FMECA application to Rainfall Hazard prevention

This paper presents a System Safety application to reduce the economical impact hazards in growings produced by Rainfall. System Safety is an engineering subdiscipline oriented to identify and mitigate the possible hazards to a system under study. Inside the System Safety area, the FMECA (Failure Mode, Effects and Criticallity Analysis) is a popular tool to analyze and identify the failures and weaknesses points of any system. Basically, it consist on identifying systematically the failure modes of a system to mitigate them as much as possible. The idea is to study three different kind of growings (stone fruits in the south of Spain, wheat production in Castilla Leon and Olive trees production in Andalucia) using this methodology in order to identify all the hazardous situations produced by rainfall. Applying the state of the art weather forecast techniques, this information would help farmers to prevent and mitigate the identified hazardous situations. The aim of the work is to prevent the economical hazards as are defined in the System Safety area: "Any real or potential condition that can cause injury, illness, or death to personnel; damage to or loss of a system, equipment or property; or damage to the environment", so the study is not reduced to the analysis of catastrophical situations but aboutany kind of economical damage produced by rainfall

Swarm intelligence and its applications in swarm robotics

This work gives an overview of the broad field of computational swarm intelligence and its applications in swarm robotics. Computational swarm intelligence is modelled on the social behavior of animals and its principle application is as an optimization technique. Swarm robotics is a relatively new and rapidly developing field which draws inspiration from swarm intelligence. It is an interesting alternative to classical approaches to robotics because of some properties of problem solving present in social insects, which is flexible, robust, decentralized and self-organized. This work highlights the possibilities for further research

Estrategias de comunicación virtual en la RSC

En este artículo, revisamos los distintos tipos de comunicación de la RSC, desde la simple información hasta la participación en la generación de la RSC a través del diálogo. La gestión de la comunicación y la identificación con los intereses de los diferentes agentes permite diferenciar diferentes niveles de compromiso y colaboración, que incluye estrategias de información, persuasión y diálogo diferentes. Este diálogo puede ser virtual, por lo que analizamos las características para poder gestionarlo. Finalmente, revisamos la situación actual de un agente, en particular, los sindicatos en España, para considerarlos como agentes interesados en la generación de estrategias de dialogo virtual como herramienta para desarrollar la RSC. La escasa experiencia sindical en torno a la RSC puede verse desarrollada a través de estrategias de comunicación virtual que permite a los diferentes agentes contactar y dialogar de forma ágil. La vida real de la gestión de las decisiones estratégicas en RSC revela que está lejos de las teorías en donde se señala lo beneficioso que es implicar a los diferentes agentes. Las vías de comunicación siguen el camino de la información, llegar al diálogo parece difícil

University knowledge transfer office and social responsibility

Numerous studies and reviews about University Knowledge Transfer Offices (UKTO) have been written, but there are few that focus on Social Responsibility (SR). We present a systematic review of the research on both fields. We consider not only logics from agency theory and resource-based view, but also the dynamic approach from institutional theory, as they aim to generate sustainable economic and social value. The evolution of Knowledge Transfer Offices depends on their role as brokers of collaborations among different stakeholders, according to their mission and capacity to confront the innovation gap. We follow the line of SR viewed as a response to the specific demands of large stakeholders. Building upon recent conceptualizations of different theories, we develop an integrative model for understanding the institutional effects of the UKTO on university social responsibilit

Probabilistic versus incremental presynaptic learning in biological plausible synapses

In this paper, the presynaptic rule, a classical rule for hebbian learning, is revisited. It is shown that the presynaptic rule exhibits relevant synaptic properties like synaptic directionality, and LTP metaplasticity (long-term potentiation threshold metaplasticity). With slight modifications, the presynaptic model also exhibits metaplasticity of the long-term depression threshold, being also consistent with Artola, Brocher and Singer’s (ABS) influential model. Two asymptotically equivalent versions of the presynaptic rule were adopted for this analysis: the first one uses an incremental equation while the second, conditional probabilities. Despite their simplicity, both types of presynaptic rules exhibit sophisticated biological properties, specially the probabilistic versio

A Wavelet neural network for detection of signals in communications

Our objective is the design and simulation of an efficient system for detection of signals in communications in terms of speed and computational complexity. The proposed scheme takes advantage of two powerful frameworks in signal processing: Wavelets and Neural Networks. The decision system will take a decision based on the computation of the a priori probabilities of the input signal. For the estimation of such probability density functions, a Wavelet Neural Network (WNN) has been chosen. The election has arosen under the following considerations: (a) neural networks have been established as a general approximation tool for fitting nonlinear models from input/output data and (b) the increasing popularity of the wavelet decomposition as a powerful tool for approximation. The integration of the above factors leads to the wavelet neural network concept. This network preserve the universal approximation property of wavelet series, with the advantage of the speed and efficient computation of a neural network architecture. The topology and learning algorithm of the network will provide an efficient approximation to the required probability density functions

Data mining for the diagnosis of type 2 diabetes

Diabetes is the most common disease nowadays in all populations and in all age groups. diabetes contributing to heart disease, increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. Different techniques of artificial intelligence has been applied to diabetes problem. The purpose of this study is apply the artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining (DM) technique for the diabetes disease diagnosis. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with decision tree (DT), Bayesian classifier (BC) and other algorithms, recently proposed by other researchers, that were applied to the same database. The robustness of the algorithms are examined using classification accuracy, analysis of sensitivity and specificity, confusion matrix. The results obtained by AMMLP are superior to obtained by DT and BC

Multiple proportion case-basing driven CBRE and its application in the evaluation of possible failure of firms

Case-based reasoning (CBR) is a unique tool for the evaluation of possible failure of firms (EOPFOF) for its eases of interpretation and implementation. Ensemble computing, a variation of group decision in society, provides a potential means of improving predictive performance of CBR-based EOPFOF. This research aims to integrate bagging and proportion case-basing with CBR to generate a method of proportion bagging CBR for EOPFOF. Diverse multiple case bases are first produced by multiple case-basing, in which a volume parameter is introduced to control the size of each case base. Then, the classic case retrieval algorithm is implemented to generate diverse member CBR predictors. Majority voting, the most frequently used mechanism in ensemble computing, is finally used to aggregate outputs of member CBR predictors in order to produce final prediction of the CBR ensemble. In an empirical experiment, we statistically validated the results of the CBR ensemble from multiple case bases by comparing them with those of multivariate discriminant analysis, logistic regression, classic CBR, the best member CBR predictor and bagging CBR ensemble. The results from Chinese EOPFOF prior to 3 years indicate that the new CBR ensemble, which significantly improved CBRs predictive ability, outperformed all the comparative methods

A Neural Network Approach for Analyzing the Illusion of Movement in Static Images

The purpose of this work is to analyze the illusion of movement that appears when seeing certain static images. This analysis is accomplished by using a biologically plausible neural network that learned (in a unsupervised manner) to identify the movement direction of shifting training patterns. Some of the biological features that characterizes this neural network are: intrinsic plasticity to adapt firing probability, metaplasticity to regulate synaptic weights and firing adaptation of simulated pyramidal networks. After analyzing the results, we hypothesize that the illusion is due to cinematographic perception mechanisms in the brain due to which each visual frame is renewed approximately each 100 msec. Blurring of moving object in visual frames might be interpreted by the brain as movement, the same as if we present a static blurred object

Application of Fractal and Wavelets in Microcalcification Detection

Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection