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- On the model updating operators in univariate estimation of distribution algorithmsPublication . Bronevich, Andrey G.; Oliveira, José Valente deThe role of the selection operation-that stochastically discriminate between individuals based on their merit-on the updating of the probability model in univariate estimation of distribution algorithms is investigated. Necessary conditions for an operator to model selection in such a way that it can be used directly for updating the probability model are postulated. A family of such operators that generalize current model updating mechanisms is proposed. A thorough theoretical analysis of these operators is presented, including a study on operator equivalence. A comprehensive set of examples is provided aiming at illustrating key concepts, main results, and their relevance.
- Exploiting generative adversarial networks as an oversampling method for fault diagnosis of an industrial robotic manipulatorPublication . Pu, Ziqiang; Cabrera, Diego; Sánchez, René-Vinicio; Cerrada, Mariela; Li, Chuan; Valente de Oliveira, JoséData-driven machine learning techniques play an important role in fault diagnosis, safety, and maintenance of the industrial robotic manipulator. However, these methods require data that, more often that not, are hard to obtain, especially data collected from fault condition states and, without enough and appropriated (balanced) data, no acceptable performance should be expected. Generative adversarial networks (GAN) are receiving a significant interest, especially in the image analysis field due to their outstanding generative capabilities. This paper investigates whether or not GAN can be used as an oversampling tool to compensate for an unbalanced data set in an industrial manipulator fault diagnosis task. A comprehensive empirical analysis is performed taking into account six different scenarios for mitigating the unbalanced data, including classical under and oversampling (SMOTE) methods. In all of these, a wavelet packet transform is used for feature generation while a random forest is used for fault classification. Aspects such as loss functions, learning curves, random input distributions, data shuffling, and initial conditions were also considered. A non-parametric statistical test of hypotheses reveals that all GAN based fault-diagnosis outperforms both under and oversampling classical methods while, within GAN based methods, an average accuracy difference as high as 1.68% can be achieved.
- Human-computer interaction: the agency perspectivePublication . Zacarias, Marielba; Oliveira, José Valente deAgent-centric theories, approaches and technologies are contributing to enrich interactions between users and computers. This book aims at highlighting the influence of the agency perspective in Human-Computer Interaction through a careful selection of research contributions. Split into five sections; Users as Agents, Agents and Accessibility, Agents and Interactions, Agent-centric Paradigms and Approaches, and Collective Agents, the book covers a wealth of novel, original and fully updated material, offering: - a coherent, in depth, and timely material on the agency perspective in HCI - an authoritative treatment of the subject matter presented by carefully selected authors - a balanced and broad coverage of the subject area, including, human, organizational, social, as well as technological concerns. - a hands-on-experience by covering representative case studies and offering essential design guidelines The book will appeal to a broad audience of researchers and professionals associated to software engineering, interface design, accessibility, as well as agent-based interaction paradigms and technology.
- On asynchronous parallelization of order-based GA over grid-enabled heterogenous commodity hardwarePublication . Valente de Oliveira, JOSÉ; Baltazar, Sérgio; Daniel, HelderIn real-world applications, the runtime of genetic algorithms (GAs) can be computationally demanding, an issue that can be mitigated using parallelization. The study evaluates the parallelization of order-based GAs using the island model in an asynchronous heterogeneous computing environment. The island model allows for a considerable number of migration topologies. The study offers a systematic review of the studies on migration topologies and observes that no study is available yet on the performance of these migration topologies over asynchronous heterogeneous environments. Based on a statistical analysis of a comprehensive set of experiments, using real-world TSPLIB instances, the study researches the question: What is the fastest island model topology for order-based genetic algorithm, in an asynchronous distributed heterogeneous grid-enabled commodity computing environment, without losing significant fitness comparatively to the correspondent sequential panmictic implementation of the same algorithm?. Moreover, a new speedup index, the expected root speedup, is also proposed. A diversity of topology types and characteristics are considered: the single node, star, ring, cartwheel, rooted ordered tree, rooted full binary tree, coordinated tree-ring, and feedforward fully connected layered type. Different number of nodes are also considered. While some of the types of topologies are well known, the coordinated tree-ring topology is a novelty. These types of topologies allow us to assess three notable cases: (i) no migration (isolated island), (ii) migration toward the coordinator only, and (iii) migration flows to, and from, the coordinator.
- VGAN: generalizing MSE GAN and WGAN-GP for robot fault diagnosisPublication . Pu, Ziqiang; Cabrera, Diego; Li, Chuan; Valente de Oliveira, JOSÉGenerative adversarial networks (GANs) have shown their potential for data generation. However, this type of generative model often suffers from oscillating training processes and mode collapse, among other issues. To mitigate these, this work proposes a generalization of both mean square error (mse) GAN and Wasserstein GAN (WGAN) with gradient penalty, referred to as VGAN. Within the framework of conditional WGAN with gradient penalty, VGAN resorts to the Vapnik V-matrix-based criterion that generalizes mse. Also, a novel early stopping-like strategy is proposed that keeps track during training of the most suitable model. A comprehensive set of experiments on a fault-diagnosis task for an industrial robot where the generative model is used as a data augmentation tool for dealing with imbalance datasets is presented. The statistical analysis of the results shows that the proposed model outperforms nine other models, including vanilla GAN, conditional WGAN with and without conventional regularization, and synthetic minority oversampling technique, a classic data augmentation technique.
