Maximum Power Point Tracking (MPPT) Using Artificial Bee Colony Based Algorithm for Photovoltaic System
Hassan Salmi,
Abdelmajid Badri,
Mourad Zegrari
Issue:
Volume 5, Issue 1, February 2016
Pages:
1-4
Received:
29 December 2015
Accepted:
5 January 2016
Published:
15 January 2016
Abstract: The Artificial Bee Colony based algorithm (ABC) studied in this paper is assigned as an intelligent control of photovoltaic system. The output power of a photovoltaic panel depends on solar irradiation and temperature. Therefore, it is important to operate the photovoltaic (PV) panel in its maximum power point. In this aim, the ABC consists to track the optimal duty cycle of the electronic converter, in order to lead to the Maximum Power Point (MPP) of the PV system. Moreover, the classical method Perturb and Observe (P&O) [1-2] is studied in the sake of comparison with the ABC method in Matlab/Simulink, by taking into consideration the efficiency, the speed and the robustness performance when the meteorological conditions change.
Abstract: The Artificial Bee Colony based algorithm (ABC) studied in this paper is assigned as an intelligent control of photovoltaic system. The output power of a photovoltaic panel depends on solar irradiation and temperature. Therefore, it is important to operate the photovoltaic (PV) panel in its maximum power point. In this aim, the ABC consists to trac...
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Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans
Mahmoud Saleh Jawarneh,
Mohammed Said Abual-Rub
Issue:
Volume 5, Issue 1, February 2016
Pages:
5-16
Received:
1 January 2016
Accepted:
11 January 2016
Published:
4 February 2016
Abstract: The advice of automating computer applications is being increase to reduce the human interaction. Medical image segmentation is one of these applications, when done manually; it turns into a time-consuming and knowledge intensive task. As a result, automatic segmentation is in the focus of work to speed up segmentation processes. Fast and accurate segmentation would allow physicians to analyze and visualize the human structures and re-plan radiation therapy and surgery. This paper introduces a knowledge system based on different sources of medical knowledge to automate medical image segmentation through active contour methods. The way of getting benefit of the knowledge provided by medical atlas, expert’s rules, image features, image multiple views and image Meta data introduced by this knowledge system. We classify the system in different domains in way can be manage properly to guide active contour segmentation methods for abdominal CT scans. The obtained results are very promising showing significant improvements over other methods where the volume measurements error is 7% and the processing time was improved by 68%.
Abstract: The advice of automating computer applications is being increase to reduce the human interaction. Medical image segmentation is one of these applications, when done manually; it turns into a time-consuming and knowledge intensive task. As a result, automatic segmentation is in the focus of work to speed up segmentation processes. Fast and accurate ...
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Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment
Mojisola Grace Asogbon,
Olatubosun Olabode,
Oluwatoyin Catherine Agbonifo,
Oluwarotimi Williams Samuel,
Victoria Ifeoluwa Yemi-Peters
Issue:
Volume 5, Issue 1, February 2016
Pages:
17-24
Received:
23 January 2016
Accepted:
1 February 2016
Published:
19 February 2016
Abstract: Mortgage lending is one of the major businesses of mortgage institutions which usually involve the granting of loan to potential customers who want to own a home but do not have sufficient capital to do so. The granting of mortgage loan to customers usually comes with a lot of risks which may eventually affect the continuity of such institution if not properly managed. In recent times, several techniques for mortgage loan risk assessment have been proposed. However, a technique that can learn and adapt at the same time incorporate current knowledge of mortgage loan practices is still lacking. Therefore, this research proposed a hybrid decision support system in which neural networks was used to build learning and adaptive capabilities into a fuzzy inference module for mortgage loan risk assessment. The performance of the proposed hybrid system was investigated based on the accuracy of loan risk prediction and the mean absolute deviation metrics. Experimental results show that the hybrid system has better performance than the non-adaptive fuzzy inference system. Our findings suggest that the proposed method would efficiently predict the risk associated with mortgage loan applicants and thereby promote mortgage lending in such institutions.
Abstract: Mortgage lending is one of the major businesses of mortgage institutions which usually involve the granting of loan to potential customers who want to own a home but do not have sufficient capital to do so. The granting of mortgage loan to customers usually comes with a lot of risks which may eventually affect the continuity of such institution if ...
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