Nowadays, batteries play an important role in a lot of different applications like energy storage, electro-mobility, consumer electronic and so on. All the battery types have a common factor that is their complexity, independently of its nature. Usually, the batteries have an electrochemical nature. Several different test are accomplished to check the batteries performance, and commonly, it is predictable how they work depending of their technology. The present research describes the hybrid intelligent system created to accomplish fault detection over a (...) Lithium Iron Phosphate—LiFePO4 power cell type, commonly used in electro-mobility applications. The approach is based on the cell temperatures behaviour for voltage and current specific values. Taken into account the operating range of a real system based on a LiFePO4 cell, a large set of points of operation have been used to achieve the dataset. The different behaviour zones have been obtained by clustering as a first step. Then, different regression techniques have been used over each cluster. Polynomial regression, artificial neural networks and support vector regression were the combined techniques to develop the hybrid intelligent model proposed. The intelligent system gives very good results over the operating range, detecting all the faults tested during the validation. (shrink)

One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study (...) is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery. (shrink)

Closed-loop administration of propofol for the control of hypnosis in anesthesia has evidenced an outperformance when comparing it with manual administration in terms of drug consumption and post-operative recovery of patients. Unlike other systems, the success of this strategy lies on the availability of a feedback variable capable of quantifying the current hypnotic state of the patient. However, the appearance of anomalies during the anesthetic process may result in inaccurate actions of the automatic controller. These anomalies may come from the (...) monitors, the syringe pumps, the actions of the surgeon or even from alterations in patients. This could produce adverse side effects that can affect the patient postoperative and reduce the safety of the patient in the operating room. Then, the use of anomaly detection techniques plays a significant role to avoid this undesirable situations. This work assesses different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining real data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final model presents successful performance. (shrink)

Automatic control of physiological variables is one of the most active areas in biomedical engineering. This paper is centered in the prediction of the analgesic variables evolution in patients undergoing surgery. The proposal is based on the use of hybrid intelligent modelling methods. The study considers the Analgesia Nociception Index to assess the pain in the patient and remifentanil as intravenous analgesic. The model proposed is able to make a one-step-ahead prediction of the remifentanil dose corresponding to the current state (...) of the patient. The input information is the previous remifentanil dose, the ANI variable and the electromyogram signal. Modelling techniques used are Artificial Neural Networks and Support Vector machines for Regression combined with clustering methods. Both training and validation were done with a real dataset from different patients. Results obtained show the potential of this methodology to calculate the drug dose corresponding to a given analgesic state of the patient. (shrink)

Student performance and its evaluation remain a serious challenge for education systems. Frequently, the recording and processing of students’ scores in a specific curriculum have several flaws for various reasons. In this context, the absence of data from some of the student scores undermines the efficiency of any future analysis carried out in order to reach conclusions. When this is the case, missing data imputation algorithms are needed. These algorithms are capable of substituting, with a high level of accuracy, the (...) missing data for predicted values. This research presents the hybridization of an algorithm previously proposed by the authors called adaptive assignation algorithm, with a well-known technique called multivariate imputation by chained equations. The results show how the suggested methodology outperforms both algorithms. (shrink)

A large part of technological advances, especially in the field of industry, have been focused on the optimization of productive processes. However, the detection of anomalies has turned out to be a great challenge in fields like industry, medicine or stock markets. The present work addresses anomaly detection on a control level plant. We propose the application of different intelligent techniques, which allow to obtain one-class classifiers using real data taken from the correct plant operation. The performance of each classifier (...) is assessed and validated with real created faults, achieving successful overall results. (shrink)

Detecting intrusions in large networks is a highly demanding task. In order to reduce the computation demand of analysing every single packet travelling along one of such networks, some years ago flows were proposed as a way of summarizing traffic information. Very few research works have addressed intrusion detection in flows from a visualizations perspective. In order to bridge this gap, the present paper proposes the application of a novel projection method (Beta Hebbian Learning) under this framework. With the aim (...) to validate this method, 8 traffic segments, containing many flows, have been analysed by means of this projection method. The promising results obtained for these segments, extracted from the University of Twente dataset, validate the proposed application. (shrink)

