Autonomous robot systems and competitions: proceedings of the 12th International Conference

This is the 2012’s edition of the scientific meeting of the Portuguese Robotics Open (ROBOTICA’ 2012). It aims to disseminate scientific contributions and to promote discussion of theories, methods and experiences in areas of relevance to Autonomous Robotics and Robotic Competitions. All accepted contributions are included in this proceedings book. The conference program has also included an invited talk by Dr.ir. Raymond H. Cuijpers, from the Department of Human Technology Interaction of Eindhoven University of Technology, Netherlands.The conference is kindly sponsored by the IEEE Portugal Section / IEEE RAS ChapterSPR-Sociedade Portuguesa de Robótic

Deep Recurrent Neural Networks for Fault Detection and Classification

Deep Learning is one of the fastest growing research topics in process systems engineering due to the ability of deep learning models to represent and predict non-linear behavior in many applications. However, the application of these models in chemical engineering is still in its infancy. Thus, a key goal of this work is assessing the capabilities of deep-learning based models in a chemical engineering applications. The specific focus in the current work is detection and classification of faults in a large industrial plant involving several chemical unit operations. Towards this goal we compare the efficacy of a deep learning based algorithm to other state-of-the-art multivariate statistical based techniques for fault detection and classification. The comparison is conducted using simulated data from a chemical benchmark case study that has been often used to test fault detection algorithms, the Tennessee Eastman Process (TEP). A real time online scheme is proposed in the current work that enhances the detection and classifications of all the faults occurring in the simulation. This is accomplished by formulating a fault-detection model capable of describing the dynamic nonlinear relationships among the output variables and manipulated variables that can be measured in the Tennessee Eastman Process during the occurrence of faults or in the absence of them. In particular, we are focusing on specific faults that cannot be correctly detected and classified by traditional statistical methods nor by simpler Artificial Neural Networks (ANN). To increase the detectability of these faults, a deep Recurrent Neural Network (RNN) is programmed that uses dynamic information of the process along a pre-specified time horizon. In this research we first studied the effect of the number of samples feed into the RNN in order to capture more dynamical information of the faults and showed that accuracy increases with this number e.g. average classification rates were 79.8%, 80.3%, 81% and 84% for the RNN with 5, 15, 25 and 100 number of samples respectively. As well, to increase the classification accuracy of difficult to observe faults we developed a hierarchical structure where faults are grouped into subsets and classified with separate models for each subset. Also, to improve the classification for faults that resulted in responses with low signal to noise ratio excitation was added to the process through an implementation of a pseudo random signal(PRS). By applying the hierarchical structure there is an increment on the signal-to-noise ratio of faults 3 and 9, which translates in an improvement in the classification accuracy in both of these faults by 43.0% and 17.2% respectively for the case of 100 number of samples and by 8.7% and 23.4% for 25 number samples. On the other hand, applying a PRS to excite the system has showed a dramatic increase in the classification rates of the normal state to 88.7% and fault 15 up to 76.4%. Therefore, the proposed method is able to improve considerably both the detection and classification accuracy of several observable faults, as well as faults considered to be unobservable when using other detection algorithms. Overall, the comparison of the deep learning algorithms with Dynamic PCA (Principal Component Analysis) techniques showed a clear superiority of the deep learning techniques in classifying faults in nonlinear dynamic processes. Finally, we develop these same techniques to different operational modes of the TEP simulation, achieving comparable improvements to the classification accuracies

Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design

Ethics as Harmony and Improvisation in Responsive Equilibrium: the Core Psychophysical Process as a bio-logical foundation for ethical engagement

In this thesis I address the ethics of corporeal being at a foundational level. Rather than starting the discussion of ethics at an abstract level founded in propositions and logical arguments about principles, I offer an holistic view of human engagement that recognises sensori-motor processes and our embodied engagements with the world as foundational to and integral with cognition and higher functions and social skills. I propose that the capacity of human beings to act in an ethically responsible way is built into our biological, psychosocial natures, and that ethical interaction is informed and enhanced by intentionally cultivating a particular psychophysical process. The Core Psychophysical Process (the CPP) that I have identified naturally underlies our interactions in the world as vertebrate creatures, grounds our primary and ongoing developmental and learning processes, and is integral with the process of developing our ethical ‘second nature.’ The CPP is expressed at a fundamental level in a reflexive neuro-musculo-skeletal expansive and contractive process that is integral with an experiential sequence of perception, reaction, and reflection leading to choice of action. There is a constant ebb and flow of contraction and expansion throughout the body which resonates with, in and through all of our experiences. It is integrated into processes of reasoning, interpretation, intentionality, emotion, valuing and habit, all of which, along with the abilities to inhibit, deliberate, and choose, are foundational to ethical action. Elements of the CPP are active at every level of corporeal being, from the fluent maintenance of equilibrium at neuronal level through to the dynamics of ethical deliberations and negotiations between people in society. In this thesis the Alexander Technique and processes in the Arts provide exemplars wherein the foundational intrinsic aspects and expressions of the CPP can be understood. In order to fully explore the impact of the CPP in human experience, I examine both theoretical and practical experimental experience with the CPP in relation to: historical and contemporary readings from different cultures in bioethics, ethics, philosophy, feminist philosophy, and the philosophy of mind; empirical investigations in cognitive science, physiology, and neuroscience; and Susan Hurley’s Shared Circuits Model. This is a phenomenological study, from a feminist and arts-based perspective. Arts Phenomenology starts with the question: ‘What is the experience of being with, acting with, with the intention to?’ That perspective leaves behind subject/object, mind/body dualities to understand human experience as extended and grounded in the embodied interactions of social being. I offer alternate conceptions of embodiment, and explore Bodily ‘I’dentity that reflects multi-sensory meaning-making grounded in experience

Handbook of the Cultural Foundations of Learning

Edited by a diverse group of expert collaborators, the Handbook of the Cultural Foundations of Learning is a landmark volume that brings together cutting-edge research examining learning as entailing inherently cultural processes. Conceptualizing culture as both a set of social practices and connected to learner identities, the chapters synthesize contemporary research in elaborating a new vision of the cultural nature of learning, moving beyond summary to reshape the field toward studies that situate culture in the learning sciences alongside equity of educational processes and outcomes. With the recent increased focus on culture and equity within the educational research community, this volume presents a comprehensive, innovative treatment of what has become one of the field’s most timely and relevant topics

Undergraduate and Graduate Course Descriptions, 2013 Summer

Wright State University undergraduate and graduate course descriptions from Summer 2013