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A Deployment Optimization for Wireless Sensor Networks Based on Stacked Auto Encoder and Probabilistic Neural Network

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Digital Communications and Networks 2024年

作者： Hui Feng Chen Xu Bo Jin Min Zhang School of Intelligent Engineering Technology Jiangsu Vocational College of Finance and Economics Huai'an 223003 China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai 200235 China Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra 3030-290 Portugal

In the field of consumer electronics, Wireless Sensor Networks (WSNs) can be used to build large-scale IoT systems and achieve intelligent data collection and processing. This paper proposes a deep neural network model SAE-PNN by Stacked Auto Encoder (SAE) and Probabilistic Neural Network (PNN) in a stack manner. A comprehensive training algorithm of SAE-PNN based on a group of orthogonal function bases is established by introducing a time-varying input and connection weight function, based on the conventional algorithm of unsupervised layer-by-layer initialization and gradient descent of deep neural network. Our proposed SAE-PNN model is used to predict the distance from unknown nodes to known nodes. Based on the collaborative work of nodes and network coverage, an optimal set of working nodes is established to reduce the energy loss of network nodes. A balance relationship between network coverage and energy consumption has been established to solve the problem of optimizing the balance in location coverage and the contradiction between individual nodes and the overall performance of the network. The experimental results show that the proposed method can improve the coverage of the monitored area and the perceived quality of service, and uses a distributed node synchronous scheduling mechanism to reduce the overall energy consumption of the network.

关键词： Wireless sensor networks Stacked auto encoder Probabilistic neural network Deep neural network Deployment strategy Node energy consumption

Experimental Analysis of Three-Phase Induction Motors with Multiflux, Dual-Winding Configurations 45

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Experimental Analysis of Three-Phase Induction Motors with M...

45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019

作者： Alberto, Jose T. E. Ferreira, Fernando J. De Almeida, Anibal T. Institute of Systems and Robotics University of Coimbra Department of Electrical and Computer Engineering Coimbra3030-290 Portugal

ISBN: (纸本)9781728148786

In this paper, several multiflux, three-phase induction motors, dual-winding configurations are experimentally analysed. Using a motor with two reconfigurable stator windings sharing the same slots, several dual-winding configurations with different spatial displacement between the two partial windings, are experimentally tested. A programmable voltage source is used to adjust the line-to-line voltage at the terminals of the motor so that the fundamental flux level is the same for all the configurations. Then, the experimentally determined value is compared to the calculated value of the electromotive force and the per-unit values of line-to-line voltage per flux or current per flux are measured and compared to simulated results. Finally, the locked-rotor impedance was measured in order to assess its variation with the fundamental winding factor. © 2019 IEEE.

关键词： Induction motors

Reputation and Pricing Dynamics in Online Markets

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arXiv 2021年

作者： Ma, Qian Huang, Jianwei Bąsar, Tamer Liu, Ji Chen, Xudong The School of Intelligent Systems Engineering Sun Yat-sen University China The School of Science and Engineering Chinese University of Hong Kong Shenzhen Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China The Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign United States The Department of Electrical and Computer Engineering Stony Brook University United States The Department of Electrical University of Colorado Boulder Computer and Energy Engineering United States

We study the economic interactions among sellers and buyers in online markets. In such markets, buyers have limited information about the product quality, but can observe the sellers' reputations which depend on their past transaction histories and ratings from past buyers. Sellers compete in the same market through pricing, while considering the impact of their heterogeneous reputations. We consider sellers with limited as well as unlimited capacities, which correspond to different practical market scenarios. In the unlimited seller capacity scenario, buyers prefer the seller with the highest reputation-price ratio. If the gap between the highest and second highest seller reputation levels is large enough, then the highest reputation seller dominates the market as a monopoly. If sellers' reputation levels are relatively close to each other, then those sellers with relatively high reputations will survive at the equilibrium, while the remaining relatively low reputation sellers will get zero market share. In the limited seller capacity scenario, we further consider two different cases. If each seller can only serve one buyer, then it is possible for sellers to set their monopoly prices at the equilibrium while all sellers gain positive market shares;if each seller can serve multiple buyers, then it is possible for sellers to set maximum prices at the equilibrium. Simulation results show that the dynamics of reputations and prices in the longerterm interactions will converge to stable states, and the initial buyer ratings of the sellers play the critical role in determining sellers' reputations and prices at the stable state. Copyright © 2021, The Authors. All rights reserved.

