DeepQ Residue Representation of Moving Object Images using YOLO in Video Surveillance Environment

The IAEA photo evaluation software does have functions for scene-alternate recognition, black photo detection, and deficient scene analysis, even though its capabilities are not at their highest. The current workflows for detecting safeguards-relevant activities heavily rely on inspectors' laborious visual examination of surveillance videos, which is a time-consuming and error-prone process. The paper proposes using item-based totally movement detection and deep gadget learning to identify fun items in video streams in order to improve method accuracy and reduce inspector workload. An attitude transformation model is used to estimate historical movements, and a deep learning classifier trained on manually categorized datasets is used to identify shifting applicants within the history subtracted image. Through optical glide matching, we identify spatio-temporal tendencies for each and every shifting item applicant and then prune them solely based on their movement patterns in comparison to the past. In order to improve the temporal consistency of the various candidate detections, a Kalman clear out is performed on pruned shifting items. A UAV-derived video dataset was used to demonstrate the rules. The results demonstrate that our set of rules can effectively target small UAVs with limited computing power

Identification of Solar Photovoltaic Model Parameters using an Improved Gradient-Based Optimization Algorithm with Chaotic Drifts

When discussing the commercial applications of photovoltaic (PV) systems, one of the most critical problems is to estimate the efficiency of a PV system because current (I) – voltage (V) and power (P) – voltage (V) characteristics are highly non-linear. It should be noted that most of the manufacturer’s datasheets do not have complete information on the electrical equivalent parameters of PV systems that are necessary for simulating an effective PV module. Compared to conventional approaches, computational optimization and global research strategies are more acceptable as an effective alternative to parameter estimation of solar PV modules. Recently, a Gradient-based optimizer (GBO) is reported to solve the engineering design optimization problems. However, the basic GBO algorithm is stuck in local optima when handling complex non-linear problems. In this sense, this paper presents a new optimization technique called the Chaotic-GBO (CGBO) algorithm to derive the parameters of PV modules while offering precise I-V and P-V curves. To this end, the CGBO algorithm is based on a chaotic generator to obtain the PV parameters combined with the GBO algorithm. There are five case studies considered to validate the performance of the proposed CGBO algorithm. A quantitative and qualitative performance evaluation reveals that the proposed CGBO algorithm has improved results than other state-of-the-art algorithms in terms of accuracy and robustness when obtaining PV parameters. The average RMSE values and runtime of five case studies are equal to 9.8427E-04, 2.3700E-04, 2.4251E-03, 4.3524E-03 and 1.8349E-03, and 18.44, 17.78, 18.18, 18.28 and 17.97, respectively. The results proved the superiority of the proposed CGBO algorithm over the different selected algorithms. For future research, this study will be backed up with external support at https://premkumarmanoharan.wixsite.com/mysite.UCD Energy Institut