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This paper introduces an intelligent computational approach to automatically authenticate fingerprint for personal identification and verification. The feature vector is formed using combined features obtained from Gabor filtering technique and deep learning technique such as Convolutional Neural Network (CNN). Principle Component Analysis (PCA) has been performed on the feature vectors to reduce the overfitting problems in order to make the classification results more accurate and reliable. A multiclass classifier has been trained using the extracted features. Experiments performed using standard public databases demonstrated that the proposed approach showed better performance with regard to accuracy (99.87%) compared to the more recent classification techniques such as Support Vector Machine (97.86%) or Random Forest (95.47%). However, the proposed method also showed higher accuracy compared to other validation approaches such as K-fold (98.89%) and generalization (97.75%). Furthermore, these results were supported by confusion matrix results where only 10 failures were found when tested with 5000 images.
Nur- A- Alam; M. Ahsan; M.A. Based; J. Haider; M. Kowalski. An intelligent system for automatic fingerprint identification using feature fusion by Gabor filter and deep learning. Computers & Electrical Engineering 2021, 95, 107387 .
AMA StyleNur- A- Alam, M. Ahsan, M.A. Based, J. Haider, M. Kowalski. An intelligent system for automatic fingerprint identification using feature fusion by Gabor filter and deep learning. Computers & Electrical Engineering. 2021; 95 ():107387.
Chicago/Turabian StyleNur- A- Alam; M. Ahsan; M.A. Based; J. Haider; M. Kowalski. 2021. "An intelligent system for automatic fingerprint identification using feature fusion by Gabor filter and deep learning." Computers & Electrical Engineering 95, no. : 107387.
This paper proposes a three-port Zeta-KY dc-dc converter which is fed with hybrid sources like photovoltaic (PV) cells and batteries. The converter proposed here is a multi-input single-output (MISO) structure which harnesses the benefits of Zeta and KY converters. The combination of these converters is highly advantageous since the Zeta converter provides lesser output voltage ripples with high gain and the KY converter topology suits well for withstanding load transients. The KY converter used in this research work is subjected to a topological change to facilitate bidirectional power flow. The bidirectional flow is essential to save the excess power in PV source in batteries during low load conditions. This novel multiport topology with bidirectional facility is first of its kind and has not been discussed earlier in the research arena. In the proposed work, two control algorithms are developed and deployed: the first one ensures the maximum power extraction from the PV and the second one maintains constant dc bus voltage and manages bidirectional power flow. MATLAB Simulink and hardware prototype of the proposed system has been realized for a 72 V dc bus and a 500 W electric vehicular drive. The simulation and experimental results reveal that the proposed system is viable for medium power electric shuttle applications. The proposed system is subjected to various test cases and it is observed that the source and load intermittencies are catered very well by the proposed three port Zeta-KY converter. The developed multiport converter is feasible for renewable energy applications.
Ilambirai Chandran; Sridhar Ramasamy; Mominul Ahsan; Julfikar Haider; Eduardo Rodrigues. Implementation of Non-Isolated Zeta-KY Triple Port Converter for Renewable Energy Applications. Electronics 2021, 10, 1681 .
AMA StyleIlambirai Chandran, Sridhar Ramasamy, Mominul Ahsan, Julfikar Haider, Eduardo Rodrigues. Implementation of Non-Isolated Zeta-KY Triple Port Converter for Renewable Energy Applications. Electronics. 2021; 10 (14):1681.
Chicago/Turabian StyleIlambirai Chandran; Sridhar Ramasamy; Mominul Ahsan; Julfikar Haider; Eduardo Rodrigues. 2021. "Implementation of Non-Isolated Zeta-KY Triple Port Converter for Renewable Energy Applications." Electronics 10, no. 14: 1681.
Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.
Nazmus Sakib; Rodina Binti Ahmad; Mominul Ahsan; Abdul Based; Khalid Haruna; Julfikar Haider; Saravanakumar Gurusamy. A Hybrid Personalized Scientific Paper Recommendation Approach Integrating Public Contextual Metadata. IEEE Access 2021, 9, 83080 -83091.
AMA StyleNazmus Sakib, Rodina Binti Ahmad, Mominul Ahsan, Abdul Based, Khalid Haruna, Julfikar Haider, Saravanakumar Gurusamy. A Hybrid Personalized Scientific Paper Recommendation Approach Integrating Public Contextual Metadata. IEEE Access. 2021; 9 ():83080-83091.
