Garbage Classification and Management Control System Based on Convolutional Neural Networks

Yi Wang

doi:10.54097/x0hysn75

Authors

Yi Wang

DOI:

https://doi.org/10.54097/x0hysn75

Keywords:

Intelligent management, Control system, Discrete-time integrator, Pressure trasducer.

Abstract

With the rapid development of technology and economy, research on the industrial control field is becoming more and more professional. In recent years, garbage collection and management have become more and more difficult. Thus, it is vital to make a control system to deal with this problem. In past studies, most research paid attention to developing waste disposal. This study analyze a kind of control system which used MATLAB and Simulink to manage garbage more easily than traditional control systems. The main aim of this research is to find out a control system that can help manage and recycle garbage in a better and greener way. By using CNN in Simulink, this control system can suit both constant garbage pouring speed and ramp garbage pouring speed. Compared with a light sensor, a pressure transducer has wider use. Although a light sensor can receive light to send signals, which is convenient, it still has lots of drawbacks and limitations. Because of the pressure transducer, this control system is sensitive to the garbage. This kind of sensor makes a big contribution to putting the control system into use. In the future, this control system can make a big contribution to managing garbage and protecting the environment.

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References

[1] Yam B., Zhao R., Elshakankiri M. Aqua garbage collector: utilizing AI and IoT for efficient underwater garbage classification. Cluster Computing, 28 (1): 386, 2025.

[2] Zucai T., Luping W., Miaoyan Q., et al. Design of a garbage classification system based on deep transfer learning. Journal of Ambient Intelligence and Humanized Computing, 16 (1): 225–232, 2025.

[3] Mustafy T., Rahman M.T.U. MATLAB. In: Statistics and Data Analysis for Engineers and Scientists. Transactions on Computer Systems and Networks. Singapore: Springer, 2024.

[4] Huang L., Yao C., Zhang L., et al. Enhancing computer image recognition with improved image algorithms. Scientific Reports, 14 (1): 13709, 2024.

[5] Papageorgiou V.E. Boosting epidemic forecasting performance with enhanced RNN-type models. Operational Research International Journal, 25 (1): 77, 2025.

[6] Zhou Y., Wang Z., Zheng S., Zhou L., Dai L., Luo H., Zhang Z., Sui M. Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development. Alexandria Engineering Journal, 108: 1–12, 2024.

[7] Deng S., Zhou J. Prediction of remaining useful life of aero-engines based on CNN-LSTM-Attention. International Journal of Computational Intelligence Systems, 17 (1): 232, 2024.

[8] Alshingiti Z., Alaqel R., Al-Muhtadi J., Haq Q.E.U., Saleem K., Faheem M.H. A deep learning-based phishing detection system using CNN, LSTM, and LSTM-CNN. Electronics, 12 (1): 232, 2023.

[9] Zhang Y., Liu F., Zhou Z., et al. Near-zero nonlinear error pressure sensor based on piezoresistor sensitivity matching for wind tunnel pressure test. Microsystems & Nanoengineering, 11 (1): 122, 2025.

[10] Shi N., Yang J., Cao Z., Jin X. A programmable ambient light sensor with dark current compensation and wide dynamic range. Sensors, 24 (1): 3396, 2024.