关键词： Competition

A Lightweight Flexible Semi-Cylindrical Valve for Seamless Integration in Soft Robots Based on the Giant Electrorheological Fluid

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SSRN

SSRN 2022年

作者： Huang, Tiantian Xu, Denan Zhang, Hang Bai, Ou Aravelli, Aparna Zhou, Xiaoxiao Han, Bin School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430070 China Human Cyber-Physical Systems Laboratory The Department of Electrical and Computer Engineering Florida International University Florida33174 United States Applied Research Center Florida International University Florida33174 United States Guangdong Intelligent Robotics Institute Dongguan523808 China

Complete compliance of soft robots is a trending research topic. One significant aspect is the control element. In the current work, a flexible electro-rheological (ER) valve development is chosen due to the advantage of its dynamic power density, convenient electronic control mode and no mechanical moving parts. These properties enable the ER valve to be miniaturized and easily integrated into a fluid-driven soft robot. However, the integration effect of the existing flexible ER valve configuration in soft robots is not ideal. In this study, a novel concentric semi-cylindrical flexible giant ER (GER) valve with a dual flowing direction is proposed for a soft robot to enable a larger deformation bending motion with multiple motion modes. The resulting valve has an overall volume of 50 mm 3 , a mass of 0.88 g, and a maximum controlling pressure of 84 kPa with a pressure change rate of 13.9, which is suitable for most soft robot applications. The pressure difference calculation method for the concentric semi-cylindrical GER valve is discussed. The effects of size, voltages, and flow rates on the valve’s static and dynamic performances, as well as the characteristics of the two pressure regulation modes, are investigated through simulations and experiments. The seamless integration and flexibility of the proposed valve are demonstrated by controlling a typical network actuator with six motion modes. Finally, a soft robot composed of two actuators is fabricated and realized the grasping of multiple everyday objects using different motion modes. © 2022, The Authors. All rights reserved.

关键词： Nanofluidics

Bio-inspired modality fusion for active speaker detection

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arXiv 2020年

作者： Assunção, Gustavo Gonçalves, Nuno Menezes, Paulo University of Coimbra Department of Electrical and Computer Engineering Portugal Institute of Systems and Robotics Coimbra3030-194 Portugal

Human beings have developed fantastic abilities to integrate information from various sensory sources exploring their inherent complementarity. Perceptual capabilities are therefore heightened, enabling, for instance, the well-known "cocktail party" and McGurk effects, i.e., speech disambiguation from a panoply of sound signals. This fusion ability is also key in refining the perception of sound source location, as in distinguishing whose voice is being heard in a group conversation. Furthermore, neuroscience has successfully identified the superior colliculus region in the brain as the one responsible for this modality fusion, with a handful of biological models having been proposed to approach its underlying neurophysiological process. Deriving inspiration from one of these models, this paper presents a methodology for effectively fusing correlated auditory and visual information for active speaker detection. Such an ability can have a wide range of applications, from teleconferencing systems to social robotics. The detection approach initially routes auditory and visual information through two specialized neural network structures. The resulting embeddings are fused via a novel layer based on the superior colliculus, whose topological structure emulates spatial neuron cross-mapping of unimodal perceptual fields. The validation process employed two publicly available datasets, with achieved results confirming and greatly surpassing initial expectations. © 2020, CC BY.

关键词： Neural networks

CNN-based approaches for cross-subject classification in motor imagery: From the state-of-the-art to DynamicNet

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arXiv 2021年

作者： Zancanaro, Alberto Cisotto, Giulia Paulo, João Ruivo Pires, Gabriel Nunes, Urbano J. Dept. Information Engineering University of Padova Italy National Centre for Neurology and Psychiatry Tokyo Japan Padova Italy Institute of Systems and Robotics University of Coimbra Coimbra Portugal Engineering Department Polytechnic Institute of Tomar Tomar Portugal Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

Motor imagery (MI)-based brain-computer interface (BCI) systems are being increasingly employed to provide alternative means of communication and control for people suffering from neuro-motor impairments, with a special effort to bring these systems out of the controlled lab environments. Hence, accurately classifying MI from brain signals, e.g., from electroencephalography (EEG), is essential to obtain reliable BCI systems. However, MI classification is still a challenging task, because the signals are characterized by poor signal-to-noise-ratio, high intra-subject and cross-subject variability. Deep learning approaches have started to emerge as valid alternatives to standard machine learning techniques, e.g., filter bank common spatial pattern (FBCSP), to extract subject-independent features and to increase the cross-subject classification performance of MI BCI systems. In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance. Second, we propose DynamicNet, a Python-based tool for quick and flexible implementations of deep learning models based on convolutional neural networks. We show-case the potentiality of DynamicNet by implementing EEGNet, a well-established architecture for effective EEG classification. Finally, we compare its performance with FBCSP in a 4-class MI classification over public datasets. To explore its cross-subject classification ability, we applied three different cross-validation schemes. From our results, we demonstrate that DynamicNet-implemented EEGNet outperforms FBCSP by about 25%, with a statistically significant difference when cross-subject validation schemes are applied. We conclude that deep learning approaches might be particularly helpful to provide higher cross-subject classification performance in multi-class MI classification problems with respect to FBCSP. Moreover, in the future DynamicNet could be useful to imp

关键词： Brain computer interface

Power generation expansion planning with complementarity between renewable sources and regions for 100% renewable energy systems

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Power generation expansion planning with complementarity bet...

