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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Manuel Diaz-Zapata Özgür Erkent Christian Laugier Univ. Grenoble Alpes INRIA Chroma Team France INRIA Rhône-Alpes France

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Detection of the objects around a vehicle is important for a safe and successful navigation of an autonomous vehicle. Instance segmentation provides a fine and accurate classification of the objects such as cars, trucks, pedestrians, etc. In this study, we propose a fast and accurate approach which can detect and segment the object instances which can be adapted to new conditions without requiring the labels from the new condition. Furthermore, the performance of the instance segmentation does not degrade in detection of the objects in the original condition after it adapts to the new condition. To our knowledge, currently there are not other methods which perform unsupervised domain adaptation for the task of instance segmentation using non-synthetic datasets. We evaluate the adaptation capability of our method on two datasets. Firstly, we test its capacity of adapting to a new domain; secondly, we test its ability to adapt to new weather conditions. the results show that it can adapt to new conditions with an improved accuracy while preserving the accuracy of the original condition.

关键词： Image segmentation Semantics Adaptation models Meteorology Autonomous vehicles Gallium nitride Training

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Domain Adaptation for Degraded Remote Scene Classification

Domain Adaptation for Degraded Remote Scene Classification

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Jianfei Yang Hailin Chen Yuecong Xu Ziji Shi Ruikang Luo Lihua Xie Rong Su School of Electical and Electronic Engineering Nanyang Technological University Singapore Spasee Technology Pte. Ltd. Singapore

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Remote scene classification serves a vital role in many applications. However, satellite images are often blurred and degraded due to aerosol scattering under fog, haze, and other weather conditions, reducing the image contrast and color fidelity. State-of-the-art remote sensing classification models building upon convolutional neural networks (CNNs) are mostly trained on annotated datasets of clear satellite images. When applied to blurred images, they will suffer a great degradation in performance. To address this problem, we adopt the domain adaptation algorithm TADA and propose Transferable Attention enhanced Adversarial Adaptation Network (TA 3 N), which utilizes annotated data in clear images by applying knowledge transferring from clear image domain to blurred image domain. Our TA 3 N first integrates spatial attention to focus on salient areas which are discriminative and transferable. In addition, domain discriminator and adversarial training via gradient reversal layer are used to minimize the discrepancies in extracted features from clear and degraded domains. We synthesize degraded remote scene classification dataset SSI based on FoHIS model. Experiments on degraded SSI showed that TA 3 N significantly outperforms baseline and other state-of-the-art domain adaptation methods.

关键词： Feature extraction Remote sensing Satellites Task analysis Adaptation models Training Meteorology

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Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

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INFORMATION FUSION 2023年第1期100卷

