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.

VibNet: Application of Wireless Sensor Network for Vibration Monitoring Using ARM

Abstract: Vibration in electrical and mechanical systems is an important parameter which is related to the systems health, stability and efficiency. In practical condition, the system is distributed and most of the time unreachable during their operating time. In this paper, triple axis wireless vibration monitoring system for distributed system is described. The system also includes development of real time graphical user interface with MATLAB. Here triple axis accelerometer ADXL335 is used for acceleration measurement , cost effective ARM M4 processor TM4C123GXL is used as processing unit and low power Zigbee transceiver is used to transfer the vibration data. To prevent data tapping, the data is encrypted by Advanced Encryption Standard (AES) algorithm in sending side and then decrypted in receiver end. The overall latency of the system is also measured. The received signal strength based performance analysis is also done where the RSSI value is approximately -43dBm at 587.94m distance. The system is tested for vibration measurement of a single phase induction motor.

Identification and Classification of Textual Aggression in Social Media: Resource Creation and Evaluation

Abstract: Recently, social media has gained substantial attention as people can share opinions, expressions, emotions and carry out meaningful interactions through it spontaneously. Unfortunately, with this rapid advancement, social media misuse has also been proliferated, which leads to an increase in aggressive, offensive and abusive activities. Most of these unlawful activities performed through textual communication. Therefore, it is monumental to create intelligent systems that can identify and classify these texts. This paper presents an aggressive text classification system in Bengali. To serve our purpose a corpus (hereafter we called, ‘ATxtC’) is developed using hierarchical annotation schema that contains 7591 annotated texts (3888 for aggressive and 3703 for non-aggressive). Furthermore, the proposed system can classify aggressive Bengali text into religious, gendered, verbal and political aggression classes. Data annotation obtained a 0.74 kappa score in coarse-grained and 0.61 kappa score in fine-grained categories, which ensures the data’s acceptable quality. Several classification algorithms such as LR, RF, SVM, CNN and BiLSTM are implemented on AtxtC. The experimental result shows that the combined CNN and BiLSTM model achieved the highest weighted \(f_1\) score of 0.87 (identification task) and 0.80 (classification task).

Partial discharge modeling with increasing applied voltages across different void sizes

Abstract: Power transformer, bushing, insulator, cable and other related parts of electrical equipment are usually used in the power system network. Catastrophic event like explosion may occur in those network components due to some unexpected reasons. This kind of events triggers the power outage, which in turn results in a financial loss. One of the main causes for such terrible incidents is the imperfect condition of the insulation systems, which leads to deterioration and breakdown of high voltage equipment due to partial discharge. In this paper, the effect of an increase in applied voltage across the three-capacitance model, which represents the phenomena of partial discharge within the solid type insulation, is studied using the EMTP-RV software. Different void sizes and types of dielectric materials are also studied in this paper. The results obtained by the simulation software EMTP-RV and MATLAB are summarized.

Dealing with Suboptimality in Multistep Model Predictive Control for Transient Operations

Abstract: Recently, a computational issue of sphere decoding algorithm (SDA) during transient operation of multistep model predictive control has been addressed in [1] and achieved its real-time implementation in [2] for a medium-voltage electrical drive system. This is achieved by projecting the unconstrained solution onto the convex-hull of the finite control set during transient operation. Therefore, a new initial sphere that guarantees feasibility and includes a significant smaller number of candidate solutions is obtained. This reduces the computation time required to solve the optimization problem. However, the reduction of the computational burden comes at the expense of (mild) suboptimal results [3]. This paper analyses the possibility to obtain a suboptimal solution by the SDA based optimization during transient operation. To deal with this suboptimality issue, this work explores the possibility to enlarge the convex-hull, whose size is by definition tied to the original finite control set. Therefore, in this work, the convex-hull is treated as a SDA initialization parameter during transient operation. As will be demonstrated, enlarging the convex-hull size reduces the possibility to obtain a suboptimal solution during the transient operation retaining, thus, the optimality during the whole converter operation.

E-MIIM: an ensemble-learning-based context-aware mobile telephony model for intelligent interruption management

Abstract: Nowadays, mobile telephony interruptions in our daily life activities are common because of the inappropriate ringing notifications of incoming phone calls in different contexts. Such interruptions may impact on the work attention not only for the mobile phone owners, but also for the surrounding people. Decision tree is the most popular machine-learning classification technique that is used in existing context-aware mobile intelligent interruption management (MIIM) model to overcome such issues. However, a single-decision tree-based context-aware model may cause over-fitting problem and thus decrease the prediction accuracy of the inferred model. Therefore, in this paper, we propose an ensemble machine-learning-based context-aware mobile telephony model for the purpose of intelligent interruption management by taking into account multi-dimensional contexts and name it “E-MIIM”. The experimental results on individuals’ real-life mobile telephony data sets show that our E-MIIM model is more effective and outperforms existing MIIM model for predicting and managing individual’s mobile telephony interruptions based on their relevant contextual information.