A dimensional reduction algorithm is applied to an intelligent classification model with the purpose of improving the efficiency and accuracy. The proposed classification model, used to distinguish the operating mode: Hard- and Soft-Switching, is presented and an analysis of the synchronized rectified step-down converter is done. With the aim of improving the accuracy and reducing the computational cost of the model, three different methods for dimensional reduction are applied to the input dataset of the model: self-organizing maps, principal component analysis (...) and correlation matrix. The obtained results show how the number of variable is highly reduced and the performance of the classification model is boosted: the results manifest an improve in the accuracy and efficiency of the classification. (shrink)

The use of batteries became essential in our daily life in electronic devices, electric vehicles and energy storage systems in general terms. As they play a key role in many devices, their design and implementation must follow a thorough test process to check their features at different operating points. In this circumstance, the appearance of any kind of deviation from the expected operation must be detected. This research deals with real data registered during the testing phase of a lithium iron (...) phosphate—LiFePO4—battery. The process is divided into four different working points, alternating charging, discharging and resting periods. This work proposes a hybrid classifier, based on one-class techniques, whose aim is to detect anomalous situations during the battery test. The faults are created by modifying the measured cell temperature a slight ratio from their real value. A detailed analysis of each technique performance is presented. The average performance of the chosen classifier presents successful results. (shrink)

In this research work a novel two-step system for anomaly detection is presented and tested over several real datasets. In the first step the novel Exploratory Projection Pursuit, Beta Hebbian Learning algorithm, is applied over each dataset, either to reduce the dimensionality of the original dataset or to face nonlinear datasets by generating a new subspace of the original dataset with lower, or even higher, dimensionality selecting the right activation function. Finally, in the second step Principal Component Analysis anomaly detection (...) is applied to the new subspace to detect the anomalies and improve its classification capabilities. This new approach has been tested over several different real datasets, in terms of number of variables, number of samples and number of anomalies. In almost all cases, the novel approach obtained better results in terms of area under the curve with similar standard deviation values. In case of computational cost, this improvement is only remarkable when complexity of the dataset in terms of number of variables is high. (shrink)

This paper aims to enhance security in IoT device networks through a visual tool that utilizes three projection techniques, including Beta Hebbian Learning (BHL), t-distributed Stochastic Neighbor Embedding (t-SNE) and ISOMAP, in order to facilitate the identification of network attacks by human experts. This work research begins with the creation of a testing environment with IoT devices and web clients, simulating attacks over Message Queuing Telemetry Transport (MQTT) for recording all relevant traffic information. The unsupervised algorithms chosen provide a set (...) of projections that enable human experts to visually identify most attacks in real-time, making it a powerful tool that can be implemented in IoT environments easily. (shrink)

In this study, a hybrid model based on intelligent techniques is developed to predict the active power generated in a bioclimatic house by a low power wind turbine. Contrary to other researches that predict the generated power taking into account the speed and the direction of the wind, the model developed in this paper only uses the speed of the wind, measured mainly in a weather station from the government meteorological agency (MeteoGalicia). The wind speed is measured at different heights, (...) against the usual measurements in others researches, which uses the wind speed and the direction measured in a weather station on the wind turbine nacelle. The prediction is performed 30 minutes ahead, what ensures that the Building Management System knows the energy generated by the low power wind turbine 30 minutes before, and it can adapt the consumption of different equipment in the house to optimize the power use. The main objective is to allow the Building Management System to optimize the uses of energy, taking into account the predicted amount of energy that will be produced and the energy consumed in the house. The developed model uses a hybrid topology with four clusters to improve the prediction, achieving an error lower than 6.5% for Mean Absolute Error measured in a final test. To perform this test, part of the original dataset was isolated from the beginning of the training process to check the model with a dataset that is not used before, simulating the model as it is receiving new data. (shrink)

