Today, failure modes characterization and early detection is a key issue in complex assets. This ... more Today, failure modes characterization and early detection is a key issue in complex assets. This is due to the negative impact of corrective operations and the conservative strategies usually put in practice, focused on preventive maintenance. In this paper anomaly detection issue is addressed in new monitoring sensor data by characterizing and modeling operational behaviors. The learning framework is performed on the basis of a machine learning approach that combines constrained K-means clustering for outlier detection and fuzzy modeling of distances to normality. A final score is also calculated over time, considering the membership degree to resulting fuzzy sets and a local outlier factor. Proposed solution is deployed in a CBM+ platform for online monitoring of the assets. In order to show the validity of the approach, experiments have been conducted on real operational faults in an auxiliary marine diesel engine. Experimental results show a fully comprehensive yet accurate prognostics approach, improving detection capabilities and knowledge management. The performance achieved is quite high (precision, sensitivity and specificity above 93% and κ = 0 . 93 ), even more so given that a very small percentage of real faults are present in data.
Software is becoming an increasingly important part of complex manmade systems. There is a tenden... more Software is becoming an increasingly important part of complex manmade systems. There is a tendency that the software itself adds significant complexity to a system. This chapter surveys important issues in software for complex control systems. The results are illustrated with software for complex controllers. K. Åström et al. (Eds.
The use of registered names, trademarks etc. in this publication does not imply, even in the abse... more The use of registered names, trademarks etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made.
This paper discusses the application of fuzzy logic to solve data validation problems. We show ho... more This paper discusses the application of fuzzy logic to solve data validation problems. We show how human expert knowledge is easily represented by means of fuzzy logic, a technique that allows one to manage lingUistic terms, and so imprecision and uncertainty. The way in which this technique may be integrated, not only in a real-time system, but with other techniques carrying out the same task, is also discussed. Finally, an industrial application of these ideas is presented.
Historically, mining operations have faced numerous challenges, including safety hazards, ineffic... more Historically, mining operations have faced numerous challenges, including safety hazards, inefficiencies, and environmental concerns. However, recent advances in robotics, automation, and artificial intelligence have presented opportunities for the mining industry. The ROBOMINERS project, a Horizon 2020 European Union initiative, aims to revolutionize the mining ecosystem by implementing disruptive robotic concepts. One such concept is resilience, which involves enabling mining robots to reconfigure morphologically during operation. This article presents the development of a modular robotic system that focuses on modularity and self-assembly to provide insight into developing a highly adaptable and compact solution for future mining robots. The robotic system is composed of a set of highly configurable modular robotic platforms that can be reconfigured with other robotic modules or submodules to form more complex systems to perform different tasks. Several module configurations are ...
In the ICEA project we are concerned with the extraction of general designs from rat brains. When... more In the ICEA project we are concerned with the extraction of general designs from rat brains. When extracting control design patterns from mammal brains-something we have been doing in the CEA Project-there are several objectives that tend to confound the methods and the results. The three main motivations are i) doing mammal brain theory, ii) building mammal-inspired animats and iii) having better-performant controllers. At the end of the day, however, the strive for unifiying cognitive science threads shall render just one single science and a collection of derived technologies. This single science can be easily profiled as the science of robust autonomous behavior. We are interested in a theory that describes systems that operate robustly in uncertain environments attaining some goals that are critical for their dwelling. Function, viability, survivability are terms used to describe the character of this activity of the autonomous system. In this paper we will provide insights on this picture, recognising epistemology as the critical science to be developed, but not in the broad and somewhat weak sense of the philosophers but in the precise terms of mathematical physics: Epistemology is the study of the processes of systemic congruence. In this paper we will describe a fundamental pattern of mind design-the epistemic control loop-that captures the fundamental essence of cognition.
In the ICEA Project we are concerned with the extraction of general designs from rat brains. We a... more In the ICEA Project we are concerned with the extraction of general designs from rat brains. We are interested in designs that capture the core integrational aspects of emotion and cognition. The role that emotions play in the control the behaviour of animals and eventually may play in robots is still unclear. This is so, because the huge amount of research and theorising on emotions notwithstanding, there is still no deep, causal, functional theory of the emotional phenomena.
