We present a distributed control strategy that lets a swarm of spacecrafts autonomously form a lattice in orbit around a planet. The system, based on the artificial potential field approach, proposes a novel way to divide the artificial field into two main terms: a global artificial potential field mainly based on the famous C-W equations that gathers the spacecrafts around a predefined meeting point, and a local term exploited the well-known Tersoff-Brenner potential that allows a spacecraft to place itself in the correct position relative to its closest neighbors. Moreover, in order to obtain convergence from all initial distributions of the spacecrafts, a dissipation term depended on the velocity of agent is introduced. The new methodology is demonstrated in the problem of forming a hexagon lattice, the structure unit of graphite. It is shown that a pattern formation can operate around a planet. By slightly changing the scenario our method can be easily applied to shape other configurations, such as a regular tetrahedron (with central point), the structure unit, etc.

This paper presents the application of the Modified Particle Swarm Optimization (MPSO) technique for optimal settings zones for MHO distance relay protect 400 kV single transmission line of Eastern Algerian transmission networks at Algerian Company of Electrical and Gas (Group Sonelgaz) compensated by series Flexible Alternative current Transmission System (FACTS) i.e. Thyristor Controlled Series Capacitor (TCSC) connected at midpoint. The effects of TCSC insertion on the total impedance of a protected transmission line with respect to injected variable reactance value (X_(TCSC)) in capacitive and inductive boost mode depending of the firing angle (α) is considered. The modified setting zone protection for three zones (Z_(1), Z_(2) and Z_(3)) is have been investigate in order to prevent circuit breaker nuisance tripping and improve the performances of distance relay protection. In this work our aim is to compare the performance of the proposed MPSO algorithm with an analytical method (AM). The findings demonstrate the outstanding performance of the proposed MPSO in terms of computation speed, rate of convergence, and feasibility. The simulation results are compared with each other, and then the more perfect algorithm is considered.

This paper proposes a novel system to classify three types of electrocardiogram beats, namely normal beats and two manifestations of heart arrhythmia. This system includes three main modules: a feature extraction module, a classifier module, and an optimization module. In the feature extraction module, a proper set combining the shape features and timing features is proposed as the efficient characteristic of the patterns. In the classifier module, a multi-class support vector machine (SVM)-based classifier is proposed. For the optimization module, a particle swarm optimization algorithm is proposed to search for the best value of the SVM parameters and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy. This high efficiency is achieved with only little features, which have been selected using particle swarm optimizer.

The chatter vibration stands for a sudden relative vibration appeared between a material and a tool while processing with a machine. This vibration is a factor that seriously affects the quality of processed materials as well as being a factor which causes serious damages to the tool and the machine. This study is related to the detection and compensation of chatter vibration that can compensate chatter vibration faster and produce processed goods with more precision by autonomous compensation. 
The above-mentioned chatter vibration compensator includes the chatter vibration sensor and the chatter compensator that estimates the compensation value according to the sensor detecting the chatter vibration of machine tool and the chatter vibration detected from the sensor while having a feature of being organized by interlocking with the machine tool controller.

A chaos-ANFIS approach is presented for analysis of EEG signals for epileptic seizure recognition. The non-linear dynamics of the original EEGs are quantified in the form of the hurst exponent (H) and largest lyapunov exponent (λ). The process of EEG analysis consists of two phases, namely the qualitative and quantitative analysis. The classification ability of the H and λ measures is tested using ANFIS classifier. This method is evaluated with using a benchmark EEG dataset, and qualitative and quantitative results are presented. Our inter-ictal EEG based diagnostic approach achieves 97.4% accuracy with using 4-fold cross validation. Diagnosis based on ictal data is also tested in ANFIS classifier, reaching 96.9% accuracy. Therefore, our method can be successfully applied to both inter-ictal and ictal data.

In this article, we would like to revisit and comment on the widely used definition of cardinality of fuzzy sets. For this purpose we have given a brief description of the history of development of fuzzy cardinality. In the process, we can find that the existing definition fails to give a proper cardinality while dealing with complementation of fuzzy sets. So there arises the need of defining the cardinality in a different manner. Here a new definition of cardinality is proposed which is rooted in the definition of complementation of fuzzy sets on the basis of reference function. This definition of cardinality will inevitably play an important role in any problem area that involves complementation. Further, some important results are proven with the help of the proposed definition and it is found that these properties are somewhat analogus to those obtained with the help of the existing definition.

Film industry is the most important component of entertainment industry. Both profit and loss are very high for this business. Like every other business, business prediction system plays a vital role for this industry. Before release of a particular movie, if the Production Houses or distributors get any type of prediction that how the film will do business, then it will be very useful to reduce the risk of the investors. In this paper we have proposed a method using back propagation neural network for prediction about a given movie’s profitability. Initially the entire range of profit-loss has been divided into a number of groups. The proposed algorithm can assign a given movie to it’s appropriate profit-loss group. Note that, a similar such method has been successfully applied in the field of Stock Market Prediction, Weather Prediction and Image Processing.

Underwater sensor network is one of the potential research arenas that opens the window of pleasing a lot of researcher in studying the field. Network layer of the underwater sensor networks must be one of the most attractive fields to build up anew protocol. In this paper, we proposed a underwater sensor network routing protocol named Energy Efficient Fitness based routing protocol for under water sensor networks (EEF) that promises the best use of total energy consumptions. The proposed routing protocol takes into account residual energy, depth and distance from the forwarding node to the sink node to guide a packet from source to the destination node. The prominent advantages of the proposed protocol are to confirm higher network life time and less end to end delay. The proposed protocol does not use control packet that causes much energy consumption and end to end delay. Simulation has been performed to certify the better result of the proposed routing protocol.

In this paper we consider the inclusion properties for upper and lower approximation of union and intersection of sets for both pessimistic and optimistic multigranulations. We find that two inclusions for pessimistic cases are actually equalities. For other six cases we provide examples to show that actually the proper inclusions hold true. On the approximation of classifications a theorem was proved in Tripathy et al to establish sufficient type properties. We establish here that actually the result is both necessary and sufficient one. Also, we consider types of elements in classifications with respect to both types of multigranulations and establish a general theorem on them.

Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., internal combustion engine) is one of the most important challenging works. This paper focuses on the comparative study between two important nonlinear controllers namely; computed torque controller (CTC) and sliding mode controller (SMC) and applied to internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller and computed torque controller are selected. Pure SMC and CTC can be used to control of partly known nonlinear dynamic parameters of IC engine. Pure sliding mode controller and computed torque controller have difficulty in handling unstructured model uncertainties. To solve this problem applied linear error-based tuning method to sliding mode controller and computed torque controller for adjusting the sliding surface gain (λ ) and linear inner loop gain (K). Since the sliding surface gain (λ) and linear inner loop gain (K) are adjusted by linear error-based tuning method. In this research new λ and new K are obtained by the previous λ and K multiple gains updating factor(α). The results demonstrate that the error-based linear SMC and CTC are model-based controllers which works well in certain and uncertain system. These controllers have acceptable performance in presence of uncertainty.