作者： Gorriz, J. M. Avarez-Illan, I. Avarez-Marquina, A. Arco, J. E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S. B. Contreras, R. Cuadra, J. M. Dominguez, E. Dominguez-Mateos, F. Duro, R. J. Elizondo, D. Fernandez-Caballero, A. Fernandez-Jover, E. Formoso, M. A. Gallego-Molina, N. J. Gamazo, J. Gonzalez, J. Garcia Garcia-Rodriguez, J. Garre, C. Garrigos, J. Gomez-Rodellar, A. Gomez-Vilda, P. Grana, M. Guerrero-Rodriguez, B. Hendrikse, S. C. F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J. J. Martinez-Murcia, F. J. Martinez-Tomas, R. Mekyska, J. Nalepa, G. J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M. A. Ponticorvo, M. Psarrou, A. Ramirez, J. Rincon, M. Rodellar-Biarge, V. Rodriguez-Rodriguez, I. Roelofsma, P. H. M. P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S. H. Wang, W. Zhang, Y. D. Zhu, H. Zhu, Z. Ferrandez-Vicente, J. M. Univ Granada Data Sci & Computat Intelligence Inst SiPBA Granada Spain Univ Granada Data Sci & Computat Intelligence Inst DaSCII Granada Spain Univ Cambridge Dept Psychiat Cambridge England Univ Malaga Dept Commun Engn Malaga Spain Univ Leicester Sch Comp & Math Sci Leicester Leics England Univ Osnabruck Semant Informat Syst Grp Osnabruck Germany German Res Ctr AI DFKI Osnabruck Germany Univ Politecn Cartagena Dept Elect Comp Technol & Projects Cartagena Spain Univ Politecn Valencia Valencian Res Inst AI VRAIN Valencia Spain Jagiellonian Univ Jagiellonian Human Ctr AI Lab JAHCAI Krakow Poland Jagiellonian Univ Inst Appl Comp Sci Krakow Poland Univ Murcia Dept Informat & Commun Engn Murcia Spain Univ A Coruna Dept Comp Sci & Informat Technol Ctr Informat & Commun Technol Res CITIC La Coruna Spain Univ Oviedo Dept Comp Sci Oviedo Spain Inst Argentino Matemat Alberto Calderon Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn CONICET Buenos Aires DF Argentina Inst Tecnol Buenos Aires ITBA Buenos Aires DF Argentina Eindhoven Univ Technol NL-5612 AZ Eindhoven Netherlands Univ Malaga Dept Comp Sci Malaga Spain De Montfort Univ Leicester Leics England Univ Concepcion Dept Comp Sci Fac Engn Concepcion Chile Univ Castilla La Mancha Albacete Res Inst Informat Neurocognit & Emot Res Unit Albacete Spain Univ Concepcion Dept Educ Informat Concepcion Chile Univ Nacl Educac Distancia UNED Dept AI Madrid Spain Univ Naples Federico II Dept Humanist Studies Naples Italy Univ Rey Juan Carlos Escuela Tecn Super Ingn Informat Campus Mostoles Madrid 28933 Spain Univ Politecn Madrid Neuromorph Speech Proc Lab Ctr Biomed Technol Campus Montegancedo Madrid 28223 Spain Inst Salud Carlos III Biomed Res Networking Ctr Mental Hlth Madrid 28016 Spain Univ Edinburgh Usher Inst Fac Med Edinburgh Midlothian Scotland Univ Miguel Hernandez Inst Bioingn Alicante Spain Univ Alicante Dept Comp Technol Alicante Spain Univ Cent Ecuador Quito E

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human -level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th international conference on the Interplay between Natural and Artificial Computation (IWINAC). the works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.

关键词： Explainable Artificial Intelligence Data science Computational approaches Machine learning Deep learning Neuroscience robotics Biomedical applications Computer-aided diagnosis systems

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Adaptive neural network control for a soft robotic manipulator

Adaptive neural network control for a soft robotic manipulat...

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international conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Jiangtao Han Zhijie Liu Wei He School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School of University of Science and Technology Beijing Foshan China School of Automation and Electrical Engineering and Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China

ISBN: (数字)9781728162461

ISBN: (纸本)9781665419352

this paper mainly introduces the modeling based on Cosserat rod theory and focuses on the adaptive neural network controller design based on model. In dealing with the external interference with the environment and the unmodeled dynamics of the system, a neural network (NN) is introduced to compensate it, and using Backstepping method to design the adaptive controller, finally the stability of the closed-loop system and the convergence of the signal in the system is proved with Lyapunov function theory, ensuring the end of the manipulator can track the given signal. In addition, the simulation experiment of soft manipulator swing and constant angle tracking control are carried out, and the simulation shows the rationality of the proposed controller.

关键词： Adaptation models Adaptive systems Neural networks Soft robotics Stability analysis Manipulator dynamics Lyapunov methods

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A State-of-Charge Estimation Method based on Bidirectional LSTM Networks for Lithium-ion Batteries

A State-of-Charge Estimation Method based on Bidirectional L...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Zhen Zhang Ming Xu Longhua Ma Binchao Yu Collage of Control Science and Engineering (CSE) Zhejiang University Hangzhou PR China Institute of Automation and Electrical Engineering Zhejiang University Ningbo Institute of Technology Ningbo PR China Zhejiang Zheneng Natural Gas Operation Co. Hangzhou PR China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

State-of-charge (SOC) estimation is the key to the BMS of lithium-ion batteries. At present, data-driven methods are more popularly used to estimate the SOC. Among them, the deep learning technology especially recurrent neural networks (RNNs) performed very well. In this paper, we proposed a bidirectional long short-term memory (Bi-LSTM) neural network to perform SOC estimation. the bidirectional LSTM layer is employed to catch the forward and backward temporal dependencies of battery sequential states, and shows better performance in SOC estimation of LiBs. With a public dataset of vehicle driving cycles, we separately train our proposed network at three different constant temperatures (0 °C, 10 °C, and 25 °C), and the evaluated MAEs are 0.498%, 0.411 %, 0.738%. Besides, the Bi-LSTM network trained with overall datasets at three temperatures achieves a mean absolute error (MAE) of 0.616% and maximum absolute error (MaxE) of 3.809%. It shows that the proposed method is robust and accurate in SOC estimation.

