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3rd International Conference on Innovations and Development of Information Technologies and robotics, IDITR 2024

作者： Zou, Qi Zhang, Dan Zhang, Shuo Shi, Yuancheng Huang, Guanyu Li, Lijian Robotics Engineering Columbus State University ColumbusGA United States The Hong Kong Polytechnic University Department of Mechanical Engineering Hong Kong Lassonde School of Engineering York University Toronto Canada School of Electrical Engineering Hanyang University Ansan-si Korea Republic of Intelligent Robotics Research Center Zhejiang Lab Hangzhou China School of Mechanical Engineering North China University of Water Resources and Electric Power Zhengzhou China

ISBN: (纸本)9798350385694

The dynamic models of parallel robots are more complicated due to the highly coupled kinematic chains compared with those of their counterparts-serial robot. The approximate dynamic models can be considered if the analytical solutions high cost in time due to the sophisticated calculations. To address this issue, this paper presents an alternative approach-the dynamic model based on the neural network. A three degrees-of-freedom parallel manipulator is introduced, followed by a detailed mathematical inverse dynamic model, which is replicated by three feedforward neural networks with various performance goals. A cascade forward neural network is also constructed to be compared with the feedforward neural network. The simulation results demonstrate the effectiveness of these proposed neural networks. © 2024 IEEE.

关键词： Feedforward neural networks

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Adaptive Block Elevation Mapping for Large-scale Scene

Adaptive Block Elevation Mapping for Large-scale Scene

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhou, Yang Zhu, Shiqiang Huang, Huang Li, Yuehua Gu, Jason The Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China The Beijing Institute of Control Engineering Beijing100094 China The Beijing Research Institute Zhejiang Laboratory Beijing100094 China The Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory. © 2022 IEEE.

关键词： Motion planning

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Design and Development of a Fixed-Wing Tail-Sitter Tethered VTOL Drone with Custom Autopilot and Robust Control Techniques 16th

Design and Development of a Fixed-Wing Tail-Sitter Tether...

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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Safaee, Ahmad Aguiar, A. Pedro Santos, Filipe Neves Moreira, Antonio Paulo SYSTEC-ARISE Research Center for Systems and Technologies Faculty of Engineering University of Porto Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering Technology and Science CRIIS-Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering and DEEC/FEUP - Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

The need for aerial platforms capable of sustained operation is critical in fields such as surveillance, agricultural monitoring, disaster response, and temporary telecommunication systems. Traditional unmanned aerial vehicles (UAVs) are hampered by limited battery capacity and inefficient energy utilization. This paper introduces an autonomous vertical flight drone tethered to a ground-based platform, which overcomes these limitations. The drone is designed to optimize its position and energy usage by harnessing wind currents, functioning similarly to a kite when conditions permit. This is enabled by a custom-designed autopilot system comprising both hardware and software tailored for this application. We detail the model identification process for three vertical take-off and landing (VTOL) drone systems, employing authentic data that measures lateral positional errors from the tether. Robust mixed sensitivity controllers are developed and implemented. The effectiveness of these controllers is validated through simulations showing their capability to handle uncertainties. Furthermore, the practical application and robust performance of the system are confirmed by a real-world case study, highlighting its potential in real operational environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： VTOL/STOL aircraft

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A Multi-stage Precision Landing Method for Autonomous eVTOL Based on Multi-marker Joint Localization

A Multi-stage Precision Landing Method for Autonomous eVTOL ...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Xiang, Senwei Ye, Minxiang Zhu, Shiqiang Gu, Jason Xie, Anhuan Men, Zehua Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou310000 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Electric vertical takeoff and landing aircraft (eVTOL) has drawn more and more attention from home and abroad in recent years. It is believed autonomous eVTOL will create a new era of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM). To autonomous eVTOL, precision landing is an extremely critical operation for it directly affects flight safety. In this paper, we analyze the special issues an eVTOL will encounter when it lands in the UAM applications. A multi-stage precision landing method based on multi-marker joint localization is proposed accordingly. Our method contains three key elements: a compatible vertiport with multiple visual markers;an accurate, fast detection and localization algorithm for the vertiport and a multi-stage landing strategy. We implement our method on an eVTOL prototype named ZJ-Copter developed by Zhejiang Lab. A series of real-world experiments have been conducted to validate the effectiveness and accuracy of the proposed method. Experiment results show that our method works well in real-world scenarios for autonomous eVTOL without GPS signal during landing process. The positioning accuracy is less than 0.1m (altitude © 2022 IEEE.

