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: Computer science & Renewable energy. 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.

An evaluation of disaster risk reduction (DRR) approaches for coastal delta cities: a comparative analysis

Abstract: Deltas are the promising places with multifarious ecosystems and arable soils along with the ease of water transportation system; hence, a number of important cities are established in or near coastal delta regions. However, due to the geomorphic characteristics, those cities are extremely exposed to hydro-meteorological hazards, especially to riverine and coastal flood. Additionally, climate change, rapid urbanization and subsidence are exacerbating the existing situation and causing monumental loss. Researchers as well as various international organizations like United Nations Inter-Agency Secretariat of the International Strategy for Disaster Reduction have recognized the implications of formulating disaster risk reduction (DRR) plans for coastal delta cities. This demands for the excogitation of adaptation policies and measures in addition to the mitigation efforts to reduce flood risks. In this regard, to support the comprehensive concept development, this study elicits different components of flood risk reduction policies and measures, congenial for coastal delta cities in respect of physical and environmental perspectives. Eleven precedent (model) cities are selected to study their various initiatives for reducing coastal flood risks. Findings show that protecting cities from flooding and reducing exposure to floods are two different but interrelated approaches of DRR. Combinations of structural and non-structural measures are the prerequisites to achieve the goal of effective DRR.

Possibilities of urban flood reduction through distributed-scale rainwater harvesting

Abstract: Urban flooding in Chittagong City usually occurs during the monsoon season and a rainwater harvesting (RWH) system can be used as a remedial measure. This study examines the feasibility of rain barrel RWH system at a distributed scale within an urbanized area located in the northwestern part of Chittagong City that experiences flash flooding on a regular basis. For flood modeling, the storm water management model (SWMM) was employed with rain barrel low-impact development (LID) as a flood reduction measure. The Hydrologic Engineering Center's River Analysis System (HEC-RAS) inundation model was coupled with SWMM to observe the detailed and spatial extent of flood reduction. Compared to SWMM simulated floods, the simulated inundation depth using remote sensing data and the HEC-RAS showed a reasonable match, i.e., the correlation coefficients were found to be 0.70 and 0.98, respectively. Finally, using LID, i.e., RWH, a reduction of 28.66% could be achieved for reducing flood extent. Moreover, the study showed that 10%–60% imperviousness of the subcatchment area can yield a monthly RWH potential of 0.04–0.45 m3 from a square meter of rooftop area. The model can be used for necessary decision making for flood reduction and to establish a distributed RWH system in the study area.

PI controller and park's transformation based control of dynamic voltage restorer for voltage sag minimization

Abstract: Modern electronics based on power electronic devices such as Programmable Logic Controllers (PLC), Distributed Control System (DCS) are used frequently in industrials plants to improve efficient production. These systems are extremely sensitive to power quality problem like voltage sag. Voltage sag is considered to be one of the most severe problems that cause failure, overheating and finally a total shutdown of industrial plants. It has been observed that, Dynamic Voltage Restorer (DVR) is the most efficient and effective modern custom power device exercised in power distribution networks to minimize voltage sags. The efficiency of the DVR depends on the performance of the control technique, which involved in switching the inverters. This paper presents modeling, analysis and simulation of a Dynamic Voltage Restorer (DVR) test systems using MATLAB Simulink for minimizing voltage sag by two promising controlling strategies: control using PI controller and control based on Park's Transformation. Simulation results of DVR controlling found by MATLAB Simulink here which demonstrates the well-organized plant applications. Moreover, this work shows that, control based on Park's Transformation provides better sag improvement than PI Control.

An Efficient Method to Classify Brain Tumor using CNN and SVM

Abstract: Brain Tumor is one of the most sophisticated diseases for the human body that happens when the brain cells start increasing unconditionally. Before giving treatment, the main challenge is to detect and classify tumors from brain MRI images. Researchers have been working really hard for ages to find the best method with higher accuracy for implementing them in real life medical image classification. The main problem is that when a classifier deals with huge amount of data it becomes difficult to classify them accurately. To solve this a CNN-SVM based method is proposed to classify brain tumor with higher accuracy. Firstly, a convolutional neural network having 19 layers is constructed using three convolutional 2D layers, three max-pooling layers, two fully-connected layers, three batch-normalization layers with activation functions reLu. Secondly softmax is used as a classifier and implemented over a dataset containing 3064 images on three class of tumor images (glioma tumors, meningioma tumors, and pituitary tumors). After that, another classifier named support vector machine is used to improve the accuracy of the CNN model using the features extracted from the model. The final accuracy of this proposed CNN-SVM based method is found 97.1%.