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International Conference on Electrical Machines, ICEM

作者： Fernando J. T. E. Ferreira Dep. of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

This paper proposes using star-delta and star-series connection modes in three-phase dual-winding squirrelcage induction motors to have available 100% and 55% of the original rated power for a given supply line-to-line voltage. The connection mode can be changed on a nine-terminal block. The experimental results obtained with a commercial 4-kW, 400-V, 50-Hz, 4-pole induction motor whose stator windings were redesigned to provide 4-kW and 2.2-kW output powers are presented and validate the proposed solution. Two-rated-power motors with the proposed output power ratio could be used as spare motors to replace faulty/damaged motors with the same frame size. The proposed solution allows fine-tuning the motor power to the actual maximum power required by the driven application/load and can be considered cost-effective as it may lead to significant gains in the motor efficiency and power factor when the low-power mode is adopted. In many cases, it may avoid the underloaded operation of induction motors, while keeping their original rated power available.

关键词： Reactive power Stator cores Costs Induction motors Torque Windings Stator windings Voltage Transformers Power generation

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Exploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classification

arXiv

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arXiv 2024年

作者： Pereira, Ricardo Garrote, Luís Barros, Tiago Lopes, Ana Nunes, Urbano J. University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal Polytechnic Institute of Tomar Portugal

Indoor scenes are usually characterized by scattered objects and their relationships, which turns the indoor scene classification task into a challenging computer vision task. Despite the significant performance boost in classification tasks achieved in recent years, provided by the use of deep-learning-based methods, limitations such as inter-category ambiguity and intracategory variation have been holding back their performance. To overcome such issues, gathering semantic information has been shown to be a promising source of information towards a more complete and discriminative feature representation of indoor scenes. Therefore, the work described in this paper uses both semantic information, obtained from object detection, and semantic segmentation techniques. While object detection techniques provide the 2D location of objects allowing to obtain spatial distributions between objects, semantic segmentation techniques provide pixel-level information that allows to obtain, at a pixel-level, a spatial distribution and shape-related features of the segmentation categories. Hence, a novel approach that uses a semantic segmentation mask to provide Hu-momentsbased segmentation categories' shape characterization, designated by Segmentation-based Hu-Moments Features (SHMFs), is proposed. Moreover, a three-main-branch network, designated by GOS2F2App, that exploits deep-learning-based global features, object-based features, and semantic segmentation-based features is also proposed. GOS2F2App was evaluated in two indoor scene benchmark datasets: SUN RGB-D and NYU Depth V2, where, to the best of our knowledge, state-of-the-art results were achieved on both datasets, which present evidences of the effectiveness of the proposed approach. © 2024, CC BY.

关键词： Object detection

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A Comprehensive Study on Different Optimizations of Pure Reference Counting Garbage Collectors

A Comprehensive Study on Different Optimizations of Pure Ref...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Tabassum, Faria Islam, Md. Rahatul Ur Rahman, Md Shoaib Majumder, Jimmy Bangladesh University of Engineering and Technology Department of Cse Dhaka Bangladesh Kyushu Institute of Technology Department of Human Intelligence Systems Engineering Japan Chittagong National Engineering College Department of Mechanical Engineering Bangladesh Bangladesh Advance Robotics Research Center Bangladesh

ISBN: (纸本)9798350313796

With the gradual increase of program complexity for utilities and various appliances, memory management has always been a challenge for computer scientists. Garbage collection is a very effective memory management system that comprises techniques that aim to claim unused objects which in turn returns reusable memory space for further use. Among the array of garbage collection techniques, pure reference counting (RC) is very popular that actively tracks down any unused object reference by means of keeping reference counts. It offers better reliability compared with other passive techniques. However this process cannot track cyclic garbage references. In order to solve this cycle detection issue along with other various improvements available for other techniques different methods have been proposed and developed. This paper offers an extensive examination of diverse optimization approaches designed to improve the effectiveness of garbage collectors based on Pure Reference Counting. Some of these optimizations look for loaning concepts from tracing to improve tracking down performance while others add up cycle detection from various existing techniques to gain the pros of both techniques or others simply emphasize memory allocation for reducing time complexity overhead. Our findings provide insights for researchers seeking to leverage Pure RC as a memory management solution, shedding light on the advantages and limitations of different optimization techniques. This study contributes to the ongoing discourse on memory management, offering a perspective on the specifics of Pure RC-based garbage collectors and their potential for optimization. © 2023 IEEE.

关键词： Storage allocation (computer)

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Fractional-Order Global Fast Terminal Sliding Mode Control Strategy for Fractional-Order Hydraulic Turbine Regulating System 42

Fractional-Order Global Fast Terminal Sliding Mode Control S...

