Papers by Mariamma Chacko
A Shunt DC Electric Spring Control Strategy for MVDC Bus Voltage Stability Onboard AES
Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering)
Background: The recent trend in the all-electric ship (AES) electrical systems, especially in mil... more Background: The recent trend in the all-electric ship (AES) electrical systems, especially in military vessels, is to move towards medium voltage direct current (MVDC) distribution. Bus voltage instability is a major issue in direct current (DC) distribution systems. Nowadays, direct current electric springs (DCES) are extensively used in low-voltage direct current (LVDC) microgrids to address voltage instability issues. This paper extends the use of a shunt DCES to stabilize the bus voltage in an MVDC grid. The work proposes an addition to the MVDC onboard ship distribution system architecture, described in IEEE 1709, by integrating a shunt DCES with a novel control strategy to stabilize the bus voltage under various loading conditions, including propulsion motor (PM) and online pulsed power load (PPL). Method: The shunt DCES is designed to provide current into the MVDC bus, which reduces the bus current ripple to attain a stable bus voltage with reduced ripple. A dual loop control...
IEEE Access, 2020
Pre-launch success prediction of a product is a challenge in today's electronic world. Based on t... more Pre-launch success prediction of a product is a challenge in today's electronic world. Based on this prediction, industries can avoid huge losses by deciding on whether to launch or not to launch a product into the market. We have implemented a Multithreaded Hash join Resilient Distributed Dataset (MHRDD) with a prediction classifier for pre-launch prediction. MHRDD helps to remove the redundancy in the input dataset and improves the performance of the prediction model. Large volume of e-Word of Mouth (e-WOM) data like product reviews, comments and ratings available on internet about products can be used for pre-launch product prediction. In MHRDD, to identify features a distance similarity score is used. In order to remove duplicates, a hash key and join operations are used to create a hash table of significant features. With in-memory computations and hashing on the join operations, this model reduces redundancy of data. This model is scalable and can handle large datasets with good prediction accuracy. This paper presents a novel big data processing method that predicts product success before its launch in the market. Proposed method helps to identify features that are significant for the product to be successful. Based on the pre-launch prediction, companies can reduce cost, effort and time with improved product success. INDEX TERMS Big data analytics, product pre-launch prediction, resilient distributed dataset, redundancy elimination.
A novel strategy using H infinity theory with optimum weight selection for the robust control of sensorless brushless DC motor
A direct implementation of H infinity theory for the robust control of a sensorless brushless dir... more A direct implementation of H infinity theory for the robust control of a sensorless brushless direct current motor is proposed in this paper. This provides a better dynamic performance compared to conventional Proportional Integral controller and simulation results of the comparison are presented. For the proper weight selection of H infinity controller, particle swarm optimization technique is employed which results in small sensitivity function in low frequency region and small complementary sensitivity function in high frequency region which is the trade off required for good reference tracking, disturbance rejection, being insensitive to modelling errors and noise. The reduced torque ripples along with improved rise time and steady state error by 0.6% and 25% respectively results in low noise, less vibration and better robust performance irrespective of external disturbances compared to PI controller.
Randomized Agent-Based Model for Mobile Customer Retention Behaviour Prediction
EAI/Springer Innovations in Communication and Computing, Oct 19, 2019
Due to the development of technology, mobile phones have a crucial role in human life. Multiple s... more Due to the development of technology, mobile phones have a crucial role in human life. Multiple sim card phones and a single person using multiple mobile phones are common nowadays. Telecommunication is a major area where big data technologies are needed. Competition among the telecommunication companies is high due to customer churn. Customer retention in telecom companies is one of the major problems. In this paper, we propose a Randomized Method (RM) using Map and Reduce big data functions to avoid data duplication in the customer call data of telecommunication application. We use agent-based model (ABM) to predict the complex customer behaviour for the retention of customers with a particular telecommunication service. Agent-based model increases the prediction accuracy due to its dynamic nature of agents. ABM suggests rules based on mobile user variable features using multiple agents. This paper shows the effectiveness RM with MapReduce along with agent-based model to predict customer retention behaviour. The benefit of this proposed system is simple, cost-effective and flexible prediction model with high business value.
