A Hybrid Intelligent System to Forecast Solar Energy Production

Manuscrito aceptado[Abstarct]: There is wide acknowledgement that solar energy is a promising and renewable source of electricity. However, complementary sources are sometimes required, due to its limited capacity, in order to satisfy user demand. A Hybrid Intelligent System (HIS) is proposed in this paper to optimize the range of possible solar energy and power grid combinations. It is designed to predict the energy generated by any given solar thermal system. To do so, the novel HIS is based on local models that implement both supervised learning (artificial neural networks) and unsupervised learning (clustering). These techniques are combined and applied to a realworld installation located in Spain. Alternative models are compared and validated in this case study with data from a whole year. With an optimum parameter fit, the proposed system managed to calculate the solar energy produced by the panel with an error that was lower than 10-4 in 86% of cases

Delving into Android Malware Families with a Novel Neural Projection Method

[Abstract] Present research proposes the application of unsupervised and supervised machine-learning techniques to characterize Android malware families. More precisely, a novel unsupervised neural-projection method for dimensionality-reduction, namely, Beta Hebbian Learning (BHL), is applied to visually analyze such malware. Additionally, well-known supervised Decision Trees (DTs) are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones. The proposed techniques are validated when facing real-life Android malware data by means of the well-known and publicly available Malgenome dataset. Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malwar

Delving into Android Malware Families with a Novel Neural Projection Method

Present research proposes the application of unsupervised and supervised machine-learning techniques to characterize Android malware families. More precisely, a novel unsupervised neural-projection method for dimensionality-reduction, namely, Beta Hebbian Learning (BHL), is applied to visually analyze such malware. Additionally, well-known supervised Decision Trees (DTs) are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones. The proposed techniques are validated when facing real-life Android malware data by means of the well-known and publicly available Malgenome dataset. Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malware.This work is partially supported by Instituto Nacional de Ciberseguridad (INCIBE) and developed by Research Institute of Applied Sciences in Cybersecurity (RIASC)

Machine learning to study and predict malfunctioning in robot software

La creciente modernización que se está viviendo en la industria, con la adaptación de la misma a la denominada industria 4.0, implica un mayor control de los sistemas de producción y con ello una abundancia de datos, generando la necesidad de su análisis y comprensión. Esto permite entre otras cosas, llevar a cabo tareas de mantenimiento, tratando de minimizar los periodos de inactividad de dichos sistemas. Para optimizar la realización de esta tarea, en esta tesis doctoral se desarrollan y validan diferentes estrategias para monitorizar sistemas robóticos y detectar automáticamente anomalías en los mismos que afecten al rendimiento de su componente software. En primer lugar, se valida la aplicación de técnicas de Machine Learning que permitan visualizar datos de rendimiento para la adecuada monitorización y entendimiento de los mismos. Por otra parte, se ha investigado la aplicación de otras técnicas, basadas en aprendizaje supervisado, para la detección de anomalías. Una de las dificultades de los conjuntos de datos de este tipo de problemas es la cantidad de datos disponibles asociados a fallos o anomalías. Habitualmente, es significativamente menor que los asociados a un normal funcionamiento del sistema. Para resolver el impacto que este fenómeno tiene en las técnicas aplicadas, se valida el empleo de diferentes algoritmos de balanceo de datos. Otra problemática que afrontar con datos reales es la ausencia de ciertos datos, que se puede deber a varios motivos. En el presente trabajo de investigación se proponen estrategias para hacer frente a esta problemática en el caso específico de datos relacionados con el rendimiento del software de robots. La combinación de estas innovadoras soluciones ha sido validada con datos reales y públicamente disponibles. Los satisfactorios resultados obtenidos confirman la viabilidad y utilidad de la propuesta planteada

