# Applications of Machine Learning and Computer Vision in Industry 4.0
## 3 An Overview of Published Articles
(contribution 3) delves into the realm of predicting rutting development in pavements that have been overlaid with flexible materials, employing artificial neural networks (ANNs) as the tool of choice. Pavement maintenance and repair are crucial aspects of ensuring safe and efficient transportation infrastructure, and accurate prediction of rutting development plays a pivotal role in optimizing these processes. The authors leverage data meticulously compiled from the Canadian Long-Term Pavement Performance (LTPP) program to establish a robust model. Their meticulous analysis reveals that a multitude of factors significantly influence rutting development, including traffic volume and composition, prevailing climatic conditions, the inherent characteristics of the pavement materials themselves, and any maintenance interventions that have been undertaken. By incorporating these diverse factors into the ANN model, the researchers empower it to discern the complex, often nonlinear, relationships that exist between these variables and the observed rutting development. This empowers transportation authorities and pavement maintenance crews to make more informed decisions regarding pavement maintenance strategies, ultimately contributing to the longevity and overall performance of our transportation infrastructure. In general, this article presents a valuable contribution to the field of pavement maintenance by proposing a novel and effective method for predicting rutting development using artificial neural networks. By leveraging the power of ANNs and meticulously considering the various influencing factors, the authors provide valuable insights that can be harnessed to optimize pavement maintenance strategies and ensure the enduring integrity of our transportation infrastructure. A study by [PERSON] and [PERSON] (contribution 4) presents TextControlGAN, a novel text-to-image synthesis model based on generative adversarial networks (GANs). TextControlGAN addresses the limitations of existing models that utilize the conditional GAN (cGAN) framework. It leverages the ControlGAN framework, incorporating an independent regressor trained with data augmentation (DA) techniques to enhance its text-conditioning capabilities. Evaluations were conducted using a bird image dataset containing roughly 30,000 image-text pairs. Compared to the cGAN-based GAN-INT-CLS model, TextControlGAN achieved a significant improvement of 17.6% in Inception Score (IS) and a substantial reduction of 36.6% in Frechet Inception Distance (FID). Qualitative comparisons revealed that images generated by TextControlGAN more faithfully adhered to the provided textual descriptions, unlike alternative models that sometimes failed to accurately capture the context. The key to TextControlGAN's success lies in its unique approach. By employing an independent regressor trained with DA techniques, the model effectively learns the intricate relationship between text and image features. This learning method, applicable to various model structures, paves the way for further exploration and development in the field of text-to-image synthesis. [PERSON] et al. (contribution 5) present an article about a study on the relationship between light environment parameters and driver pupil diameter. It discusses using a convolutional neural network (CNN) to analyze the data. The study found that the way the lights are laid out in the tunnel has the biggest effect on pupil diameter. Other factors that affect pupil diameter include the color temperature of the light source, the height of the reflective coating on the sidewalls, and the color of the reflective coating on the sidewalls. The researchers also looked at how these factors interact with each other. For example, they found that the effect of color temperature was more pronounced when the lights were laid out in a certain way.