Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Author: search.filters.author.Patil, M.A.Has files: No

Search Results

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    Effect of porosity and gradation of Galfenol-D on vibration suppression of bidirectional functionally graded beam
    (Elsevier Ltd, 2022) Patil, M.A.; Kadoli, R.
    A differential quadrature technique is used to investigate the vibration control response of bidirectional functionally graded imperfect aluminium-Terfenol-D and aluminium-Galfenol-D. In both transverse and axial orientations, the Terfenol-D and Galfenol-D materials are functionally graded. The bidirectional FG imperfect aluminium-Terfenol-D and aluminium-Galfenol-D governing equation is based on generalised beam theory. With consideration of uniform and uneven porosity distribution, the influence of porosity and an externally applied magnetic field on free vibration suppression behaviour is recorded. In terms of damping performance, Galfenol-D exceeds Terfenol-D. The effect of Lorentz force on the fundamental frequencies is discussed. It's worth noting that the overdamping response of the aluminium-Galfenol-D beam takes less than 0.1 s, but the exponential decay of the aluminium-Terfenol-D beam takes just 0.9 s. © 2022
  • No Thumbnail Available
    Item
    Smart Attendance Management System using IoT
    (Institute of Electrical and Electronics Engineers Inc., 2022) Patil, M.A.; Parane, K.; Sivaprasad, D.D.; Poojara, S.; Lamani, M.R.
    Taking student attendance is mandatory in an educational organization, and maintaining those attendance plays a vital role. The conventional way of taking student attendance in any institution is time-consuming and challenging, because in the conventional procedure taking attendance/Roll call is performed manually by calling student names as per their roll numbers and marking 'absent(A)' or 'present(P)' on the attendance/logbook accordingly in every class per day. To improve teaching efficiency/teaching time in classrooms by reducing the time required for Roll call's, we have proposed a biometric student attendance system based on IoT. The proposed system records students' attendance using the facial-based biometric system and stores the attendance details on the server through the internet. In this system, the Raspberry pi camera captures the student face images and compares them with the stored images in the database. If the captured image is comparable with the stored image, then the student's attendance is recorded on the remote server as a present(P) in class; otherwise, attendance is recorded as absent (A). The developed system has been tested for sample classes, and the results proved that the system is simple, cost-effective, and portable for managing students' attendance. © 2022 IEEE.
  • No Thumbnail Available
    Item
    Terfenol-D Composite Actuator For Vibration Suppression Applications: A Review
    (Springer Science and Business Media Deutschland GmbH, 2023) Patil, M.A.; Kadoli, R.
    Terfenol-D can be employed as a sensor and/or actuator in applications such as vibration control, noise reduction, health monitoring and energy harvesting. Several scholars have paid significant attention in the previous research to theoretical modelling and formulation for free and force vibration of laminated composite beams/plates. The free vibration control of beams and plates that have been combined with a Terfenol-D layer is the only topic of this review paper. This article discusses the potential scope for future study on laminated composite beams and plates combined with Terfenol-D layer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
  • No Thumbnail Available
    Item
    Hybrid Deep Learning-Based Potato and Tomato Leaf Disease Classification
    (Springer Science and Business Media Deutschland GmbH, 2024) Patil, M.A.; Manur, M.; Laxuman, C.; Parane, K.; Dodamani, B.M.; Sunkad, G.
    Predicting potato and tomato leaf disease is vital to global food security and economic stability. Potatoes and tomatoes are among the most important staple crops, providing essential nutrition to millions worldwide. However, many tomato and potato leaf diseases can seriously reduce food productivity and yields. We are proposing a hybrid deep learning model that combines optimized CNN (OCNN) and optimized LSTM (OLSTM). The weight values of LSTM and CNN models are optimized using the modified raindrop optimization (MRDO) algorithm and the modified shark smell optimization (MSSO) algorithm, respectively. The OCNN model is used to extract potato leaf image features and then fed into the OLSTM model, which handles data sequences and captures temporal dependencies from the extracted features. Precision, recall, F1-score, and accuracy metrics are used to analyze the output of the proposed OCNN-OLSTM model. The experimental performance is compared without optimizing the CNN-LSTM model, individual CNN and LSTM models, and existing MobileNet and ResNet50 models. The presented model results are compared with existing available work. We have received an accuracy of 99.25% potato and 99.31% for tomato. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.