A. B. M. Shawkat Ali

Bio: A. B. M. Shawkat Ali is an academic researcher from University of Fiji. The author has contributed to research in topics: Support vector machine & Renewable energy. The author has an hindex of 16, co-authored 70 publications receiving 1236 citations. Previous affiliations of A. B. M. Shawkat Ali include Central Queensland University.

Potential challenges of integrating large-scale wind energy into the power grid–A review

Abstract: Global warming is attracting a growing interest worldwide for the generation of large-scale energy from renewable energy sources as it is free from greenhouse gas emissions. Wind energy is one of the most promising renewable energy sources due to its availability and low cost and due to the fact that it is more efficient and advanced in technology. Hence, harvesting of large-scale wind energy is of prime interest today. However, large-scale integration of wind energy sources creates environmental, economic, social and technical impacts that need to be investigated and mitigated as part of developing a sustainable power system for the future. Government, utilities and research communities are working together to increase penetration of wind energy into the power grid and overcome potential barriers associated with this. This paper presents an extensive and useful survey on wind energy technology and associated implementation issues including effects of wind farms on the nearby locality. This paper also reviews the social, environmental and cost-economic impacts of installing large-scale wind energy plants. Finally, potential technical challenges to the integration of large-scale wind energy into the power grid are reviewed in regard to current research with their available mitigation techniques.

HMM based hand gesture recognition: A review on techniques and approaches

Abstract: Gesture is one of the most natural and expressive ways of communications between human and computer in a virtual reality system. We naturally use various gestures to express our own intentions in everyday life. Hand gesture is one of the important methods of non-verbal communication for human beings for its freer in movements and much more expressive than any other body parts. Hand gesture recognition has a number of potential applications in human-computer interaction, machine vision, virtual reality, machine control in industry, and so on. As a gesture is a continuous motion on a sequential time series, the HMMs (Hidden Markov Models) must be a prominent recognition tool. The most important thing in hand gesture recognition is what the input features are that best represent the characteristics of the moving hand gesture.This paper presents part of literature review on ongoing research and findings on different technique and approaches in gesture recognition using HMMs for vision-based approach.

Potential challenges: Integrating renewable energy with the smart grid

Abstract: Renewable energy offers alternative sources of energy which is in general pollution free, climate friendly, sustainable and unlimited. Therefore in the starting of 21st century, Government, utilities and research communities are working together to develop an intelligent power system that has potential to better integrate renewable energy sources with the grid. However, there are a number of potential challenges in integrating renewable energy with the existing grid due to its intermittent nature. This paper investigates about the potential challenges of integrating renewable energy with the smart power grid including the possible deployment issues for a sustainable future both nationally and internationally. The paper also proposes a prediction model that informs the typical variation of energy production as well as effect on grid integration using modern machine learning techniques.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Renewable energy resources: Current status, future prospects and their enabling technology

Abstract: Electric energy security is essential, yet the high cost and limited sources of fossil fuels, in addition to the need to reduce greenhouse gasses emission, have made renewable resources attractive in world energy-based economies. The potential for renewable energy resources is enormous because they can, in principle, exponentially exceed the world׳s energy demand; therefore, these types of resources will have a significant share in the future global energy portfolio, much of which is now concentrating on advancing their pool of renewable energy resources. Accordingly, this paper presents how renewable energy resources are currently being used, scientific developments to improve their use, their future prospects, and their deployment. Additionally, the paper represents the impact of power electronics and smart grid technologies that can enable the proportionate share of renewable energy resources.