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A Switched-Gain Nonlinear Observer for LED Optical Communication ⁎

IFAC-PapersOnLine 2020年第2期53卷 4941-4946页

作者： Ibrahima N’Doye Ding Zhang Ali Zemouche Rajesh Rajamani Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China University of Lorraine CRAN CNRS UMR 7039 54400 Cosnes et Romain France Department of Mechanical Engineering University of Minnesota MN 55455 USA

Alignment for maintaining Line-of-Sight (LoS) between the receiver and the transmitter in a LED optical communication system is a challenging problem due to the constant movement of the underlying optical platform that is caused by vibration effects and atmospheric turbulence. In this paper, we propose a robust switched-gain nonlinear observer to estimate the angular position and velocity of the receiver orientation, which is used subsequently to follow the receiver orientation. The optical communication system model involves highly nonlinear and non-monotonic output measurement equations. Furthermore, tracking the receiver with perfect alignment results in loss of local observability. Hence, a two-receiver system is utilized to provide a robust tracking system that retains observability over the entire range of operation of the system. Lyapunov function-based analysis that ensures global stability is used to design the observer; then, a static feedback controller drives the receiver orientation based on the state estimate. Finally, numerical simulations are presented on the performance of the observer-based static feedback control in accurately estimating and tracking the angular position and velocity of the receiver orientation.

关键词： Light-emitting diode (LED)-based optical communication model nonlinear systems nonlinear observer design monotonic systems hybrid systems linear matrix inequality (LMI)

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Cost-effective bi-functional resin reinforced with a nano-inclusion blend for vat photopolymerization additive manufacturing: The effect of multiple antibacterial nanoparticle agents

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Biomedical engineering Advances 2023年 5卷

作者： Nectarios Vidakis Markos Petousis Amalia Moutsopoulou Nikolaos Mountakis Sotirios Grammatikos Vassilis Papadakis Dimitris Tsikritzis Department of Mechanical Engineering Hellenic Mediterranean University Heraklion 71410 Greece Advanced and Sustainable Engineering Materials Laboratory (ASEMlab) Group of Sustainable Composites Department of Manufacturing and Civil Engineering Norwegian University of Science and Technology 2815 Gjøvik Norway Institute of Molecular Biology and Biotechnology Foundation for Research and Technology – Hellas 71110 Heraklion Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion 71410 Greece

Herein, a blend of nanoparticle (NP) inclusions has been arranged at various loadings into a photosensitive resin, while bi-functional three-dimensional (3D) printed specimens were fabricated through a vat photopolymerization process, to elucidate physicochemical mechanisms and synergistic effects, over the filler loading. Energy-dispersive X-ray spectroscopy (EDS), Raman, and thermogravimetric analysis (TGA) revealed the chemical/spectroscopic and thermal properties of the fabricated specimens. Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM) depicted the surface morphology, while SEM fractography demonstrated the morphology of tensile test specimens’ fractured surfaces. mechanical tests exhibited a strong reinforcement mechanism. The highest reinforcement of 20.8% in the tensile strength is reported for the 2 wt.% nanocomposite. All the prepared recipes exhibited a boosted antibacterial performance, as was documented via a screening agar well diffusion method. The research herein leads towards a novel generation of low-cost bi-functional materials for 3D printing bioactive applications, where mechanical reinforcement and antibacterial performance are required in the operational environment.

关键词： Stereolithography (SLA) Biocidal nanocomposites Antibacterial powder Vat photopolymerization (VPP) 3D printing Additive manufacturing (AM)

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PV system with high voltage gain based DC-DC boost converter for grid applications

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AIP Conference Proceedings 2022年第1期2519卷

作者： S. Peniel Pauldoss K. Sekar Balachandra Pattanaik V. Senthil Nayagam 1Department of Mechanical Engineering Brilliant Group of Technical Institutions Hyderabad Telangana India 2Department of Electrical and Electronics Engineering Hindustan College of Engineering and Technology Coimbatore Tamil Nadu India 3Department of Electrical and Computer Engineering Wollega University Nekemte Ethiopia Africa 4School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

The production of electricity from renewable sources is growing. The study proposes to use the wind power grid-based PV system and to use a PWM control system to regulate the solar system with a three-phase grid. This study aims to create a system that enhances power on the spring and controls harmonics on the grid. The converters linked up with the 3-phase RL load of the generated PV arrays. The system is comprised of the inverter, PV module, and non-linear power boost converter. The proposed way of controlling PWM makes the system robust and reliable. The converter is connected to the input side voltage source inverter to step up the generated PV array voltage. A three-phase inverter uses sliding mode control to switch signals and control the transferred active power to the grid system and load. The suggested arrangement model is developed using MATLAB/Simulink.

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Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

arXiv

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arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh The Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan The Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupied spaces in radar images by leveraging lidar label supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the two sensors. For example, some objects visible to lidar are invisible to radar, and some objects occluded in lidar scans are visible in radar images because of the radar's penetrating capability. These sensing differences cause false positive and penetrating capability degeneration, respectively. In this paper, we propose training data preprocessing and polar sliding window inference to solve the issues. The data preprocessing aims to reduce the effect caused by radar-invisible measurements in lidar scans. The polar sliding window inference aims to solve the limited sensing range issue by applying a near-range trained network to the long-range region. Instead of using common Cartesian representation, we propose to use polar representation to reduce the shape dissimilarity between long-range and near-range data. We find that extending a near-range trained network to long-range region inference in the polar space has 4.2 times better IoU than in Cartesian space. Besides, the polar sliding window inference can preserve the radar penetrating capability by changing the viewpoint of the inference region, which makes some occluded measurements seem non-occluded for a pretrained network. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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Real-time traffic congestion analysis based on collected tweets

Real-time traffic congestion analysis based on collected twe...

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2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Al-Qaness, Mohammed A. A. Elaziz, Mohamed Abd Hawbani, Ammar Abbasi, Aaqif Afzaal Zhao, Liang Kim, Sunghwan School of Computer Science Wuhan University Wuhan China Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt School of Computer Science and Technology University of Science and Technology of China Anhui China Department of Software Engineering Foundation University Islamabad Pakistan School of Computer Science and Technology Shenyang Aerospace University Shenyang China School of Electrical Engineering University of Ulsan Ulsan Korea Republic of

ISBN: (纸本)9781728152097

Due to the rapid increase in the use of and advancement of social media platforms, the amount of data available on the internet is increasing. The available information on the internet can be used to gain insights about searching trends and public interests. The advancements in machine learning and deep learning techniques drastically improved data analytics and processing solutions for social media and infotainment industry. It is no doubt that the majority of regular commuter encounters traffic congestion daily. There is a growing number of population in Metropolitans. This leads to higher population density rates and traditional methods for collecting traffic information using physical sensors are expensive, however, by using social media tools information regarding traffic jam, road and traffic congestion can be improved. In this paper, we analyze traffic congestion using Twitter data (tweets) in real-time. The proposed model extracts traffic-related tweets from Twitter and classifies the extracted information for traffic commute estimation road. In this study, tweets from Los Angeles, USA are taken into consideration as an analysis example. A machine learning classifier and a deep learning classier are used to classify traffic information. The model was trained to collect tweets containing the word 'traffic' in a real-time environment. © 2019 IEEE.

关键词： Traffic congestion

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反向连边在大型分层网络中的影响（英文）

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engineering 2024年第5期 240-249页

作者：曹浩森胡斌斌莫小雨陈都鑫高建喜袁烨陈关荣 Tamás Vicsek 管晓宏张海涛 MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Mechanical and Aerospace Engineering Nanyang Technological University Jiangsu Key Laboratory of Networked Collective Intelligence School of Mathematics Southeast University Department of Computer Science Rensselaer Polytechnic Institute Department of Electronic Engineering City University of Hong Kong Department of Biological Physics E?tv?s University MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi'an Jiaotong University

Hierarchical networks are frequently encountered in animal groups, gene networks, and artificial engineering systems such as multiple robots, unmanned vehicle systems, smart grids, wind farm networks,and so forth. The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes, such as lagging birds' howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group. This study reveals that, for most large-scale real hierarchical networks, the majority of the reverse edges do not affect the synchronization process of the entire network; the synchronization process is influenced only by a small part of these reverse edges along specific paths. More surprisingly, a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%. The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork. The overwhelming majority of active reverse edges turn out to have some kind of ‘‘bunchingo effect on the information flows of hierarchical networks, which slows down synchronization processes. This finding refines the current understanding of the role of reverse edges in many natural, social, and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks. Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

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Comparative analysis on imbalanced multi-class classification for malware samples using CNN

Comparative analysis on imbalanced multi-class classificatio...