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42nd Chinese Control Conference, CCC 2023

作者： Xi, Shiyu Chen, Zhihuan Engineering Research Center for Metallurgical Automation and Measurement Technology Ministry of Education Wuhan430081 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (纸本)9789887581543

Hydraulic turbine regulating system (HTRS) directly affects the safety and stability of hydro-generator units. In this paper, the control and modeling problems of the HTRS are mainly investigated based on the fractional-order theory, aiming to design a novel control strategy to raise the dynamic response of the HTRS and ensure the frequency stability of the hydro-generator units. Firstly, a nonlinear model of the fractional-order HTRS is established mathematically. Secondly, a fractional-order global fast terminal sliding mode controller is designed to improve the dynamic performance as well as the robustness of the system. Finally, relevant simulations are carried out to illustrate the effectiveness and rationality of the proposed control strategy in comparison with two other fractional-order control strategies. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Hydraulic motors

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Time-Series Embeddings from Language Models:A Tool for Wind Direction Nowcasting

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Journal of Meteorological Research 2024年第3期38卷 558-569页

作者： Decio ALVES Fabio MENDONCA Sheikh Shanawaz MOSTAFA Fernando MORGADO-DIAS University of Madeira Campus Universitário da Penteada9020-105FunchalPortugal Interactive Technologies Institute/Laboratory for Robotics and Engineering Systems and Agência Regional para o Desenvolvimento da Investigação Tecnologia e InovaçãoEdif.Madeira TecnopoloCaminho da Penteada piso-29020-105FunchalPortugal

Wind direction nowcasting is crucial in various sectors,particularly for ensuring aviation operations and *** this context,the TELMo(Time-series Embeddings from Language Models)model,a sophisticated deep learning architecture,has been introduced in this work for enhanced wind-direction *** by using three years of data from multiple stations in the complex terrain of an international airport,TELMo incorporates the horizontal u(east-west)and v(north-south)wind components to significantly reduce forecasting *** a day with high wind direction variability,TELMo achieved mean absolute error values of 5.66 for 2-min,10.59 for 10-min,and 14.79 for 20-min forecasts,processed within a swift 9-ms/step *** degree-based analysis,in comparison,yielded lower performance,emphasizing the effectiveness of the u and v *** contrast,a Vanilla neural network,representing a shallow-learning approach,underperformed in all analyses,highlighting the superiority of deep learning methodologies in wind direction *** is an efficient model,capable of accurately forecasting wind direction for air traffic operations,with an error less than 20°in 97.49%of the predictions,aligning with recommended international *** model design enables its applicability across various geographical locations,making it a versatile tool in global aviation meteorology.

关键词： wind nowcasting wind components wind direction time series prediction deep learning

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3D fully-enclosed triboelectric nanogenerator with bionic fish-like structure for harvesting hydrokinetic energy

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Nano Research 2022年第6期15卷 5098-5104页

作者： Zhaoxu Jing Jiacheng Zhang Jianlong Wang Mingkang Zhu Xinxian Wang Tinghai Cheng Jianyang Zhu Zhong Lin Wang Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of SciencesBeijing 101400China School of Mechatronic Engineering Changchun University of TechnologyChangchunJilin 130012China Institute of Robotics and Intelligent Systems Wuhan University of Science and TechnologyWuhanHubei 430081China CUSTech Institute of Technology WenzhouZhejiang 325024China School of Materials Science and Engineering Georgia Institute of TechnologyAtlantaGA 30332-0245USA

The hydrokinetic energy of river current,as one of the essential and widespread renewable energies,is difficult to be harvested in low flow velocity and shallow water *** this work,a three-dimensional(3D)fully-enclosed triboelectric nanogenerator(FETENG)with bionic fish-like structure for harvesting hydrokinetic energy is reported,which is comprised of the triboelectric powergeneration unit,bionic fish-like structure and connection *** the bionic structure,the FE-TENG realizes zero head power generation in shallow water with low flow ***’s more,the effect of external excitations and bionic structures on the electrical performance are systematically studied in this *** FE-TENG can generate peak power density of 7 and 0.36 W/m^(3)respectively under the simulated swing state with frequency of 1.25 Hz and simulated river current with flow velocity of 0.81 m/*** practical applications,due to the 3D fully-enclosed design,the FE-TENG immersed in water for 35 days demonstrates excellent immersion durability with undiminished electrical ***,the work proposes an efficient method realizing zero head power generation,and provides a good candidate for long-term service in the river current.

关键词： hydrokinetic energy three-dimensional(3D)fully-enclosed design triboelectric nanogenerator bionic fish-like structure zero head power generation water immersion durability

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High-Precision Localization of Mobile Robot Based on Particle Filters Combined with Triangle Matching 7th

High-Precision Localization of Mobile Robot Based on Parti...

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7th International Conference on Cognitive systems and Information Processing, ICCSIP 2022

作者： Zhou, Huaidong Tuo, Wanchen Chou, Wusheng Robotics Institute School of Mechanical Engineering and Automation Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

ISBN: (纸本)9789819906161

Robot location is a fundamental technology in the field of autonomous robotics and has been widely studied. However, high-precision localization of the mobile robot is still a serious challenge. To investigate this point further, this paper proposes a method to improve the autonomous robot localization accuracy, which adjusts the number of particles in the AMCL algorithm by fusing the information of LRL. LRL data is used to improve the localization accuracy of mobile platforms and prevent kidnapped robot problems. The algorithm is conducted and verified in a real robot system. The experiment results show that the proposed method can achieve high-precision locations in real-world scenarios, and verify the effectiveness of the improved localization system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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Multispectral Image Segmentation in Agriculture: Evaluating Deep Learning Models with Train-Test Split and Cross-Validation Strategies

Multispectral Image Segmentation in Agriculture: Evaluating ...