Nowadays, the quality standards of higher education institutions pay special attention to the performance and evaluation of the students. Then, having a complete academic record of each student, such as number of attempts, average grade and so on, plays a key role. In this context, the existence of missing data, which can happen for different reasons, leads to affect adversely interesting future analysis. Therefore, the use of imputation techniques is presented as a helpful tool to estimate the value of missing (...) data. This work deals with the academic records of engineering students, in which imputation techniques are applied. More specifically, it is assessed and compared to the performance of the multivariate imputation by chained equations methodology, the adaptive assignation algorithm based on multivariate adaptive regression splines and a hybridization based on self-organisation maps with Mahalanobis distances and AAA algorithm. The results show that proposed methods obtain successfully results regardless the number of missing values, in general terms. (shrink)

The present research describes a novel adaptive anomaly detection method to optimize the performance of nonlinear and time-varying systems. The proposal integrates a centroid-based approach with the real-time identification technique Recursive Least Squares. In order to find anomalies, the approach compares the present system dynamics with the average (centroid) of the dynamics found in earlier states for a given setpoint. The system labels the dynamics difference as an anomaly if it rises over a determinate threshold. To validate the proposal, two (...) different datasets obtained from a level control plant operation have been used, to which anomalies have been artificially added. The results shown have determined a satisfactory performance of the method, especially in those processes with low noise. (shrink)

Batteries are a fundamental storage component due to its various applications in mobility, renewable energies and consumer electronics among others. Regardless of the battery typology, one key variable from a user’s perspective is the remaining energy in the battery. It is usually presented as the percentage of remaining energy compared to the total energy that can be stored and is labeled State Of Charge (SOC). This work addresses the development of a hybrid model based on a Lithium Iron Phosphate (LiFePO4) (...) power cell, due to its broad implementation. The proposed model calculates the SOC, by means of voltage and electric current as inputs and the latter as the output. Therefore, four models based on k-Means, Agglomerative Clustering, Gaussian Mixture and Spectral Clustering techniques have been tested in order to obtain an optimal solution. (shrink)

Background:The acquisition of experience is a major concern for nurses in intensive care units. Although the emotional component of the clinical practice of these nurses has been widely studied, greater examination is required to determine how this component influences their learning and practical experience.Objective:To discover the relationships between emotion, memory and learning and the impacts on nursing clinical practice.Research design:This is a qualitative phenomenological study. The data were collected from open, in-depth interviews. A total of 22 intensive care unit nurses (...) participated in this research between January 2012 and December 2014.Ethical considerations:The School of Nursing Ethics Committee approved the study, which complied with ethical principles and required informed consent.Findings:We found a clear relationship between emotion, memory and the acquisition of experience. This relationship grouped three dimensions: (1) satisfaction, to relieve the patient’s pain or discomfort, give confidence and a sense of security to the patient, enable the presence of family members into the intensive care unit and provide family members with a realistic view of the patient’s situation; (2) error experience, which nurses feel when a patient dies, when they fail to accompany a patient in his or her decision to abandon the struggle to live or when they fail to lend support to the patient’s family; and (3) the feel bad–feel good paradox, which occurs when a mistake in the patient’s care or handling of his or her family is repaired.Conclusion:Emotion is a capacity that impacts on nurses’ experience and influences improvements in clinical practice. Recalling stories of satisfaction helps to reinforce good practice, while recalling stories of errors helps to identify difficulties in the profession and recognise new forms of action. The articulation of emotional competencies may support the development of nursing ethics in the intensive care unit to protect and defend their patients and improve their relationships with families in order to maximise the potential for patient care. (shrink)

Drawing on political, legal, national, post-national, as well as American and European perspectives, this collection of essays offers a diverse and balanced discussion of the current arguments concerning deliberative democracy. The essays consider the thr.

