Multi-argument fuzzy measures on lattices of fuzzy sets

In this paper, we axiomatically introduce fuzzy multi-measures on bounded lattices. In particular, we make a distinction between four different types of fuzzy set multi-measures on a universe X, considering both the usual or inverse real number ordering of this lattice and increasing or decreasing monotonicity with respect to the number of arguments. We provide results from which we can derive families of measures that hold for the applicable conditions in each case.

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.

Data Fusion based on Game Theory for Speaker Diarization

A novel algorithm based on bimatrix game theory has been developed to improve the accuracy and reliability of a speaker diarization system. This algorithm fuses the output data of two open-source speaker diarization programs, LIUM and SHoUT, taking advantage of the best properties of each one. The performance of this new system has been tested by means of audio streams from several movies. From preliminary results on fragments of five movies, improvements of 63% in false alarms and missed speech mistakes have been achieved with respect to LIUM and SHoUT systems working alone. Moreover, we also improve in a 20% the number of recognized speakers, getting close to the real number of speakers in the audio stream

Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-election of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested on decentralized solution where the robots themselves autonomously and in an individual manner, are responsible of 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-tasks distribution problem and we propose a solution using two different approaches by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithm, 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.

Application of self-organizing techniques for the distribution of heterogeneous multi-tasks in multi-robot systems

This paper focuses on the general problem of coordinating of multi-robot systems, more specifically, it addresses the self-election of heterogeneous and specialized tasks by autonomous robots. In this regard, it has proposed experimenting with two different techniques based chiefly on selforganization and emergence biologically inspired, by applying response threshold models as well as ant colony optimization. Under this approach it can speak of multi-tasks selection instead of multi-tasks allocation, that means, as 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 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. It has 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.

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.

Fuzzy min-max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes.

UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand- avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions.

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand-avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

Development of a fuzzy control based self-adaptive system for energy saving in constrained networks for the Internet of Things

Las redes del futuro, incluyendo las redes de próxima generación, tienen entre sus objetivos de diseño el control sobre el consumo de energía y la conectividad de la red. Estos objetivos cobran especial relevancia cuando hablamos de redes con capacidades limitadas, como es el caso de las redes de sensores inalámbricos (WSN por sus siglas en inglés). Estas redes se caracterizan por estar formadas por dispositivos de baja o muy baja capacidad de proceso y por depender de baterías para su alimentación. Por tanto la optimización de la energía consumida se hace muy importante. Son muchas las propuestas que se han realizado para optimizar el consumo de energía en este tipo de redes. Quizás las más conocidas son las que se basan en la planificación coordinada de periodos de actividad e inactividad, siendo una de las formas más eficaces para extender el tiempo de vida de las baterías. La propuesta que se presenta en este trabajo se basa en el control de la conectividad mediante una aproximación probabilística. La idea subyacente es que se puede esperar que una red mantenga la conectividad si todos sus nodos tienen al menos un número determinado de vecinos. Empleando algún mecanismo que mantenga ese número, se espera que se pueda mantener la conectividad con un consumo energético menor que si se empleara una potencia de transmisión fija que garantizara una conectividad similar. Para que el mecanismo sea eficiente debe tener la menor huella posible en los dispositivos donde se vaya a emplear. Por eso se propone el uso de un sistema auto-adaptativo basado en control mediante lógica borrosa. En este trabajo se ha diseñado e implementado el sistema descrito, y se ha probado en un despliegue real confirmando que efectivamente existen configuraciones posibles que permiten mantener la conectividad ahorrando energía con respecto al uso de una potencia de transmisión fija. ABSTRACT. Among the design goals for future networks, including next generation networks, we can find the energy consumption and the connectivity. These two goals are of special relevance when dealing with constrained networks. That is the case of Wireless Sensors Networks (WSN). These networks consist of devices with low or very low processing capabilities. They also depend on batteries for their operation. Thus energy optimization becomes a very important issue. Several proposals have been made for optimizing the energy consumption in this kind of networks. Perhaps the best known are those based on the coordinated planning of active and sleep intervals. They are indeed one of the most effective ways to extend the lifetime of the batteries. The proposal presented in this work uses a probabilistic approach to control the connectivity of a network. The underlying idea is that it is highly probable that the network will have a good connectivity if all the nodes have a minimum number of neighbors. By using some mechanism to reach that number, we hope that we can preserve the connectivity with a lower energy consumption compared to the required one if a fixed transmission power is used to achieve a similar connectivity. The mechanism must have the smallest footprint possible on the devices being used in order to be efficient. Therefore a fuzzy control based self-adaptive system is proposed. This work includes the design and implementation of the described system. It also has been validated in a real scenario deployment. We have obtained results supporting that there exist configurations where it is possible to get a good connectivity saving energy when compared to the use of a fixed transmission power for a similar connectivity.

