Optimizing degradable plastic density prediction: a coarse-to-fine Deep Neural Network approach

Density is an important property for the production of high-quality degradable plastics. Density is useful to determine the type of plastic material and to detect physical changes in the plastic material. In this paper, a novel technique for predicting the density of degradable plastics using Deep Neural Networks (DNN) is presented. The aim was to reduce the dimension of the inputs in order to establish a strong relationship between the inputs using principal component analysis (PCA). The results show that the combination of polyethylene, oil palm biomass, starch and palm oil has a greater impact on predicting the density of degradable plastics. Subsequently, the number of hidden neurons is determined by a coarse-to-fine search to develop the network topology of the DNN model for predicting the density of degradable plastics. The developed DNN model consists of 4 input neurons, 62 neurons in the first hidden layer, 31 neurons in the second hidden layer and one output neuron. The developed DNN model showed high accuracy with the lowest values for RMSE, MAE and MSE, indicating that the use of a DNN model is a suitable method for predicting the density of degradable plastics. Furthermore, this study has the potential to make rapid and accurate predictions about the physical properties of degradable plastics in the context of polymers

Novel improvements of empirical wireless channel models and proposals of machine-learning-based path loss prediction models for future communication networks.

Doctoral Degree. University of KwaZulu-Natal, Durban.Path loss is the primary factor that determines the overall coverage of networks. Therefore, designing reliable wireless communication systems requires accurate path loss prediction models. Future wireless mobile systems will rely mainly on the super-high frequency (SHF) and the millimeter-wave (mmWave) frequency bands due to the massively available bandwidths that will meet projected users’ demands, such as the needs of the fifth-generation (5G) wireless systems and other high-speed multimedia services. However, these bands are more sensitive and exhibit a different propagation behavior compared to the frequency bands below 6 GHz. Hence, improving the existing models and developing new models are vital for characterizing the wireless communication channel in both indoor and outdoor environments for future SHF and mmWave services. This dissertation proposes new path loss and LOS probability models and efficiently improves the well-known close-in (CI) free space reference distance model and the floating-intercept (FI) model. Real measured data was taken for both line-of-sight (LOS) and non-line-of-sight (NLOS) communication scenarios in a typical indoor corridor environment at three selected frequencies within the SHF band, namely 14 GHz, 18 GHz, and 22 GHz. The research finding of this work reveals that the proposed models have better performance in terms of their accuracy in fitting real measured data collected from measurement campaigns. In addition, this research studies the impact of the angle of arrival and the antenna heights on the current and improved CI and FI models. The results show that the proposed improved models provide better stability and sensitivity to the change of these parameters. Furthermore, the mean square error between the models and their improved versions was presented as another proof of the superiority of the proposed improvement. Moreover, this research shows that shadow fading’s standard deviation can have a notable reduction in both the LOS and NLOS scenarios (especially in the NLOS), which means higher precision in predicting the path loss compared to the existing standard models. After that, the dissertation presents investigations on high-ordering the dependency of the standard CI path loss model on the distance between the transmitting and the receiving antennas at the logarithmic scale. Two improved models are provided and discussed: second-order CI and third-order CI models. The main results reveal that the proposed two models outperform the standard CI model and notable reductions in the shadow fading’s standard deviation values as the model’s order increases, which means that more precision is provided. This part of the dissertation also provides a trade-off study between the model’s accuracy and simplicity