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Iberian robotics Conference (ROBOT)

作者： Wilgo Cardoso Tiago Barros Gil Gonçalves Cristiano Premebida Urbano J. Nunes Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal Dept. of Mathematics Institute for Systems Engineering and Computers at Coimbra University of Coimbra Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

In agricultural robotics, the integration of multi-spectral image processing and deep learning (DL) has become the state-of-the-art (SOTA) in crop monitoring, yield estimation, and efficient land management. This work addresses the impact of different DL-segmentation models and evaluation protocols on multispectral imagery datasets collected by a UAV over vineyards. In terms of evaluation protocols, we have considered train-test split, standard k-fold cross-validation, and group k-fold cross-validation. While the first two assume that the training and test data are drawn from the same underlying distribution, the group k-fold cross-validation protocol assumes that each fold represents distinct distributions. Most works either adopt train-test split or k-fold cross-validation under the assumption that both the training and test sets are drawn from the same distribution. However, this assumption is rarely met in real-world applications. Therefore, the objective of this study is to evaluate and compare different evaluation protocols within the context of a real-world agricultural task, highlighting their limitations and weaknesses. Two SOTA DL-based segmentation models, SegNet and DeepLabV3, are employed to perform semantic segmentation on datasets of three Vineyards. The models have been trained and tested considering single-modality representations. In addition to the RGB modality, models trained on NDVI, GNDVI and early fusion are also evaluated. The performance of the models are evaluated using the IoU metric across different dataset configurations. The results indicate that the early fusion representation achieves the highest performance across the various splitting protocols, compared to the single-input representations. The results also show that the train-test and random k-fold splitting approaches report similar results. However, when employing group k-fold the performance drops consistently across both models and the modalities. This indicates that the models

关键词： Deep learning Training Precision agriculture Measurement Protocols Data models Yield estimation Robots Monitoring Standards

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Modeling and Planar Trajectory Tracking Control of an MJ-AUV 42

Modeling and Planar Trajectory Tracking Control of an MJ-AUV

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42nd Chinese Control Conference, CCC 2023

作者： Omar, Zakarya Liu, Ke-Xian Ren, Chao Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin300072 China Sana'a University Faculty of Engineering Sana'a Yemen

ISBN: (纸本)9789887581543

A Multi-Joint Autonomous Underwater Vehicle (MJ-AUV) is designed and equipped with multiple movable segments or joints that allow for greater maneuverability and flexibility in the water. These joints allow the MJ-AUV to change its shape, orientation, and direction of movement, so it can access tight spaces or navigate in complex underwater environments, making it useful for a variety of tasks in the deep sea, such as surveillance, exploration, and maintenance. However, the multiple joint structure of the MJ-AUV complicates the dynamics of the system. Furthermore, the joints' rotation causes misalignment of the cabins, causing deviations in the desired forces and torques. To address these issues, this study focuses on the 2-dimensional (2D) modeling as well as the trajectory tracking controller of the MJ-AUV. In this setting, the proposed controller utilizes a hierarchically structured robust nonlinear back-stepping and sliding-mode approach. Hence, this controller is simulated under various uncertainties and disturbances and the results show that it presents a global finite-time convergence to a bounded neighborhood of the origin for both position and velocity tracking errors, demonstrating its effectiveness and robustness. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Autonomous underwater vehicles

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Probing Local Cellular Mechanics by Atomic Force Microscopy with Modified Spherical Tip

Probing Local Cellular Mechanics by Atomic Force Microscopy ...

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2022 IEEE International Conference on Cyborg and Bionic systems, CBS 2022

作者： Zuo, Zhaofeng Liu, Xiaoming Tang, Xiaoqing Liu, Fengyu Liu, Dan Li, Yuyang Huang, Qiang Arai, Tatsuo Beijing Institute of Technology Beijing Adv. Innov. Ctr. for Intelligent Robots and Systems The Intelligent Robotics Inst. School of Mechatronical Engineering Beijing100081 China

ISBN: (纸本)9781665490283

The mechanical characterization of cell is important for knowing the physiological state and studying diseases of organism. While many approaches are available for measuring cellular elasticity, distinguishing the stiffness variation among different cellular areas is still a challenge. In this paper, we reported a method to modify spherical atomic force microscopy (AFM) tip for accurate measurement of Young's modulus in several areas on single adherent living cancer cells (Hela cells). The micrometer size spheres were transported to an AFM probe tip by dual micropipettes and fixed by ultraviolet (UV) curable glue, which is reproducible and nondestructive to the cantilever. The force-displacement curves were measured along major axis of cells with the modified AFM probe in indentation experiments. The results demonstrate that the modulus value increased with the detected point approaching the nucleus center, and areas closed to nucleus showed have higher stiffness. Our study provides a quantitative method for static measurement of different locations of cell, and the result has the potential to reveal how intracellular structures have effect on the cell mechanical characterization. © 2023 IEEE.

关键词： Probes

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