Accuracy study of image classification for reverse vending machine waste segregation using convolutional neural network

This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1-score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks

Development of Nonlinear Adaptive PI Controller For Improved Pneumatic Actuator System

The wide application of pneumatic actuator in electrical and electronics sectors are undeniable hence ask for a good control environment. PID controller is always known with easy implementation and good control performance. But the limitation of the PID static gains to effectively control the complex nonlinear system is unavoidable. This suggests the enhancement of the PI controller with a nonlinear adaptive interaction algorithm (AIA). The modification is introduced by integrating a nonlinear gain function that adaptively tunes the AIA parameter, hence resulting the best tuning of the PI control gains. The uncertainties and nonlinearities inherent in the system parameters are believed to be handled by the integration, therefore improving the controller performances while maintaining the pneumatic actuator at the desired position. It was proved that improved error performance criteria’s, settling time and overshoot were resulted by the nonlinear AIA PI compared to fix AIA PI. Besides, the nonlinear AIA PI has successfully reduced the overshoot to 5.35% and 6.70% compared to optimal AIA PI and optimal PI controller, respectively. To conclude, the development of the proposed controller is demonstrated to function well and offers an alternative tuning strategy in other electronical and electronic engineering applications

Development of Nonlinear Adaptive PI Controller For Improved Pneumatic Actuator System

The wide application of pneumatic actuator in electrical and electronics sectors are undeniable hence ask for a good control environment. PID controller is always known with easy implementation and good control performance. But the limitation of the PID static gains to effectively control the complex nonlinear system is unavoidable. This suggests the enhancement of the PI controller with a nonlinear adaptive interaction algorithm (AIA). The modification is introduced by integrating a nonlinear gain function that adaptively tunes the AIA parameter, hence resulting the best tuning of the PI control gains. The uncertainties and nonlinearities inherent in the system parameters are believed to be handled by the integration, therefore improving the controller performances while maintaining the pneumatic actuator at the desired position. It was proved that improved error performance criteria’s, settling time and overshoot were resulted by the nonlinear AIA PI compared to fix AIA PI. Besides, the nonlinear AIA PI has successfully reduced the overshoot to 5.35% and 6.70% compared to optimal AIA PI and optimal PI controller, respectively. To conclude, the development of the proposed controller is demonstrated to function well and offers an alternative tuning strategy in other electronical and electronic engineering applications

Direct Adaptive Predictive Control For Wastewater Treatment Plant

The purpose of this paper was to design a much simpler control method for a wastewater treatment plant. The work proposes a direct adaptive predictive control (DAMPC) also known as subspace predictive control (SPC) as a solution to the conventional one. The adaptive control structure is based on the linear model of the process and combined with numerical algorithm for subspace state space system identification (N4SID). This N4SID plays the role of the software sensor for on-line estimation of prediction matrices and control matrices of the bioprocess, joint together with model predictive control (MPC) in order to obtain the optimal control sequence. The performances of both estimation and control algorithms are illustrated by simulation results. Stability analysis is done to investigate the response of the system-proposed when parameter changes exist. This project proves that subspace-adaptive method has a large number of important and useful advantages, primarily the application ability to Multi Input Multi Output (MMO) systems, and the low requirements on prior system information. Given the advantages observed, the most likely areas of application for the proposed algorithm are multivariable processes, about which little information is known such as this wastewater treatment plant. Hence, direct adaptive predictive control (DAMPC) can provide simplicity and good performance in of an activated sludge process