Many different PD controller modeling, configurations and control algorithms have been developed. These methods differ in their theoretical basis and performance under the changes of system conditions. In the present paper we review the methods used in the design of PD control systems. We highlight the main difficulties and summarize the more recent developments in their control techniques. Intelligent control systems like PD fuzzy control can be used to emulate the qualitative aspects of human knowledge with several advantages such as universal approximation theorem and rule-based algorithms.

Optimal coordination of Inverse Definite Minimum Time (IDMT) direction overcurrent relays in the power systems in the presence of multiple Thyristor Controller Series Capacitor (TCSC) on inductive and capacitive operation mode on meshed power system is studied in this paper. 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 (T) 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 evolutionary optimization techniques named Biogeography Based Optimization (BBO) is used. The proposed algorithm is validated on IEEE 14-bus transmission network test system considering various scenarios. The obtained results show a high efficiency of the proposed method to solve such complex optimization problem, in such a way the relays coordination is guaranteed for all simulation scenarios with minimum operating time. The results of new relay setting are compared to other optimization algorithms.

A modification of the neo-fuzzy neuron is proposed (an extended neo-fuzzy neuron (ENFN)) that is characterized by improved approximating properties. An adaptive learning algorithm is proposed that has both tracking and smoothing properties and solves prediction, filtering and smoothing tasks of non-stationary “noisy” stochastic and chaotic signals. An ENFN distinctive feature is its computational simplicity compared to other artificial neural networks and neuro-fuzzy systems.

The advantages and disadvantages of discrete GM(1,1) model and least squares support vector machine are analyzed respectively, this article proposes a new time series forecasting model of discrete grey least squares support vector machine. The new model adopts structural risk minimization principle, at the same time develops the advantages of accumulation generation in the grey forecasting method, weakens the effect of stochastic-disturbing factors in original sequence, and avoids the theoretical defects existing in the grey forecasting model. The simulation results show that the forecasting model is effective and reliable, and consolidates the advantage of the discrete GM(1,1) model and least squares support vector machine. It offers a new way to improve the time series forecasting accuracy.

Dual Population Genetic Algorithm is an effective optimization algorithm that provides additional diversity to the main population. It addresses the premature convergence problem as well as the diversity problem associated with Genetic Algorithm. Thus it restricts their individuals to be trapped in the local optima. This paper proposes Dual Population Genetic Algorithm for solving Constrained Optimization Problems. A novel method based on maximum constrains satisfaction is applied as constrains handling technique and Dual Population Genetic Algorithm is used as meta-heuristic. This method is verified against 9 problems from Problem Definitions and Evaluation Criteria for the Congress on Evolutionary Computation 2006 Special Session on Constrained Real-Parameter Optimization problem set. The results are compared with existing algorithms such as Ant Bee Colony Algorithm, Differential Evolution Algorithm and Genetic Algorithm that have been used for solving same problem set. Analysis shows that this technique gives results close to optimum value but fails to obtain exact optimum solution. In future Dual Population Genetic Algorithm can produce more efficient solutions using alternative constrains handling technique.

The next generation of Internet of things that connects the things, people and the process through which the people and things interact is coined as Internet of Everything. Safety management is constructed as a complex system of systems design coordinating with each other like the Fire Alarm System or Gas Detection System as well as the Emergency response like the Fire Fighters and Para-Medicals like the Ambulatory services. The governments have been setting up national broadband plans and separate dedicated spectrum for Public Safety Communications used for effective information rich emergency management and response. This paper outlines the evolution of the public safety LTE network and its applicability in the safety management system and safety preparedness. It also describes the role of Smart Objects and Internet of Everything in Safety Management. To achieve this, this paper develops the information models for safety management systems to be used in IoE utilizing the broad-band LTE networks.

The World report on road traffic injury prevention presents some assessments and conclusions regarding road traffic accidents, in which they state that more than 1.2 million deaths per year occur on the world’s roads and around 50 million more of injured people. To prevent this people are working for intelligent transport system (ITS). ITS is trying to make an intelligent car which will be able to avoid collation. In this paper we have tried to add a new goal in ITS system which will be activated in the intelligence fails. This SMS system will help to locate a car using GPS, if the car collides. Our total development work is divided into two parts. In first part we have tried to develop a system which will help the driver by providing the road scenario using dopple radar. Doppler radar will measure the velocity of the nearby car or passing by car, depending upon the information our car will be controlled. In second part of development we have developed a auto generated SMS sending system to a specific number if the Car collide. Both systems are described in details in next part of this article.

One of the challenges in natural language understanding is to determine which entities to be referred in the discourse and how they relate to each other. Anaphora resolution needs to be addressed in almost every application dealing with natural language such as language understanding and processing, dialogue system, system for machine translation, discourse modeling, information extraction. This paper represents a system that uses the combination of constraint-based and preferences-based architectures; each uses a different source of knowledge and proves effective on computational and theoretical basis, instead of using a monolithic architecture for anaphora resolution. This system identifies both inter-sentential and intra-sentential antecedents of “Third person pronoun anaphors” and “Pleonastic it”. This system uses Charniak Parser (parser05Aug16) as an associated tool, and it relays on the output generated by it. Salience measures derived from parse tree are used in order to find out accurate antecedents from the list of all potential antecedents. We have tested the system extensively on 'Reuters Newspaper corpus' and efficiency of the system is found to be 81.9%.