A Virtual Work Based Algorithm for Solving Direct Dynamics Problem of a 3-RRP Spherical Parallel Manipulator

In this paper, we use principle of virtual work to obtain the direct dynamics analysis of a 3-RRP spherical parallel manipulator, also called spherical star-triangle (SST) manipulator (Enferadi et al., Robotica 27, 2009). This manipulator has good accuracy and relatively a large workspace which is free of singularities (Enferadi et al., Robotica, 2009). The direct kinematics problem of this manipulator has eight solution (Enferadi et al., Robotica 27, 2009). Given a desired actuated joint trajectories, we first present an algorithm for selecting the admissible solution. Next, direct velocity and direct acceleration analysis are obtained in invariant form. The concept of direct link Jacobian matrices is introduced. The direct link Jacobian matrix relates motion of any link to vector velocity of actuated joints. Finally, dynamical equations of the manipulator are obtained using the principle of virtual work and the concept of direct link Jacobian matrices. This method allows elimination of constraint forces and moments at the passive joints from motion equations. Two examples are presented and trajectory of moving platform are obtained. Results are verified using a commercial dynamics modeling package as well as inverse dynamics analysis (Enferadi et al., Nonlinear Dyn 63, 2010).     

ANN model to predict the effects of composition and heat treatment parameters on transformation start temperature of microalloyed steels

The paper presents some results of the research connected with the development of new approach based on the artificial neural network (ANN) of predicting the transformation start temperature of the phase constituents occurring in five steels after continuous cooling. The independent variables in the model are chemical compositions (C, Mn, Nb, Mo, Ti, N, Cu, P, S, Si, Al, V), austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. For purpose of constructing these models, 138 different experimental data were gathered from the literature. The data used in the ANN model are arranged in a format of fourteen input parameters that cover the chemical compositions, initial austenite grain size and cooling rate, and output parameter which is transformation start temperature. In this model, the training and testing results in the ANN have shown strong potential for prediction of effects of chemical compositions and heat treatments on phase transformation of microalloyed steels.     

Semi-Fluid content distribution model for fast data dissemination in heterogeneous overlay networks

Overlay networks and peer-to-peer networking have emerged as alternative solutions to solve many problems related to massive information distribution and processing tasks by providing enhanced services in the application layer. For instance, overlay networks can improve data dissemination in P2P file sharing overlays and Content Distribution Networks (CDNs). In such overlay networks, delay is considered as a critical performance metric. A viable content distribution model could considerably minimize the completion time and would lead to efficient utilization of resources such as network bandwidth. The main objective of this paper is to design a new flexible content distribution model for fast data dissemination in overlay networks with heterogeneous nodes. The proposed “Semi-Fluid” content distribution model is a hybrid model that enables fast data dissemination in overlay networks by combining Chunk and Fluid content distribution models. In this new content distribution model, the chunk content is distributed into heterogeneous overlay nodes in a fluid manner. The proposed hybrid content distribution model eliminates both a backpressure problem caused by the Fluid content distribution model, as well as a chunk transition delay caused by the Chunk content distribution model. The performance of the proposed Semi-Fluid content distribution model has been evaluated by mathematical analysis as well as by real implementation over the “PlanetLab” platform, and the obtained results show that the Semi-Fluid content distribution model efficiently reduces the total download time of the clients. Hence, the Semi-Fluid content distribution model can be deployed as a promising solution for fast data dissemination in heterogeneous overlay networks.     

Design and Implementation of Robust-Fixed Structure Controller for Telerobotic Systems

The controller design for bilateral teleoperation systems involves a delicate trade-off between performance and stability. To achieve high performance, high order robust controllers may not be feasible for real-time implementation because of hardware and computational limitations. The main purpose of this paper is to achieve stability and transparency in the presence of time delay in communication channel as well as model uncertainty. To address these problems, a novel robust fixed-structure controller is proposed for uncertain bilateral teleoperation systems. Here, the traditional conventional Proportional-Integral-Derivative (PID) controller is employed to achieve the requirements. The simplicity and straightforward design are the significant advantageous of the proposed method. Robust stability analysis of the proposed technique is also provided. Results demonstrate that the structure is effective in providing stability and transparency in teleoperation systems.     