Data technologies and applications, Aug 20, 2019
Purpose -Telecommunication has a decisive role in the development of technology in the current er... more Purpose -Telecommunication has a decisive role in the development of technology in the current era. The number of mobile users with multiple SIM cards is increasing every second. Hence, telecommunication is a significant area in which big data technologies are needed. Competition among the telecommunication companies is high due to customer churn. Customer retention in telecom companies is one of the major problems. The paper aims to discuss this issue. Design/methodology/approach -The authors recommend an Intersection-Randomized Algorithm (IRA) using MapReduce functions to avoid data duplication in the mobile user call data of telecommunication service providers. The authors use the agent-based model (ABM) to predict the complex mobile user behaviour to prevent customer churn with a particular telecommunication service provider. Findings -The agent-based model increases the prediction accuracy due to the dynamic nature of agents. ABM suggests rules based on mobile user variable features using multiple agents. Research limitations/implications -The authors have not considered the microscopic behaviour of the customer churn based on complex user behaviour. Practical implications -This paper shows the effectiveness of the IRA along with the agent-based model to predict the mobile user churn behaviour. The advantage of this proposed model is as follows: the user churn prediction system is straightforward, cost-effective, flexible and distributed with good business profit. Originality/value -This paper shows the customer churn prediction of complex human behaviour in an effective and flexible manner in a distributed environment using Intersection-Randomized MapReduce Algorithm using agent-based model.
Hardware implementation of FFT-8086 based system
A system for the hardware implementation of the FFT (fast Fourier transform) is developed in conj... more A system for the hardware implementation of the FFT (fast Fourier transform) is developed in conjunction with an SDK-86. The FFT of any number of points can be computed provided the system has sufficient memory capacity. A scheme for implementing this hardware on a PC is also described
Journal of Big Data, Feb 28, 2020
Launching new products in the consumer electronics market is challenging. Developing and marketin... more Launching new products in the consumer electronics market is challenging. Developing and marketing the same in limited time affect the sustainability of such companies. This research work introduces a model that can predict the success of a product. A Feature Information Gain (FIG) measure is used for significant feature identification and Distributed Memory-based Resilient Dataset Filter (DMRDF) is used to eliminate duplicate reviews, which in turn improves the reliability of the product reviews. The pre-processed dataset is used for prediction of product pre-launch in the market using classifiers such as Logistic regression and Support vector machine. DMRDF method is fault-tolerant because of its resilience property and also reduces the dataset redundancy; hence, it increases the prediction accuracy of the model. The proposed model works in a distributed environment to handle a massive volume of the dataset and therefore, it is scalable. The output of this feature modelling and prediction allows the manufacturer to optimize the design of his new product.
Data centric redundancy elimination for network data traffic
International journal of cloud computing, 2023
As I complete my research work, I realize and recognize numerous hands that have helped me in man... more As I complete my research work, I realize and recognize numerous hands that have helped me in many ways, and I thank them all with my whole heart. I express my sincere gratitude to my supervising guide Dr. Mariamma Chacko, for the zeal with which she guided me in carrying out my Ph. D study and research work. It has been a great learning experience working with her. I am deeply indebted to her for her valuable guidance, patience, constant encouragement and suggestions throughout the course of my work. I would like to thank Dr. Sumam Mary Idicula, Computer Science Department who is a member of Doctoral Committee, for her valuable guidance and insightful comments for the improvement of my work. I am also indebted to Dr. James Kurian & Dr. Nandakumar, for supporting me all throughout the process. I would like to thank all the department research committee members for their comments, encouragement and also for their questions which widened my research from various perspectives. I remember my father with deep love for his blessings bestowed upon me. I am also thankful to my mother, my husband Ranjan, son Vaishnav and daughter Reshma for their support and prayers in my hard times without which I would not be able to complete this thesis. I also extend my gratitude to my fellow research scholars and my friends who always stood by my side during my difficult times. I thank God Almighty for the uncountable blessings bestowed upon me all through my life and especially during the period of my thesis work. With a heart full of gratitude, I submit this thesis. Once again I thank all who walked with me to make this venture a grand success.
