Computer-Assisted Real-Time Rice Variety Learning Using Deep Learning Network

Due to the inconsistency of rice variety,agricultural industry faces an important challenge of rice grading and classification by the traditional grading system.The existing grading system is manual,which introduces stress and strain to humans due to visual inspection.Automated rice grading system development has been proposed as a promising research area in computer vision.In this study,an accurate deep learning-based non-contact and cost-effective rice grading system was developed by rice appearance and characteristics.The proposed system provided real-time processing by using a NI-myRIO with a high-resolution camera and user interface.We firstly trained the network by a rice public dataset to extract rice discriminative features.Secondly,by using transfer learning,the pre-trained network was used to locate the region by extracting a feature map.The proposed deep learning model was tested using two public standard datasets and a prototype real-time scanning system.Using AlexNet architecture,we obtained an average accuracy of 98.2%with 97.6%sensitivity and 96.4%specificity.To validate the real-time performance of proposed rice grading classification system,various performance indices were calculated and compared with the existing classifier.Both simulation and real-time experiment evaluations confirmed the robustness and reliability of the proposed rice grading system.

Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

The power transfer capability of the smart transmission gridconnected networks needs to be reduced by inter-area oscillations.Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks.This fact is more noticeable in smart grid-connected systems.The smart grid infrastructure has more renewable energy resources installed for its operation.To overcome this problem,a deep learning widearea controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes.The proposed Deep Wide Area Controller(DWAC)uses the Deep Belief Network(DBN).The network weights are updated based on real-time data from Phasor measurement units.Resilience assessment based on failure probability,financial impact,and time-series data in grid failure management determine the norm H2.To demonstrate the effectiveness of the proposed framework,a time-domain simulation case study based on the IEEE-39 bus system was performed.For a one-channel attack on the test system,the resiliency index increased to 0.962,and inter-area dampingξwas reduced to 0.005.The obtained results validate the proposed deep learning algorithm’s efficiency on damping inter-area and local oscillation on the 2-channel attack as well.Results also offer robust management of power system resilience and timely control of the operating conditions.