关键词： State of charge Batteries Estimation Temperature measurement Battery charge measurement Logic gates Recurrent neural networks

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Stress analysis recorded in the EEG signal based on mathematical markers

Stress analysis recorded in the EEG signal based on mathemat...

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international conference on methods and models in automation and robotics (MMAR)

作者： Magda Zolubak Barbara Grochowicz Mariusz Pelc Aleksandra Kawala-Sterniuk Department of Computer Science Opole University of Technology Opole Poland Department of Robotics and Applied Informatics Opole University of Technology Opole Poland Department of Computing and Information Systems University of Greenwich London UK

Stress is an increasingly common problem. thanks to the use of various mathematical methods, it can be described mathematically based on the EEG signal. Generally, the stress in mathematical analysis can be divided into several models. To determine the variability in the stress-related EEG signal, the periodograms used for the overall assessment are checked.

关键词： Stress Electroencephalography Brain modeling Correlation Stress measurement Publishing Training

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Adaptive, nonlinear control of a third-order Duffing-Holmes type chaotic oscillator

Adaptive, nonlinear control of a third-order Duffing-Holmes ...

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international conference on methods and models in automation and robotics (MMAR)

作者： Jacek Kabziński Przemysław Mosiołek Institute of Automatic Control Lodz University of Technology Lodz Poland

A general tracking problem is formulated and solved for a third-order Duffing-Holmes type chaotic oscillator implemented as an electronic circuit with unknown parameters. Two nonlinear controllers, based on adaptive backstepping techniques, are derived and compared. Both are sufficiently robust and accurate for practical implementation. the same approach may be used for chaos stabilization and synchronization.

关键词： Oscillators Backstepping Chaotic communication Synchronization Lyapunov methods Electronic circuits

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Application of an adaptive Golomb block code using asymmetric inter-channel dependencies

Application of an adaptive Golomb block code using asymmetri...

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international conference on methods and models in automation and robotics (MMAR)

作者： Cezary Wernik Grzegorze Ulacha Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin Szczecin Poland

In this paper the lossless block encoder application using the adaptive Golomb code and asymmetric inter-channel dependencies are presented. Using the sets of settings obtained from the study of inter-channel dependencies, the lowest bit averages were found within the channels. In the next step the algorithm was improved by adaptation within the blocks, what allowed to improving the degree of compression.

关键词： Codecs Predictive models Prediction algorithms Adaptation models Audio compression Audio coding

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Robust stabilization of a discrete-time large-scale interconnected system composed of identical subsystems

Robust stabilization of a discrete-time large-scale intercon...

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international conference on methods and models in automation and robotics (MMAR)

作者： Branislav Rehák Volodymyr Lynnyk Czech Academy of Sciences Institute of Information Theory and Automation Praha 8 Czech Republic

Stabilization of a linear discrete-time large-scale interconnected systems composed of identical subsystems is studied. the controls of every subsystem are delayed. the control design is based on a state transformation that decouples the subsystems. then, a suitable design method is used. Robustness to deal with systems with uncertainties is guaranteed. the results are illustrated by an example.

关键词： Symmetric matrices Interconnected systems Delay effects Linear matrix inequalities Delays Control systems Large-scale systems

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Trajectory control of the wheeled mobile robots in dynamic environment

Trajectory control of the wheeled mobile robots in dynamic e...

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international conference on methods and models in automation and robotics (MMAR)

作者： Botao Zhang Aleksandr Y. Krasnov Sergey A. Chepinskiy Valery V. Grigoriev Kirill A. Artemov Duzhesheng Liao Shengyi Zhang Jian Wang school of Automation Hangzhou Dianzi University Hangzhou China faculty of Control Systems and Robotics ITMO University St. Petersburg Russia

this article deals with the problem of the trajectory control of wheeled mobile robots in presence of moving external objects. A procedure of the controllers design based on stabilisation of geometric manifolds using transformation of the mathematical model to the task-oriented basis without velocities measure is proposed. Efficiency of proposed algorithms is proven by numerical simulation results and experimental implementation on wheeled mobile robot.

关键词： Trajectory Mobile robots Task analysis Mathematical model Manifolds

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