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.

Turbulent energy motion of fiber suspensions in a rotating frame

Abstract: Turbulent flows play a major role in many fields of science and industry. Noticeable attention is seen on turbulent flows of suspending fibers because of the sensitivity of the electrical, thermal, and mechanical properties of the connecting fiber composites to the spatial configuration and orientation of fibers. The involvement of fibers in the turbulent flow greatly affects the turbulent energy. It is more influenced when the turbulent flow occurs in a rotating system. The effect of fibers on the turbulent energy in the rotating frame must therefore be investigated. For turbulent energy with fiber suspension, a mathematical model can be built in a rotating system that is very important to enhance the quality of industrial goods. This paper, therefore, develops a mathematical model for turbulent energy motion in a rotating frame with a fiber suspension. The model was formulated using the averaging procedure. The momentum equation for incompressible and viscous fluid turbulent flow was considered to develop the model. The turbulent energy motion of the fiber suspensions was presented in the rotating frame in second-order correlation tensors, W i , j , S i , j , L i , j , F i , j , G i , j , D i , j , Q i , j , and H i , j , where all the tensors are the function of time, distance, and space coordinates.

Numerical study on ITO surface modification technique to improve single emissive layer based organic light emitting diode performance

Abstract: To improve the performance and operational life time of an organic light emitting diode (OLED), a single uniformly mixed-host (MH) emissive layer (EML) based OLED structure with modified anode surface has been proposed in this work. Through the numerical simulation, the feasibility of the proposed modified work function of ITO based device structure has been justified from the electrical characteristics and efficiency curves. By the ITO surface modification technique, the interfacial energy barrier of ITO/EML is reduced enough to increase hole injection rate into the EML. Therefore, the carriers become more balanced which raise the recombination rate in the EML. From the calculated current balance factor from the simulation results, it is confirmed that due to ITO surface modification, the proposed MH-OLED able to give better performance in terms of brightness, efficiency and longer operating time over unmodified ITO based MH-OLED.

IoT based medical assistant robot (Docto-Bot)

Abstract: The world's population is growing every day, and so is the number of patients. People's life expectancy is increasing due to technology's welfare, but the problem is that the health sector has always faced a shortage of inadequate doctors. This research main objective was to design & implement a biomedical-based medical assistant robot named "Docto-Bot" to deal with this problem. This research concerns this medical assistant robot's design and development for the disabled and the patients in need. Such a robot's prime utilization is to minimize person-to-person contact and ensure the cleaning, sterilization, and support in hospitals and similar facilities such as quarantine. This prototype robot consists of a medicine reminding and medicine providing system, Automatic hand sanitizer and IoT based Physiological Monitoring System (Body Temperature, Pulse rate, ECG, Oxygen saturation level). A direct one-to-one server-based communication method and user-end android app maintaining system designed. It also included the controlling part, which control automatically and manually by users. Docto-Bot will play a very significant factor in bio-medical robot applications. Though the achievements described in the paper look fruitful and advanced, shortcomings still exist.

Flexural capacity assessment of brick aggregated pre-cracked rc beam strengthened with carbon fiber polymer

Abstract: Ageing and improvements to design code has led to many existing RC structures made of locally available brick aggregates are now found structurally deficient and are in need of rehabilitation. This research emphases on flexural capacity assessment and investigation of failure modes of Carbon Fiber Reinforced Polymers (CFRP) strengthened brick aggregated RC beams. Flexural performance of the RC beam specimens are evaluated using four point bending method. Six RC beams (initially cracked) with CFRP strengthening were tested by varying (i) type of CFRP, (ii) reinforcing area, (iii) anchorage type; and (iv) number of CFRP layers. Two beams were tested as control specimens. Unidirectional carbon fiber sheet (Tow Sheet) and individually hardened continuous fiber strands woven into sheet form (Strand Sheet) were used. Simple flexure failure was obtained for unstrengthened RC beams while end plate and interfacial debonding were observed for the initially cracked CFRP strengthened RC beams. Strengthening of pre-cracked beams using Strand Sheet gave better performance compared to Tow sheet. Overall flexural strength improvement of CFRP strengthened beams varied from 12% to 34% with respect to unstrengthened beams depending on strengthening methods.

Multi-class Textual Emotion Categorization using Ensemble of Convolutional and Recurrent Neural Network

Abstract: Categorizing emotion refers to extracting the individuals’ behaviour from texts and assigning textual units into an emotion from predefined emotional connotations. Identification and categorization of emotion content have mostly been made for English, French, Chinese, Arabic, and other high-resource languages. However, very few studies have investigated emotion from the under-resourced language like Bengali. This work proposes an ensemble-based technique for classifying textual emotions into six classes: anger, disgust, fear, joy, sadness and surprise. An emotion corpus containing 9000 Bengali texts is developed to perform the emotion classification. This work investigates 22 standard classifier models developing based on three deep learning techniques (Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Bidirectional Long Short Term Memory (BiLSTM) with different ensemble strategies and embedding models (i.e., Word2Vec, FastText). All the models are tuned, trained and tested on the developed dataset (EBEmoD-Extended Bengali Emotion Dataset) and a publicly available emotion dataset (BYCD-Bengali Youtube Comment dataset). The experimental result demonstrates that the ensemble of CNN and BiLSTM (i.e., CNN+BiLSTM) outdoes all other models by acquiring the highest weighted $$f_1$$ -score of 62.46% (for EBEmoD) and 67.57% (for BYCD), respectively.