The use of artificial intelligence in healthcare contexts is highly controversial for the (bio)ethical conundrums it creates. One of the main problems arising from its implementation is the lack of transparency of machine learning algorithms, which is thought to impede the patient’s autonomous choice regarding their medical decisions. If the patient is unable to clearly understand why and how an AI algorithm reached certain medical decision, their autonomy is being hovered. However, there are alternatives to prevent the negative impact of (...) AI opacity in shared (healthcare professional–patient) decision-making processes, and benefit from the high accuracy of such systems. Despite these ethical issues, this paper advocates for its usage and also defends the positive role that it could have in enhancing patients’ autonomy. I advocate for a system where the implementation of AI is only partial, and humans will remain in control of oversighting algorithm interpretation of medical images and patients’ data. (shrink)

Aprender de los griegos es posible, pero solo si partimos de dos bases necesarias: la primera, que nuestro mundo es profundamente distinto al suyo; la segunda, que el pensamiento libre requiere no quedar prisionero de ningún dogma. La forma primitiva de pensar de los griegos es una escuela que nos puede ayudar a desnudar la enorme cantidad de supuestos en los que se apoya la mentalidad contemporánea.

One of the most urgent debates of our time is about the exact role that new technologies can and should play in our societies and particularly in our public decision-making processes. This paper is a first attempt to introduce the idea of CrowdLaw, defined as online public participation leveraging new technologies to tap into diverse sources of information, judgments and expertise at each stage of the law and policymaking cycle to improve the quality as well as the legitimacy of the (...) resulting laws and policies. First, we explain why CrowdLaw differs from many previous forms of political participation. Second,we reproduce and explain the CrowdLaw Manifesto that the rising CrowdLaw community has elaborated to foster such approaches around the world. Lastly, we introduce some preliminary considerations on the notions of justice, legitimacy and quality of lawmaking and public decision-making, which are central to the idea of CrowdLaw. (shrink)

The central aim of this paper is to explain how protocol sentences establish a specific space for scientific discussion at the level of observation in the social sciences. In this space, the four levels of the theory of protocol sentences work as a coordinate system that allows to specify the degree of quality of data production. To do so, we elaborate upon Jean-Claude Passeron’s epistemology by combining it with Otto Neurath’s theory of protocol sentences. We start by discussing Passeron’s interpretation (...) of Neurath’s work in Le raissonnement sociologique and then present Neurath’s theory, following Thomas Uebel’s interpretation, with the purpose of developing the potential of Passeron’s epistemology in this regard. (shrink)

En este artículo analizo aportaciones de Michel Foucault y Antoni Domènech, relacionadas ambas con una filosofía política del cuerpo. Para ello estudio sus acercamientos respectivos a la ética clásica, ya sea como una estética de la existencia o como una razón erótica que se opone a una razón inerte. Posteriormente, y tras confirmar sus concomitancias, propongo completarlos con una lectura de El banquete de Platón. Finalmente ejemplifico cómo podría funcionar este programa analizando la conformidad y la resistencia contra la imposición (...) de una ortodoxia corporal. (shrink)

Euthanasia and the duty to die have both been thoroughly discussed in the field of bioethics as morally justifiable practices within medical healthcare contexts. The existence of a narrow connection between both could also be established, for people having a duty to die should be allowed to actively hasten their death by the active means offered by euthanasia. Choosing the right time to end one’s own life is a decisive factor to retain autonomy at the end of our lives. However, (...) there is no definitive consensus on why physicians should be the ones performing the medical procedure to end a person’s life. The moral problems arising from such assertion are not to be taken lightly, for medical tradition has long regarded the duty not to kill, not to actively end a patient’s life, as the core moral obligation that gives meaning to the medical profession. Our concern is to question the moral justifiability of the arguments offered by physicians not to actively help patients die. (shrink)