关键词： Aircraft landing

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Leaf Classification of Jackfruit (Artocarpus heterophyllus) and Cempedak (Radermachera integra) Using Deep Learning 14

Leaf Classification of Jackfruit (Artocarpus heterophyllus) ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Budiman, Gelar Utoro, Rio Korio Rizqyawan, Muhammad Ilham Rudawan, Rikman A. Novianty, Astri Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University The University Center of Excellence for Advanced Intelligent Communications Bandung Indonesia Research Center of Smart Mechatronics National Research and Innovation Agency Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia

ISBN: (纸本)9798350335095

Plant identification and categorization tasks require accurate leaf classification. In this study, we investigate the use of deep learning approaches for categorizing the leaves of cempedak (Artocarpus integra) and jackfruit (Artocarpus heterophyllus). We use the well-known deep learning architectures VGG16 and Inception v3 to correctly categorize the leaf images. A dataset of 204 leaf images, with equal representation from jackfruit and cempedak, is used to train and assess the models. According to the experimental findings, the VGG16 and Inception v3 architectures both perform exceptionally well when classifying leaves. Jackfruit and cempedak leaves are accurately classified with 97.57% accuracy by the VGG16 model. These high accuracy rates demonstrate how well deep learning models are able to differentiate between the two leaf varieties. © 2023 IEEE.

关键词： Deep learning

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STAR-RIS Assisted Covert Wireless Communications with Randomly Distributed Blockages

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4690-4705页

作者： Li, Xingwang Zhao, Jingwei Chen, Gaojie Hao, Wanming Da Costa, Daniel Benevides Nallanathan, Arumugam Shin, Hyundong Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Shenzhen518107 China 5GIC 6GIC University of Surrey United Kingdom Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Kyung Hee University Department of Electronic Engineering Yongin-si17104 Korea Republic of Kyung Hee University Department of Electronics and Information Convergence Engineering Korea Republic of Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore

As one of the promising technologies, reconfigurable intelligent surface (RIS) and simultaneous transmitting and reflecting RIS (STAR-RIS) have attracted great interest. However, the existing RISs offer broadband tuning capability without filtering function due to the absence of radio frequency (RF) units, which easily leads to the unexpected tuning of the RIS undesired signals, especially in large-scale deployments. For the target network, it is difficult to obtain the parameter settings of RISs to serve other networks, which causes the unpredictability of the wireless environment. In this paper, we consider the covert communication in a STAR-RIS assisted random wireless network with randomly distributed blockages. We investigate the impact of STAR-RIS large-scale deployment on covert communication and leverage its inherent unpredictability for improving the covertness. We derive the average detection error probability for warden within the random wireless networks. Furthermore, we optimize the passive beamforming of STAR-RIS to maximize the covert communication rate, considering both direct and indirect line-of-sight (LoS) links. To address this, we employ an alternating optimization (AO) algorithm based on the semi-definite programming (SDP) method. Finally, numerical results demonstrate significant enhancements and increase covert capability achieved through the large-scale deployment of STAR-RIS. 2002-2012 IEEE.

关键词： Numerical methods

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Not so griddy: Internal representations of RNNs path integrating more than one agent 38

Not so griddy: Internal representations of RNNs path integra...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Redman, William T. Acosta, Francisco Acosta-Mendoza, Santiago Miolane, Nina Intelligent Systems Center Johns Hopkins Applied Physics Laboratory United States Department of Physics University of California Santa Barbara United States Dynamical Neuroscience University of California Santa Barbara United States Department of Electrical & Computer Engineering University of California Santa Barbara United States

Success in collaborative and competitive environments, where agents must work with or against each other, requires individuals to encode the position and trajectory of themselves and others. Decades of neurophysiological experiments have shed light on how brain regions [e.g., medial entorhinal cortex (MEC), hippocampus] encode the self's position and trajectory. However, it has only recently been discovered that MEC and hippocampus are modulated by the positions and trajectories of others. To understand how encoding spatial information of multiple agents shapes neural representations, we train a recurrent neural network (RNN) model that captures properties of MEC to path integrate trajectories of two agents simultaneously navigating the same environment. We find significant differences between these RNNs and those trained to path integrate only a single agent. At the individual unit level, RNNs trained to path integrate more than one agent develop weaker grid responses, stronger border responses, and tuning for the relative position of the two agents. At the population level, they develop more distributed and robust representations, with changes in network dynamics and manifold topology. Our results provide testable predictions and open new directions with which to study the neural computations supporting spatial navigation. © 2024 Neural information processing systems foundation. All rights reserved.