Modular Multilevel Converter with a Sensing and Balancing (SAB) Technique for Capacitor Voltage Balancing
Iranian Journal of Science and Technology, Transactions of Electrical Engineering
Data centric redundancy elimination for network data traffic
International Journal of Cloud Computing
International journal of electrical and computer engineering systems, 2022
Due to the large size of conventional electrical power transmission systems and the large number ... more Due to the large size of conventional electrical power transmission systems and the large number of uncertainties involved, achieving the most favourable Transmission Expansion Planning solution turns out to be almost impossible. The proposed method intends to develop a novel method to solve Transmission Expansion Planning problems in electric power systems incorporating renewable energy sources like wind turbines and Photo Voltaic array using IEEE 24 Reliability Test System. For enhancing the efficiency of search processes and to make its use easier on diverse networks and operations, the hybridization of two renowned meta-heuristic algorithms known as Grey Wolf Optimization (GWO) and Genetic Algorithm (GA) termed as Grey Wolf with Genetic Algorithm (GWGA) is adopted. A novel distance factor based on the best position and current position of the solution in Grey wolf optimization is introduced and proposed for the hybridization technique and gives a quick and promising solution wit...
Back propagation network based prediction on map-reduce platforms
Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems, 2019
In this paper, we provide a significant model illustrating the challenges to handle terabytes of ... more In this paper, we provide a significant model illustrating the challenges to handle terabytes of data efficiently. Proposed paper aims to develop a model which predicts the success rate of a product before its in launch into the market. Artificial Neural Network using back propagation (BANN) has been implemented for product prediction. Huge volume of e-commerce customer reviews and rating dataset is used for testing this model. This nonlinear method uses Broyden-Fletcher-Goldforb-Shanno (BFGS) training on multiple network layer. Our method gives improved prediction accuracy. The results of this model is to introduce a sustainable and successful product into the market, which in turn helps to improve the quality of the product.
Randomized Agent-Based Model for Mobile Customer Retention Behaviour Prediction
EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, 2019
Due to the development of technology, mobile phones have a crucial role in human life. Multiple s... more Due to the development of technology, mobile phones have a crucial role in human life. Multiple sim card phones and a single person using multiple mobile phones are common nowadays. Telecommunication is a major area where big data technologies are needed. Competition among the telecommunication companies is high due to customer churn. Customer retention in telecom companies is one of the major problems. In this paper, we propose a Randomized Method (RM) using Map and Reduce big data functions to avoid data duplication in the customer call data of telecommunication application. We use agent-based model (ABM) to predict the complex customer behaviour for the retention of customers with a particular telecommunication service. Agent-based model increases the prediction accuracy due to its dynamic nature of agents. ABM suggests rules based on mobile user variable features using multiple agents. This paper shows the effectiveness RM with MapReduce along with agent-based model to predict c...
As I complete my research work, I realize and recognize numerous hands that have helped me in man... more As I complete my research work, I realize and recognize numerous hands that have helped me in many ways, and I thank them all with my whole heart. I express my sincere gratitude to my supervising guide Dr. Mariamma Chacko, for the zeal with which she guided me in carrying out my Ph. D study and research work. It has been a great learning experience working with her. I am deeply indebted to her for her valuable guidance, patience, constant encouragement and suggestions throughout the course of my work. I would like to thank Dr. Sumam Mary Idicula, Computer Science Department who is a member of Doctoral Committee, for her valuable guidance and insightful comments for the improvement of my work. I am also indebted to Dr. James Kurian & Dr. Nandakumar, for supporting me all throughout the process. I would like to thank all the department research committee members for their comments, encouragement and also for their questions which widened my research from various perspectives. I remember my father with deep love for his blessings bestowed upon me. I am also thankful to my mother, my husband Ranjan, son Vaishnav and daughter Reshma for their support and prayers in my hard times without which I would not be able to complete this thesis. I also extend my gratitude to my fellow research scholars and my friends who always stood by my side during my difficult times. I thank God Almighty for the uncountable blessings bestowed upon me all through my life and especially during the period of my thesis work. With a heart full of gratitude, I submit this thesis. Once again I thank all who walked with me to make this venture a grand success.
