Reliability and quality in the area of statistical thinking in Engineering

There is remarkable growing concern about the quality control at the time, which has led to the search for methods capable of addressing effectively the reliability analysis as part of the Statistic. Managers, researchers and Engineers must understand that 'statistical thinking' is not just a set of statistical tools. They should start considering 'statistical thinking' from a 'system', which means, developing systems that meet specific statistical tools and other methodologies for an activity. The aim of this article is to encourage them (engineers, researchers and managers) to develop a new way of thinking.

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.

A fuzzy MCDA framework for safety assessment in the remediation of a uranium mill tailings site in Ukraine

The Pridneprovsky Chemical Plant was one of the largest uranium processing enterprises in the former USSR, producing a huge amount of uranium residues. The Zapadnoe tailings site contains most of these residues. We propose a theoretical framework based on multicriteria decision analysis and fuzzy logic to analyze different remediation alternatives for the Zapadnoe tailings, which simultaneously accounts for potentially conflicting economic, social and environmental objectives. We build an objective hierarchy that includes all the relevant aspects. Fuzzy rather than precise values are proposed for use to evaluate remediation alternatives against the different criteria and to quantify preferences, such as the weights representing the relative importance of criteria identified in the objective hierarchy. Finally, we suggest that remediation alternatives should be evaluated by means of a fuzzy additive multi-attribute utility function and ranked on the basis of the respective trapezoidal fuzzy number representing their overall utility.

An interactive method of fuzzy probability elicitation in risk analysis

Expert knowledge is used to assign probabilities to events in many risk analysis models. However, experts sometimes find it hard to provide specific values for these probabilities, preferring to express vague or imprecise terms that are mapped using a previously defined fuzzy number scale. The rigidity of these scales generates bias in the probability elicitation process and does not allow experts to adequately express their probabilistic judgments. We present an interactive method for extracting a fuzzy number from experts that represents their probabilistic judgments for a given event, along with a quality measure of the probabilistic judgments, useful in a final information filtering and analysis sensitivity process.

A Knowledge Model for Automatic Configuration of Traffic Messages

This paper describes a knowledge model for a configuration problem in the do-main of traffic control. The goal of this model is to help traffic engineers in the dynamic selection of a set of messages to be presented to drivers on variable message signals. This selection is done in a real-time context using data recorded by traffic detectors on motorways. The system follows an advanced knowledge-based solution that implements two abstract problem solving methods according to a model-based approach recently proposed in the knowledge engineering field. Finally, the paper presents a discussion about the advantages and drawbacks found for this problem as a consequence of the applied knowledge modeling ap-proach.

Using Electronic Documents for Knowledge Acquisition and Model Maintenance

Knowledge acquisition and model maintenance are key problems in knowledge engineering to improve the productivity in the development of intelligent systems. Although historically a number of technical solutions have been proposed in this area, the recent experience shows that there is still an important gap between the way end-users describe their expertise and the way intelligent systems represent knowledge. In this paper we propose an original way to cope with this problem based on electronic documents. We propose the concept of intelligent document processor as a tool that allows the end-user to read/write a document explaining how an intelligent system operates in such a way that, if the user changes the content of the document, the intelligent system will react to these changes. The paper presents the structure of such a document based on knowledge categories derived from the modern knowledge modeling methodologies together with a number of requirements to be understandable by end-users and problem solvers.

Model Based On-Line Energy Prediction System for Semi-Autonomous Mobile Robots

Maximizing energy autonomy is a consistent challenge when deploying mobile robots in ionizing radiation or other hazardous environments. Having a reliable robot system is essential for successful execution of missions and to avoid manual recovery of the robots in environments that are harmful to human beings. For deployment of robots missions at short notice, the ability to know beforehand the energy required for performing the task is essential. This paper presents a on-line method for predicting energy requirements based on the pre-determined power models for a mobile robot. A small mobile robot, Khepera III is used for the experimental study and the results are promising with high prediction accuracy. The applications of the energy prediction models in energy optimization and simulations are also discussed along with examples of significant energy savings.