Chicago/Turabian StyleNazmus Sakib; Rodina Binti Ahmad; Mominul Ahsan; Abdul Based; Khalid Haruna; Julfikar Haider; Saravanakumar Gurusamy. 2021. "A Hybrid Personalized Scientific Paper Recommendation Approach Integrating Public Contextual Metadata." IEEE Access 9, no. : 83080-83091.
The existing solutions for reducing total harmonic distortion (THD) using different control algorithms in shunt active power filters (SAPFs) are complex. This work proposes a split source inverter (SSI)-based SAPF for improving the power quality in a nonlinear load system. The advantage of the SSI topology is that it is of a single stage boost inverter with an inductor and capacitor where the conventional two stages with an intermediate DC-DC conversion stage is discarded. This research proposes inventive control schemes for SAPF having two control loops; the outer control loop regulates the DC link voltage whereas the inner current loop shapes the source current profile. The control mechanism implemented here is an effective, less complex, indirect scheme compared to the existing time domain control algorithms. Here, an intelligent fuzzy logic control regulates the DC link voltage which facilitates reference current generation for the current control scheme. The simulation of the said system was carried out in a MATLAB/Simulink environment. The simulations were carried out for different load conditions (RL and RC) using a fuzzy logic controller (FLC) and PI controllers in the outer loop (voltage control) and hysteresis current controller (HCC) and sinusoidal pulse width modulation (SPWM) in the inner loop (current control). The simulation results were extracted for dynamic load conditions and the results demonstrated that the THD can be reduced to 0.76% using a combination of SPWM and FLC. Therefore, the proposed system proved to be effective and viable for reducing THD. This system would be highly applicable for renewable energy power generation such as Photovoltaic (PV) and Fuel cell (FC).
Poornima Panati; Sridhar Ramasamy; Mominul Ahsan; Julfikar Haider; Eduardo Rodrigues. Indirect Effective Controlled Split Source Inverter-Based Parallel Active Power Filter for Enhancing Power Quality. Electronics 2021, 10, 892 .
AMA StylePoornima Panati, Sridhar Ramasamy, Mominul Ahsan, Julfikar Haider, Eduardo Rodrigues. Indirect Effective Controlled Split Source Inverter-Based Parallel Active Power Filter for Enhancing Power Quality. Electronics. 2021; 10 (8):892.
Chicago/Turabian StylePoornima Panati; Sridhar Ramasamy; Mominul Ahsan; Julfikar Haider; Eduardo Rodrigues. 2021. "Indirect Effective Controlled Split Source Inverter-Based Parallel Active Power Filter for Enhancing Power Quality." Electronics 10, no. 8: 892.
Carbon neutral buildings are dependent on effective energy management systems and harvesting energy from unpredictable renewable sources. One strategy is to utilise the capacity from electric vehicles, while renewables are not available according to demand. Vehicle to grid (V2G) technology can only be expanded if there is funding and realisation that it works, so investment must be in place first, with charging stations and with the electric vehicles to begin with. The installer of the charging stations will achieve the financial benefit or have an incentive and vice versa for the owners of the electric vehicles. The paper presents an effective V2G strategy that was developed and implemented for an operational university campus. A machine learning algorithm has also been derived to predict energy consumption and energy costs for the investigated building. The accuracy of the developed algorithm in predicting energy consumption was found to be between 94% and 96%, with an average of less than 5% error in costs predictions. The achieved results show that energy consumption savings are in the range of 35%, with the potentials to achieve about 65% if the strategy was applied at all times. This has demonstrated the effectiveness of the machine learning algorithm in carbon print reductions.
Connor Scott; Mominul Ahsan; Alhussein Albarbar. Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings. Sustainability 2021, 13, 4003 .
AMA StyleConnor Scott, Mominul Ahsan, Alhussein Albarbar. Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings. Sustainability. 2021; 13 (7):4003.
Chicago/Turabian StyleConnor Scott; Mominul Ahsan; Alhussein Albarbar. 2021. "Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings." Sustainability 13, no. 7: 4003.
In the era of Industry 4.0, remote monitoring and controlling appliance/equipment at home, institute, or industry from a long distance with low power consumption remains challenging. At present, some smart phones are being actively used to control appliances at home or institute using Internet of Things (IoT) systems. This paper presents a novel smart automation system using long range (LoRa) technology. The proposed LoRa based system consists of wireless communication system and different types of sensors, operated by a smart phone application and powered by a low-power battery, with an operating range of 3–12 km distance. The system established a connection between an android phone and a microprocessor (ESP32) through Wi-Fi at the sender end. The ESP32 module was connected to a LoRa module. At the receiver end, an ESP32 module and LoRa module without Wi-Fi was employed. Wide Area Network (WAN) communication protocol was used on the LoRa module to provide switching functionality of the targeted area. The performance of the system was evaluated by three real-life case studies through measuring environmental temperature and humidity, detecting fire, and controlling the switching functionality of appliances. Obtaining correct environmental data, fire detection with 90% accuracy, and switching functionality with 92.33% accuracy at a distance up to 12 km demonstrated the high performance of the system. The proposed smart system with modular design proved to be highly effective in controlling and monitoring home appliances from a longer distance with relatively lower power consumption.
Nur- A- Alam; Mominul Ahsan; Abdul Based; Julfikar Haider; Eduardo Rodrigues. Smart Monitoring and Controlling of Appliances Using LoRa Based IoT System. Designs 2021, 5, 17 .
AMA StyleNur- A- Alam, Mominul Ahsan, Abdul Based, Julfikar Haider, Eduardo Rodrigues. Smart Monitoring and Controlling of Appliances Using LoRa Based IoT System. Designs. 2021; 5 (1):17.
Chicago/Turabian StyleNur- A- Alam; Mominul Ahsan; Abdul Based; Julfikar Haider; Eduardo Rodrigues. 2021. "Smart Monitoring and Controlling of Appliances Using LoRa Based IoT System." Designs 5, no. 1: 17.
The detection of microorganisms like Pseudomonas are very important as they trigger an infection in human blood, lungs, and different parts of the body causing various ailments. In this paper, a surface plasmon resonance (SPR) biosensor based on photonic crystal fiber (PCF) has been proposed to detect the presence of Pseudomonas bacteria with attractive performance characteristics. The sensor is designed using a simple circular lattice of PCF, coated with a thin chemically stable gold layer. The performance investigation of the sensor is numerically carried out by using a finite element (FE) based simulation tool where the highest wavelength and amplitude sensitivity are found as 20,000 nm/RIU and 1380 RIU −1 , respectively. The sensor shows an excellent spectral resolution of the highest value of
N. Jahan; M. Rahman; Mominul Ahsan; Abdul Based; Masud Rana; Saravanakumar Gurusamy; Julfikar Haider. Photonic Crystal Fiber Based Biosensor for Pseudomonas Bacteria Detection: A Simulation Study. IEEE Access 2021, 9, 42206 -42215.
AMA StyleN. Jahan, M. Rahman, Mominul Ahsan, Abdul Based, Masud Rana, Saravanakumar Gurusamy, Julfikar Haider. Photonic Crystal Fiber Based Biosensor for Pseudomonas Bacteria Detection: A Simulation Study. IEEE Access. 2021; 9 ():42206-42215.
Chicago/Turabian StyleN. Jahan; M. Rahman; Mominul Ahsan; Abdul Based; Masud Rana; Saravanakumar Gurusamy; Julfikar Haider. 2021. "Photonic Crystal Fiber Based Biosensor for Pseudomonas Bacteria Detection: A Simulation Study." IEEE Access 9, no. : 42206-42215.
Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is responsible for a large number of deaths. Earlier detection of the COVID-19 through accurate diagnosis, particularly for the cases with no obvious symptoms, may decrease the patient’s death rate. Chest X-ray images are primarily used for the diagnosis of this disease. This research has proposed a machine vision approach to detect COVID-19 from the chest X-ray images. The features extracted by the histogram-oriented gradient (HOG) and convolutional neural network (CNN) from X-ray images were fused to develop the classification model through training by CNN (VGGNet). Modified anisotropic diffusion filtering (MADF) technique was employed for better edge preservation and reduced noise from the images. A watershed segmentation algorithm was used in order to mark the significant fracture region in the input X-ray images. The testing stage considered generalized data for performance evaluation of the model. Cross-validation analysis revealed that a 5-fold strategy could successfully impair the overfitting problem. This proposed feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%. When compared to other classification techniques, such as ANN, KNN, and SVM, the CNN technique used in this study showed better classification performance. K-fold cross-validation demonstrated that the proposed feature fusion technique (98.36%) provided higher accuracy than the individual feature extraction methods, such as HOG (87.34%) or CNN (93.64%).
