Browsing by Author "HOSSAIN, MD SHAKHAWAT"

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Fuzzy Logic - Based Maximum Power Point Tracking Of Photovoltaic System
(Department of Electrical and Electronic Engineering, 2022-04) HOSSAIN, MD SHAKHAWAT; KABIR, MAHMUDUL
In this modern era, the use of Solar energy is increasing day by day. Keeping in mind the needs of the future demand of Solar energy, it is very essential for us to get maximum power tracking point in order to utilize Solar energy properly. So, in this thesis work, we proposed a Fuzzy Logic Controller system to track maximum power point. This system is used to get the Maximum Point of a PV array. It is the most suitable way for the human decision making mechanism, providing the operation and showing every step on the basis of Boolean logic system. This technique contact about on the artificial intelligence with the help of membership function which is inspired on the basis of human perception. We knew that there are some techniques to find the maximum power point, but our proposed system has better effectiveness among these techniques. In this work, we compared with classical PI, PD, PID, FPID controllers and also compared performance analysis with P&O and INC method of maximum power point technique. In our study, a Boost converter is used with fuzzy sets method. In our proposed system, we also showed how to design a fuzzy system combining with boost converter and find maximum power of a PV array on fixed temperature and fixed irradiance. At finally we get a result, that the fuzzy controller has an excellent performance to track solar MPPT over P&O, INC method and others controllers.
Tissue Artifact Segmentation and Severity Assessment for Automatic Analysis using WSI
(Independent University, Bangladesh, 2023-02) HOSSAIN, MD SHAKHAWAT; SHAHRIAR, GALIB MUHAMMAD; SYEED, M M MAHBUBUL; FAISAL UDDIN, MOHAMMAD; HASAN, MAHADY; HOSSAIN, MD SAKIR; BARI, RUBINA
Traditionally, pathological analysis and diagnosis are performed by manually eyeballing glass-slide specimens under a microscope by an expert. The whole slide image (WSI) is the digital specimen produced from the glass slide. WSI enabled specimens to be observed on a computer screen and led to computational pathology where computer vision and artificial intelligence are utilized for automated analysis and diagnosis. With the current computational advancement, the entire WSI can be analyzed autonomously without human supervision. However, the analysis could fail or lead to wrong diagnosis if the WSI is affected by tissue artifacts such as tissue fold or air bubbles depending on the severity. Existing artifact detection methods rely on experts for severity assessment to eliminate artifact-affected regions from the analysis. This process is time-consuming, exhausting and undermines the goal of automated analysis or removal of artifacts without evaluating their severity, which could result in the loss of diagnostically important data. Therefore, it is necessary to detect artifacts and then assess their severity automatically. In this paper, we propose a system that incorporates severity evaluation with artifact detection utilizing convolutional neural networks (CNN). The proposed system uses DoubleUNet to segment artifacts and an ensemble network of six fine-tuned CNN models to determine severity. This method outperformed current state-of-the-art in accuracy by 9% for artifact segmentation and achieved a strong correlation of 97% with the pathologist’s evaluation for severity assessment. The robustness of the system was demonstrated using our proposed heterogeneous dataset and practical usability was ensured by integrating it with an automated analysis system.