5 research outputs found
Position control of a DC Servo multi-motor for Lower-Limb Exoskeletons
Full text linkThe angular position control of DC servo multimotor using PID control has been proposed. The PID controller tuning is applied to the Ziegler-Nichols (ZN) method. The angular position control of the DC motor is built in the mixed
(cascade) system consisting of six electric motors to drive the joint movement of the lower-limb exoskeleton (hip, knees, and ankle). The PID control design is implemented in the lower-limb exoskeleton and experimental setup and simulation are carried out to obtain proper proportional, integral, and derivative gains. Based on these gains, the PID controller gives a good response, without overshoot, fixed settling time, and is precise to get the desired angle
Design of real-time weather monitoring system based on mobile application using automatic weather station
No full textComparative performance analysis of convolutional neural network-architectures on coffee-bean roast classification
Full text linkThe classification of coffee bean roast levels using Agtron standards has evolved from traditional subjective methods to technology-driven approaches employing advanced artificial intelligence. Recent advancements in computer vision have demonstrated the capability of convolutional neural networks (CNNs) in providing objective and consistent roast level classification compared to human visual assessment, which is prone to variability and subjectivity. This research presents a performance analysis of five CNN architectures (AlexNet, ResNet, MobileNet, VGGNet, and DenseNet) for classifying coffee beans into eight distinct Agtron roast levels. The comprehensive methodology encompasses four phases: i) data acquisition, ii) image preprocessing, iii) model training and validation, and iv) evaluation metric. During training-validation, DenseNet outperformed other models, achieving 99.702% training accuracy and 77.68% validation accuracy. In the testing evaluation, DenseNet also led with an average testing accuracy of 93.8%, followed by ResNet at 92.6%, VGGNet and AlexNet both at 92.4%, and MobileNet at 89.7%. The results show that the DenseNet shows promise in classifying Agtron coffee-bean roast classification
Implementation of Image Processing and CNN for Roasted-Coffee Level Classification
Full text linkThe roasting process of coffee beans plays a crucial role in the development of chemicals responsible for the rich color and complex flavors characteristic of well-roasted coffee. One approach to understanding this process involves assessing the roast level, which varies in color from light to dark, with intermediate levels in between. In this study, image processing was performed using Convolutional Neural Networks (CNNs), a widely used method for image classification. The objective was to utilize the LAB color model and the CNN framework to classify the roast levels of coffee beans based on images from files or video streams. The study also details the hardware and software tools employed. A user-friendly graphical interface was developed to ensure ease of use, requiring minimal training for efficient operation. The research successfully designed, developed, and implemented an application for classifying coffee bean roast levels using two methods: LAB color model image processing and the CNN model. Consequently, the system can recognize roast levels based on the outputs from both the LAB model and the CNN model. This research represents a preliminary effort and requires further development to support more extensive studies. Ultimately, it serves as a foundation for future exploration and the application of embedded system-based solutions for assessing coffee bean maturity levels in alignment with Agtron classification standards