Nur- A- Alam; Mominul Ahsan; Abdul Based; Julfikar Haider; Marcin Kowalski. COVID-19 Detection from Chest X-Ray Images Using Feature Fusion and Deep Learning. Sensors 2021, 21, 1480 .
AMA StyleNur- A- Alam, Mominul Ahsan, Abdul Based, Julfikar Haider, Marcin Kowalski. COVID-19 Detection from Chest X-Ray Images Using Feature Fusion and Deep Learning. Sensors. 2021; 21 (4):1480.
Chicago/Turabian StyleNur- A- Alam; Mominul Ahsan; Abdul Based; Julfikar Haider; Marcin Kowalski. 2021. "COVID-19 Detection from Chest X-Ray Images Using Feature Fusion and Deep Learning." Sensors 21, no. 4: 1480.
Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language.
Nur-A-Alam Nur-A-Alam; Mominul Ahsan; Abdul Based; Julfikar Haider. Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks. Technologies 2021, 9, 9 .
AMA StyleNur-A-Alam Nur-A-Alam, Mominul Ahsan, Abdul Based, Julfikar Haider. Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks. Technologies. 2021; 9 (1):9.
Chicago/Turabian StyleNur-A-Alam Nur-A-Alam; Mominul Ahsan; Abdul Based; Julfikar Haider. 2021. "Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks." Technologies 9, no. 1: 9.
This paper begins with a comprehensive review into the existing GaN device models. Secondly, it identifies the need for a more accurate GaN switching model. A simple practical process based on radio frequency techniques using vector network analyser is introduced in this paper as an original contribution. It was applied to extract the impedances of the GaN device to develop an efficient behavioural model. The switching behaviour of the model was validated using both simulation and real time double pulse test experiments at 500 V, 15 A conditions. The proposed model is much easier for power designers to handle, without the need for knowledge about the physics or geometry of the device. The proposed model for Transphorm GaN HEMT was found to be 95.2% more accurate when compared to the existing LT-spice manufacturer model. This work additionally highlights the need to adopt established RF techniques into power electronics to reduce the learning curve while dealing with these novel high-speed switching devices.
Nikita Hari; Sridhar Ramasamy; Mominul Ahsan; Julfikar Haider; Eduardo M. G. Rodrigues. An RF Approach to Modelling Gallium Nitride Power Devices Using Parasitic Extraction. Electronics 2020, 9, 2007 .
AMA StyleNikita Hari, Sridhar Ramasamy, Mominul Ahsan, Julfikar Haider, Eduardo M. G. Rodrigues. An RF Approach to Modelling Gallium Nitride Power Devices Using Parasitic Extraction. Electronics. 2020; 9 (12):2007.
Chicago/Turabian StyleNikita Hari; Sridhar Ramasamy; Mominul Ahsan; Julfikar Haider; Eduardo M. G. Rodrigues. 2020. "An RF Approach to Modelling Gallium Nitride Power Devices Using Parasitic Extraction." Electronics 9, no. 12: 2007.
Intelligent fault diagnosis methods have replaced time consuming and unreliable human analysis, increasing anomaly detection efficiency. Deep learning models are clear cut techniques for this purpose. This paper’s fundamental purpose is to automatically detect leakage in tanks during production with more reliability than a manual inspection, a common practice in industries. This research proposes an inspection system to predict tank leakage using hydrophone sensor data and deep learning algorithms after production. In this paper, leak detection was investigated using an experimental setup consisting of a plastic tank immersed underwater. Three different techniques for this purpose were implemented and compared with each other, including fast Fourier transform (FFT), wavelet transforms, and time-domain features, all of which are followed with 1D convolution neural network (1D-CNN). Applying FFT and converting the signal to a 1D image followed by 1D-CNN showed better results than other methods. Experimental results demonstrate the effectiveness and the superiority of the proposed methodology for detecting real-time leakage inaccuracy.
Masoumeh Rahimi; Alireza Alghassi; Mominul Ahsan; Julfikar Haider. Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal. Informatics 2020, 7, 49 .
