Self-learning of fault diagnosis identification

A good and early fault detection and isolation system along with efficient alarm management and fine sensor validation systems are very important in today¿s complex process plants, specially in terms of safety enhancement and costs reduction. This paper presents a methodology for fault characterization. This is a self-learning approach developed in two phases. An initial, learning phase, where the simulation of process units, without and with different faults, will let the system (in an automated way) to detect the key variables that characterize the faults. This will be used in a second (on line) phase, where these key variables will be monitored in order to diagnose possible faults. Using this scheme the faults will be diagnosed and isolated in an early stage where the fault still has not turned into a failure.

Virtual 3D Support Contents Oriented to Interactive Self-Learning

In the educational project described in this paper, new virtual 3D didactical contents have been developed to achieve specific outcomes, within the frame of a new methodology oriented to objectives of the European Higher Education Area directives. The motivation of the project was to serve as a new assessment method, to create a link between new programs of study with the older ones. In this project, new rubrics have been developed to be employed as an objective method of evaluation of specific and transversal outcomes, to accomplish the certification criteria of institutions like ABET (Accreditation Board for Engineering and Technology).

Self Learning and self evaluation exercises database in basic mechanics: a theoretical-practical approach

In the School of Mines of the Technical University of Madrid (UPM) the first course of different degrees has been implemented and adapted to the European Higher Educational Area (EHEA). In all of the degrees there is a first semester course which gathers all the contents of basic mechanics: from the first kinematics concepts to the rigid solid plane motion Before the Bologna process took place, the authors had established the final assessment of the theoretical contents through open questions of theoretical-practical character In the present work, the elaboration of a wide database containing theoretical-practical questions that students can access on line is presented. The questions are divided in thirteen different questionnaires composed of a number of questions randomly chosen from a certain group in the database. Each group corresponds to a certain learning objective that the student knows. After answering the questionnaire and checking the grade assigned according to the performance of the student, the pupils can see the correct response displayed on the screen and widely explained by the professors. This represents a 10% of the final grade. As the student can access the questionnaires as many times as they want, the main goal is the self-assessment of each learning objective and therefore, getting the students involved in their own learning process so they can decide how much time they need to acquire the required level.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

An example of Learning based on competences: Use of Maxima in Linear Algebra for Engineers

This paper analyzes the role of Computer Algebra Systems (CAS) in a model of learning based on competences. The proposal is an e-learning model Linear Algebra course for Engineering, which includes the use of a CAS (Maxima) and focuses on problem solving. A reference model has been taken from the Spanish Open University. The proper use of CAS is defined as an indicator of the generic ompetence: Use of Technology. Additionally, we show that using CAS could help to enhance the following generic competences: Self Learning, Planning and Organization, Communication and Writing, Mathematical and Technical Writing, Information Management and Critical Thinking.

Fusion of probabilistic knowledge-based classification rules and learning automata for automatic recognition of digital images

In this paper, the fusion of probabilistic knowledge-based classification rules and learning automata theory is proposed and as a result we present a set of probabilistic classification rules with self-learning capability. The probabilities of the classification rules change dynamically guided by a supervised reinforcement process aimed at obtaining an optimum classification accuracy. This novel classifier is applied to the automatic recognition of digital images corresponding to visual landmarks for the autonomous navigation of an unmanned aerial vehicle (UAV) developed by the authors. The classification accuracy of the proposed classifier and its comparison with well-established pattern recognition methods is finally reported.

Distributed Fault Diagnosis Using Bayesian Reasoning in MAGNETO

Many of the emerging telecom services make use of Outer Edge Networks, in particular Home Area Networks. The configuration and maintenance of such services may not be under full control of the telecom operator which still needs to guarantee the service quality experienced by the consumer. Diagnosing service faults in these scenarios becomes especially difficult since there may be not full visibility between different domains. This paper describes the fault diagnosis solution developed in the MAGNETO project, based on the application of Bayesian Inference to deal with the uncertainty. It also takes advantage of a distributed framework to deploy diagnosis components in the different domains and network elements involved, spanning both the telecom operator and the Outer Edge networks. In addition, MAGNETO features self-learning capabilities to automatically improve diagnosis knowledge over time and a partition mechanism that allows breaking down the overall diagnosis knowledge into smaller subsets. The MAGNETO solution has been prototyped and adapted to a particular outer edge scenario, and has been further validated on a real testbed. Evaluation of the results shows the potential of our approach to deal with fault management of outer edge networks.