Solving electrical power system problems by harmony search: a review

Today, electric power plays a highly significant role in the development of various sectors of the countries. Most often, power system optimization problems have non-linear and non-convex objective functions with intense equality and inequality constraints along with various types of decision variables (continuous, discrete and integer). As modern electrical power systems become more complex, planning, operation and control of such systems using traditional methods face increasing difficulties. Owing to the ability of escaping local optima, meta-heuristic optimization algorithms can be efficient alternatives to solve power system optimization problems. Inspired by the improvisation process of music, harmony search (HS) algorithm is a meta-heuristic search method which has received a considerable attention to solve different power system optimization problems. HS has simple concept, is easy to implement, converges rapidly to the solution and has high efficiency. In this paper, technical literature about HS applied to power system optimization problems is reviewed. This review will enable the researchers to open the mind to explore possible applications in this field as well as beyond this area.     

On Infinite Horizon Optimal Control Problems with a Feed Forward Neural Network Scheme

Neural Processing Letters - In this paper, a class of infinite-horizon nonlinear optimal control problems is considered. The main idea is to convert the infinite horizon problem to an equivalent...     

Reverse engineering the way humans rank textures

We argue that in order to understand which features are used by humans to group textures, one must start by computing thousands of features of diverse nature, and select from those features those that allow the reproduction of perceptual groups or perceptual ranking created by humans. We use the Trace transform to produce such features here. We compare these features with those produced from the co-occurrence matrix and its variations. We show that when one is not interested in reproducing human behaviour, the elements of the co-occurrence matrix used as features perform best in terms of texture classification accuracy. However, these features cannot be “trained” or “selected” to imitate human ranking, while the features produced from the Trace transform can. We attribute this to the diverse nature of the features computed from the Trace transform.

C-trust: A trust management system to improve fairness on circular P2P networks

An important issue in Peer-to-Peer networks is to encourage users to share with others as they use the resources of the network. However, some nodes may only consume from users without giving anything in return. To fix this problem, we can incorporate trust management systems with network infrastructures. Current trust managements are usually made for unstructured overlays and have several shortcomings. They are made to be very similar to e-commerce scoring websites which may not be the best design for fairness in P2P networks. Several problems may arise with their designs such as difficulties to provide a complete history of freeloaders or lack of an autonomous removal mechanism in case of severe attacks. In this paper, we argue that such systems can be deployed more efficiently by using a structured paradigm. For this purpose, we propose C-Trust, a trust management system which is focused on fairness for P2P networks. This is done by getting help from current circular structured designs. This method is able to mark freeloaders, identify their severity of abusion and punish them accordingly. We are also able to effectively protect both Seeder-to-Leecher and Leecher-to-Leecher transactions. This feature is specially important for fairness which other trust systems have not considered so far.     

Use of NARX neural networks for Meteosat Second Generation SEVIRI very short-term cloud mask forecasting

In this article, a novel technique based on artificial neural networks (NN) is proposed for cloud coverage short-term forecasting (nowcasting). In particular, the capabilities of multi-layer perceptron NN and time series analysis with nonlinear autoregressive with exogenous input NN are explored and applied to the European meteorological system ‘Meteosat Second Generation’ with its payload Spinning Enhanced Visible and InfraRed Imager. The general neural architecture consists of a first stage addressing the prediction of the radiance images at six bands (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm). In a second stage a cloud masking algorithm, always based on NN, is applied to the predicted images for the cloud coverage nowcasting. The scheme was compared with the most basic forecast algorithm for the prediction: the persistent model. Two test areas characterized by different climatology have been considered for the performance analysis. The results show that about 85% of the changes occurring in the time window were recognized by the proposed technique.