Communication Middleware for Distributed Hard Real-Time Systems in Java

Distributed real-time embedded systems are becoming increasingly important to society. More demands will be made on them and greater reliance will be placed on the delivery of their services. A relevant subset of them is high-integrity or hard real-time systems, where failure can cause loss of life, environmental harm, or significant financial loss. Additionally, the evolution of communication networks and paradigms as well as the necessity of demanding processing power and fault tolerance, motivated the interconnection between electronic devices; many of the communications have the possibility of transferring data at a high speed. The concept of distributed systems emerged as systems where different parts are executed on several nodes that interact with each other via a communication network. Java’s popularity, facilities and platform independence have made it an interesting language for the real-time and embedded community. This was the motivation for the development of RTSJ (Real-Time Specification for Java), which is a language extension intended to allow the development of real-time systems. The use of Java in the development of high-integrity systems requires strict development and testing techniques. However, RTJS includes a number of language features that are forbidden in such systems. In the context of the HIJA project, the HRTJ (Hard Real-Time Java) profile was developed to define a robust subset of the language that is amenable to static analysis for high-integrity system certification. Currently, a specification under the Java community process (JSR- 302) is being developed. Its purpose is to define those capabilities needed to create safety critical applications with Java technology called Safety Critical Java (SCJ). However, neither RTSJ nor its profiles provide facilities to develop distributed realtime applications. This is an important issue, as most of the current and future systems will be distributed. The Distributed RTSJ (DRTSJ) Expert Group was created under the Java community process (JSR-50) in order to define appropriate abstractions to overcome this problem. Currently there is no formal specification. The aim of this thesis is to develop a communication middleware that is suitable for the development of distributed hard real-time systems in Java, based on the integration between the RMI (Remote Method Invocation) model and the HRTJ profile. It has been designed and implemented keeping in mind the main requirements such as the predictability and reliability in the timing behavior and the resource usage. iThe design starts with the definition of a computational model which identifies among other things: the communication model, most appropriate underlying network protocols, the analysis model, and a subset of Java for hard real-time systems. In the design, the remote references are the basic means for building distributed applications which are associated with all non-functional parameters and resources needed to implement synchronous or asynchronous remote invocations with real-time attributes. The proposed middleware separates the resource allocation from the execution itself by defining two phases and a specific threading mechanism that guarantees a suitable timing behavior. It also includes mechanisms to monitor the functional and the timing behavior. It provides independence from network protocol defining a network interface and modules. The JRMP protocol was modified to include two phases, non-functional parameters, and message size optimizations. Although serialization is one of the fundamental operations to ensure proper data transmission, current implementations are not suitable for hard real-time systems and there are no alternatives. This thesis proposes a predictable serialization that introduces a new compiler to generate optimized code according to the computational model. The proposed solution has the advantage of allowing us to schedule the communications and to adjust the memory usage at compilation time. In order to validate the design and the implementation a demanding validation process was carried out with emphasis in the functional behavior, the memory usage, the processor usage (the end-to-end response time and the response time in each functional block) and the network usage (real consumption according to the calculated consumption). The results obtained in an industrial application developed by Thales Avionics (a Flight Management System) and in exhaustive tests show that the design and the prototype are reliable for industrial applications with strict timing requirements. Los sistemas empotrados y distribuidos de tiempo real son cada vez más importantes para la sociedad. Su demanda aumenta y cada vez más dependemos de los