Amirkabir University of Technology

About: Amirkabir University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Fuzzy logic. The organization has 15254 authors who have published 31165 publications receiving 487551 citations. The organization is also known as: Tehran Polytechnic & Tehran Polytechnic University.

Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development

Abstract: Clustering algorithms aim at finding dense regions of data based on similarities and dissimilarities of data points. Noise and outliers contribute to the computational procedure of the algorithms as well as the actual data points that leads to inaccurate and misplaced cluster centers. This problem also arises when sizes of the clusters are different that moves centers of small clusters towards large clusters. Mass of the data points is important as well as their location in engineering and physics where non-uniform mass distribution results displacement of the cluster centers towards heavier clusters even if sizes of the clusters are identical and the data are noise-free. Fuzzy C-Means (FCM) algorithm that suffers from these problems is the most popular fuzzy clustering algorithm and has been subject of numerous researches and developments though improvements are still marginal. This work revises the FCM algorithm to make it applicable to data with unequal cluster sizes, noise and outliers, and non-uniform mass distribution. Revised FCM (RFCM) algorithm employs adaptive exponential functions to eliminate impacts of noise and outliers on the cluster centers and modifies constraint of the FCM algorithm to prevent large or heavier clusters from attracting centers of small clusters. Several algorithms are reviewed and their mathematical structures are discussed in the paper including Possibilistic Fuzzy C-Means (PFCM), Possibilistic C-Means (PCM), Robust Fuzzy C-Means (FCM-σ), Noise Clustering (NC), Kernel Fuzzy C-Means (KFCM), Intuitionistic Fuzzy C-Means (IFCM), Robust Kernel Fuzzy C-Mean (KFCM-σ), Robust Intuitionistic Fuzzy C-Means (IFCM-σ), Kernel Intuitionistic Fuzzy C-Means (KIFCM), Robust Kernel Intuitionistic Fuzzy C-Means (KIFCM-σ), Credibilistic Fuzzy C-Means (CFCM), Size-insensitive integrity-based Fuzzy C-Means (siibFCM), Size-insensitive Fuzzy C-Means (csiFCM), Subtractive Clustering (SC), Density Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Models (GMM), Spectral clustering, and Outlier Removal Clustering (ORC). Some of these algorithms are suitable for noisy data and some others are designed for data with unequal clusters. The study shows that the RFCM algorithm works for both cases and outperforms the both categories of the algorithms.

PLGA/gelatin hybrid nanofibrous scaffolds encapsulating EGF for skin regeneration

Abstract: The novel strategies of skin regenerative treatment are aimed at the development of biologically responsive scaffolds capable of delivering multiple bioactive agents and cells to the target tissues. In this study, nanofibers of poly(lactic-co-glycolic acid) (PLGA) and gelatin were electrospun and the effect of parameters viz polymer concentration, acid concentration, flow rate and voltage on the morphology of the fibers were investigated. PLGA nanofibers encapsulating epidermal growth factor were also prepared through emulsion electrospinning. The core-sheath structure of the nanofibers was verified by transmission electron microscopy. The hemostatic attributes and the biocompatibility of the scaffolds for human fibroblast cell were scrutinized. Furthermore, gene expression of collagen type I and type III by the cells on the scaffolds was quantified using real-time reverse transcriptase polymerase chain reaction. The results indicated desirable bioactivity and hemostasis of the scaffolds with the capability of encapsulation and controlled release of the protein which can be served as skin tissue engineering scaffolds and wound dressings.

Multi-objective optimal power management and sizing of a reliable wind/PV microgrid with hydrogen energy storage using MOPSO

Abstract: In this paper, a multi-objective algorithm is presented for optimal power management and design of a hybrid Wind/Photovoltaic/ generation system with hydrogen energy storage system including electrolyzer, fuel cell and hydrogen tank to supply power demand in a microgrid system. The generation units are intrinsically non-dispatchable and moreover, the major components of the system i.e. wind turbine generators, photovoltaic arrays and DC/AC converter may be subjected to failure. Also, solar radiation, wind speed and load data are assumed to be entirely deterministic. The goal of this design is to use a novel multi-objective optimization algorithm to minimize the objective functions i.e. annualized cost of the system, loss of load expected and loss of energy expected and provide optimal energy management in the microgrid. The system costs involve investment, replacement and operation and maintenance costs. Prices are all empirical and components are commercially available. The simulation results for different cases reveal the impact of components outage on the reliability and cost of the system. So, they are directly depends on component’s reliabilities, i.e. outages lead to need for a larger and more expensive generation system to supply the load with the acceptable level of reliability. In addition, an approximate method for reliability evaluation of hybrid system is presented which lead to reduce computation time. Simulation results show the effectiveness of proposed multi-objective algorithm to solve optimal sizing problem in contrast with traditional single objective methods. 8