A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers

The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope(SEM)images of the electrospun nanofiber,to ensure that no structural defects are produced.The presence of anomalies prevents practical application of the electrospun nanofibrous material in nanotechnology.Hence,the automatic monitoring and quality control of nanomaterials is a relevant challenge in the context of Industry 4.0.In this paper,a novel automatic classification system for homogenous(anomaly-free)and non-homogenous(with defects)nanofibers is proposed.The inspection procedure aims at avoiding direct processing of the redundant full SEM image.Specifically,the image to be analyzed is first partitioned into subimages(nanopatches)that are then used as input to a hybrid unsupervised and supervised machine learning system.In the first step,an autoencoder(AE)is trained with unsupervised learning to generate a code representing the input image with a vector of relevant features.Next,a multilayer perceptron(MLP),trained with supervised learning,uses the extracted features to classify non-homogenous nanofiber(NH-NF)and homogenous nanofiber(H-NF)patches.The resulting novel AE-MLP system is shown to outperform other standard machine learning models and other recent state-of-the-art techniques,reporting accuracy rate up to92.5%.In addition,the proposed approach leads to model complexity reduction with respect to other deep learning strategies such as convolutional neural networks(CNN).The encouraging performance achieved in this benchmark study can stimulate the application of the proposed scheme in other challenging industrial manufacturing tasks.

Affective Spaces in Urban Transformation’s Contexts

According to the contributions coming from different fields of research-from aesthetics to cognitive science-the paper intends to address the topic of urban transformation within the framework of the concept of “affective space”, which associates the emotions with all stimuli both internal to the agent and within its environment. The central research question will be： what is the influence of the affective sphere on changes that take place in the city and vice versa how much do these changes affect the emotional sphere？ By placing subjects at the center of the research, the paper intends to study the relationship between individuals-as well as groups and communities-and urban spaces they inhabit. This can be done by guaranteeing centrality to the pre-reflective emotional impact that spatial situations produce on subjects, where for “spatial situation” it is intended the inclusive description of a specific condition, including both the material articulation of space and its intangible qualities that influence the subject’s emotional sphere.