End-to-end epidemic multicast loss recovery: Analysis of scalability and robustness

For ensuring reliability at the transport level end-to-end multicasting, an efficient loss recovery mechanism is indispensable. We consider scalability, topology independence and robustness as the significant features that such a mechanism should offer, and demonstrate that an epidemic loss recovery approach is superior in all these aspects. We also show that the epidemic approach transparently handles network link failures by using pair-wise propagation of information, and compare it with feedback controlled loss recovery on identical network settings. The contribution of this work is the simulative analysis of recovery overhead distribution on multicast group members in the case of various link failures on the network, the impact of group size, randomized system-wide noise and message rate on scalability, and examination of various scenarios modeling the overlay networks. We investigate the important features of epidemic multicast loss recovery extensively together and reach concrete results on realistic network scenarios.     

Geno-mathematical identification of the multi-layer perceptron

In this paper, we will focus on the use of the three-layer backpropagation network in vector-valued time series estimation problems. The neural network provides a framework for noncomplex calculations to solve the estimation problem, yet the search for optimal or even feasible neural networks for stochastic processes is both time consuming and uncertain. The backpropagation algorithm—written in strict ANSI C—has been implemented as a standalone support library for the genetic hybrid algorithm (GHA) running on any sequential or parallel main frame computer. In order to cope with ill-conditioned time series problems, we extended the original backpropagation algorithm to a K nearest neighbors algorithm (K-NARX), where the number K is determined genetically along with a set of key parameters. In the K-NARX algorithm, the terminal solution at instant t can be used as a starting point for the next t, which tends to stabilize the optimization process when dealing with autocorrelated time series vectors. This possibility has proved to be especially useful in difficult time series problems. Following the prevailing research directions, we use a genetic algorithm to determine optimal parameterizations for the network, including the lag structure for the nonlinear vector time series system, the net structure with one or two hidden layers and the corresponding number of nodes, type of activation function (currently the standard logistic sigmoid, a bipolar transformation, the hyperbolic tangent, an exponential function and the sine function), the type of minimization algorithm, the number K of nearest neighbors in the K-NARX procedure, the initial value of the Levenberg–Marquardt damping parameter and the value of the neural learning (stabilization) coefficient α. We have focused on a flexible structure allowing addition of, e.g., new minimization algorithms and activation functions in the future. We demonstrate the power of the genetically trimmed K-NARX algorithm on a representative data set.     

Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease

In this paper, we have made medical application of a new artificial immune system named the information gain based artificial immune recognition system (IG-AIRS) which minimizes the negative effects of taking into account all attributes in calculating Euclidean distance in shape–space representation which is used in many artificial immune systems. For medical data, thyroid disease data set was applied in the performance analysis of our proposed system. Our proposed system reached 95.90% classification accuracy with 10-fold CV method. This result ensured that IG-AIRS would be helpful in diagnosing thyroid function based on laboratory tests, and would open the way to various ill diagnoses support by using the recent clinical examination data, and we are actually in progress.     

An integrated decision making approach for ERP system selection

Enterprise resource planning (ERP) systems have gained major prominence by enabling companies to streamline their operations, leverage and integrate business data process. In order to implement an ERP project successfully, it is necessary to select an ERP system which can be aligned with the needs of the company. Thus, a robust decision making approach for ERP software selection requires both company needs and characteristics of the ERP system and their interactions to be taken into account. This paper develops a novel decision framework for ERP software selection based on quality function deployment (QFD), fuzzy linear regression and zero–one goal programming. The proposed framework enables both company demands and ERP system characteristics to be considered, and provides the means for incorporating not only the relationships between company demands and ERP system characteristics but also the interactions between ERP system characteristics through adopting the QFD principles. The presented methodology appears as a sound investment decision making tool for ERP systems as well as other information systems. The potential use of the proposed decision framework is illustrated through an application.     

Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks

In wind energy conversion systems, one of the operational problems is the changeability and discontinuity of wind. In most cases, wind speed can fluctuate rapidly. Hence, quality of produced energy becomes an important problem in wind energy conversion plants. Several control techniques have been applied to improve the quality of power generated from wind turbines. Pitch control is the most efficient and popular power control method, especially for variable-speed wind turbines. It is a useful method for power regulation above the rated wind speed. This paper proposes an artificial neural network-based pitch angle controller for wind turbines. In the simulations, a variable-speed wind turbine is modeled, and its operation is observed by using two types of artificial neural network controllers. These are multi-layer perceptrons with back propagation learning algorithm and radial basis function network. It is shown that the power output was successfully regulated during high wind speed, and as a result overloading or outage of the wind turbine was prevented.     

Supporting students’ participation in authentic proof activities in computer supported collaborative learning (CSCL) environments

In this paper, I review both mathematics education and CSCL literature and discuss how we can better take advantage of CSCL tools for developing mathematical proof skills. I introduce a model of proof in school mathematics that incorporates both empirical and deductive ways of knowing. I argue that two major forces have given rise to this conception of proving: a particular learning perspective promoted in reform documents and a genre of computer tools, namely dynamic geometry software, which affords this perspective of learning within the context of mathematical proof. Tracing the move from absolutism to fallibilism in the philosophy of mathematics, I highlight the vital role of community in the production of mathematical knowledge. This leads me to an examination of a certain CSCL tool whose design is guided by knowledge-building pedagogy. I argue that knowledge building is a suitable pedagogical approach for the proof model presented in this paper. Furthermore, I suggest software modifications that will better support learners’ participation in authentic proof tasks.     

Design and Testing of a Robotic System for mr Image-guided Interventions

The effective integration of robotics together with magnetic resonance (mr) technology is expected to facilitate the real-time guidance of various diagnostic and therapeutic interventions. Specially designed robotic manipulators are required for this purpose, the development of which is a challenging task given the strong magnetic fields and the space limitations that characterize the mr scanning environment. A prototype mr-compatible manipulator is presented, designed to operate inside cylindrical mr scanners. It was developed for the study of minimally invasive diagnostic and therapeutic interventions in the abdominal and thoracic area with real-time mr image guidance. Initial tests were performed inside a high-field clinical mr scanner and included mr-compatibility tests and phantom studies on image-guided targeting.