La visita a campo como metodología para el proceso de enseñanza-aprendizaje

Dentro de la enseñanza de la geotecnia los viajes a campo son una herramienta útil para superar las limitaciones asociadas a la enseñanza en el aula así como para promover el autoaprendizaje del alumno, el cual se enfrenta en primera persona a la información en estado bruto. Mediante esta comunicación compartimos la experiencia de la visita a las obras de construcción de los Túneles de Sorbas y El Almendral dentro del Máster de "Geología Aplicada a la Obra Civil y los Recursos Hídricos" ofertado por la Universidad de Granada, comentando, con un enfoque docente, la planificación de la actividad en función de los resultados de aprendizaje deseados. Fieldtrips are a good tool to overcome the inherent difficulties associated to teaching engineering geology at the classroom and to encourage student self-learning, when they face raw data. In this paper, we share our recent experience with the organization of a fieldtrip to two tunneling construction site (Sorbas Tunnel and El Almendral Tunnel) for the MSc program of “Applied Geology in Civil Engineering and Water Resources” offered by the University of Granada, discussing, with a educational point of view, the planning and learning outcomes.

Desarrollo de herramientas de autoevaluación de prácticas de procesado digital de la señal.

El objetivo de este proyecto es desarrollar un conjunto de herramientas de auto aprendizaje y autoevaluación del laboratorio de la asignatura "Procesado Digital de la Señal", perteneciente al plan de grado de la Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación de la Universidad Politécnica de Madrid. Con ello se pretende de mejorar el rendimiento académico de los alumnos en dicha asignatura y en la materia "Señales y Sistemas" en general. Para la realización de las prácticas se emplea Matlab, de modo que es necesario integrar esta herramienta en el laboratorio con MOODLE, plataforma de e-learning utilizada para la gestión de las asignaturas a nivel docente, para proporcionar material de estudio y programar actividades de aprendizaje y evaluación. Será fundamental el análisis de la integración de Matlab con MOODLE, de modo que en función de los resultados de los alumnos, se les propongan repeticiones de apartados erróneos, revisiones de resultados y otros aspectos, como autoaprendizaje y autoevaluación que permitan la obtención de las competencias y alcanzar los resultados de aprendizaje, y a los profesores que imparten la asignatura, como herramienta para detectar las deficiencias más significativas en la programación y en las metodologías empleadas en la asignatura para corregir las carencias de los alumnos. ABSTRACT: The aim of this project will be the development of self-learning and self- assessment lab tools for the course "Procesado Digital de la Señal" in order to improve student’s performance in that subject and in the matter "Señales y Sistemas " for grades taught at the Escuela Universitaria de Ingeniería Técnica de Telecomunicación of the Universidad Politécnica de Madrid today. Matlab is used to perform laboratory practices of "Procesado Digital de la Señal “. Matlab is a numerical calculation program. A very powerful tool with a great mathematical processing performance level, so it is necessary to integrate this tool in the laboratory with MOODLE, the current e-learning platform used at the Universidad Politécnica de Madrid for the management of teaching subjects to provide material and to program learning and assessment activities for students. It is therefore essential the analysis of the Matlab integration with Moodle. Thus, depending on the results and grades that students get along the way in the various activities evaluators should conduct, they propose, for example, repetitions of erroneous exercises, reviews of some results and other aspects such as self-learning and self-assessment. This would allow students to obtain the skills and learning to achieve the results set as a target. For teachers who teach the subject will also be a preview of the notes as these tools will be used to identify the most significant shortcomings both in programming and in the methodologies used in "Procesado Digital de la Señal " to act accordingly and correcting shortcomings of the enrolled students.

