Ramesh Kumar

Ramesh Kumar, Dilawar Hussain, Ruchita

Non-linear controller is preferred to linear controller due to non-linear operation of DSTATCOM. System dynamic can be improved by regulating and fixing the capacitor DC voltage in DSTATCOM. The nonlinear control is based on exact linearization via feedback. There is a PI controller in this system to regulate DC voltage. In conventional scheme, the trial and error method is used to determine PI values. Exact calculation to optimize PI coefficients can be carried out to reduce disturbances in DC link voltage and thus, in this paper, Particle Swarm Optimization is applied. As a result, Capacitor voltage tracks the reference values which have less vibration than conventional status. Both trial and error method and PSO are implemented. A set of corresponding diagrams achieved by these two methods are offered to demonstrate the effectiveness of new method. Optimizations and Simulations are worked out in MATLAB environment.

Kailas PS, Selvakumaran R, Palani Murugan et al.

In recent years, significant advancements have been made in deep learning-based object detection algorithms, revolutionizing basic computer vision tasks, notably in object detection, tracking, and segmentation. This paper delves into the intricate domain of Small-Object-Detection (SOD) within satellite imagery, highlighting the unique challenges stemming from wide imaging ranges, object distribution, and their varying appearances in bird's-eye-view satellite images. Traditional object detection models face difficulties in detecting small objects due to limited contextual information and class imbalances. To address this, our research presents a meticulously curated dataset comprising 3000 images showcasing cars, ships, and airplanes in satellite imagery. Our study aims to provide valuable insights into small object detection in satellite imagery by empirically evaluating state-of-the-art models. Furthermore, we tackle the challenges of satellite video-based object tracking, employing the Byte Track algorithm on the SAT-MTB dataset. Through rigorous experimentation, we aim to offer a comprehensive understanding of the efficacy of state-of-the-art models in Small-Object-Detection for satellite applications. Our findings shed light on the effectiveness of these models and pave the way for future advancements in satellite imagery analysis.