作者： da Luz, Thiago Moura, Pedro Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Polo II Coimbra3030-290 Portugal

The positive impact of the complementarity between renewable generation sources has been presented in several studies, but this concept has been neglected in many studies of expansion planning. This paper presents a new approach to introduce the complementarity between renewable energy resources and regions into the expansion planning for 100% renewable energy systems. A novel objective function is proposed to optimize the electricity mix and the water flow of hydropower reservoirs, considering the daily and yearly variations. A case study is presented with the objective of assessing the optimum mix (for the complementarity perspective) between solar, wind, hydro, and biomass power to ensure the expected monthly and hourly energy demand for Brazil in 2050. In the Brazilian case, when the complementarity between regions was considered, it was possible to ensure the electricity demand increase without new large reservoirs and with a high contribution from all renewable generation sources. A mix to ensure the daily and annual demand in 2050, which promotes the best complementarity, is presented, resulting in a share of 27%, 16%, 34%, and 23% for photovoltaic, wind, hydro, and biomass power, respectively. © 2019 John Wiley & Sons, Ltd.

关键词： Wind power

Teaching Autonomous systems Hands-On: Leveraging Modular Small-Scale Hardware in the robotics Classroom

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arXiv 2022年

作者： Betz, Johannes Zheng, Hongrui Zang, Zirui Sauerbeck, Florian Zheng, Y. Rosa Biswas, Joydeep Walas, Krzysztof Dimitrov, Velin Behl, Madhur Krovi, Venkat Mangharam, Rahul The Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States The Institute of Automotive Technology Technical University of Munich Germany The College of Engineering and Applied Science Lehigh University BethlehemPA18015 United States The Department of Computer Science University of Texas Austin AustinTX United States The Institute of Robotics and Machine Intelligence Poznan University of Technology Poland The Department of Computer Science University of Virginia CharlottesvilleVA United States College of Engineering Computing and Applied Sciences Clemson University ClemsonSC29634 United States The Toyota Research Institute CambridgeMA02139 United States

Although robotics courses are well established in higher education, the courses often focus on theory and sometimes lack the systematic coverage of the techniques involved in developing, deploying, and applying software to real hardware. Additionally, most hardware platforms for robotics teaching are low-level toys aimed at younger students at middle-school levels. To address this gap, an autonomous vehicle hardware platform, called F1TENTH, is developed for teaching autonomous systems hands-on. This article describes the teaching modules and software stack for teaching at various educational levels with the theme of "racing" and competitions that replace exams. The F1TENTH vehicles offer a modular hardware platform and its related software for teaching the fundamentals of autonomous driving algorithms. From basic reactive methods to advanced planning algorithms, the teaching modules enhance students’ computational thinking through autonomous driving with the F1TENTH vehicle. The F1TENTH car fills the gap between research platforms and low-end toy cars and offers hands-on experience in learning the topics in autonomous systems. Four universities have adopted the teaching modules for their semester-long undergraduate and graduate courses for multiple years. Student feedback is used to analyze the effectiveness of the F1TENTH platform. More than 80% of the students strongly agree that the hardware platform and modules greatly motivate their learning, and more than 70% of the students strongly agree that the hardware enhanced their understanding of the subjects. The survey results show that more than 80% of the students strongly agree that the competitions motivate them for the course. © 2022, CC BY.

关键词： Students

A discrete-time reputation-based resilient consensus algorithm for synchronous or asynchronous communications

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arXiv 2021年

作者： Ramos, Guilherme Silvestre, Daniel Silvestre, Carlos The Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Portugal NOVA University of Lisbon The Institute for Systems and Robotics Instituto Superior Técnico University of Lisbon Lisbon1049-001 Portugal The Dep. of Electrical and Computer Engineering The Faculty of Science and Technology The University of Macau China Instituto Superior Técnico Technical University of Lisbon Lisbon Portugal

We tackle the problem of a set of agents achieving resilient consensus in the presence of attacked agents. We present a discrete-time reputation-based consensus algorithm for synchronous and asynchronous networks by developing a local strategy where, at each time, each agent assigns a reputation (between zero and one) to each neighbor. The reputation is then used to weigh the neighbors' values in the update of its state. Under mild assumptions, we show that: (i) the proposed method converges exponentially to the consensus of the regular agents;(ii) if a regular agent identifies a neighbor as an attacked node, then it is indeed an attacked node;(iii) if the consensus value of the normal nodes differs from that of any of the attacked nodes' values, then the reputation that a regular agent assigns to the attacked neighbors goes to zero. Further, we extend our method to achieve resilience in the scenarios where there are noisy nodes, dynamic networks and stochastic node selection. Finally, we illustrate our algorithm with several examples, and we delineate some attacking scenarios that can be dealt by the current proposal but not by the state-of-the-art approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems