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ieee International Conference on Robotics and automation (ICRA)

作者： G. Fadini T. Flayols A. Del Prete N. Mansard P. Souères LAAS-CNRS Université de Toulouse CNRS Toulouse France University of Trento Artifical and Natural Intelligence Toulouse Insitute (ANITI)

This paper presents a computational framework for the design of high-performance legged robotic systems. The framework relies on the concurrent optimization of hardware parameters and control trajectories to find the best robot design for a given task. In particular, we focus on energy efficiency, presenting novel electro-mechanical models to account for the losses of the actuators due to friction and Joule effects. Thanks to a bi-level optimization scheme, featuring a genetic algorithm in the outer loop, our framework can also optimize for the duration of the motion, the actuators, and the size of the robot. We present a novel approach to scale both the actuators and the robot structure in a way that ensures structural integrity by maintaining constant the normalized deflection of the links. We validated our approach by designing a two-joint monoped robot to execute a jumping task. Our simulation results show that our framework can lead to remarkable energy savings (up to 60%) thanks to the concurrent optimization of robot size, motion duration, and actuators.

关键词： Legged locomotion Actuators Simulation Energy efficiency Hardware Trajectory Task analysis

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PI regulation of the aeromechanical model of a Direct-Drive Wind Turbine

PI regulation of the aeromechanical model of a Direct-Drive ...

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ieee International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)

作者： Martinez-Vazquez, Daniel-Librado Sandoval-Gutierrez, Jacobo Liceaga-Castro, Eduardo Liceaga-Castro, Jesus-Ulises Univ Autonoma Metropolitana Dept Proc Prod Mexico City DF Mexico IPN ESIME Ticoman Ingn Aeronaut Cdmx Mexico Univ Autonoma Metropolitana Dept Elect Cdmx Mexico

ISBN: (纸本)9781728199047

Although Direct-Drive Wind Turbines (DDWT) were previously studied, there is no consensus in the role played by the highly non-linear aerodynamic behaviour of the wind turbine rotor. In this work, some results are reported in terms of the aerodynamic derivatives. Further, a numerical example of the use of Proportional Integral (PI) control applied to the aeromechanical model of a DDWT is presented. The designed control accomplish a typical wind turbine control specification. It can be concluded that, for the linearized model, the PI control is enough to guarantee regulation.

关键词： Aerodynamic Derivatives Direct-Drive Wind Turbine PI control

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Towards Explainable, Privacy-Preserved Human-Motion Affect Recognition

Towards Explainable, Privacy-Preserved Human-Motion Affect R...

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ieee symposium Series on computational intelligence (SSCI)

作者： Matthew Malek–Podjaski Fani Deligianni School of Computing Science University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9781728190495

Human motion characteristics are used to monitor the progression of neurological diseases and mood disorders. Since perceptions of emotions are also interleaved with body posture and movements, emotion recognition from human gait can be used to quantitatively monitor mood changes. Many existing solutions often use shallow machine learning models with raw positional data or manually extracted features to achieve this. However, gait is composed of many highly expressive characteristics that can be used to identify human subjects, and most solutions fail to address this, disregarding the subject's privacy. This work introduces a novel deep neural network architecture to disentangle human emotions and biometrics. In particular, we propose a cross-subject transfer learning technique for training a multi-encoder autoencoder deep neural network to learn disentangled latent representations of human motion features. By disentangling subject biometrics from the gait data, we show that the subject's privacy is preserved while the affect recognition performance outperforms traditional methods. Furthermore, we exploit Guided Grad-CAM to provide global explanations of the model's decision across gait cycles. We evaluate the effectiveness of our method to existing methods at recognizing emotions using both 3D temporal joint signals and manually extracted features. We also show that this data can easily be exploited to expose a subject's identity. Our method shows up to 7% improvement and highlights the joints with the most significant influence across the average gait cycle.

关键词： Training Deep learning Privacy Emotion recognition Biometrics (access control) Mood Neural networks

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Design of an Electronic Coupling to control Speed of Motor on a Conveyor Belt Using IoT

Design of an Electronic Coupling to Control Speed of Motor o...

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ieee International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)

作者： Ulices, Camacho Irma, Martinez Carlos, Juarez Autonomous Univ Mexico State UAEM Tianguistenco Profess Acad Unit Toluca Estado De Mexic Mexico

ISBN: (纸本)9781728199047

An advanced production ecosystem, automated and interconnected by the development of new technologies in industrial systems is known as Industry 4.0. The contribution of different technologies supported by cyber-physical systems whose principle of operation is to monitor and control manufacturing processes through computer-based algorithms connected to the Internet. this allows to have a Machine-to-Machine (M2M) communication system supervised and controlled for decision making. This paper will focus on the update of the design and construction of an electronic coupling device for making Industrial Internet of Things (IIoT) connections in a conventional Programmable Logic controllers (PLC). In this work a SIMATIC S7-200 PLC is used, where a Nodemcu Esp8266 WIFI card is coupled through the inputs and outputs of the device to monitor and control the shifting process of a product distribution hand. A method is proposed to modify the speed of motor using an HC-SR04 sensor to determine the amount of material corresponding to the PLC.