servicios que proporcionan. Los sistemas de alta integridad constituyen un subconjunto de gran importancia. Se caracterizan por que un fallo en su funcionamiento puede causar pérdida de vidas humanas, daños en el medio ambiente o cuantiosas pérdidas económicas. La necesidad de satisfacer requisitos temporales estrictos, hace más complejo su desarrollo. Mientras que los sistemas empotrados se sigan expandiendo en nuestra sociedad, es necesario garantizar un coste de desarrollo ajustado mediante el uso técnicas adecuadas en su diseño, mantenimiento y certificación. En concreto, se requiere una tecnología flexible e independiente del hardware. La evolución de las redes y paradigmas de comunicación, así como la necesidad de mayor potencia de cómputo y de tolerancia a fallos, ha motivado la interconexión de dispositivos electrónicos. Los mecanismos de comunicación permiten la transferencia de datos con alta velocidad de transmisión. En este contexto, el concepto de sistema distribuido ha emergido como sistemas donde sus componentes se ejecutan en varios nodos en paralelo y que interactúan entre ellos mediante redes de comunicaciones. Un concepto interesante son los sistemas de tiempo real neutrales respecto a la plataforma de ejecución. Se caracterizan por la falta de conocimiento de esta plataforma durante su diseño. Esta propiedad es relevante, por que conviene que se ejecuten en la mayor variedad de arquitecturas, tienen una vida media mayor de diez anos y el lugar ˜ donde se ejecutan puede variar. El lenguaje de programación Java es una buena base para el desarrollo de este tipo de sistemas. Por este motivo se ha creado RTSJ (Real-Time Specification for Java), que es una extensión del lenguaje para permitir el desarrollo de sistemas de tiempo real. Sin embargo, RTSJ no proporciona facilidades para el desarrollo de aplicaciones distribuidas de tiempo real. Es una limitación importante dado que la mayoría de los actuales y futuros sistemas serán distribuidos. El grupo DRTSJ (DistributedRTSJ) fue creado bajo el proceso de la comunidad de Java (JSR-50) con el fin de definir las abstracciones que aborden dicha limitación, pero en la actualidad aun no existe una especificacion formal. El objetivo de esta tesis es desarrollar un middleware de comunicaciones para el desarrollo de sistemas distribuidos de tiempo real en Java, basado en la integración entre el modelo de RMI (Remote Method Invocation) y el perfil HRTJ. Ha sido diseñado e implementado teniendo en cuenta los requisitos principales, como la predecibilidad y la confiabilidad del comportamiento temporal y el uso de recursos. El diseño parte de la definición de un modelo computacional el cual identifica entre otras cosas: el modelo de comunicaciones, los protocolos de red subyacentes más adecuados, el modelo de análisis, y un subconjunto de Java para sistemas de tiempo real crítico. En el diseño, las referencias remotas son el medio básico para construcción de aplicaciones distribuidas las cuales son asociadas a todos los parámetros no funcionales y los recursos necesarios para la ejecución de invocaciones remotas síncronas o asíncronas con atributos de tiempo real. El middleware propuesto separa la asignación de recursos de la propia ejecución definiendo dos fases y un mecanismo de hebras especifico que garantiza un comportamiento temporal adecuado. Además se ha incluido mecanismos para supervisar el comportamiento funcional y temporal. Se ha buscado independencia del protocolo de red definiendo una interfaz de red y módulos específicos. También se ha modificado el protocolo JRMP para incluir diferentes fases, parámetros no funcionales y optimizaciones de los tamaños de los mensajes. Aunque la serialización es una de las operaciones fundamentales para asegurar la adecuada transmisión de datos, las actuales implementaciones no son adecuadas para sistemas críticos y no hay alternativas. Este trabajo propone una serialización predecible que ha implicado el desarrollo de un nuevo compilador para la generación de código optimizado acorde al modelo computacional. La solución propuesta tiene la ventaja que en tiempo de compilación nos permite planificar las comunicaciones y ajustar el uso de memoria. Con el objetivo de validar el diseño e implementación se ha llevado a cabo un exigente proceso de validación con énfasis en: el comportamiento funcional, el uso de memoria, el uso del procesador (tiempo de respuesta de extremo a extremo y en cada uno de los bloques funcionales) y el uso de la red (consumo real conforme al estimado). Los buenos resultados obtenidos en una aplicación industrial desarrollada por Thales Avionics (un sistema de gestión de vuelo) y en las pruebas exhaustivas han demostrado que el diseño y el prototipo son fiables para aplicaciones industriales con estrictos requisitos temporales.