Watching subgraphs to improve efficiency in maximum clique search

This paper describes a new technique referred to as watched subgraphs which improves the performance of BBMC, a leading state of the art exact maximum clique solver (MCP). It is based on watched literals employed by modern SAT solvers for boolean constraint propagation. In efficient SAT algorithms, a list of clauses is kept for each literal (it is said that the clauses watch the literal) so that only those in the list are checked for constraint propagation when a (watched) literal is assigned during search. BBMC encodes vertex sets as bit strings, a bit block representing a subset of vertices (and the corresponding induced subgraph) the size of the CPU register word. The paper proposes to watch two subgraphs of critical sets during MCP search to efficiently compute a number of basic operations. Reported results validate the approach as the size and density of problem instances rise, while achieving comparable performance in the general case.

Smart Products: Una Revisión Sistemática de la Literatura

Existe una proliferación de los llamados Smart Products. Ello es debido a que cada vez se apueste más por este tipo de productos tanto en la vida cotidiana como en el sector industrial. Sin embargo el término Smart Product se utiliza con diferentes acepciones en diferentes contextos o dominios de aplicación. La utilización del término con una semántica diferente de la habitual en un contexto puede llevar a problemas serios de compresión. El objetivo de este trabajo es analizar las diferentes definiciones de Smart Products—Productos Inteligentes, Smart Products en terminología inglesa, ampliamente utilizada—que aparecen en la literatura con el objeto de estudiar los diferentes matices y alcances que ofrecen para valorar si es posible obtener una definición de consenso que satisfaga a todas las partes, y especificarla. Con el fin de poder abarcar definiciones conexas introducimos el concepto Smart Thing—este concepto incluirá aquellas definiciones que puedan estar relacionadas con los Smart Products, como es el caso de los Intelligent Products, Smart Objects, Intelligent Systems, Intelligent Object. Para poder analizar las diferentes definiciones existentes en la literatura existente realizamos una Revisión Sistemática de la Literatura. El enfoque de Computación Autonómica—Autonomic Computing—tiene varios aspectos en común con Smart Products. Por ello una vez analizadas las diferentes definiciones existentes en la literatura hemos procedido a estudiar los puntos en común que tienen con Autonomic Computing, con el fin de valorar si Autonomic Computing es un enfoque adecuado en el que nos podamos apoyar para especificar, y diseñar Smart Products.

Conditional density approximations with mixtures of polynomials

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multi-dimensional (marginal) MoPs from data have recently been proposed. In this paper we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and we demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.

Autonomous Acquisition of Natural Language

An important part of human intelligence is the ability to use language. Humans learn how to use language in a society of language users, which is probably the most effective way to learn a language from the ground up. Principles that might allow an artificial agents to learn language this way are not known at present. Here we present a framework which begins to address this challenge. Our auto-catalytic, endogenous, reflective architecture (AERA) supports the creation of agents that can learn natural language by observation. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime mock television interview, using gesture and situated language. Results show that S1 can learn multimodal complex language and multimodal communicative acts, using a vocabulary of 100 words with numerous sentence formats, by observing unscripted interaction between the humans, with no grammar being provided to it a priori, and only high-level information about the format of the human interaction in the form of high-level goals of the interviewer and interviewee and a small ontology. The agent learns both the pragmatics, semantics, and syntax of complex sentences spoken by the human subjects on the topic of recycling of objects such as aluminum cans, glass bottles, plastic, and wood, as well as use of manual deictic reference and anaphora.

Identificación de los síntomas de daño o merma de capacidad resistente en estructuras en zonas sísmicas