CAS: A tool for improving autonomous work

The EHEA proposes a student-centered teaching model. Therefore, it seems necessary to actively involve the students in the teaching-learning process. Increasing the active participation of the students is not always easy in mathematical topics, since, when the students just enter the University, their ability to carry out autonomous mathematical work is scarce. In this paper we present some experiences related with the use of Computer Algebra Systems (CAS). All the experiences are designed in order to develop some mathematical competencies and mainly self-learning, the use of technology and team-work. The experiences include some teachers? proposals including: small projects to be executed in small groups, participation in competitions, the design of different CAS-Toolboxes, etc. The results obtained in the experiences, carried out with different groups of students from different engineering studies at different universities, makes us slightly optimistic about the educational value of the model.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-selection of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested in a decentralized solution where the robots themselves autonomously and in an individual manner, are responsible for selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-task distribution problem and we propose a solution using two different approaches by applying Response Threshold Models as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Peer Assessment and Self-assessment: Effective Learning Tools in Higher Education.

When used appropriately, self- and peer-assessment are very effective learning tools. In the present work, instructor formative assessment and feedback, self-assessment (SA), and peer-assessment (PA) have been compared. During the first part of a semester, the students followed a continuous formative assessment. Subsequently, they were divided into two subgroups based on similar performances. One subgroup performed SAs, and the other followedPAduring the last part of the course. The performances of the two groups in solving problems were compared. Results suggest that PA is a more effective learning tool than SA, and both are more effective than instructor formative assessment. However, a survey that was conducted at the end of the experiment showed higher student confidence in instructor assessment than in PA. The students recognized the usefulness of acting as peer assessors, but believed that SA helped them more than PA.

Behavior competence development through e-learning: experience at the undergraduate level in the context of Aula a Distancia Abierta (ADA) Madrid, Spain

This communication presents the results of an innovative approach for competencedevelopment suggesting a new methodology for the integration of these elements in professional development within the ADA initiative (AulaaDistanciaAbierta, Distance and Open Classroom) of the Community of Madrid. The main objective of this initiative is to promote the use of Information and Communication Technologies (ICTs) for educational activities by creating a new learning environment structured on the premises of commitment to self–learning, individual work, communication and virtual interaction, and self and continuous assessment. Results from this experience showed that conceptualization is a positive contribution to learning, as students added names and characteristics to competences and abilities that were previously unknown or underestimated. Also, the diversity of participants’ disciplines indicated multidimensional interest in this idea and supported the theory that this approach to competencedevelopment could be successful in all knowledge areas.

Using modular neural networks to model self-consciousness and self-representation for artificial entities

Self-consciousness implies not only self or group recognition, but also real knowledge of one’s own identity. Self-consciousness is only possible if an individual is intelligent enough to formulate an abstract self-representation. Moreover, it necessarily entails the capability of referencing and using this elf-representation in connection with other cognitive features, such as inference, and the anticipation of the consequences of both one’s own and other individuals’ acts. In this paper, a cognitive architecture for self-consciousness is proposed. This cognitive architecture includes several modules: abstraction, self-representation, other individuals'representation, decision and action modules. It includes a learning process of self-representation by direct (self-experience based) and observational learning (based on the observation of other individuals). For model implementation a new approach is taken using Modular Artificial Neural Networks (MANN). For model testing, a virtual environment has been implemented. This virtual environment can be described as a holonic system or holarchy, meaning that it is composed of autonomous entities that behave both as a whole and as part of a greater whole. The system is composed of a certain number of holons interacting. These holons are equipped with cognitive features, such as sensory perception, and a simplified model of personality and self-representation. We explain holons’ cognitive architecture that enables dynamic self-representation. We analyse the effect of holon interaction, focusing on the evolution of the holon’s abstract self-representation. Finally, the results are explained and analysed and conclusions drawn.