Fuzzy Logic Based Dam Water Shutter Control System by Using Water Level and Rainfall Condition in Raining Season

Nay Thazin Htun, Moh Moh Nyunt Aung — Sun, 18 Jul 2021 15:14:24 +0000

A robust water shutter management system ensures that the water does not overflow and destroy or damage the dam. During the rainy season, care must be taken when dams conserve water, if the reservoir volume is too high, the risk of dam failure may be increased. So water level control is a special matter in the rainy season. Fuzzy logic sets provide better control than binary logic-based methods because they are used to determine the meaning of qualitative values for controller inputs and outputs, such as small, medium, and large control actions. This system used the fuzzy logic control theory in the water shutter management system to get smoothness motor control values of small, very small, medium, large, and very large. The system uses ultrasonic sensors to detect water levels, rain sensors to detect rain, and fuzzy logic controls to control the PWM duty cycle to the shutter gate motor driver circuit based on the detection of these two sensors. This control strategy is implemented with Arduino Uno.

Recognition of West African Indigenous Fruits using a Convolutional Neural Network Model

Wed, 04 Aug 2021 07:44:53 +0000

The. Fruit recognition involves the extraction and processing of relevant features from fruit images in order to deduce the categories of that fruit. Due to its importance to human health and sustainability, various systems exist for recognition of fruits, although none exist for recognition of west Africa's indigenous fruits. This research developed a fruit recognition system using a convolutional neural network (CNN) based model. Five west Africa indigenous fruits were selected, while “images were directly used as input to CNN based model of (3 convolutional layers, 3 max pooling layers and 1 fully connected layer) for training and recognition without features extraction process. The study further presents a transfer learning on visual geometry group 16 and ResNet models for result comparison. Using the optimal training set, the proposed CNN based model produced a recognition rate of 96%.

International Journal of Computer (IJC)

Fuzzy Logic Based Dam Water Shutter Control System by Using Water Level and Rainfall Condition in Raining Season

Recognition of West African Indigenous Fruits using a Convolutional Neural Network Model