Los sismos afectan a las estructuras en función de su intensidad. Normalmente se espera de las estructuras daños irreparables por motivos de ductilidad en los sismos nominales o de diseño, para protección de las personas y sus bienes. No obstante, las estructuras en zonas sísmicas sufren terremotos de baja o media intensidad de manera continuada y éstos pueden afectar a la capacidad resistente residual de las mismas, es por eso que en el presente trabajo se plantea lo siguiente: a) Identificar cuál es la estrategia o nivel de protección, que consideran las diferentes Normativas y Reglamentos frente a sismos de baja o mediana intensidad, puesto que durante la vida útil de una estructura, esta puede verse afectada por sismos de intensidad baja o moderada, los cuales también provocan daños; es por ello que es de mucha importancia conocer y estudiar el aporte, estrategias y demás parámetros que consideran las Normas, esto mediante la técnica de revisión de documentación o Literatura. b) Identificar la manera con que un terremoto de baja o media intensidad afecta a la capacidad resistente de las estructuras, sus señales, sus síntomas de daño, etc. Esto a través de tres técnicas de Investigación : Revisión en Literatura, Tormenta de ideas con un grupo de expertos en el tema, y mediante la Técnica Delphi; para finalmente aplicar una método de refinamiento para elaborar un listado y un mapa de síntomas esperables en las estructuras, consecuencia de eventos sísmicos de baja o mediana intensidad. Los cuales se podrían controlar con sistemas inteligentes y así monitorizar las construcciones para prever su comportamiento futuro. Earthquakes affect structures depending on its intensity. Normally it expected of the irreparable damage structures. It due to ductility in nominal earthquakes to protect people and property. Structures in seismic areas suffer earthquakes of low to medium intensity continually, and it may affect the residual resistant ability, therefore posed in this investigation is the following: (a) Identifying what is the strategy or level of protection, which consider different guidelines and regulations against earthquakes of low to medium intensity. Since during the service life of a structure may be affected by low or moderate intensity earthquakes, which also cause damage. For this reason it is very important also to meet and study the contribution, strategies and other parameters considered by the Guidelines by reviewing the documentation or literature technique. b) Identifying the way an earthquake of low to medium intensity affects the resistant ability of structures, their signs, their symptoms of injury, etc. Through three research techniques: review of documentation or literature, brainstorming technique with a group of experts, and using the Delphi technique. Finally applying a method of refining to produce a list and a map of symptoms expected in structures, consequence of low to medium intensity earthquakes. It could be controlled with intelligent systems and thus to monitor structures to predict its future behavior

MedVir: an interactive representation system of multidimensional medical data applied to Traumatic Brain Injury's rehabilitation prediction

Clinicians could model the brain injury of a patient through his brain activity. However, how this model is defined and how it changes when the patient is recovering are questions yet unanswered. In this paper, the use of MedVir framework is proposed with the aim of answering these questions. Based on complex data mining techniques, this provides not only the differentiation between TBI patients and control subjects (with a 72% of accuracy using 0.632 Bootstrap validation), but also the ability to detect whether a patient may recover or not, and all of that in a quick and easy way through a visualization technique which allows interaction.

Herramienta web para la ayuda a la generación de consenso

En la actualidad encontramos una gran y creciente cantidad de información en las redes sociales. Esta información en su mayoría se encuentra desestructurada o no organizada de forma adecuada, esto produce que sea difícil alcanzar consensos en argumentaciones y además impide la rápida participación de nuevos agentes en las mismas. Se han estudiado diferentes soluciones para alcanzar consensos en áreas concretos y en su mayoría centrados en el entorno académico, sin embargo se pueden encontrar pocas aplicaciones que traten de acercarse a una solución dentro de un contexto abierto como son las redes sociales. El contexto de las redes sociales es complejo pues no existe un control sobre los usuarios, los hilos de argumentación pueden desvirtuarse y es complejo alcanzar consensos cuando no existe una figura de experto bien definida como suele ocurrir en el contexto académico. Este trabajo trata de crear una herramienta web en forma de red social, con una base en sistemas inteligentes que permita a los usuarios poder obtener suficiente información de una conversación minimizando el esfuerzo para poder participar activamente.---ABSTRACT---Nowadays a large and an increasing amount of information can be found on social networks. This information is mostly unstructured and not properly organized, which is a problem when conclusions are needed to reach a consensus in argumentations. In addition new participants can find difficulties to join argumentations. Different solutions have been studied to solve these problems focused in academic contexts, however few applications which attempt to solve these problems on social networks can be found. It is not a simple task to handle the complexity of arguments on a social network. Besides, the free context and the lack of control over users make reaching a consensus even harder. This academic work seeks to create a tool in the form of an intelligent systems based social networks which may allow users to minimize the effort needed to join and participate in an argumentation.

Crowdsourcing without a crowd : Reliable online species identification using Bayesian models to minimize crowd size

Acknowledgment This research is supported by an award made by the RCUK Digital Economy program to the University of Aberdeen’s dot.rural Digital Economy Hub (ref. EP/G066051/1).