El presente artículo se basa en una investigación llevada a cabo por profesores de la Universidad Pablo de Olavide de Sevilla, adscritos al Grupo de Investigación de Trabajo Social y Políticas Sociales (Plan Andaluz de Investigación, Desarrollo e Innovación -Ciencias Sociales, Económicas y Jurídicas (PAIDI, SEJ-452), a lo largo de dos años y dentro del marco de colaboración con la Fundación Mercasevilla. El objeto de dicha investigación ha sido el análisis de la sociabilidad de las personas mayores en el medio (...) urbano desde la sociabilidad a la dependencia. El estudio pone de manifiesto las diferencias existentes en el uso de los espacios según la edad de la población, los déficits que esos espacios muestran, y su relación con la situación de dependencia de las personas mayores. En relación a los datos obtenidos, se plantean conclusiones y orientaciones que pueden resultar de interés a técnicos, investigadores, planificadores del territorio y para aquellos que pueden decidir acerca de políticas que modifiquen el espacio urbano. El equipo de investigación establece una relación directa entre la integración social del colectivo de mayores, con el grado de participación en la sociedad en la que se encuentran, y por tanto, con el uso y acceso a los distintos espacios de sociabilidad, que es en definitiva donde ejercen esta participación social. (shrink)

Based on the results obtained from primary education students—fifth and sixth graders—the aim of this work is to check the appropriate self-perceived behaviors during and at the end of the game. The study population was made up of 698 students from fifth and sixth grade in the Autonomous Region of Castilla–La Mancha (Spain). Data were collected through a questionnaire (scale) on the social skills of primary school students linked to the adequate skills when losing, the adequate skills when winning, and (...) the adequate skills during the game. Study results revealed that analyzed behaviors vary, depending on the moment of the game, being it more frequently during the development of the game than when losing or winning. (shrink)

Este artículo examina el reciente debate entre J. Habermas y C. Lafont sobre la ética del discurso. Se intenta mostrar que la propuesta de Lafont de interpretar la ética del discurso como una versión del realismo moral se enfrenta a problemas difíciles de resolver dentro del marco teórico de la ética discursiva. El artículo examina en primer lugar cómo la posición de Lafont extiende al terreno de la razón práctica varias objeciones importantes contra la teoría consensual de la verdad. A (...) continuación, se critican los argumentos con que Lafont avala su realismo moral, y se concluye examinando la distinción entre consensos racionales y compromisos de intereses desde una perspectiva estrictamente procedimental. This paper analyses the recent debate between J. Habermas and C. Lafont on discourse ethics. The aim is to show that Lafont's proposal of a realist interpretation of discourse ethics must face several problems which are difficult to resolve from within the theoretical frame of discourse ethics. First of all, the paper sets out how Lafont's position extends to the field of practical reason some important objections against Habermas¿s consensus theory of truth. Subsequently Lafont's arguments in support of moral realism are criticised, and the paper concludes with an analysis of the difference between rational agreements and compromises of interests from a procedural point of view. (shrink)

The aim of this paper is to focus on Wittgenstein's conception about Psychology as Science. For that purpose we analyse first Wittgenstein's approach to the psychological questions conected with his new theory of meaning. Then, we intend to concentrate on his idea of Psychology as Science, separating the scientist's from the philosopher's tasks. Finally, we intend to place wittgensteinian view about Psychology as Science in relation to the different views defending a methodological dualism connected with the methodological tension unity-diversity of (...) Science. (shrink)

En la actividad científica se pueden distinguir tres tipos principales de representación científica: proyectiva, subsuntiva y reductiva. Tras unas breves considcraciones introductorias, se presentan las características más destacadas de cada uno de estos tres tipos principales de representación científica y se abstrae a partir de ellas el esquema al que toda Teoría General de la Representación Científica se debe adecuar. A continuación se exponen las lineas generales de la principal propuesta presente en la literatura para desarrollar TGRC y se hacen (...) algunas consideraciones criticas. La conclusión provisional es que esta propuesta tiene, al menos por el momento, el problema de ser, o bien demasiado liberal, o bien demasiado conservadora. Por último, se propone un enfoque alternativo a explorar que pretende estar libre de los problemas deI anterior.We can distinguish three main types of representation in scientific practice: projective, subsuntive and reductive. After some brief introductory remarks, we present the more salient characteristics of these three main types of scientific representation and we abstract from them a scheme that any General Theory for Scientific Representation must fit. Then we offer the general traits of the main existing proposal for a GTSR and we make some criticisms. The provisional conclusion is that this proposal is, as it stands, either too liberal or too conservative. Finally, we propose to explore an alternative approach which intends to solve these difficulties. (shrink)