AMA StyleMasoumeh Rahimi, Alireza Alghassi, Mominul Ahsan, Julfikar Haider. Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal. Informatics. 2020; 7 (4):49.
Chicago/Turabian StyleMasoumeh Rahimi; Alireza Alghassi; Mominul Ahsan; Julfikar Haider. 2020. "Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal." Informatics 7, no. 4: 49.
Recent advances in micro electro-mechanical systems (MEMS) have produced wide variety of wearable sensors. Owing to their low cost, small size and interfacability, those MEMS based devices have become increasingly commonplace and part of daily life for many people. Large amount of data from heart and breath rates to electrocardiograph (ECG) signals, which contain a wealth of health-related information, can be measured. Hence, there is a timely need for novel interrogation and analysis methods for extracting health related features from such a Big Data. In this paper, the prospects from smart clothing such as wearable devices in generating Big Data are critically analyzed with a focus on applications related to healthcare, sports and fashion. The work also covers state-of-the-art data analytics methods and frameworks for health monitoring purposes. Subsequently, a novel data analytics framework that can provide accurate decision in both normal and emergency health situations is proposed. The proposed novel framework identifies and discusses sources of Big Data from the human body, data collection, communication, data storage, data analytics and decision making using artificial intelligence (AI) algorithms. The paper concludes by identifying challenges facing the integration of Big Data analytics with smart clothing. Recommendation for further development opportunities and directions for future work are also suggested.
Mominul Ahsan; Siew Teay Hon; Alhussein Albarbar. Development of Novel Big Data Analytics Framework for Smart Clothing. IEEE Access 2020, 8, 146376 -146394.
AMA StyleMominul Ahsan, Siew Teay Hon, Alhussein Albarbar. Development of Novel Big Data Analytics Framework for Smart Clothing. IEEE Access. 2020; 8 (99):146376-146394.
Chicago/Turabian StyleMominul Ahsan; Siew Teay Hon; Alhussein Albarbar. 2020. "Development of Novel Big Data Analytics Framework for Smart Clothing." IEEE Access 8, no. 99: 146376-146394.
This paper studies the phase and frequency estimation problem of single-phase grid voltage signal in the presence of DC offset and harmonics. For this purpose, a novel parameterized linear model of the grid voltage signal is considered where the unknown frequency of the grid is considered as the parameter. Based on the developed model, a linear observer (Luenberger type) is proposed. Then using Lyapunov stability theory, an estimator of the unknown grid frequency is developed. In order to deal with the grid harmonics, multiple parallel observers are then proposed. The proposed technique is inspired by other Luenberger observers already proposed in the literature. Those techniques use coordinate transformation that requires real-time matrix inverse calculation. The proposed technique avoids real-time matrix inversion by using a novel state-space model of the grid voltage signal. In comparison to similar other techniques available in the literature, no coordinate transformation is required. This significantly reduces the computational complexity w.r.t. similar other techniques. Comparative experimental results are provided with respect to two other recently proposed nonlinear techniques to show the dynamic performance improvement. Experimental results demonstrate the suitability of the proposed technique.
Hafiz Ahmed; Mominul Ahsan; Mohamed Benbouzid; Alhussein Albarbar; Mohammad Shahjalal; Samet Biricik. Coordinate Transformation-Free Observer-Based Adaptive Estimation of Distorted Single-Phase Grid Voltage Signal. IEEE Access 2020, 8, 74280 -74290.
AMA StyleHafiz Ahmed, Mominul Ahsan, Mohamed Benbouzid, Alhussein Albarbar, Mohammad Shahjalal, Samet Biricik. Coordinate Transformation-Free Observer-Based Adaptive Estimation of Distorted Single-Phase Grid Voltage Signal. IEEE Access. 2020; 8 (99):74280-74290.
Chicago/Turabian StyleHafiz Ahmed; Mominul Ahsan; Mohamed Benbouzid; Alhussein Albarbar; Mohammad Shahjalal; Samet Biricik. 2020. "Coordinate Transformation-Free Observer-Based Adaptive Estimation of Distorted Single-Phase Grid Voltage Signal." IEEE Access 8, no. 99: 74280-74290.
