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The analysis of time series of electricity consumption can be very useful when establishing energy saving policies. Even more so, in the case of institutions with a large number of buildings in which poor management not only entails greater expenditure but also greater damage to the environment. That is why the automatic generation of linguistic descriptions from this data is a critical element of management aid. In this paper, a Linguistic Petri net is proposed to describe the daily cycle of electricity consumption of buildings in large institutions. Such descriptions are based on the idea of category defined as the set of consecutive trends that satisfy a series of conditions. The designed network generates a description of each category which will allow to obtain high level descriptions of the buildings’ behaviour. The results of the proposal are validated with real data on electricity consumption from different buildings of the University of Castilla-La Mancha.
Juan Moreno-Garcia; Luis Jimenez-Linares; Luis Rodriguez-Benitez. Automatic Generation of Linguistic Descriptions of Electricity Consumption in the Buildings of a Large Institution. Studies in Computational Intelligence 2021, 101 -109.
AMA StyleJuan Moreno-Garcia, Luis Jimenez-Linares, Luis Rodriguez-Benitez. Automatic Generation of Linguistic Descriptions of Electricity Consumption in the Buildings of a Large Institution. Studies in Computational Intelligence. 2021; ():101-109.
Chicago/Turabian StyleJuan Moreno-Garcia; Luis Jimenez-Linares; Luis Rodriguez-Benitez. 2021. "Automatic Generation of Linguistic Descriptions of Electricity Consumption in the Buildings of a Large Institution." Studies in Computational Intelligence , no. : 101-109.
The aim of this study was to characterise all the goal scoring patterns during open play (elaborate attacks versus counterattacks) related to zone pitch division and the number of players involved in the 2018 FIFA World Cup in Russia. An Iterative Dichotomiser 3 (ID3) decision tree algorithm was used to classify all the goal scoring patterns (94 goals in 64 matches). The results did not show statistical differences between the type of scoring goal during the 2018 FIFA World Cup (p > 0.05; ES = Moderate). According to the result of the patterns of how the goals were achieved, an ID3 algorithm decision tree with seven classification decision nodes was calculated. Consequently, this study may aid national team coaches for the next World Cup to establish notational analyses and spatial-temporal relations to understand how scoring patterns during open play are related to zone pitch division and the number of players involved.
Joaquin Cerda; Javier Sanchez-Sanchez; David Viejo-Romero; Luis Jimenez-Linares; Jesus Vicente Gimenez; Jorge Garcia-Unanue; Jose Luis Felipe. Characterisation of Goal Scoring Patterns during Open Play Related to Zone Pitch Division and Number of Players Involved in the 2018 FIFA World Cup. Sensors 2021, 21, 5601 .
AMA StyleJoaquin Cerda, Javier Sanchez-Sanchez, David Viejo-Romero, Luis Jimenez-Linares, Jesus Vicente Gimenez, Jorge Garcia-Unanue, Jose Luis Felipe. Characterisation of Goal Scoring Patterns during Open Play Related to Zone Pitch Division and Number of Players Involved in the 2018 FIFA World Cup. Sensors. 2021; 21 (16):5601.
Chicago/Turabian StyleJoaquin Cerda; Javier Sanchez-Sanchez; David Viejo-Romero; Luis Jimenez-Linares; Jesus Vicente Gimenez; Jorge Garcia-Unanue; Jose Luis Felipe. 2021. "Characterisation of Goal Scoring Patterns during Open Play Related to Zone Pitch Division and Number of Players Involved in the 2018 FIFA World Cup." Sensors 21, no. 16: 5601.
The aim of this study was to analyse different success models and split time on cut-off point values on physical demands to keep category in semi-professional football players. An ad hoc observational controlled study was carried out with a total of ten (840 match data) outfield main players (25.2 ± 6.3 years, 1.79 ± 0.75 m, 74.9 ± 5.8 kg and 16.5 ± 6 years of football experience) and monitored using 15 Hz GPS devices. During 14 official matches from the Spanish division B in the 2016/2017 season, match data were coded considering the situational variable (score) and classified by match results (winning, losing or drawing). The results show significant differences between high-intensity attributes criteria that considered split time in velocity zones of 0–15 min (p = 0.043, ηp2 = 0.065, medium), 30–45 min (p = 0.010, ηp2 = 0.094, medium) and 60–75 min (p = 0.015, ηp2 = 0.086, medium), as well as sprint 60–75 min (p = 0.042, ηp2 = 0.066, medium) and 75–90 min (p = 0.002, ηp2 = 0.129, medium). Decision tree induction was applied to reduce the disparity range of data according to six 15-min intervals and to determine the cut-off point values for every parameter combination. It was possible to establish multivariate models for the main high-intensity actions criteria, allowing the establishment of all rules with their attributes and enabling the detection and visualisation of relationships and the pattern sets of variables for determining success.
Jesus Vicente Gimenez; Luis Jimenez-Linares; Jorge Garcia-Unanue; Javier Sanchez-Sanchez; Leonor Gallardo; Jose Luis Felipe. Analyse Success Model of Split Time and Cut-Off Point Values of Physical Demands to Keep Category in Semi-Professional Football Players. Applied Sciences 2020, 10, 5299 .
AMA StyleJesus Vicente Gimenez, Luis Jimenez-Linares, Jorge Garcia-Unanue, Javier Sanchez-Sanchez, Leonor Gallardo, Jose Luis Felipe. Analyse Success Model of Split Time and Cut-Off Point Values of Physical Demands to Keep Category in Semi-Professional Football Players. Applied Sciences. 2020; 10 (15):5299.
Chicago/Turabian StyleJesus Vicente Gimenez; Luis Jimenez-Linares; Jorge Garcia-Unanue; Javier Sanchez-Sanchez; Leonor Gallardo; Jose Luis Felipe. 2020. "Analyse Success Model of Split Time and Cut-Off Point Values of Physical Demands to Keep Category in Semi-Professional Football Players." Applied Sciences 10, no. 15: 5299.
The backpropagation (BP) algorithm is a gradient-based algorithm used for training a feedforward neural network (FNN). Despite the fact that BP is still used today when FNNs are trained, it has some disadvantages, including the following: (i) it fails when non-differentiable functions are addressed, (ii) it can become trapped in local minima, and (iii) it has slow convergence. In order to solve some of these problems, metaheuristic algorithms have been used to train FNN. Although they have good exploration skills, they are not as good as gradient-based algorithms at exploitation tasks. The main contribution of this article lies in its application of novel memetic approaches based on the Gravitational Search Algorithm (GSA) and Chaotic Gravitational Search Algorithm (CGSA) algorithms, called respectively Memetic Gravitational Search Algorithm (MGSA) and Memetic Chaotic Gravitational Search Algorithm (MCGSA), to train FNNs in three classical benchmark problems: the XOR problem, the approximation of a continuous function, and classification tasks. The results show that both approaches constitute suitable alternatives for training FNNs, even improving on the performance of other state-of-the-art metaheuristic algorithms such as ParticleSwarm Optimization (PSO), the Genetic Algorithm (GA), the Adaptive Differential Evolution algorithm with Repaired crossover rate (Rcr-JADE), and the Covariance matrix learning and Bimodal distribution parameter setting Differential Evolution (COBIDE) algorithm. Swarm optimization, the genetic algorithm, the adaptive differential evolution algorithm with repaired crossover rate, and the covariance matrix learning and bimodal distribution parameter setting differential evolution algorithm.
Ricardo García-Ródenas; Luis Jimenez Linares; Julio Alberto López-Gómez. Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm. Neural Computing and Applications 2020, 33, 2561 -2588.
AMA StyleRicardo García-Ródenas, Luis Jimenez Linares, Julio Alberto López-Gómez. Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm. Neural Computing and Applications. 2020; 33 (7):2561-2588.
Chicago/Turabian StyleRicardo García-Ródenas; Luis Jimenez Linares; Julio Alberto López-Gómez. 2020. "Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm." Neural Computing and Applications 33, no. 7: 2561-2588.
We here describe an algorithm based on an evolutionary strategy to find the prototype series of a set of time series, and we use Dynamic Time Warping (DTW) as a distance measure between series, and do not restrict the search space to the series in the set. The problem of calculating the centroid of a set of time series can be addressed as a minimization problem, using genetic algorithms. Our proposal may be considered among the set of non-classical approaches to genetic algorithms, where an individual gene is a candidate time series for being the centroid or representative of the whole set of series. The representation and operators of genetic algorithms are redesigned, in order to generate efficient summaries, the fitness function of each candidate series to be a prototype is approximated, comparing them only with a subset of randomly selected time series from the original dataset. Three areas are looked at in order to assess the goodness of our proposal: the performance of the prototype generated in terms of a fitness function, the consistency of the prototype generation for use in classical grouping algorithms, and its use in classification algorithms based on the nearest prototypes.
Pablo Leon-Alcaide; Luis Rodriguez-Benitez; Ester Castillo-Herrera; Juan Moreno-Garcia; Luis Jimenez-Linares. An evolutionary approach for efficient prototyping of large time series datasets. Information Sciences 2020, 511, 74 -93.
AMA StylePablo Leon-Alcaide, Luis Rodriguez-Benitez, Ester Castillo-Herrera, Juan Moreno-Garcia, Luis Jimenez-Linares. An evolutionary approach for efficient prototyping of large time series datasets. Information Sciences. 2020; 511 ():74-93.
Chicago/Turabian StylePablo Leon-Alcaide; Luis Rodriguez-Benitez; Ester Castillo-Herrera; Juan Moreno-Garcia; Luis Jimenez-Linares. 2020. "An evolutionary approach for efficient prototyping of large time series datasets." Information Sciences 511, no. : 74-93.
It is very common to use time series in a large number of areas, and it is necessary to obtain as much detailed information as possible from these series. There are different possibilities for displaying this information, for example, in the form of a graphical representation. However, the need to represent information using natural language, that is to say, by means of a linguistic description, is becoming more and more frequent. This paper presents a technique for obtaining linguistic descriptions from time series using a representation called Fuzzy Piecewise Linear Segments. It is shown how to obtain the information of a modelled series using this representation and the necessary steps to generate the description using templates. Finally, some examples of its use are shown.
Juan Moreno-Garcia; Antonio Moreno-Garcia; Luis Jimenez-Linares; Luis Rodriguez-Benitez. Describing Time Series Using Fuzzy Piecewise Linear Segments. Econometrics for Financial Applications 2019, 149 -155.
AMA StyleJuan Moreno-Garcia, Antonio Moreno-Garcia, Luis Jimenez-Linares, Luis Rodriguez-Benitez. Describing Time Series Using Fuzzy Piecewise Linear Segments. Econometrics for Financial Applications. 2019; ():149-155.
Chicago/Turabian StyleJuan Moreno-Garcia; Antonio Moreno-Garcia; Luis Jimenez-Linares; Luis Rodriguez-Benitez. 2019. "Describing Time Series Using Fuzzy Piecewise Linear Segments." Econometrics for Financial Applications , no. : 149-155.
Metaheuristic optimization algorithms address two main tasks in the process of problem solving: i) exploration (also called diversification) and ii) exploitation (also called intensification). Guaranteeing a trade-off between these operations is critical to good performance. However, although many methods have been proposed by which metaheuristics can achieve a balance between the exploration and exploitation stages, they are still worse than exact algorithms at exploitation tasks, where gradient-based mechanisms outperform metaheuristics when a local minimum is approximated. In this paper, a quasi-Newton method is introduced into a Chaotic Gravitational Search Algorithm as an exploitation method, with the purpose of improving the exploitation capabilities of this recent and promising population-based metaheuristic. The proposed approach, referred to as a Memetic Chaotic Gravitational Search Algorithm, is used to solve forty-five benchmark problems, both synthetic and real-world, to validate the method. The numerical results show that the adding of quasi-Newton search directions to the original (Chaotic) Gravitational Search Algorithm substantially improves its performance. Also, a comparison with the state-of-the-art algorithms: Particle Swarm Optimization, Genetic Algorthm, Rcr-JADE, COBIDE and RLMPSO, shows that the proposed approach is promising for certain real-world problems.
