R. S. Milton

Studies on rough sets in multiple tables

Rough Set Theory is a mathematical tool to deal with vagueness and uncertainty. Rough Set Theory uses a single information table. Relational Learning is the learning from multiple relations or tables. This paper studies the use of Rough Set Theory and Variable Precision Rough Sets in a multi-table information system (MTIS). The notion of approximation regions in the MTIS is defined in terms of those of the individual tables. This is used in classifying an example in the MTIS, based on the elementary sets in the individual tables to which the example belongs. Results of classification experiments in predictive toxicology based on this approach are presented.

Rough Sets and Relational Learning

Rough Set Theory is a mathematical tool to deal with vagueness and uncertainty. Rough Set Theory uses a single information table. Relational Learning is the learning from multiple relations or tables. Recent research in Rough Set Theory includes the extension of Rough Set Theory to Relational Learning. A brief overview of the work in Rough Sets and Relational Learning is presented.

Multiagent Systems Based Modeling and Implementation of Dynamic Energy Management of Smart Microgrid Using MACSimJX

The objective of this paper is implementation of multiagent system (MAS) for the advanced distributed energy management and demand side management of a solar microgrid. Initially, Java agent development environment (JADE) frame work is used to implement MAS based dynamic energy management of solar microgrid. Due to unstable nature of MATLAB, when dealing with multithreading environment, MAS operating in JADE is linked with the MATLAB using a middle ware called Multiagent Control Using Simulink with Jade Extension (MACSimJX). MACSimJX allows the solar microgrid components designed with MATLAB to be controlled by the corresponding agents of MAS. The microgrid environment variables are captured through sensors and given to agents through MATLAB/Simulink and after the agent operations in JADE, the results are given to the actuators through MATLAB for the implementation of dynamic operation in solar microgrid. MAS operating in JADE maximizes operational efficiency of solar microgrid by decentralized approach and increase in runtime efficiency due to JADE. Autonomous demand side management is implemented for optimizing the power exchange between main grid and microgrid with intermittent nature of solar power, randomness of load, and variation of noncritical load and grid price. These dynamics are considered for every time step and complex environment simulation is designed to emulate the distributed microgrid operations and evaluate the impact of agent operations.

Paraphrase identification in short texts using grammar patterns

We can determine whether two texts are paraphrases of each other by finding out the extent to which the texts are similar. The typical lexical matching technique works by matching the sequence of tokens between the texts to recognize paraphrases, and fails when different words are used to convey the same meaning. We can improve this simple method by combining lexical with syntactic or semantic representations of the input texts. The present work makes use of syntactical information in the texts and computes the similarity between them using word similarity measures based on WordNet and lexical databases. The texts are converted into a unified semantic structural model through which the semantic similarity of the texts is obtained. An approach is presented to assess the semantic similarity and the results of applying this approach is evaluated using the Microsoft Research Paraphrase (MSRP) Corpus.

Multi agent systems based distributed control and automation of micro-grid using MACSimJX

The objective of this paper is to develop a model for distributed automation of micro-grid using Multi Agent System(MAS) for the advanced control and distributed energy management of a solar micro-grid. A grid connected solar micro-grid model, which contains two solar Photo Voltaic (PV) systems, one in department and other in hostel, each contains a local consumer, a solar PV system and a battery, is modelled in Simulink. Due to unstable nature of MATLAB, when dealing with multi threading environment, MAS operating in Java Agent Development Environment(JADE) is linked with the MATLAB using a middleware, Multi Agent Control using Simulink with Jade extension (MACSimJX). MACSimJX allows the solar micro-grid system designed with MATLAB to be controlled by solar micro-grid agents for realizing the advantages of decentralized approach of MAS. All the agents of solar micro-grid components are programmed in JADE and the results of the coordinated action of these agents are sent to the environment for distributed control and automation of the hardware. JADE leverages the advantage of MAS by its inherent features and hence the operational efficiency of solar micro-grid is further increased. Also the power exchange between main grid and solar micro-grid is optimized by effetive demand side management. Simulation is designed to evaluate impact of autonamous operations of agents.

Probability measures for prediction in multi-table infomation systems

Rough Set Theory is a mathematical tool to deal with vagueness and uncertainty. Rough Set Theory uses a single information table. Relational Learning is the learning from multiple relations or tables. This paper presents a new approach to the extension of Rough Set Theory to multiple relations or tables. The utility of this approach is shown in classification experiments in predictive toxicology.