This paper reviews the recent developments in the field of sensorless control of Brushless Direct... more This paper reviews the recent developments in the field of sensorless control of Brushless Direct Current motors. Moreover a survey of application of H∞ control theory for robust control, considering the uncertainties like modeling errors and external disturbances in various motors and systems has been carried out. This provides a deeper insight into the recent developments and future scope in the robust control of sensorless BLDC motors to mitigate the effects of parametric uncertainties and unmodeled dynamics.
Marine Photovoltaics: A review Of Research And Developments, Challenges And Future Trends
International Journal of Scientific & Technology Research, 2019
The four-quadrant operation of a BLDC motor used as thruster motor coupled with a propeller in an... more The four-quadrant operation of a BLDC motor used as thruster motor coupled with a propeller in an Autonomous Underwater Vehicle (AUV) is studied through simulation in MATLAB/SIMULINK. The robust control of thruster motor is an essential requisite for the smooth operation of AUV in the presence of uncertainties such as un-modeled vehicle parameters and external disturbances due to weather. An H infinity speed controller whose coefficients of weights being optimized by Particle Swarm Optimization (PSO) is proposed for achieving robust control of BLDC motor when there is a change in reference speed and load variation. The MATLAB function hinfsyn is used for synthesizing H infinity controller. The design of H infinity controller and PI controller with their weights and gains optimized by PSO respectively are discussed and their simulation results are compared. It is observed that during the forward braking region, the torque ripples with the proposed controller strategy are found to be ...
International Journal of Power Electronics and Drive Systems (IJPEDS), 2021
The hardware implementation of sensorless brushless direct current motor drive incorporating H-in... more The hardware implementation of sensorless brushless direct current motor drive incorporating H-infinity control strategy with optimized weights by particle swarm optimization in the speed control is carried out in this work. The methodology involved in the design of brushless direct current (BLDC) motor control with sensorless position detection technique, the design of H-infinity speed controller, steps involved in particle swarm optimization for optimizing coefficients of its weights and the hardware implementation is discussed in detail in this paper. Texas Instruments microcontroller board C2000 Delfino Launchpad LAUNCHXL F28377S and driver BOOSTXL DRV8301 are used for realization of the speed controller. The code is developed using C2000 hardware support package in MATLAB/SIMULINK platform. A comprehensive performance analysis is accomplished during starting of the motor and during the fast application and removal of load. This strategy is found to be robust resulting in faster...
An optimized H infinity strategy for robust control of sensorless BLDC propulsion motor in submarines for improved maneuverability
2016 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 2016
Brushless direct current motor finds its applications in electric propulsion due to its reliabili... more Brushless direct current motor finds its applications in electric propulsion due to its reliability, reduced electromagnetic interference and improved life cycle cost. Rotor position sensing with sensorless techniques exhibit better performance with varying temperature and humidity conditions as well as continual motion of the vessels and also overthrow the use of hall sensors. A control technique with direct implementation of an optimal H infinity controller in the speed control feedback loop of a sensorless BLDC motor that is insensitive to disturbances and uncertainties is proposed in this paper. This provides a robust dynamic performance compared to conventional PI controller and simulation results of the comparison are presented. For optimum weight selection of H infinity controller, particle swarm optimization technique is employed so that sensitivity function is small for low frequency region and complementary sensitivity function is low for high frequency region which is the trade off required for good reference tracking, disturbance rejection, being insensitive to modeling errors and noise. Results of the case study conducted based on the operational profile of submarine VII C available with British admiralty report have been presented. The reduced torque ripples along with 50% reduction of steady state error results in enhanced maneuverability, low noise and vibration.
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Papers by Mariamma Chacko