@article {295, title = {Reservoir Computing for Detection of Steady State in Performance Tests of Compressors}, journal = {Neurocomputing}, volume = {275}, year = {2018}, month = {01/2018}, pages = {607}, chapter = {598}, abstract = {

Fabrication of devices in industrial plants often includes undergoing quality assurance tests or tests that seek to determine some attributes or capacities of the device. For instance, in testing refrigeration compressors, we want to find the true refrigeration capacity of the compressor being tested. Such test (also called an episode) may take up to four hours, being an actual hindrance to applying it to the total number of compressors produced. This work seeks to reduce the time spent on such industrial trials by employing Recurrent Neural Networks (RNNs) as dynamical models for detecting when a test is entering the so-called steady-state region. Specifically, we use Reservoir Computing (RC) networks which simplify the learning of RNNs by speeding up training time and showing convergence to a global optimum. Also, this work proposes a self-organized subspace projection method for RC networks which uses information from the beginning of the episode to define a cluster to which the episode belongs to. This assigned cluster defines a particular binary input that shifts the operating point of the reservoir to a subspace of trajectories for the duration of the episode. This method is shown to turn the RC model robust in performance with respect to varying combination of reservoir parameters, such as spectral radius and leak rate, when compared to a standard RC network.

}, doi = {10.1016/j.neucom.2017.09.005}, author = {Eric A Antonelo and Carlos A. Flesch and Filipe Schmitz} } @article {Antonelo2016, title = {Echo State Networks for Data-driven Downhole Pressure Estimation in Gas-lift Oil Wells}, journal = {Neural Networks}, volume = {85}, year = {2017}, note = {(in press)}, pages = {106-117}, abstract = {

Process measurements are of vital importance for monitoring and control of industrial plants. When we consider offshore oil production platforms, wells that require gas-lift technology to yield oil production from low pressure oil reservoirs can become unstable under some conditions. This undesir- able phenomenon is usually called slugging flow, and can be identified by an oscillatory behavior of the downhole pressure measurement. Given the importance of this measurement and the unreliability of the related sensor, this work aims at designing data-driven soft-sensors for downhole pressure estimation in two contexts: one for speeding up first-principle model sim- ulation of a vertical riser model; and another for estimating the downhole pressure using real-world data from an oil well from Petrobras based only on topside platform measurements. Both tasks are tackled by employing Echo State Networks (ESN) as an efficient technique for training Recurrent Neu- ral Networks. We show that a single ESN is capable of robustly modeling both the slugging flow behavior and a steady state based only on a square wave input signal representing the production choke opening in the vertical riser. Besides, we compare the performance of a standard network to the performance of a multiple timescale hierarchical architecture in the second task and show that the latter architecture performs better in modeling both large irregular transients and more commonly occurring small oscillations.

}, doi = {10.1016/j.neunet.2016.09.009}, url = {http://authors.elsevier.com/sd/article/S0893608016301393}, author = {Eric A Antonelo and Eduardo Camponogara and Bjarne Foss} } @conference {296, title = {Predicting Air Compressor Failures with Echo State Networks}, booktitle = {Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016}, year = {2016}, publisher = {PHM Society}, organization = {PHM Society}, abstract = {

Modern vehicles have increasing amounts of data streaming continuously on-board their controller area networks. These data are primarily used for controlling the vehicle and for feedback to the driver, but they can also be exploited to de- tect faults and predict failures. The traditional diagnostics paradigm, which relies heavily on human expert knowledge, scales poorly with the increasing amounts of data generated by highly digitised systems. The next generation of equipment monitoring and maintenance prediction solutions will therefore require a different approach, where systems can build up knowledge (semi-)autonomously and learn over the lifetime of the equipment. A key feature in such systems is the ability to capture and encode characteristics of signals, or groups of signals, on-board vehicles using different models. Methods that do this robustly and reliably can be used to describe and compare the operation of the vehicle to previous time periods or to other similar vehicles. In this paper two models for doing this, for a single signal, are presented and compared on a case of on-road failures caused by air compressor faults in city buses. One ap- proach is based on histograms and the other is based on echo state networks. It is shown that both methods are sensitive to the expected changes in the signal\’s characteristics and work well on simulated data. However, the histogram model, de- spite being simpler, handles the deviations in real data better than the echo state network.