Advanced Energy Management of a Micro-grid Using Arduino and Multi-agent System

The objective of this paper is to develop Arduino-based multi-agent system (MAS) for advanced distributed energy management of a solar-wind micro-grid. High penetration of renewable energy resources needs new coordination and control approaches to meet the stochastic nature of the environment and dynamic loadings. We use multi-agent system for advanced distributed, autonomous energy management of micro-grid to dynamically and flexibly adapt to the changes in the environment as renewable energy resources are intermittent in nature. We consider that a micro-grid which contains two systems each contains solar photo voltaic (PV) system, wind generator system, local consumer, and a battery. We develop a simulation model using Java Agent Development Environment (JADE) in Eclipse IDE for dynamic energy management, which considers the intermittent nature of solar power, randomness of load, dynamic pricing of grid, and variation of critical loads, and choose the best possible action every hour to stabilize and optimize the micro-grid. Furthermore, environment variables are sensed through Arduino Mega micro-controller and given to agents of MAS. The agents take the strategic action, and the resulting actions are reflected in the LED outputs which can be readily deployed in the actual field. MAS increases responsiveness, stability, flexibility, and fault tolerance, thereby increasing operational efficiency and leading to economic and environmental optimization. All the smart grid features are tested using JADE simulations and practically verified through Arduino micro-controller to make micro-grid into smart micro-grid.

Application of Multi Agent Systems in Automation of Distributed Energy Management in Micro-grid using MACSimJX

AbstractThe objective of this paper is to monitor and control a micro-grid model developed in MATLAB-Simulink through Multi Agent System (MAS) for autonomous and distributed energy management. Since MATLAB/Simulink is not compatible with parallel operations of MAS, MAS operating in Java Agent Development Environment (JADE) is linked with MATLAB/Simulink through Multi Agent Control using Simulink with Jade extension (MACSimJX). This allows the micro-grid system designed with Simulink to be controlled by MAS for realizing the advantages of MAS in distributed and decentralized micro-grid systems. JADE agents receive environmental information through Simulink and they coordinate to take best possible action, which is reflected in MATLAB/Simulink simulations. After validation and performance evaluation through dynamic simulations, the operations of the agents at various scenarios are practically verified by using the Arduino microcontroller. These validation and verification moves MAS closer to Smartgrid appli...

Advanced automation in energy management of micro-grid using multi agent system

The objective of this paper is to implement a Multi Agent System (MAS) in Java Agent Development Environment (JADE) for the automation and optimization in advanced distributed energy management of a micro-grid. We consider two grid connected micro-grids each contains wind turbine, solar Photo Voltaic (PV) systems and load. The power generated by solar and wind along with the load variations in the two micro-grids are calculated initially and then Multi Agent System is used for distributed energy management of micro-grid, which considers the intermittent nature of solar power, randomness of load, dynamic pricing of grid and variation of critical loads and automatically choose the best possible action every hour to optimize ther micro-grid. The advantages of MAS approach is leveraged through JADE to increase the operational efficiency and thereby maximizes the power production of micro-grid and minimizes the operational cost. Thus MAS in micro-grid obviously leads to economic and environmental optimization of the micro-grid due to its inherent features. Simulated operation of the micro-grids are studied by performing simulations under different agent objectives.

Energy Optimization of Solar Micro-Grid Using Multi Agent Reinforcement Learning

In this paper, two solar Photovoltaic (PV) systems are considered; one in the department with capacity of 100 kW and the other in the hostel with capacity of 200 kW. Each one has battery and load. The capital cost and energy savings by conventional methods are compared and it is proved that the energy dependency from grid is reduced in solar micro-grid element, operating in distributed environment. In the smart grid frame work, the grid energy consumption is further reduced by optimal scheduling of the battery, using Reinforcement Learning. Individual unit optimization is done by a model free reinforcement learning method, called Q-Learning and it is compared with distributed operations of solar micro-grid using a Multi Agent Reinforcement Learning method, called Joint Q-Learning. The energy planning is designed according to the prediction of solar PV energy production and observed load pattern of department and the hostel. A simulation model was developed using Python programming.