A visual tool for monitoring and detecting anomalies in robot performance

In robotic systems, both software and hardware components are equally important. However, scant attention has been devoted until now in order to detect anomalies/failures affecting the software component of robots while many proposals exist aimed at detecting physical anomalies. To bridge this gap, the present paper focuses on the study of anomalies affecting the software performance of a robot by using a novel visualization tool. Unsupervised visualization methods from the machine learning field are applied in order to upgrade the recently proposed Hybrid Unsupervised Exploratory Plots (HUEPs). Furthermore, Curvilinear Component Analysis and t-distributed stochastic neighbor embedding are added to the original HUEPs formulation and comprehensively compared. Furthermore, all the different combinations of HUEPs are validated in a real-life scenario. Thanks to this intelligent visualization of robot status, interesting conclusions can be obtained to improve anomaly detection in robot performance.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

A hybrid machine learning system to impute and classify a component-based robot

In the field of cybernetic systems and more specifically in robotics, one of the fundamental objectives is the detection of anomalies in order to minimize loss of time. Following this idea, this paper proposes the implementation of a Hybrid Intelligent System in four steps to impute the missing values, by combining clustering and regression techniques, followed by balancing and classification tasks. This system applies regression models to each one of the clusters built on the instances of data set. Subsequently, a variety of balancing techniques are applied to improve the classifier’s ability to discern whether it is in an error or a normal state. These techniques support to obtain better classification ratios in which a robot is close to error and allow us to bring the behavior back to a normal state. The experimentation is performed using a modern and public data set, which has been extracted from a component-based robotic system, in which different anomalies are induced by software in their components

Imputation of Missing Values Affecting the Software Performance of Component-based Robots

Intelligent robots are foreseen as a technology that would be soon present in most public and private environments. In order to increase the trust of humans, robotic systems must be reliable while both response and down times are minimized. In keeping with this idea, present paper proposes the application of machine learning (regression models more precisely) to preprocess data in order to improve the detection of failures. Such failures deeply a ect the performance of the software components embedded in human-interacting robots. To address one of the most common problems of real-life datasets (missing values), some traditional (such as linear regression) as well as innovative (decision tree and neural network) models are applied. The aim is to impute missing values with minimum error in order to improve the quality of data and consequently maximize the failure-detection rate. Experiments are run on a public and up-to-date dataset and the obtained results support the viability of the proposed models

A Hybrid Intelligent Modeling approach for predicting the solar thermal panel energy production

There is no doubt that the European Union is undergoing an ecological transition, with renewable energies accounting for an increasing share of energy consumption in the Member States. In Spain, solar energy is one of these rapidly expanding renewable sources. This study analyzes the solar energy production of a panel in the Spanish region of Galicia. It has been demonstrated that the solar energy produced by this panel can be predicted using a hybrid stepwise system. The missing value imputation is a key step in the process. This involves combining regression and clustering techniques on different subdivisions of the complete dataset, starting with a smaller and less complete dataset and performing appropriate imputations to create a larger and more complete collection. Finally, the dataset is divided into more relevant subsets for regression analysis to calculate the amount of solar energy generated. The imputing missing values using an Artificial Neural Network resulted in a more valid dataset for further processing than eliminating rows with corrupted or empty values. Also, properly applying clustering techniques gives better results than working on the whole dataset.Míriam Timiraos’s research was supported by the “Xunta de Galicia” (Regional Government of Galicia), Spain through grants to industrial PhD (http://gain.xunta.gal/), under the “Doutoramento Industrial 2022” grant with reference: 04_IN606D_2022_2692965. Funding for open access charge: Universidade da Coruña/CISUG. CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01)

A Hybrid Intelligent System to forecast solar energy production

There is wide acknowledgement that solar energy is a promising and renewable source of electricity. However, complementary sources are sometimes required, due to its limited capacity, in order to satisfy user demand. A Hybrid Intelligent System (HIS) is proposed in this paper to optimize the range of possible solar energy and power grid combinations. It is designed to predict the energy generated by any given solar thermal system. To do so, the novel HIS is based on local models that implement both supervised learning (artificial neural networks) and unsupervised learning (clustering). These techniques are combined and applied to a realworld installation located in Spain. Alternative models are compared and validated in this case study with data from a whole year. With an optimum parameter fit, the proposed system managed to calculate the solar energyproduced by the panel with an error that was lower than 10-4 in 86% of cases