- Addressing Unequal Area Facility Layout Problems with the Coral Reef Optimization algorithm with Substrate LayersPublication . Garcia-Hernandez, L.; Garcia-Hernandez, J. A.; Salas-Morera, L.; Carmona-Munoz, C.; Alghamdi, N. S.; Valente de Oliveira, José; Salcedo-Sanz, S.The Unequal Area Facility Layout Problem (UA-FLP) is a relevant task in industrial manufacturing, in which the disposition of a number of facilities (or departments) in a manufacturing system must be obtained, under several optimization criteria and different constraints. The UA-FLP is a hard optimization problem, in which traditional optimization techniques do not obtain good results. Thus, it has been successfully tackled with different heuristics and meta-heuristics in the last years. In this work we address the UA-FLP with a multi-method ensemble approach, the Coral Reefs Optimization algorithm with Substrate Layers (CRO-SL). It is a novel multi-method evolutionary algorithm that encourages the evolution of several searching procedures at the same time over a single population. The CRO-SL has been previously applied to very difficult optimization problems, obtaining excellent performance. In this case, we adapt the CRO-SL to the UA-FLP, by means of increasing the diversity generation within the algorithm, which is helpful to improve the exploration of the searching space, avoiding to fall into local minima. Specifically, we propose to include several reproduction mechanisms (adapted to the UA-FLP) within each substrate of the algorithm, which will highly increase the diversity generation in the CRO-SL. An exhaustive experimental study of the CRO-SL performance in a large number of UA-FLP instances is carried out, including a comparison with the state-of-the-art algorithms for this problem. We will show the ability of the CRO-SL to reach or surpass the best-known solutions in most of the tested UA-FLP cases.
- Fuzzy determination of informative frequency band for bearing fault detectionPublication . Li, Chuan; Oliveira, José Valente de; Sanchez, Rene-Vinicio; Cerrada, Mariela; Zurita, Grover; Cabrera, DiegoDetecting early faults in rolling element bearings is a crucial measure for the health maintenance of rotating machinery. As faulty features of bearings are usually demodulated into a high-frequency band, determining the informative frequency band (IFB) from the vibratory signal is a challenging task for weak fault detection. Existing approaches for IFB determination often divide the frequency spectrum of the signal into even partitions, one of which is regarded as the IFB by an individual selector. This work proposes a fuzzy technique to select the IFB with improvements in two aspects. On the one hand, an IFB-specific fuzzy clustering method is developed to segment the frequency spectrum into meaningful sub-bands. Considering the shortcomings of the individual selectors, on the other hand, three commonly-used selectors are combined using a fuzzy comprehensive evaluation method to guide the clustering. Among all the meaningful sub-bands, the one with the minimum comprehensive cost is determined as the IFB. The bearing faults, if any, can be detected from the demodulated envelope spectrum of the IFB. The proposed fuzzy technique was evaluated using both simulated and experimental data, and then compared with the state-of-the-art peer method. The results indicate that the proposed fuzzy technique is capable of generating a better IFB, and is suitable for detecting bearing faults.
- Observer-biased bearing condition monitoring: from fault detection to multi-fault classificationPublication . Li, Chuan; Oliveira, José Valente de; Cerrada, Mariela; Pacheco, Fannia; Cabrera, Diego; Sanchez, Vinicio; Zurita, GroverBearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.
- Fusing convolutional generative adversarial encoders for 3D printer fault detection with only normal condition signalsPublication . Li, Chuan; Cabrera, Diego; Sancho, Fernando; Sanchez, Rene-Vinicio; Cerrada, Mariela; Long, Jianyu; Oliveira, José Valente deCollecting data from mechanical systems in abnormal conditions is expensive and time consuming. Consequently, fault detection approaches based on classical supervised learning working with both normal and abnormal data are not applicable in some condition-based maintenance tasks. To address this problem, this paper proposes Fusing Convolutional Generative Adversarial Encoders (fCGAE) method to create fault detection models from only normal data. Firstly, to obtain an adequate deep feature space, encoder models based on 1D convolutional neural networks are created. Then, these encoders are optimized in an unsupervised way through Bidirectional Generative Adversarial Networks. Finally, the multi-channel features collected from the system are merged with One-Class Support Vector Machine. fCGAE is applied to fault detection in 3D printers, where experimental results in two fault detection cases show excellent generalization capabilities and better performance compared to peer methods. (C) 2020 Elsevier Ltd. All rights reserved.
- Applying the coral reefs optimization algorithm for solving unequal area facility layout problemsPublication . Garcia-Hernandez, L.; Salas-Morera, L.; Garcia-Hernandez, J. A.; Salcedo-Sanz, S.; Valente de Oliveira, JOSÉCoral Reefs Optimization (CRO) is a recently proposed evolutionary-type algorithm which has shown promising results to tackle many complex optimization problems. This paper discusses the performance of this meta-heuristic in Unequal Area Facility Layout Problems (UA-FLPs). The UA-FLP is an important problem in industrial production, which considers a rectangular region and a set of rectangular facilities. These facilities must be allocated in the plant in the most adequate way satisfying certain constraints. The Flexible Bay Structure has been selected in order to represent solutions for the UA-FLP in the proposed CRO algorithm. In this paper, we detail the implementation of the algorithm and provide the results of different tests in several UA-FLP instances with different size and setting. The obtained results confirm the excellent performance of the proposed algorithm in solving UA-FLPs, improving alternative algorithms devoted to this problem in the literature.
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