Ricardo García-Ródenas; Luis Jiménez Linares; Julio Alberto López-Gómez. A Memetic Chaotic Gravitational Search Algorithm for unconstrained global optimization problems. Applied Soft Computing 2019, 79, 14 -29.
AMA StyleRicardo García-Ródenas, Luis Jiménez Linares, Julio Alberto López-Gómez. A Memetic Chaotic Gravitational Search Algorithm for unconstrained global optimization problems. Applied Soft Computing. 2019; 79 ():14-29.
Chicago/Turabian StyleRicardo García-Ródenas; Luis Jiménez Linares; Julio Alberto López-Gómez. 2019. "A Memetic Chaotic Gravitational Search Algorithm for unconstrained global optimization problems." Applied Soft Computing 79, no. : 14-29.
Time series (TSs) are usually represented numerically although there are many situations where a linguistic description is preferable. Granular Linguistic Model of Phenomena (GLMP) is a paradigm used in the generation of linguistic descriptions of static situations without considering the temporal relationship of data present, for instance, in TSs. This paper presents a new method to generate linguistic descriptions of TSs with an operation similar to Petri Nets (PNs) and inspired by GLMP. The presented approach maintains the operation of PNs, adding a mechanism to generate linguistic descriptions based on GLMP. The main components of GLMP are added to places and transitions of PNs. This extension is called Linguistic Petri Nets (LPNs) and is a language that can be used to generate linguistic descriptions of systems. GLMP is a method that can be used by experts to design how the linguistic descriptions are synthesized and generated. So, LPNs also allow incorporating expert knowledge and combining descriptions in an appropriate way. The experimental part is focused on showing how LPNs can be used to linguistically describe TSs including the occurrence of maxima or minima, and trends.
Juan Moreno-Garcia; Luis Rodriguez-Benitez; Luis Jimenez-Linares; Gracian Trivino. A Linguistic Extension of Petri Nets for the Description of Systems: An Application to Time Series. IEEE Transactions on Fuzzy Systems 2019, 27, 1818 -1832.
AMA StyleJuan Moreno-Garcia, Luis Rodriguez-Benitez, Luis Jimenez-Linares, Gracian Trivino. A Linguistic Extension of Petri Nets for the Description of Systems: An Application to Time Series. IEEE Transactions on Fuzzy Systems. 2019; 27 (9):1818-1832.
Chicago/Turabian StyleJuan Moreno-Garcia; Luis Rodriguez-Benitez; Luis Jimenez-Linares; Gracian Trivino. 2019. "A Linguistic Extension of Petri Nets for the Description of Systems: An Application to Time Series." IEEE Transactions on Fuzzy Systems 27, no. 9: 1818-1832.
In this paper we present a software architecture based on a multi-agent system whose major goal is the identification of traffic events from videos. In order to achieve this, H264/AVC motion vectors that appear in compressed video signal are taken as input. They are classified depending on their position in the scene and after that each group of motion vectors obtained from such classification is processed independently using statistical techniques. The use of this kind of techniques have been broadly used in the processing of time series like the one we take as input. After the statistical processing, individual results are compared between them in order to detect patterns related to possible traffic events. This comparison process can be understood as a cooperative process. So, to integrate the different processing components of this architecture we propose the use of a multi-agent system. Multi-agent systems allows to define a cooperative architecture using individual agents that can be run in parallel allowing to raise the performance and efficiency of the global process of event identification. The experimentation of this paper is driven to the detection of objects in complex traffic scenarios where the videos are captured from on-board cameras.
L. Rodriguez-Benitez; J. Giralt; D. Merino; L. Jimenez-Linares; J. Moreno-Garcia. Enhancing the Analysis of Video Time Series by Means of a Multi-agent Architecture. Artificial Intelligence: Foundations, Theory, and Algorithms 2018, 11 -21.