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intelligent upper-limb exoskeleton integrated with soft bioelectronics and deep learning for intention-driven augmentation

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npj Flexible Electronics 2024年第1期8卷 856-868页

作者： Jinwoo Lee Kangkyu Kwon Ira Soltis Jared Matthews Yoon Jae Lee Hojoong Kim Lissette Romero Nathan Zavanelli Youngjin Kwon Shinjae Kwon Jimin Lee Yewon Na Sung Hoon Lee Ki Jun Yu Minoru Shinohara Frank L.Hammond Woon-Hong Yeo Department of Mechanical Roboticsand Energy EngineeringDongguk UniversitySeoul 04620Republic of Korea IEN Center for Wearable Intelligent Systems and Healthcare Institute for Electronics and NanotechnologyGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA School of Industrial Design Georgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Electronic Engineering Yonsei UniversitySeoul 03722Republic of Korea School of Biological Sciences Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Materials Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaGA 30332USA

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper ***,we introduce an intelligent upper-limb exoskeleton system that utilizes deep learning to predict human intention for strength *** embedded soft wearable sensors provide sensory feedback by collecting real-time muscle activities,which are simultaneously computed to determine the user’s intended *** deep learning predicts four upper-limb joint motions with an average accuracy of 96.2%at a 500–550 ms response rate,suggesting that the exoskeleton operates just by human *** addition,an array of soft pneumatics assists the intended movements by providing 897 newtons of force while generating a displacement of 87mm at *** intent-driven exoskeleton can reduce human muscle activities by 3.7 times on average compared to the unassisted exoskeleton.

关键词： skeleton learning utilize

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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PRODUCING MULTIFUNCTIONAL PA6/ORGANOSHEET COMPOSITES WITH SUSTAINABLE PLANT BASED GRAPHENE COATINGS

PRODUCING MULTIFUNCTIONAL PA6/ORGANOSHEET COMPOSITES WITH SU...

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SAMPE 2024 Conference and Exhibition

作者： Mulqueen, Daniel W. Wright, Joseph Xiao, Shu Kravchenko, Oleksandr G. Climate Robotics INC HoustonTX77064 United States Department of Mechanical and Aerospace Engineering Old Dominion University Kaufman Hall NorfolkVA23508 United States Department of Electrical and Computer Engineering Old Dominion University Frank Reidy Research Center Suite 300 NorfolkVA23508 United States

ISBN: (纸本)9781934551455

The use of composite components in battery enclosures for electric vehicles requires the addition of electrically conductive elements to provide shielding from Electromagnetic Interference (EMI). Graphene, a highly conductive nanomaterial, can be useful for creating conductive paths on the surface and in interlaminar layers of the composite to provide shielding. In this study we examined impacts of plant-based graphene nanoplatelets (pGNP) surface coating on PA6/glass fiber organosheet with regards to EMI shielding, surface resistance, and flame retardancy. Suspensions of pGNP—derived from agricultural waste materials—were examined for their effectiveness in coating the PA6 surface. Ε-Caprolactam and polyurethane (PU) were examined for their impacts on the sprayed surface and interactions with the substrate. pGNP surface application at 10 g/m2 gave a 790 Ω/sq surface resistance, an improvement over untreated PA6 of seven orders of magnitude. Ε-Caprolactam and PU addition improve adhesion on the PA6 surface, scratch resistance of the coating, and show evidence of in-situ polymerization with pGNP. This material is cost effective for automotive applications and allows for rapid manufacturing using thermoforming processes. We propose a conductive pGNP coating that can be applied after the forming operation, thus avoiding limitations of thermoforming of the conductive veils. © 2024 Soc. for the Advancement of Material and Process engineering. All rights reserved.

关键词： Thermoforming

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