关键词： Internet of Things Programmable Logic controllers Speed control Wifi

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Fractional-Order Sliding-Mode Fault-Tolerant Neural Adaptive control of Fixed-Wing UAV with Prescribed Tracking Performance 2

Fractional-Order Sliding-Mode Fault-Tolerant Neural Adaptive...

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2nd International Conference on Industrial Artificial intelligence, IAI 2020

作者： Yu, Ziquan Badihi, Hamed Zhang, Youmin Ma, Yajie Jiang, Bin Su, Chun-Yi College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Industrial and Aerospace Engineering Concordia University Department of Mechanical MontrealQCH3G 1M8 Canada

ISBN: (纸本)9781728182162

In this paper, a fractional-order sliding-mode fault-tolerant tracking control scheme is proposed for a fixed-wing UAV with prescribed performance. The outer-loop position dynamics is first transformed to the second-order nonlinear model. By using neural networks, the unknown nonlinear functions containing actuator faults are identified. Moreover, the minimum learning parameter of neural networks is constructed to reduce the computational burden. Fractional-order calculus is further utilized in the sliding-mode control for improving the fault-tolerant tracking performance. Simulation results are presented to show the effectiveness. © 2020 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Harnessing Unlabeled Data to Improve Generalization of Biometric Gender and Age Classifiers

Harnessing Unlabeled Data to Improve Generalization of Biome...

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ieee symposium Series on computational intelligence (SSCI)

作者： Aakash Varma Nadimpalli Narsi Reddy Sreeraj Ramachandran Ajita Rattani School of Computing Wichita State University Wichita USA School of Computing University of Missouri Kansas City USA

ISBN: (纸本)9781728190495

With significant advances in deep learning, many computer vision applications have reached the inflection point. However, these deep learning models need large amount of labeled data for model training and optimum parameter estimation. Limited labeled data for model training results in overfitting and impacts their generalization performance. However, the collection and annotation of large amount of data is a very time consuming and expensive operation. Further, due to privacy and security concerns, the large amount of labeled data could not be collected for certain applications such as those involving medical field. Self-training, Co-training, and Self-ensemble methods are three types of semi-supervised learning methods that can be used to exploit unlabeled data. In this paper, we propose self-ensemble based deep learning model that along with limited labeled data, harness unlabeled data for improving the generalization performance. We evaluated the proposed self-ensemble based deep-learning model for soft-biometric gender and age classification. Experimental evaluation on CelebA and VISO B datasets suggest gender classification accuracy of 94.46 % and 81.00 %, respectively, using only 1000 labeled samples and remaining 199k samples as unlabeled samples for CelebA dataset and similarly,1000 labeled samples with remaining 107k samples as unlabeled samples for VISOB dataset. Comparative evaluation suggest that there is 5.74% and 8.47% improvement in the accuracy of the self-ensemble model when compared with supervised model trained on the entire CelebA and VISOB dataset, respectively. We also evaluated the proposed learning method for age-group prediction on Adience dataset and it outperformed the baseline supervised deep-learning learning model with a better exact accuracy of 55.55 ± 4.28 which is 3.92% more than the baseline.

关键词： Deep learning Training Data privacy Parameter estimation Biometrics (access control) computational modeling Semisupervised learning

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Distributing intelligence to the Edge and Beyond

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ieee computational intelligence MAGAZINE 2019年第4期14卷 65-+页

作者： Ramos, Edgar Morabito, Roberto Kainulainen, Jani-Pekka Ericsson Res Kyrkslatt Finland

Machine intelligence (MI) technologies have revolutionized the design and applications of computational intelligence systems, by introducing remarkable scientific and technological enhancements across domains. MI can improve Internet of Things (IoT) in several ways, such as optimizing the management of large volumes of data or improving automation and transmission in large-scale IoT deployments. When considering MI in the IoT context, MI services deployment must account for the latency demands and network bandwidth requirements. To this extent, moving the intelligence towards the IoT end-device aims to address such requirements and introduces the notion of Distributed MI (D-MI) also in the IoT context. However, current D-MI deployments are limited by the lack of MI interoperability. Currently, the intelligence is tightly bound to the application that exploits it, limiting the provisioning of that specific intelligence service to additional applications. The objective of this article is to propose a novel approach to cope with such constraints. It focuses on decoupling the intelligence from the application by revising the traditional device's stack and introducing an intelligence layer that provides services to the overlying application layer. This paradigm aims to provide final users with more control and accessibility of intelligence services by boosting providers' incentives to develop solutions that could theoretically reach any device. Based on the definition of this emerging paradigm, we explore several aspects related to the intelligence distribution and its impact in the whole MI ecosystem.

