In this paper, a fuzzy predictive control scheme is proposed for controlling output voltage of a step-down DC-DC converter in presence of disturbance and uncertainty. The DC-DC converter is considered as a hybrid system and modeled by Mixed Logical Dynamical modeling approach. The main objective of the paper is to design a Fuzzy Predictive Control to achieve desired voltage output without increasing complexity of the hybrid model of DC-DC converter in various conditions. A model predictive control is designed based on the hybrid model and applied to the system. Although the performance of the model predictive control method is satisfactory in normal condition, it suffers from lack of robustness in presence of disturbance and uncertainty. So, to succeed in facing up to the problem a fuzzy supervisor is utilized to adjust the main predictive controller based on the measured states of the system. In this paper it is shown that the proposed fuzzy predictive control scheme has advantages such as simplicity and efficiency in normal operation and robustness in presence of disturbance and uncertainty. Through simulations effectiveness of the proposed method is shown.

Cancer research revolves around the study of diseases that involve unregulated cell growth. This direction facilitated the development of a wide range of cancer genomics projects that are designed to support the identification of mutated driver pathways in several cancer types. In this research, a maximum weight submatrix problem is used to identify the driver pathway in a specific type of cancer. To solve this problem, we propose two new metaheuristic algorithms. The first is an improved harmony search (IHS) algorithm and the second is an enhanced genetic algorithm (EGA). Results show that EGA enables better performance and entails less computational time than does conventional GA. Furthermore, the new IHS offers a higher number of suggested gene set solutions for mutated genes than does the standard genetic algorithm.

Modern technologies have reached our garage to secure the cars and entrance to the residences for the demand of high security and automated infrastructure. The concept of intelligent secure garage systems in modern transport management system is a remarkable example of the computer interfaced controlling devices. License Plate Recognition (LPR) process is one of the key elements of modern intelligent garage security setups. This paper presents a design of an automated secure garage system featuring LPR process. A study of templates matching approach by using Optical Character Recognition (OCR) is implemented to carry out the LPR method. We also developed a prototype design of the secured garage system to verify the application for local use. The system allows only a predefined enlisted cars or vehicles to enter the garage while blocking the others along with a central-alarm feature. Moreover, the system maintains an update database of the cars that has left and entered into the garage within a particular duration. The vehicle is distinguished by the system mainly based on their registration number in the license plates. The tactics are tried on several samples of license plate’s image in both indoor and outdoor setting.

This paper deals on cargo train scheduling between source station and destination station in Indian railways scenario. It uses Ant Colony Optimization (ACO) technique which is based on ant’s food finding behavior. Iteration wise convergence process and the convergence time for the algorithm are studied and analyzed. Finally, the run time analysis of Ant Colony Optimization Train Scheduling (ACOTS) and Standard Train Scheduling (STS) algorithm has been performed.

Chaos control is an important subject in control theory. Chaos control usually confronts with some problems due to unavailability of states or losing the system characteristics during the modeling process. In this situation, using an appropriate observer in control strategy may overcome the problem. In this paper, states are estimated using an observer without having complete prior information from nonlinear term based on neural network. Simulation results verify performance of the proposed structure in estimating nonlinear term specifically for an online practical use.

This paper considers the impact of the Inductive Fault Current Limiter (IFCL) on directional overcurrent relays coordination. The coordination problem is formulated as a non-linear constrained mono-objective optimization problem. The objective function of this optimization problem is the minimization of the operation time of the associated relays in the systems, and the decision variables are: the time dial setting (TDS) and the pickup current setting (IP) of each relay. To solve this complex non linear optimization problem, a variant of optimization algorithms named Firefly Algorithm (FA) is used. The proposed method is tested on 8-bus power transmission systems test systems considering the influences of current relay under three-phases short-circuit with and without IFCL for different locality. The results show the effectiveness of the solution.

Now a day’s everybody email inbox is full with spam mails. The problem with spam mails is that they are not malicious in nature so generally don’t get blocked with firewall or filters etc., however, they are unwanted mails received by any internet users. In 2012, more that 50% emails of the total emails were spam emails. In this paper, a genetic algorithm based method for spam email filtering is discussed with its advantages and dis-advantages. The results presented in the paper are promising and suggested that GA can be a good option in conjunction with other e-mail filtering techniques can provide more robust solution.

Network Intrusion Detection Systems (NIDSs) can be composed of a potentially large number of sensors, which monitor the traffic flowing in the network. Deciding where sensors should be placed and what information they need in order to detect the desired attacks can be a demanding task for network administrators, one that should be made as automatic as possible. Some few works have been done on positioning sensors using attack graph analysis, formal logic-based approach and Network Simulator NS2 which were studied to determine a strategy for sensors placement on the network. This paper analysed the major considerations for sensors placements, typical sensors deployments in NIDS, and established an extended model for sensors deployment to further strengthen the network for intrusion detection which was based on the escape of some malicious activities through the firewall.

This paper includes performance analysis of a multipurpose microcontroller based system which has various modes to control distinct applications. The paper elucidates how a single chip microcontroller can process different signals and accomplish different tasks. The system discussed in this paper has seven modes. Each mode controls different applications. Radio frequency signals are used for controlling the system wirelessly and each mode is triggered by giving a suitable 4-bit logic by the transmitter. The different modes which the system works in consist of security tracking, temperature measurement, sound actuated control, voltmeter mode, door automation, pits avoider and obstacle avoider. The versatility of the system is tested using Xilinx software v10.1. It is found that the system is functioning properly under normal conditions and the variations of the different parameters for a particular mode have been plotted in MATLAB R2013a v8.1 to validate the accuracy of the system.

In this research, manage the Internal Combustion (IC) engine modeling and a multi-input-multi-output artificial intelligence baseline chattering free sliding mode methodology scheme is developed with guaranteed stability to simultaneously control fuel ratios to desired levels under various air flow disturbances by regulating the mass flow rates of engine PFI and DI injection systems. Modeling of an entire IC engine is a very important and complicated process because engines are nonlinear, multi inputs-multi outputs and time variant. One purpose of accurate modeling is to save development costs of real engines and minimizing the risks of damaging an engine when validating controller designs. Nevertheless, developing a small model, for specific controller design purposes, can be done and then validated on a larger, more complicated model. Analytical dynamic nonlinear modeling of internal combustion engine is carried out using elegant Euler-Lagrange method compromising accuracy and complexity. A baseline estimator with varying parameter gain is designed with guaranteed stability to allow implementation of the proposed state feedback sliding mode methodology into a MATLAB simulation environment, where the sliding mode strategy is implemented into a model engine control module (“software”). To estimate the dynamic model of IC engine fuzzy inference engine is applied to baseline sliding mode methodology. The fuzzy inference baseline sliding methodology performance was compared with a well-tuned baseline multi-loop PID controller through MATLAB simulations and showed improvements, where MATLAB simulations were conducted to validate the feasibility of utilizing the developed controller and state estimator for automotive engines. The proposed tracking method is designed to optimally track the desired FR by minimizing the error between the trapped in-cylinder mass and the product of the desired FR and fuel mass over a given time interval.