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.

Enhanced probabilistic neural network with local decision circles: A robust classifier

Abstract: In recent years the Probabilistic Neural Network (PPN) has been used in a large number of applications due to its simplicity and efficiency. PNN assigns the test data to the class with maximum likelihood compared with other classes. Likelihood of the test data to each training data is computed in the pattern layer through a kernel density estimation using a simple Bayesian rule. The kernel is usually a standard probability distribution function such as a Gaussian function. A spread parameter is used as a global parameter which determines the width of the kernel. The Bayesian rule in the pattern layer estimates the conditional probability of each class given an input vector without considering any probable local densities or heterogeneity in the training data. In this paper, an enhanced and generalized PNN (EPNN) is presented using local decision circles (LDCs) to overcome the aforementioned shortcoming and improve its robustness to noise in the data. Local decision circles enable EPNN to incorporate local information and non-homogeneity existing in the training population. The circle has a radius which limits the contribution of the local decision. In the conventional PNN the spread parameter can be optimized for maximum classification accuracy. In the proposed EPNN two parameters, the spread parameter and the radius of local decision circles, are optimized to maximize the performance of the model. Accuracy and robustness of EPNN are compared with PNN using three different benchmark classification problems, iris data, diabetic data, and breast cancer data, and five different ratios of training data to testing data: 90:10, 80:20, 70:30, 60:40, and 50:50. EPNN provided the most accurate results consistently for all ratios. Robustness of PNN and EPNN is investigated using different values of signal to noise ratio (SNR). Accuracy of EPNN is consistently higher than accuracy of PNN at different levels of SNR and for all ratios of training data to testing data.

A Decentralized Cooperative Control Scheme With Obstacle Avoidance for a Team of Mobile Robots

Abstract: The problem of formation control of a team of mobile robots based on the virtual and behavioral structures is considered in this paper. In the virtual structure, each mobile robot is modeled by an electric charge. The mobile robots move toward a circle, and due to repulsive forces between the identical charges, regular polygon formations of the mobile robots will be realized. For swarm formation, a virtual mobile robot is located at the center of the circle, and other mobile robots follow it. In the introduced approach, each mobile robot finds its position in the formation autonomously, and the formation can change automatically in the case of change in the number of the mobile robots. This paper also proposes a technique for avoiding obstacles based on the behavioral structure. In this technique, when a mobile robot gets close to an obstacle, while moving toward its target, a rotational potential field is applied to lead the mobile robot to avoid the obstacle, without locating in local minimum positions.

Carbon Dioxide Separation from Flue Gases: A Technological Review Emphasizing Reduction in Greenhouse Gas Emissions

Abstract: Increasing concentrations of greenhouse gases (GHGs) such as CO2 in the atmosphere is a global warming. Human activities are a major cause of increased CO2 concentration in atmosphere, as in recent decade, two-third of greenhouse effect was caused by human activities. Carbon capture and storage (CCS) is a major strategy that can be used to reduce GHGs emission. There are three methods for CCS: pre-combustion capture, oxy-fuel process, and post-combustion capture. Among them, post-combustion capture is the most important one because it offers flexibility and it can be easily added to the operational units. Various technologies are used for CO2 capture, some of them include: absorption, adsorption, cryogenic distillation, and membrane separation. In this paper, various technologies for post-combustion are compared and the best condition for using each technology is identified.