Lifetime of power electronic devices, in particular those used for wind turbines, is short due to the generation of thermal stresses in their switching device e.g., IGBT particularly in the case of high switching frequency. This causes premature failure of the device leading to an unreliable performance in operation. Hence, appropriate thermal assessment and implementation of associated mitigation procedure are required to put in place in order to improve the reliability of the switching device. This paper presents two case studies to demonstrate the reliability assessment of IGBT. First, a new driving strategy for operating IGBT based power inverter module is proposed to mitigate wire-bond thermal stresses. The thermal stress is characterised using finite element modelling and validated by inverter operated under different wind speeds. High-speed thermal imaging camera and dSPACE system are used for real time measurements. Reliability of switching devices is determined based on thermoelectric (electrical and/or mechanical) stresses during operations and lifetime estimation. Second, machine learning based data-driven prognostic models are developed for predicting degradation behaviour of IGBT and determining remaining useful life using degradation raw data collected from accelerated aging tests under thermal overstress condition. The durations of various phases with increasing collector-emitter voltage are determined over the device lifetime. A data set of phase durations from several IGBTs is trained to develop Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models, which is used to predict remaining useful life (RUL) of IGBT. Results obtained from the presented case studies would pave the path for improving the reliability of IGBTs.
Mominul Ahsan; Siew Teay Hon; Canras Batunlu; Alhussein Albarbar. Reliability Assessment of IGBT Through Modelling and Experimental Testing. IEEE Access 2020, 8, 39561 -39573.
AMA StyleMominul Ahsan, Siew Teay Hon, Canras Batunlu, Alhussein Albarbar. Reliability Assessment of IGBT Through Modelling and Experimental Testing. IEEE Access. 2020; 8 (99):39561-39573.
Chicago/Turabian StyleMominul Ahsan; Siew Teay Hon; Canras Batunlu; Alhussein Albarbar. 2020. "Reliability Assessment of IGBT Through Modelling and Experimental Testing." IEEE Access 8, no. 99: 39561-39573.
In electronics manufacturing, the necessary quality of electronic components and parts is ensured through qualification testing using standards and user requirements. The challenge is that product qualification testing is time-consuming and comes at a substantial cost. The work contributes to develop a novel prognostics framework for predicting qualification test outcomes of electronic components enabling the reduction of qualification test time and cost. The research focuses on the development of a new, prognostics-based approach to qualification of electronics parts that can enable “smart testing” using data-driven modelling techniques in order to ensure product robustness and reliability in operation. This work is both novel and original because at present such approach to qualification testing and the associated capability for test time reduction (respectively cost reduction) it offers are non-existent in the electronics industry. An effective way of using three different methods for development of prognostics models are identified and applied. Predictive models are constructed from historical qualification test data in the form of electrical parameter measurements using Machine Learning (ML) techniques. ML models can be imbedded within the sequential electrical tests qualification procedure and enable the forecasting of the pass/fail qualification outcome using only partial information from already completed electrical tests. Data-driven prognostics models are developed using the following machine learning techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN) and (3) K-Nearest Neighbor (KNN). The results show that with just over half of the individual tests completed, the models are capable of forecasting the final qualification outcome, pass or fail, with accuracy as high as 92.5%. The predictive power and overall performance of the researched models in predicting qualification test binary outcomes with varying ratios of Pass and Fail data in the processed datasets are analysed.
Mominul Ahsan; Stoyan Stoyanov; Chris Bailey; Alhussein Albarbar. Developing Computational Intelligence for Smart Qualification Testing of Electronic Products. IEEE Access 2020, 8, 16922 -16933.
AMA StyleMominul Ahsan, Stoyan Stoyanov, Chris Bailey, Alhussein Albarbar. Developing Computational Intelligence for Smart Qualification Testing of Electronic Products. IEEE Access. 2020; 8 (99):16922-16933.
Chicago/Turabian StyleMominul Ahsan; Stoyan Stoyanov; Chris Bailey; Alhussein Albarbar. 2020. "Developing Computational Intelligence for Smart Qualification Testing of Electronic Products." IEEE Access 8, no. 99: 16922-16933.