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2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019

作者： Alzammam, Arwa Binsalleeh, Hamad Assadhan, Basil Kyriakopoulos, Konstantinos G. Lambotharan, Sangarapillai Imam Muhammad Ibn Saud Islamic University Department of Computer Science Riyadh Saudi Arabia King Saud University Department of Electrical Engineering Riyadh Saudi Arabia Loughborough University Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough United Kingdom

ISBN: (纸本)9781728144528

Malware is considered as one of the main actors in cyber attacks. The number of unique malware samples is constantly on the rise;however, the ratio of benign software still greatly outnumbers malware samples. In machine learning, such datasets are known as imbalanced, where the majority class label greatly dominates over others. In this paper, we present a comparative analysis and evaluation of some of the proposed techniques in the literature in order to address the problem of classifying imbalanced multi-class malware datasets. More specifically, we use Convolutional Neural Network (CNN) as a classification algorithm to study the effect of imbalanced datasets on deep learning approaches. These experiments are conducted on three publicly available imbalanced datasets. Our performance analysis demonstrates that methods such as cost sensitive learning, oversampling and cross validation have positive effects on the model classification performance, albeit in varying degrees. Meanwhile others like using pre-trained models require more special parameter settings. However, best practices may change in accordance with the problem domain. © 2020 IEEE.

关键词： Convolutional neural networks

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Blockchain Based Domestic Appliances Scheduling in Community Microgrids

Blockchain Based Domestic Appliances Scheduling in Community...

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2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

作者： Afzal, M. Umer, Khalid Amin, Waqas Naeem, M. Cai, Dongshang Zhenyuan, Zhang Huang, Qi Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Electrical and Computer Engineering COMSATS University Islamabad Wah Campus Pakistan

ISBN: (纸本)9781728135205

This paper proposes an optimization model for community microgrid. The proposed scheme performs the scheduling of domestic appliances in a community microgrid using renewable energy resources. The objective of the scheme is to minimize the total operating cost in the whole community. The optimization problem is solved using the branch and bound algorithm. Blockchain (BC) technology is introduced for secure implementation of the proposed approach. Machine to machine interaction is facilitated using BC technology. The transaction information/ data is stored in the distributed shared ledger. The smart contract is used to automatically trigger the flow of electricity and the movement of funds once the necessary conditions are met. © 2019 IEEE.

关键词： Blockchain

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Semi-autonomous Robot-assisted Cooperative Therapy Exercises for a Therapist’s Interaction with a Patient

Semi-autonomous Robot-assisted Cooperative Therapy Exercises...

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Carlos Manuel Martinez Jason Fong S. Farokh Atashzar Mahdi Tavakoli Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada Department of Mechanical and Aerospace Engineering Department of Electrical and Computer Engineering New York University USA

Recent increases in demand for post-stroke motor rehabilitation services together with limited time of therapist and accessibility issues, in particular for patients living in remote areas, have created a significant burden on healthcare systems worldwide. Semi-autonomous techniques that allow for sharing the time of a therapist between multiple patients have attracted great interest. Among them Learning from Demonstration (LfD) based robots have been studied as solutions to address this growing demand. In this work, a Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) based LfD approach are proposed to generate a versatile framework to deliver rehabilitation in the absence of the therapist. To collect data for training the models, a bilateral telerehabilitation system is used to enable patient-therapist collaborative task performance is one Degree of Freedom (DOF). The performance and generalizability of the trained model are demonstrated for a variety of patient actions.

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The Impact of Charging Battery Electric Vehicles on the Load Profile in the Presence of Renewable Energy

The Impact of Charging Battery Electric Vehicles on the Load...

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2019 IEEE Canadian Conference of electrical and computer engineering, CCECE 2019

作者： Yu, Danilo Adhikari, Min Prasad Guiral, Aurélien Fung, Alan S. Mohammadi, Farahnaz Raahemifar, Kaamran Departmentt of Electrical and Computer Enginnering Ryerson University TorontoON Canada Department of Aerospace Engineering Ryerson University TorontoON Canada Institut Catholique d'Arts et Métiers Toulouse France Department of Mechanical and Industrial Engineering Ryerson University TorontoON Canada

ISBN: (纸本)9781728103198

The driving and charging patterns of battery electric vehicles can have a significant impact on the load profile of the distribution grid. Renewable resources and controlled charging can mitigate this effect by reducing the energy imported from the grid. The smart real-time electric vehicle charging scheduling proposed in this paper reduced grid peak demand through dynamic programming that uses future renewable supply and electric vehicle demand predictions to optimize the balance of energy in the electric grid. The large-scale driving, charging and parking availability patterns used in the simulations were synthesized from an actual experiment made up of a commercial fleet of battery electric vehicles. Numerical simulations showed that for commercial-use vehicles, solar photovoltaic and hybrid photovoltaic +wind technologies can reduce annual grid demand by 24%. Smart scheduling of electric vehicles and the integration of renewable energy resulted in lower peaks but no significant shift in the peak hour demand was observed. © 2019 IEEE.

关键词： Charging (batteries)

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