AMA StyleL. Rodriguez-Benitez, J. Giralt, D. Merino, L. Jimenez-Linares, J. Moreno-Garcia. Enhancing the Analysis of Video Time Series by Means of a Multi-agent Architecture. Artificial Intelligence: Foundations, Theory, and Algorithms. 2018; ():11-21.
Chicago/Turabian StyleL. Rodriguez-Benitez; J. Giralt; D. Merino; L. Jimenez-Linares; J. Moreno-Garcia. 2018. "Enhancing the Analysis of Video Time Series by Means of a Multi-agent Architecture." Artificial Intelligence: Foundations, Theory, and Algorithms , no. : 11-21.
Thanks to the presence of sensors and the boom in technologies typical of the Internet of things, we can now monitor and record the energy consumption of buildings over time. By effectively analyzing these data to capture consumption patterns, significant reductions in consumption can be achieved and this can contribute to a building’s sustainability....
Sergio Martínez-Municio; Luis Rodríguez-Benítez; Ester Castillo-Herrera; Juan Giralt-Muiña; Luis Jiménez-Linares. Linguistic Modeling and Synthesis of Heterogeneous Energy Consumption Time Series Sets. International Journal of Computational Intelligence Systems 2018, 12, 259 -272.
AMA StyleSergio Martínez-Municio, Luis Rodríguez-Benítez, Ester Castillo-Herrera, Juan Giralt-Muiña, Luis Jiménez-Linares. Linguistic Modeling and Synthesis of Heterogeneous Energy Consumption Time Series Sets. International Journal of Computational Intelligence Systems. 2018; 12 (1):259-272.
Chicago/Turabian StyleSergio Martínez-Municio; Luis Rodríguez-Benítez; Ester Castillo-Herrera; Juan Giralt-Muiña; Luis Jiménez-Linares. 2018. "Linguistic Modeling and Synthesis of Heterogeneous Energy Consumption Time Series Sets." International Journal of Computational Intelligence Systems 12, no. 1: 259-272.
Deep learning has arisen in the last years as a powerful and ultimate tool for machine learning problems. This article analyses the performance of classic and deep neural network models in a challenging problem like face recognition. The aim of this article is to study what the main advantages and disadvantages deep neural networks provide and when they will be more suitable than classic models, which have also obtained really good results in some complex problems. Is it worth using deep learning? The results show that deep models increase the learning capabilities of classic neural networks in problems with high non-linearities features.
Ricardo García-Ródenas; Luis Jiménez Linares; Julio Alberto López-Gómez. On the Performance of Classic and Deep Neural Models in Image Recognition. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 10614, 600 -608.
AMA StyleRicardo García-Ródenas, Luis Jiménez Linares, Julio Alberto López-Gómez. On the Performance of Classic and Deep Neural Models in Image Recognition. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; 10614 ():600-608.
Chicago/Turabian StyleRicardo García-Ródenas; Luis Jiménez Linares; Julio Alberto López-Gómez. 2017. "On the Performance of Classic and Deep Neural Models in Image Recognition." Transactions on Petri Nets and Other Models of Concurrency XV 10614, no. : 600-608.
The aim of this paper is to represent time series in a fuzzy way by means of a piecewise linear segment method that represents the time series in an efficient way. But in addition, this representation collects the uncertainty generated in the process of generation of the segments. This representation can be used, for instance, to compare time series or to query information from these. That is why in this approach we also propose a method to compare different parts of the output, obtaining as result a dissimilarity value representing how close their outputs are. Several tests have been done in order to know if input time series are represented correctly with this method and to know if the comparison process obtains the expected results. The input data selected for the tests is the real data obtained from the electric consumption of three buildings from the campus in the city of Toledo of our university.
Antonio Moreno-Garcia; Juan Moreno-Garcia; Luis Jimenez-Linares; Luis Rodriguez-Benitez. Time series represented by means of fuzzy piecewise lineal segments. Journal of Computational and Applied Mathematics 2017, 318, 156 -167.
AMA StyleAntonio Moreno-Garcia, Juan Moreno-Garcia, Luis Jimenez-Linares, Luis Rodriguez-Benitez. Time series represented by means of fuzzy piecewise lineal segments. Journal of Computational and Applied Mathematics. 2017; 318 ():156-167.
Chicago/Turabian StyleAntonio Moreno-Garcia; Juan Moreno-Garcia; Luis Jimenez-Linares; Luis Rodriguez-Benitez. 2017. "Time series represented by means of fuzzy piecewise lineal segments." Journal of Computational and Applied Mathematics 318, no. : 156-167.
In the last few years, hybridization has spread as an effective technique to solve hard optimization problems where metaheuristics algorithms have been unable to find global optima in a computational cost given. In this article, we propose the so-called cooperation strategy. This way is an alternative to hybridization in which different algorithms work together in order to find a global optimum following an intrinsically parallel approach. Different homogeneous and heterogeneous strategies using CEC benchmark functions have been designed using Brain Storm Optimization (BSO) metaheuristic in comparison with a hybrid BSO, showing that cooperation improves significantly hybridization results and the original BSO.
Ricardo Garcia-Rodenas; Luis Jiménez Linares; Julio Alberto Lopez-Gomez. A cooperative Brain Storm Optimization Algorithm. 2017 IEEE Congress on Evolutionary Computation (CEC) 2017, 838 -845.
AMA StyleRicardo Garcia-Rodenas, Luis Jiménez Linares, Julio Alberto Lopez-Gomez. A cooperative Brain Storm Optimization Algorithm. 2017 IEEE Congress on Evolutionary Computation (CEC). 2017; ():838-845.
Chicago/Turabian StyleRicardo Garcia-Rodenas; Luis Jiménez Linares; Julio Alberto Lopez-Gomez. 2017. "A cooperative Brain Storm Optimization Algorithm." 2017 IEEE Congress on Evolutionary Computation (CEC) , no. : 838-845.
This paper presents a system for detecting road departures by comparing linguistic representations for the trajectory of the vehicle with that for the lane marks of the road. All this information is obtained from a single camera processing exclusively the H264 motion vectors extracted from the recorded video. The process of comparison between the linguistic elements allows detecting the subset of continuous frames where there is no logical correspondence between the displacement of the vehicle and the road shape. Since the videos are captured from a moving vehicle, we propose a statistically based process to use domain changing fuzzy sets adapted to traffic scenarios that continuously change. This improves the reliability of the linguistic descriptions that, once compared, are used to detect departures. Lastly, a set of experiments using traffic videos with different characteristics are presented to validate this approach.
Juan Giralt; Juan Moreno-Garcia; Luis Jimenez-Linares; Luis Rodriguez-Benitez. A Road Departure Warning System Based on Video Motion Analysis and Fuzzy Logic. International Journal of Intelligent Systems 2017, 32, 830 -842.
AMA StyleJuan Giralt, Juan Moreno-Garcia, Luis Jimenez-Linares, Luis Rodriguez-Benitez. A Road Departure Warning System Based on Video Motion Analysis and Fuzzy Logic. International Journal of Intelligent Systems. 2017; 32 (8):830-842.
Chicago/Turabian StyleJuan Giralt; Juan Moreno-Garcia; Luis Jimenez-Linares; Luis Rodriguez-Benitez. 2017. "A Road Departure Warning System Based on Video Motion Analysis and Fuzzy Logic." International Journal of Intelligent Systems 32, no. 8: 830-842.
Juan Moreno-Garcia; Javier Abián-Vicén; Luis Jimenez-Linares; Luis Rodriguez-Benitez. Description of multivariate time series by means of trends characterization in the fuzzy domain. Fuzzy Sets and Systems 2016, 285, 118 -139.
AMA StyleJuan Moreno-Garcia, Javier Abián-Vicén, Luis Jimenez-Linares, Luis Rodriguez-Benitez. Description of multivariate time series by means of trends characterization in the fuzzy domain. Fuzzy Sets and Systems. 2016; 285 ():118-139.
Chicago/Turabian StyleJuan Moreno-Garcia; Javier Abián-Vicén; Luis Jimenez-Linares; Luis Rodriguez-Benitez. 2016. "Description of multivariate time series by means of trends characterization in the fuzzy domain." Fuzzy Sets and Systems 285, no. : 118-139.
The induction of classifiers by means of supervised learning techniques is one of the most common and extended applications in the field of the intelligent systems. Multi-classifier systems obtain a set of basic classifiers and uses it to predict the class of a data instance. In this work, a new method to reduce a set of classifiers to their equivalent minimal set is presented. For this purpose, a new fuzzy classifier called Atomic Fuzzy Classifier is defined. Furthermore, two different definitions of similarity, structural similarity and functional similarity, are considered. The combination of both produces a novel definition of a similarity function between two classifiers. This relation of similarity is used to obtain classes of equivalence, where each element of this class represents a subset of similar classifiers. The original set of classifiers is reduced to a new set of classifiers where only one of them is related to an unique equivalence class. In the experimental part, an application for the classification of elements of the IRIS database is presented.
Ester Castillo Herrera; Luis Jiménez Linares; Luis Rodriguez Benitez; Juan Giralt Muina; Juan Moreno Garcia. Reduction of a Set of Fuzzy Classifiers by Equivalence Classes. 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) 2015, 534 -539.
AMA StyleEster Castillo Herrera, Luis Jiménez Linares, Luis Rodriguez Benitez, Juan Giralt Muina, Juan Moreno Garcia. Reduction of a Set of Fuzzy Classifiers by Equivalence Classes. 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). 2015; ():534-539.
Chicago/Turabian StyleEster Castillo Herrera; Luis Jiménez Linares; Luis Rodriguez Benitez; Juan Giralt Muina; Juan Moreno Garcia. 2015. "Reduction of a Set of Fuzzy Classifiers by Equivalence Classes." 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) , no. : 534-539.
J. Moreno-Garcia; Luis Jiménez Linares; Luis Rodríguez Benitez; E. Del Castillo. Fuzzy numbers from raw discrete data using linear regression. Information Sciences 2013, 233, 1 -14.
AMA StyleJ. Moreno-Garcia, Luis Jiménez Linares, Luis Rodríguez Benitez, E. Del Castillo. Fuzzy numbers from raw discrete data using linear regression. Information Sciences. 2013; 233 ():1-14.
Chicago/Turabian StyleJ. Moreno-Garcia; Luis Jiménez Linares; Luis Rodríguez Benitez; E. Del Castillo. 2013. "Fuzzy numbers from raw discrete data using linear regression." Information Sciences 233, no. : 1-14.
This paper presents an independent component integrated into a global surveillance system named as OCULUS. The aim of this component is to classify the speed of moving objects as normal or abnormal in order to detect anomalous events, taking into account the object class and spatio-temporal information such as locations and movements. The proposed component analyses the speed of the detected objects in real-time without needing several cameras, a 3D representation of the environment, or the estimation of precise values. Unlike other works, the proposed method does require knowing the camera parameters previously (e.g. height, angle, zoom level, etc.). The knowledge used by this component is automatically acquired by means of a learning algorithm that generates a set of highly interpretable fuzzy rules. The experimental results demonstrate that the proposed method is accurate, robust and provides a real-time analysis.
J. Albusac; D. Vallejo; Jose Jesus Castro-Schez; Luis Jiménez Linares. OCULUS surveillance system: Fuzzy on-line speed analysis from 2D images. Expert Systems with Applications 2011, 38, 12791 -12806.
AMA StyleJ. Albusac, D. Vallejo, Jose Jesus Castro-Schez, Luis Jiménez Linares. OCULUS surveillance system: Fuzzy on-line speed analysis from 2D images. Expert Systems with Applications. 2011; 38 (10):12791-12806.
Chicago/Turabian StyleJ. Albusac; D. Vallejo; Jose Jesus Castro-Schez; Luis Jiménez Linares. 2011. "OCULUS surveillance system: Fuzzy on-line speed analysis from 2D images." Expert Systems with Applications 38, no. 10: 12791-12806.
In this paper a novel approach for recognizing actions in video sequences is presented, where the information obtained from the segmentation and tracking algorithms is used as input data. First of all, the fuzzification of input data is done and this process allows to successfully manage the uncertainty inherent to the information obtained from low-level and medium-level vision tasks, to unify the information obtained from different vision algorithms into a homogeneous representation and to aggregate the characteristics of the analyzed scenario and the objects in motion. Another contribution is the novelty of representing actions by means of an automaton and the generation of input symbols for the finite automaton depending on the comparison process between objects and actions, i.e., the main reasoning process is based on the operation of automata with capability to manage fuzzy representations of all video data. The experiments on several real traffic video sequences demonstrate encouraging results, especially when no training algorithms to obtain predefined actions to be identified are required.
Luis Rodríguez Benitez; C. Solana-Cipres; J. Moreno-Garcia; Luis Jiménez Linares. Approximate reasoning and finite state machines to the detection of actions in video sequences. International Journal of Approximate Reasoning 2011, 52, 526 -540.
AMA StyleLuis Rodríguez Benitez, C. Solana-Cipres, J. Moreno-Garcia, Luis Jiménez Linares. Approximate reasoning and finite state machines to the detection of actions in video sequences. International Journal of Approximate Reasoning. 2011; 52 (4):526-540.
Chicago/Turabian StyleLuis Rodríguez Benitez; C. Solana-Cipres; J. Moreno-Garcia; Luis Jiménez Linares. 2011. "Approximate reasoning and finite state machines to the detection of actions in video sequences." International Journal of Approximate Reasoning 52, no. 4: 526-540.
Since their first developments, traditional video surveillance systems have been designed to monitor environments. However, these systems have several limitations to automatically understand events and behaviours without human collaboration. In order to overcome this problem, intelligent surveillance systems arise as a possible solution. This kind of systems are not affected by negative factors such as fatigue or tiredness and they can be more effective than people when recognising certain kinds of events, such as the detection of suspicious or unattended objects. Intelligent surveillance refers to using Artificial Intelligence and Computer Vision techniques in order to improve traditional surveillance and process semantic information, obtained from low-level security devices. Normally these systems consist of a set of independent analysis modules that deal with particular problems, such as the trajectory analysis of pedestrian in parking lots, speed estimation of vehicles, gait or facial recognition, etc. However, most of them present a common problem: lack of flexibility and scalability to include new kinds of analysis and combine all of them in order to obtain a global interpretation. In this work, a formal model to define normal events and behaviours in monitored environments and to build scalable surveillance systems is presented. This model is based on the use of normality components, which are independent and reusable for environments with different characteristics and different kinds of objects. Each component specifies how an object should ideally behave according to a surveillance aspect, such as trajectory or velocity. The model also includes the fusion mechanisms required for combining the particular analysis made by each component. Finally, when a new component is designed making use of the proposed model, the system increases its abilities to detect new kind of abnormal events, and the normality of an object depends on a higher number of factors.
Javier Albusac; Jose Jesus Castro-Schez; David Vallejo; Luis Jiménez Linares; Carlos Glez-Morcillo. A Scalable Approach Based on Normality Components for Intelligent Surveillance. Econometrics for Financial Applications 2011, 336, 105 -145.
AMA StyleJavier Albusac, Jose Jesus Castro-Schez, David Vallejo, Luis Jiménez Linares, Carlos Glez-Morcillo. A Scalable Approach Based on Normality Components for Intelligent Surveillance. Econometrics for Financial Applications. 2011; 336 ():105-145.
Chicago/Turabian StyleJavier Albusac; Jose Jesus Castro-Schez; David Vallejo; Luis Jiménez Linares; Carlos Glez-Morcillo. 2011. "A Scalable Approach Based on Normality Components for Intelligent Surveillance." Econometrics for Financial Applications 336, no. : 105-145.