Advances in Experimental Medicine and Biology, 2011
It goes without saying that in science, experiments are essential; hypothesis need to be contrast... more It goes without saying that in science, experiments are essential; hypothesis need to be contrasted against empirical results in order to build scientific theories. In a system of overwhelming complexity like the brain, it is very likely that hidden variables, unknown by the experimentalist, are interacting with those few elements of which the values are expected and can be validated or rejected in the laboratory. Thus, at the end of the day, the experimentalist is refuting or validating tentative models that are somehow prisoners of the lack of knowledge about the structure of the system. The global picture being missing, a key is to look for the fundamental structure which must be found not in the objects, but in the relationships between the objects-their morphisms. How components at the same level interact (the objects here being neurons) and how superior levels constrain those levels below and emerge from those above is tackled here with a mathematical tooling. The mathematical theory of categories is proposed as a valid foundational framework for theoretical modeling in brain sciences.
The Escherichia coli is a bacterium that comfortingly lives in the human gut and one of the best ... more The Escherichia coli is a bacterium that comfortingly lives in the human gut and one of the best known living organisms. The sensitivity of this cell to environmental changes is reflected in two kind of movements that can be observed in a swimming bacterium: "run" towards an attractant, for example food, and "tumbling", in which a new direction is chosen randomly for the next "run". This simple bimodal behavior of the E. coli constitutes in itself a paradigm of adaptation in which roboticists and cognitive psychologists have found inspiration. We present a new approach to synaptic plasticity in the nervous system by scrutinizing Escherichia coli's motility and the signaling pathways that mediate its adaptive behavior. The formidable knowledge achieved in the last decade on bacterial chemotaxis, serve as the basis for a theory of a simple form of learning called habituation, that is applicable to biological and other systems. In this paper we try to establish a new framework that helps to explain what signals mean to the organisms, how these signals are integrated in patterns of behavior, and how they are sustained by an internal model of the world. The concepts of adaptation, synaptic plasticity and learning will be revisited within a new perspective, providing a quantitative basis for the understanding of how brains cope with a changing environment.
Fr F F om the initial, old-age considerations on the nature of mind different approaches have eme... more Fr F F om the initial, old-age considerations on the nature of mind different approaches have emerged. Of special relevance are dualist approaches where mind and body may have strongly different aspects but still need some form of collaboration. The embodied cognition movement-may we use this expression-tries to reconcile the apparently multiple quality (duality & unity at the same time sound kind of religious) by means of analysing the ways in which the body may affect cognition: supporting, raising, sustaining, etc. I will propose other approach that may be considered similar to this one or may be perceived as completely apart from embodied cognition and full of panpsychism. My proposal is that the mind does not emerge/is-supported-by from bodily processes but that indeed, those bodily processes are the mind itself. I'm not only referring to those processes happening in the brain but, perhaps in the line of artificial life, to all those information-centric processes that constitute the very inner workings of hierarchical structures of life.
This paper claims for a shift towards” the formal sciences” in the cognitive sciences. In order t... more This paper claims for a shift towards” the formal sciences” in the cognitive sciences. In order to explain the phenomenon of cognition, including aspects such as learning and intelligence, it is necessary to explore the concepts and methodologies offered by the formal sciences. In particular, category theory is proposed as the most fitting tool for the building of an unified theory of cognition.
In the field of convergence between research in autonomous machine construction and biological sy... more In the field of convergence between research in autonomous machine construction and biological systems understanding it is usually argued that building robots for research on autonomy by replicating extant animals is a valuable strategy for engineering autonomous intelligent systems. In this paper we will address the very issue of animat construction, the rationale behind this, their current implementations and the value they are producing. It will be shown that current activity, as it is done today, is deeply flawed and ...
Proceedings of the International Conference on Knowledge Engineering and Ontology Development, 2011
This paper describes the development of an ontology for autonomous systems, as the initial stage ... more This paper describes the development of an ontology for autonomous systems, as the initial stage of a research programme on autonomous systems' engineering within a model-based control approach. The ontology aims at providing a unified conceptual framework for the autonomous systems' stakeholders, from developers to software engineers. The modular ontology contains both generic and domain-specific concepts for autonomous systems description and engineering. The ontology serves as the basis in a methodology to obtain the autonomous system's conceptual models. The objective is to obtain and to use these models as main input for the autonomous system's model-based control system.
Day by day, new intelligent systems and autonomous machines are being developed to help and assis... more Day by day, new intelligent systems and autonomous machines are being developed to help and assist humans in a myriad of activities ranging from smart manufacturing to smart cities. Such new-generation intelligent systems need to work in teams and communicate with humans and other agents/robots to share information and coordinate activities. Furthermore, there is an increasing demand from government agencies and the private sector alike to use Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and Autonomous Underwater Vehicles (AUVs) for tasks including search and rescue, surveillance, and monitoring. As these intelligent systems have to interact with humans in several scenarios involving multi-agent collaboration, data collection, and decision-making, it is urgent to discuss the technical as well as the ethical aspects in their design and function. Hence, ontology-based models for this Robotics and Automation (R&A) domain have the p...
