network. Recently, the concept of the multilayer network has emerged from the area of complex networks, under the domain of complex systems. In the field of big data, More

Introduction: The current research examines a more complex social network called a multi-layered social network. Recently, the concept of the multilayer network has emerged from the area of complex networks, under the domain of complex systems. In the field of big data, simple and multi-layered social networks can be found everywhere and in all fields. The estimation of the importance of each node in these two types of networks is not the same, and weighting the nodes is very necessary to control the network. For this purpose, the relationship between the characteristics of the nodes and the relationship with the network structure should be examined. To find the degree of each node in the system function, parameters like reliability, controllability, and power should be considered. In this paper, a model for dynamic multi-layer social networks to discover influential groups, based on the combination of evolutionary frog jump algorithm and C-means clustering, has been presented.Method: Once data are collected, they were cleaned and normalized so that the desired data leads to the identification of effective individuals and groups. The decision matrix is constructed based on these data. Using this matrix, identification, and clustering (based on fuzzy clustering) were conducted, and the importance of the groups was also determined to determine influential people and groups in social networks. The Jumping frog algorithm was used to improve the detection of influential parameters.Results: In the evaluation and simulation of the clustering part, the proposed method was compared with the K-means method and the balance value of the method in cluster selection was 5. It should be noted that the proposed method showed better improvement compared to the compared methods. Also, the evaluation of the accuracy criterion of the proposed method has improved by 3.3 compared to similar methods and recorded an improvement of 3.3 compared to the basic M-ALCD method.Discussion: In this paper, a multi-layer local community detection model is proposed, which is based on structure and feature information. This model can use the information of the characteristics of the nodes and the information of the strength of similarity that is revealed by social exchanges and improves the accuracy of community detection in Improve multilayer networks. Due to its modularity and computational efficiency, this algorithm performs better on most data sets, unlike the classic multi-layer and global community detection algorithms Manuscript profile

Open Access Article

7 - High Performance Implementation of Fuzzy C-Means and Watershed Algorithms for MRI Segmentation
Farnaz Hoseini Ghader Mortezaie Dekahi

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Open Access Article

8 - An Energy Efficient improving the Leach protocol Scheme in Wireless Sensor Networks
Farzaneh Abdolahi Maryam Khademi

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