Artificial Intelligence in Process Engineering

In recent years, the field of Artificial Intelligence (AI) is experiencing a boom, caused by recent breakthroughs in computing power, AI techniques, and software architectures. Among the many fields being impacted by this paradigm shift, process engineering has experienced the benefits caused by AI. However, the published methods and applications in process engineering are diverse, and there is still much unexploited potential. Herein, the goal of providing a systematic overview of the current state of AI and its applications in process engineering is discussed. Current applications are described and classified according to a broader systematic. Current techniques, types of AI as well as pre- and postprocessing will be examined similarly and assigned to the previously discussed applications. Given the importance of mechanistic models in process engineering as opposed to the pure black box nature of most of AI, reverse engineering strategies as well as hybrid modeling will be highlighted. Furthermore, a holistic strategy will be formulated for the application of the current state of AI in process engineering

A sensory system for robots using evolutionary artificial neural networks.

The thesis presents the research involved with developing an Intelligent Vision System for an animat that can analyse a visual scene in uncontrolled environments. Inspiration was drawn both from Biological Visual Systems and Artificial Image Recognition Systems. Several Biological Systems including the Insect, Toad and Human Visual Systems were studied alongside popular Pattern Recognition Systems such as fully connected Feedforward Networks, Modular Neural Networks and the Neocognitron. The developed system, called the Distributed Neural Network (DNN) was based on the sensory-motor connections in the common toad, Bufo Bufo. The sparsely connected network architecture has features of modularity enhanced by the presence of lateral inhibitory connections. It was implemented using Evolutionary Artificial Neural Networks (EANN). A novel method called FUSION was used to train the DNN, which is an amalgamation of several concepts of learning in Artificial Neural Networks such as Unsupervised Learning, Supervised Learning, Reinforcement Learning, Competitive Learning, Self-organisation and Fuzzy Logic. The DNN has unique feature detecting capabilities. When the DNN was tested using images that comprised of combination of features used in the training set, the DNN was successful in recognising individual features. The combinations of features were never used in the training set. This is a unique feature of the DNN trained using Fusion that cannot be matched by any other popular ANN architecture or training method. The system proved to be robust in dealing with New and Noisy Images. The unique features of the DNN make the network suitable for applications in robotics such as obstacle avoidance and terrain recognition, where the environment is unpredictable. The network can also be used in the field of Medical Imaging, Biometrics (Face and Finger Print Recognition) and Quality Inspection in the Food Processing Industry and applications in other uncontrolled environments

Vision and learning algorithms for service robots: the task of welcoming visitors

En el context de la competició ERL-Service robots, hem desenvolupat software que permet a un robot TIAGo identificar i interactuar amb quatre persones concretes, a partir del reconeixement de la seva roba i la seva cara, i creant una màquina d'estats perquè el robot mostri diferents comportaments.In the context of the European Robotics League Service robot competition, we developed software which allowed a TIAGo robot to identify and interact with four different visitors, by recognizing their attire and face, and creating a state machine which allowed him to perform different actions

Deep Reinforcement Learning: A Brief Survey

Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higher-level understanding of the visual world. Currently, deep learning is enabling reinforcement learning (RL) to scale to problems that were previously intractable, such as learning to play video games directly from pixels. DRL algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of RL, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep RL, including the deep Q-network (DQN), trust region policy optimization (TRPO), and asynchronous advantage actor critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via RL. To conclude, we describe several current areas of research within the field

Keratan sulfate, a complex glycosaminoglycan with unique functional capability

From an evolutionary perspective keratan sulfate (KS) is the newest glycosaminoglycan (GAG) but the least understood. KS is a sophisticated molecule with a diverse structure, and unique functional roles continue to be uncovered for this GAG. The cornea is the richest tissue source of KS in the human body but the central and peripheral nervous systems also contain significant levels of KS and a diverse range of KS-proteoglycans with essential functional roles. KS also displays important cell regulatory properties in epithelial and mesenchymal tissues and in bone and in tumor development of diagnostic and prognostic utility. Corneal KS-I displays variable degrees of sulfation along the KS chain ranging from non-sulfated polylactosamine, mono-sulfated and disulfated disaccharide regions. Skeletal KS-II is almost completely sulfated consisting of disulfated disaccharides interrupted by occasional mono-sulfated N-acetyllactosamine residues. KS-III also contains highly sulfated KS disaccharides but differs from KS-I and KS-II through 2-O-mannose linkage to serine or threonine core protein residues on proteoglycans such as phosphacan and abakan in brain tissue. Historically, the major emphasis on the biology of KS has focused on its sulfated regions for good reason. The sulfation motifs on KS convey important molecular recognition information and direct cell behavior through a number of interactive proteins. Emerging evidence also suggest functional roles for the poly-N-acetyllactosamine regions of KS requiring further investigation. Thus further research is warranted to better understand the complexities of KS