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

Rice science 2022年第5期29卷 489-498页

作者： Pandia rajan JEYARAJ Siva Prakash ASOKAN edward rajan samuel nadar Department of Electrical and Electronics Engineering Mepco Schlenk Engineering CollegeSivakasi 626005India

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.

关键词： deep learning algorithm rice defect classification computer vision agriculture automated visual grading system

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Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

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Computers, Materials & Continua 2021年第7期68卷 553-567页

作者： Pandia rajan Jeyaraj Aravind Chellachi Kathiresan Siva Prakash Asokan edward rajan samuel nadar Hegazy Rezk Thanikanti Sudhakar Babu Mepco Schlenk Engineering College(Autonomous) SivakasiTamil NaduIndia College of Engineering at Wadi Addawaser Prince Sattam Bin Abdulaziz UniversityWadi Addawaser11991Saudi Arabia Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61517Egypt Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of TechnologyHyderabad500075India

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.

关键词： Neural network deep learning algorithm low-frequency oscillation resiliency assessment smart grid wide-area control

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