Two Novel Methods For The Determination Of The Number Of Components In Independent Components Analysis Models

Independent Components Analysis is a Blind Source Separation method that aims to find the pure source signals mixed together in unknown proportions in the observed signals under study. It does this by searching for factors which are mutually statistically independent. It can thus be classified among the latent-variable based methods. Like other methods based on latent variables, a careful investigation has to be carried out to find out which factors are significant and which are not. Therefore, it is important to dispose of a validation procedure to decide on the optimal number of independent components to include in the final model. This can be made complicated by the fact that two consecutive models may differ in the order and signs of similarly-indexed ICs. As well, the structure of the extracted sources can change as a function of the number of factors calculated. Two methods for determining the optimal number of ICs are proposed in this article and applied to simulated and real datasets to demonstrate their performance.

A Visual AGV-Urban Car using Fuzzy Control

The goal of the work described in this paper is to develop a visual line guided system for being used on-board an Autonomous Guided Vehicle (AGV) commercial car, controlling the steering and using just the visual information of a line painted below the car. In order to implement the control of the vehicle, a Fuzzy Logic controller has been implemented, that has to be robust against curvature changes and velocity changes. The only input information for the controller is the visual distance from the image center captured by a camera pointing downwards to the guiding line on the road, at a commercial frequency of 30Hz. The good performance of the controller has successfully been demonstrated in a real environment at urban velocities. The presented results demonstrate the capability of the Fuzzy controller to follow a circuit in urban environments without previous information about the path or any other information from additional sensors

Aerial Object Following Using Visual Fuzzy Servoing

This article presents a visual servoing system to follow a 3D moving object by a Micro Unmanned Aerial Vehicle (MUAV). The presented control strategy is based only on the visual information given by an adaptive tracking method based on the colour information. A visual fuzzy system has been developed for servoing the camera situated on a rotary wing MAUV, that also considers its own dynamics. This system is focused on continuously following of an aerial moving target object, maintaining it with a fixed safe distance and centred on the image plane. The algorithm is validated on real flights on outdoors scenarios, showing the robustness of the proposed systems against winds perturbations, illumination and weather changes among others. The obtained results indicate that the proposed algorithms is suitable for complex controls task, such object following and pursuit, flying in formation, as well as their use for indoor navigation

A Stress-Detection System Based on Physiological Signals and Fuzzy Logic

A stress-detection system is proposed based on physiological signals. Concretely, galvanic skin response (GSR) and heart rate (HR) are proposed to provide information on the state of mind of an individual, due to their nonintrusiveness and noninvasiveness. Furthermore, specific psychological experiments were designed to induce properly stress on individuals in order to acquire a database for training, validating, and testing the proposed system. Such system is based on fuzzy logic, and it described the behavior of an individual under stressing stimuli in terms of HR and GSR. The stress-detection accuracy obtained is 99.5% by acquiring HR and GSR during a period of 10 s, and what is more, rates over 90% of success are achieved by decreasing that acquisition period to 3-5 s. Finally, this paper comes up with a proposal that an accurate stress detection only requires two physiological signals, namely, HR and GSR, and the fact that the proposed stress-detection system is suitable for real-time applications.