Chittagong University of Engineering & Technology

About: Chittagong University of Engineering & Technology is a education organization based out in Chittagong, Bangladesh. It is known for research contribution in the topics: Renewable energy & Dielectric. The organization has 1200 authors who have published 1444 publications receiving 10418 citations. The organization is also known as: Engineering College, Chittagong & Bangladesh Institute of Technology, Chittagong.

Challenges for flood risk management in flood-prone Sirajganj region of Bangladesh

Abstract: Department of Civil Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh UNESCO-IHE Institute for Water Education, Delft, The Netherlands Institute of Water and Flood Management (IWFM), Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh Processing and Flood Forecasting Circle (PFFC), Bangladesh Water Development Board (BWDB), Dhaka, Bangladesh Institute of Water Modelling, Dhaka, Bangladesh

Improved gray wolf optimization algorithm for solving placement and sizing of electrical energy storage system in micro-grids.

Abstract: Micro-grids consist of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. Micro-grids are small-scale networks at low voltage levels that are use to provide thermal and electrical loads of small locations where there is no access to the main electrical grid. Given the environmental and economic issues for these areas, micro-grids can be a good solution for energy production. In this paper, determining the size and location of optimal electrical energy storage systems is presented. In other side, a new method based on the cost benefit analysis for optimal sizing of an energy storage system in a microgrid (MG) is proposed. The uncertainties associated with renewable energy sources and the occurrence of defects in the grid connection network and the effect of the contribution of load responses in a micro-grid are taken into account. The combined system consists of wind turbines and fuel cells. Basically, wind power is not definitively available. The new proposed method is based on two-stage randomization design (TSRD) for modeling the effect of wind power uncertainty so that the predicted wind energy error is considered as the main random parameter in the model. A standard probability distribution function is used to represent the error variations. Given the continuity of the mentioned function, the probability error function is extracted using the new discrete method and a certain number of scenarios with a certain probability. Finally, the problem has been transformed into an optimization problem, and a gray wolf optimization (GWO) algorithm has been used to solve it. In the proposed developed model based on local and global search, the algorithm tries to reach the final result in the shortest possible time and with the most precision. The results of the simulation show the efficiency of the proposed method in solving the micro-grid problem.

License plate recognition system based on contour properties and deep learning model

Abstract: The intent of this research is to design a license plate recognition (LPR) system in the domain of Bangla language for smart vehicle management The proposed system is designed on the basis of computer vision tools and deep supervised machine learning model The system has three modules: license plate detection, character segmentation and recognition of the characters of the License Plate (LP) The goal of detection is to localize the plate area from the vehicle image and to crop region of interest (LP) It is executed by applying following process: preprocessing the image, conversion to binary image, contour detection and filtering the contours to get the LP's character contours, tilt correction and cropping the plate area from the image Then, the cropped LP is segmented to extract the characters from the plate Finally, the recognition step classifies the characters by means of deep convolution neural network where the features of the character are crafted and learned by the convolution layers of the networks The system is implemented in Python OpenCV environment for offline car license plates images which are taken in different illuminations, road scenarios and colored cars The system performance is evaluated in terms of detection rate, segmentation rate, recognition rate and execution time The results illustrate that the performance of the system is remarkable