关键词： Artificial intelligence Biological system modeling computational modeling Data models Internet of Things Interoperability Machine intelligence

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Output-Feedback Robust MPC for a class of Nonlinear system 4

Output-Feedback Robust MPC for a class of Nonlinear system

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4th ieee International symposium on Recent Advances in Electrical Engineering and Computer Sciences, RAEE and CS 2020

作者： Hasan, Muhammad N. Haris, Muhammad Pirzada, Syed Jahanzeb H. Qin, Shiyin Beihang University School of Automation Science Electrical Engineering Beijing China

ISBN: (纸本)9780738131092

This paper compares the performance of a robust model predictive control (MPC) with a nominal MPC. The robust scheme is applied to a nonlinear system with input constraints, bounded unknown additive disturbances, and uncertainties on the system states. The design of the robust controller relies on the linearized version of the nonlinear model along its trajectory. It comprises two components, one of which is obtained by a continuous-time MPC approach with tightened constraints for stabilizing the nominal system. The other one is chosen as standard LQR gain, ensuring that the error system trajectories lie in a particular invariant set. The controller is implemented in a digital environment and utilizes a continuous-time model and equations, vastly reducing the computational burden. Furthermore, with some plausible assumptions, the closed-loop system is demonstrated to have regional exponential stability around the origin. Simulation results verify the improvement in disturbance rejection property of the employed controller over nominal MPC. © 2020 ieee.

关键词： Robust control

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Deep learning methods for Fake News detection 19

Deep learning methods for Fake News detection

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ieee Joint 19th International symposium on computational intelligence and Informatics / 7th International Conference on Recent Achievements in Mechatronics, automation, Computer Sciences and Robotics

作者： Kresnakova, Viera Maslej Sarnovsky, Martin Butka, Peter Tech Univ Kosice Dept Cybernet & Artificial Intelligence FEI Kosice Slovakia

ISBN: (纸本)9781728156255

Spreading of misinformation on the web nowadays represents a serious issue, as their influence on peoples opinions may be significant. Fake news represents a specific type of misinformation. While its detection was mostly being performed manually in the past, automated methods using machine learning and related fields became more critical. On the other hand, deep learning methods became very popular and frequently used methods in the field of data analysis in recent years. The study presented in this paper deals with the detection of fake news from the textual data using deep learning techniques. Our main idea was to train different types of neural network models using both entire texts from the articles and to use just the title text. The models were trained and evaluated on the Fake News dataset obtained from the Kaggle competition.

关键词： neural networks convolution LSTM text mining fake news

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Parallel Governance for Decentralized Autonomous Organizations enabled by Blockchain and Smart Contracts

Parallel Governance for Decentralized Autonomous Organizatio...

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Digital Twins and Parallel intelligence (DTPI), ieee International Conference on

作者： Wen Wen Ding XiaoLong Liang JiaChen Hou Ge Wang Yong Yuan Junqing Li Fei-Yue Wang Institute of Systems Engineering Macau University of Science and Technology Macau China School of Mathematics Renmin University of China Beijing China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Decentralized autonomous organizations(DAOs) enabled by blockchain and smart contracts is regarded as an effective tool to solve corporate governance problems. It can minimize the contract risks, principal-agent dilemmas, and other issues in corporate governance. However, DAOs also face a variety of governance issues, such as internal governance issues and external governance issues caused by the technical attributes of blockchain and smart contracts. DAO is a typical complex system with the characteristics of both social and engineering complexity. It is difficult for existing governance theories to solve governance problems in DAOs. The parallel intelligence theory based on the artificial systems + computational experiments + parallel execution (ACP) method is a suitable research and practical approach to solve management and control problems for complex systems. Therefore, we propose a parallel governance framework for DAO based on the parallel intelligence theory and discuss its key research issues. Finally, we construct a parallel governance system for GnosisDAO, and conduct computational experiments on its voting mechanism. The results show the defects of GnosisDAO governance mechanism and we thus propose further research directions. We consider that parallel governance represents the future trend of DAO research and practice. This paper is aimed at stimulating further effort and providing helpful guidance and reference for future research works.

关键词： computational modeling Digital twin Conferences Smart contracts Tools Systems modeling Market research

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