Md. Firoj Ali

Bio: Md. Firoj Ali is an academic researcher from Rajshahi University of Engineering & Technology. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 3, co-authored 6 publications receiving 20 citations.

An analytical review on the evaluation of wind resource and wind turbine for urban application: Prospect and challenges

Abstract: Wind energy is a promising scheme in the power generation sector due to pollution-free power production and wind resources’ sufficiency worldwide. Installing wind turbines in all the possible extents can mitigate the rising energy demand. Built-up areas possess high potential for wind energy, including the rooftop of high-rise buildings, railway track, the region between or around multistoried buildings, and city roads. Harnessing wind energy from these areas is quite challenging since it has dramatic nature and turbulence for higher roughness on urban surfaces. This review paper endeavors to highlight the present status of urban wind farm technology and its commercial and environmental aspects. Observations and upcoming research trends have been presented based on up-to-the-minute information. It is concluded that further investigation of wind mapping and the suitable design of turbines is essential to make the urban wind farm a reliable and feasible option for decentralized power generation.

A Comparative Study on Convolutional Neural Network Based Face Recognition

Abstract: This paper presents a comparative study to recognize faces from a customized dataset of 10 identities of different celebrities using Convolutional Neural Network based models such as AlexNet, VGG16, VGG19 and MobileNet. These pre-trained models previously trained on ImageNet dataset are used with the application of Transfer Learning and Fine Tuning. For our experiment we used Keras API with TensorFlow backend written in Python. The performance analysis includes training, validation, and testing on different images created from original dataset. The validation accuracy of VGG19 model is found better than the other three but MobileNet model showed better test accuracy.

Static Hand Gesture Recognition for American Sign Language using Deep Convolutional Neural Network

Abstract: One of the complicated issues of computer vision is the recognition of any sign languages. The deaf and the dumb people use these sign languages to communicate. In the area of deep learning at recent progress there are numerous applications that neural networks can have for interpreting sign languages. The recognition of American Sign Language static images employing a capable artificial intelligence tool, Convolutional Neural Network has been proposed in this paper. ASL dataset of 1815 images of 26 English alphabets has been used to train and validate our model. Validation accuracy has been found 94.34% which is better than many existing methods.

Multipath Rayleigh and Rician Fading Channel Simulation Using MATLAB

Abstract: In this paper we present a simulation of multipath Rayleigh fading and Recian fading channel. We have used MATLAB software for simulation. Simulation was carried out for two different sample rate 20kb/s and 500 kb/s. This simulation process uses five path propagation models. The data is modulated, encoded and transmitted through a frequency selective and flat Rayleigh and Recian Multipath fading channel. Quadrature phase shift keying (QPSK) modulation technique is used to modulate the data. This paper compares the simulated result of Rayleigh and Recian fading channel. KEYWORDSWireless Communication, Multipath, Fading Channel, Rayleigh Fading, Rician Fading

Golden Cut-Oriented Q-Rung Orthopair Fuzzy Decision-Making Approach to Evaluation of Renewable Energy Alternatives for Microgeneration System Investments

Abstract: This study aims to find an appropriate system for microgeneration energy investments and identify optimal renewable energy alternatives for the effectiveness of these projects. In this context, a model is constructed by multi stepwise weight assessment ratio analysis (M-SWARA) and technique for order preference by similarity to ideal solution (TOPSIS) with q-rung orthopair fuzzy sets (q-ROFSs) and golden cut. At the first stage, five different systems are weighted for the effectiveness of the microgeneration energy investments. Secondly, four different renewable energy alternatives are ranked regarding the performance of these projects. In addition, a comparative analysis is also implemented with intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets (PFSs). The findings are the same in all different fuzzy sets that demonstrates the reliability of the findings. It is determined that grid-connected with battery backup is the most important system choice. On the other hand, solar energy is the most appropriate alternative for microgeneration system investments. Grid-connected system should be implemented for the performance of the microgeneration projects. Hence, providing a sustainable access to the electricity can be possible. Sufficient amount of electricity may not be obtained from wind and solar energy because of the climate changes. In this process, grid-connected system can handle this problem effectively.

Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid

Abstract: Wind-generated energy is a fast-growing source of renewable energy use across the world. A dual-feed induction machine (DFIM) employed in wind generators provides active and reactive, dynamic and static energy support. In this document, the droop control system will be applied to adjust the amplitude and frequency of the grid following the guidelines established for the utility’s smart network supervisor. The wind generator will work with a maximum deloaded power curve, and depending on the reserved active power to compensate the frequency drift, the limit of the reactive power or the variation of the voltage amplitude will be explained. The aim of this paper is to show that the system presented theoretically works correctly on a real platform. The real-time experiments are presented on a test bench based on a 7.5 kW DFIG from Leroy Somer’s commercial machine that is typically used in industrial applications. A synchronous machine that emulates the wind profiles moves the shaft of the DFIG. The amplitude of the microgrid voltage at load variations is improved by regulating the reactive power of the DFIG and this is experimentally proven. The contribution of the active power with the characteristic of the droop control to the load variation is made by means of simulations. Previously, the simulations have been tested with the real system to ensure that the simulations performed faithfully reflect the real system. This is done using a platform based on a real-time interface with the DS1103 from dSPACE.