}, author = {Fan, Yuantao and Nowaczyk, S{\l}awomir and R{\"o}gnvaldsson, Thorsteinn and Antonelo, Eric A.} } @proceedings {antonelo2006, title = {Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors}, journal = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)}, year = {2006}, month = {Jul.}, pages = {498-505}, publisher = {IEEE}, address = {Vancouver, BC}, abstract = {

Classical reinforcement learning mechanisms and a modular neural network are unified to conceive an intelligent autonomous system for mobile robot navigation. The conception aims at inhibiting two common navigation deficiencies: generation of unsuitable cyclic trajectories and ineffectiveness in risky configurations. Different design apparatuses are considered to compose a system to tackle with these navigation difficulties, for instance: 1) neuron parameter to simultaneously memorize neuron activities and function as a learning factor, 2) reinforcement learning mechanisms to adjust neuron parameters (not only synapse weights), and 3) a inner-triggered reinforcement. Simulation results show that the proposed system circumvents difficulties caused by specific environment configurations, improving the relation between collisions and captures.

}, keywords = {reinforcement learning}, isbn = {0-7803-9490-9}, doi = {10.1109/IJCNN.2006.246723}, url = {https://ieeexplore.ieee.org/document/1716134/}, author = {Eric A Antonelo and Albert-Jan Baerlvedt and Thorsteinn Rognvaldsson and Mauricio Figueiredo} } @proceedings {292, title = {Evolutionary fuzzy system for architecture control in a constructive neural network}, journal = {6th IEEE International Symposium on Computational Intelligence in Robotics and Automation}, year = {2005}, pages = {541-546}, publisher = {IEEE}, abstract = {

This work describes an evolutionary system to control the growth of a constructive neural network for autonomous navigation. A classifier system generates Takagi- Sugeno fuzzy rules and controls the architecture of a constructive neural network. The performance of the mobile robot guides the evolutionary learning mechanism. Experiments show the efficiency of the classifier fuzzy system for analyzing if it is worth inserting a new neuron into the architecture.

}, issn = {0-7803-9355-4}, doi = {10.1109/CIRA.2005.1554333}, url = {https://ieeexplore.ieee.org/abstract/document/1554333/}, author = {Rodrigo Calvo and Eric A Antonelo and Mauricio Figueiredo} } @proceedings {antonelo2005, title = {Intelligent autonomous navigation for mobile robots: spatial concept acquisition and object discrimination}, journal = {Proceedings of the 6th IEEE International Symposium on Computational Intelligence in Robotics and Automation}, year = {2005}, pages = {553-557}, publisher = {IEEE}, address = {Helsinki, Finland}, abstract = {

An autonomous system able to construct its own navigation strategy for mobile robots is proposed. The navigation strategy is molded from navigation experiences (succeeding as the robot navigates) according to a classical reinforcement learning procedure. The autonomous system is based on modular hierarchical neural networks. Initially the navigation performance is poor (many collisions occur). Computer simulations show that after a period of learning the autonomous system generates efficient obstacle avoidance and target seeking behaviors. Experiments also offer support for concluding that the autonomous system develops a variety of object discrimination capability and of spatial concepts.

}, issn = {0-7803-9355-4}, doi = {10.1109/CIRA.2005.1554335}, url = {https://ieeexplore.ieee.org/document/1554335/}, author = {Eric A Antonelo and Mauricio Figueiredo and Albert-Jan Baerlvedt and Rodrigo Calvo} } @conference {293, title = {Autonomous intelligent systems applied to robot navigation: spatial concept acquisition and object discrimination}, booktitle = {2nd National Meeting of Intelligent Robotics (II ENRI) in the Congress of the Brazilian Computer Society (in Portuguese)}, year = {2004}, abstract = {

In this work, it is proposed an autonomous system capable of constructing its navigation strategy for mobile robots coherently with the acquired environmental knowledge: spatial proximity concept and color patterns associated to each type of object. The autonomous system, based on neural networks, doens\’t have innate behaviors of target seeking or obstacle avoidance, but it learns these specific behaviors for each type of object as the robot interacts with the environment. Simulation results are showed and confirm the system\’s potenciality, as well as its generalization capacity.

}, author = {Eric A Antonelo and Mauricio Figueiredo} } @conference {294, title = {Neural structures for modeling of biological declarative memory applied to robot autonomous navigation systems}, booktitle = {4th Forum of Informatics and Technology of Maring{\'a} (FITEM) (in Portuguese)}, year = {2002}, abstract = {

, pp. (2002)

}, author = {Eric A Antonelo and Mauricio Figueiredo} }