Grid synchronization plays an important role in the grid integration of renewable energy sources. To achieve grid synchronization, accurate information of the grid voltage signal parameters are needed. Motivated by this important practical application, this paper proposes a state observer-based approach for the parameter estimation of unbalanced three-phase grid voltage signal. The proposed technique can extract the frequency of the distorted grid voltage signal and is able to quantify the grid unbalances. First, a dynamical model of the grid voltage signal is developed considering the disturbances. In the model, frequency of the grid is considered as a constant and/or slowly-varying but unknown quantity. Based on the developed dynamical model, a state observer is proposed. Then using Lyapunov function-based approach, a frequency adaptation law is proposed. The chosen frequency adaptation law guarantees the global convergence of the estimation error dynamics and as a consequence, ensures the global asymptotic convergence of the estimated parameters in the fundamental frequency case. Gain tuning of the proposed state observer is very simple and can be done using Matlab commands. Some guidelines are also provided in this regard. Matlab/Simulink based numerical simulation results and dSPACE 1104 board-based experimental results are provided. Test results demonstrate the superiority and effectiveness of the proposed approach over another state-of-the art technique.
Hafiz Ahmed; Mohamed Benbouzid; Mominul Ahsan; Alhussein Albarbar; Mohammad Shahjalal. Frequency Adaptive Parameter Estimation of Unbalanced and Distorted Power Grid. IEEE Access 2020, 8, 8512 -8519.
AMA StyleHafiz Ahmed, Mohamed Benbouzid, Mominul Ahsan, Alhussein Albarbar, Mohammad Shahjalal. Frequency Adaptive Parameter Estimation of Unbalanced and Distorted Power Grid. IEEE Access. 2020; 8 (99):8512-8519.
Chicago/Turabian StyleHafiz Ahmed; Mohamed Benbouzid; Mominul Ahsan; Alhussein Albarbar; Mohammad Shahjalal. 2020. "Frequency Adaptive Parameter Estimation of Unbalanced and Distorted Power Grid." IEEE Access 8, no. 99: 8512-8519.
Nikita Hari; Mominul Ahsan; Sridhar Ramasamy; Padmanaban Sanjeevikumar; Alhussein Albarbar; Frede Blaabjerg. Gallium Nitride Power Electronic Devices Modeling Using Machine Learning. IEEE Access 2020, 8, 119654 -119667.
AMA StyleNikita Hari, Mominul Ahsan, Sridhar Ramasamy, Padmanaban Sanjeevikumar, Alhussein Albarbar, Frede Blaabjerg. Gallium Nitride Power Electronic Devices Modeling Using Machine Learning. IEEE Access. 2020; 8 ():119654-119667.
Chicago/Turabian StyleNikita Hari; Mominul Ahsan; Sridhar Ramasamy; Padmanaban Sanjeevikumar; Alhussein Albarbar; Frede Blaabjerg. 2020. "Gallium Nitride Power Electronic Devices Modeling Using Machine Learning." IEEE Access 8, no. : 119654-119667.
The aim of this work is to develop an intelligent wireless system for monitoring vehicle speed, identify speeding vehicles and imposing penalty for the speeding offenders. A prototype system has been developed in a laboratory environment to generate random speed data using a mechanical wheel (acts as a vehicle), measure the speed data with a Shimmer wireless sensor and transfer the data wirelessly to a server computer for further analysis. Software interface has been developed using Java based socket-programming to monitor the vehicle speed in a server computer and to send the data associated with a speeding vehicle to a remotely placed client computer. The functionality of the software has been tested by experimenting different traffic scenarios. If the vehicle speed is higher than the set speed limit for the road, the system automatically detects it and generates a report with the time of speeding, vehicle number, vehicle speed, etc. The report is saved in a central database (client computer) in order to take further necessary actions for the speeding offender. The experimental evaluation results show that the system can measure and monitor the vehicle speeds wirelessly and manage the speeding data automatically.
Mominul Ahsan; Julfikar Haider; Jennifer McManis; M. Saleem J. Hashmi. Developing intelligent software interface for wireless monitoring of vehicle speed and management of associated data. IET Wireless Sensor Systems 2016, 6, 90 -99.
AMA StyleMominul Ahsan, Julfikar Haider, Jennifer McManis, M. Saleem J. Hashmi. Developing intelligent software interface for wireless monitoring of vehicle speed and management of associated data. IET Wireless Sensor Systems. 2016; 6 (3):90-99.
Chicago/Turabian StyleMominul Ahsan; Julfikar Haider; Jennifer McManis; M. Saleem J. Hashmi. 2016. "Developing intelligent software interface for wireless monitoring of vehicle speed and management of associated data." IET Wireless Sensor Systems 6, no. 3: 90-99.