DC/AC converters are very important components that have to be chosen efficiently for each type of power station. In this article, we present in details, a comparison between three different architectures of multileve...DC/AC converters are very important components that have to be chosen efficiently for each type of power station. In this article, we present in details, a comparison between three different architectures of multilevel inverters, the flying capacitor multilevel inverter (FCMLI), the diode clamped multilevel inverter (DCMLI), and the cascaded H-bridge multilevel inverter (CHMLI). Thus the comparison is focused on the output voltage quality, the complexity of the power circuits, the cost of implementation, and the influence on a power bank inside the renewable power station. We also investigate trough simulation the efficient number of levels and suitable characteristics for the CHMLI that showed the most promising performance. The study uses Matlab Simulink platform as a tool of simulation, and aim to choose the most qualified inverter, for a potential insertion on a hybrid renewable energy platform (wind-solar). In all the simulations we use the same PWM control type (SPWM).展开更多
The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is th...The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency.展开更多
文摘DC/AC converters are very important components that have to be chosen efficiently for each type of power station. In this article, we present in details, a comparison between three different architectures of multilevel inverters, the flying capacitor multilevel inverter (FCMLI), the diode clamped multilevel inverter (DCMLI), and the cascaded H-bridge multilevel inverter (CHMLI). Thus the comparison is focused on the output voltage quality, the complexity of the power circuits, the cost of implementation, and the influence on a power bank inside the renewable power station. We also investigate trough simulation the efficient number of levels and suitable characteristics for the CHMLI that showed the most promising performance. The study uses Matlab Simulink platform as a tool of simulation, and aim to choose the most qualified inverter, for a potential insertion on a hybrid renewable energy platform (wind-solar). In all the simulations we use the same PWM control type (SPWM).
文摘The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency.