Autonomous systems are expected to maintain a dependable operation without human intervention. Th... more Autonomous systems are expected to maintain a dependable operation without human intervention. They are intended to fulfill the mission for which they were deployed, properly handling the disturbances that may affect them. Underwater robots, such as the UX-1 mine explorer developed in the UNEXMIN project, are paradigmatic examples of this need. Underwater robots are affected by both external and internal disturbances that hamper their capability for autonomous operation. Long-term autonomy requires not only the capability of perceiving and properly acting in open environments but also a sufficient degree of robustness and resilience so as to maintain and recover the operational functionality of the system when disturbed by unexpected events. In this article, we analyze the operational conditions for autonomous underwater robots with a special emphasis on the UX-1 miner explorer. We then describe a knowledge-based self-awareness and metacontrol subsystem that enables the autonomous r...
Artificial intelligence and cognitive science must look at the world of industrial process contro... more Artificial intelligence and cognitive science must look at the world of industrial process control to find the technological reifications of the concept of mind. 1 Motivation Artificial Intelligence (AI) seems to be at an impasse. The old vision of AI that started as the search for the computer-based technology of the artificial mind is not delivering. The excessive initial hype opened the door to ample criticisms after the failure to fulfill some bold predictions. In a sense, cognitive systems research has recently replaced AI as the forefront of this research programme. A new name for the same set of objectives just to elude the tagging as failure. But the problem of the AI research programme may not be in the methods but in the naïve conceptualizations that have driven and are still driving this research.
Trends in complex, software-intensive control systems show a continuous process of incorporating ... more Trends in complex, software-intensive control systems show a continuous process of incorporating mechanisms of self-representation and reflection. One of the main reasons is to improve system performance and resilience in changing uncertain environments. These approaches employ runtime models of the system itself that, to some extent, are identifiable with other modelling strategies at the system design and construction phases. Systems reflect upon themselves by means of self-models. From partial plant representations like those employed in classic model-based adaptive controllers to more encompassing plant+controller mixed models, the internal model principle is pushing software-intensive control systems designs to a scale that would match the complexity of modern models of human self-awareness and consciousness.
Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics
Cognitive processes might be seen as reciprocal items and they are usually characterized by multi... more Cognitive processes might be seen as reciprocal items and they are usually characterized by multiple feedback cycles. Emotions constitute one major source of feedback loops to assure the maintenance of well-being, providing cognitive processes with quantifiable meaning. This suggests the exploitation of models to improve the adaptation under value-based protocols. Emotion is not an isolated effect of stimuli, but it is the set of several effects of the stimuli and the relationships among them. This chapter proposes a study of the exploitation of models in artificial emotions, pointing out relationships as part of the model as well as the model exploitation method.
An important part of human intelligence, both historically and operationally, is our ability to c... more An important part of human intelligence, both historically and operationally, is our ability to communicate. We learn how to communicate, and maintain our communicative skills, in a society of communicators-a highly effective way to reach and maintain proficiency in this complex skill. Principles that might allow artificial agents to learn language this way are incompletely known at present-the multi-dimensional nature of socio-communicative skills are beyond every machine learning framework so far proposed. Our work begins to address the challenge of proposing a way for observation-based machine learning of natural language and communication. Our framework can learn complex communicative skills with minimal up-front knowledge. The system learns by incrementally producing predictive models of causal relationships in observed data, guided by goal-inference and reasoning using forward-inverse models. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime TVstyle interview, using multimodal communicative gesture and situated language to talk about recycling of various materials and objects. S1 can learn multimodal complex language and multimodal communicative acts, a vocabulary of 100 words forming natural sentences with relatively complex sentence structure, including manual deictic reference and anaphora. S1 is seeded only with high-level information about goals of the interviewer and interviewee, and a small ontology; no grammar or other information is provided to S1 a priori. The agent learns the pragmatics, semantics, and syntax of complex utterances spoken and gestures from scratch, by observing the humans compare and contrast the cost and pollution related to recycling aluminum cans, glass bottles, newspaper, plastic, and wood. After 20 hours of observation S1 can perform an unscripted TV interview with a human, in the same style, without making mistakes.
Uploads
Papers by Ricardo Sanz