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Trajectory-based fast ball detection and tracking for an autonomous industrial robot system

International Journal of Intelligent systems Technologies and Applications 2021年第2期20卷 126-145页

作者： AbdElKhalek, Youssef M. Awad, Mohammed Ibrahim Abd El Munim, Hossam E. Maged, Shady A. School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Mechatronics Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Engineering Galala University Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt

Autonomising industrial robots is the main goal in this paper;imagine humanoid robots that have several degrees of freedom (DOF) mechanisms as their arms. What if the humanoid's arms could be programmed to be responsive to their surrounding environment, without any hard-coding assigned? This paper presents the idea of an autonomous system, where the system observes the surrounding environment and takes action on its observation. The application here is that of rebuffing an object that is thrown towards a robotic arm's workspace. This application mimics the idea of high dynamic responsiveness of a robot's arm. This paper will present a trajectory generation framework for rebuffing incoming flying objects. The framework bases its assumptions on inputs acquired through image processing and object detection. After extensive testing, it can be said that the proposed framework managed to fulfil the real-time system requirements for this application, with an 80% successful rebuffing rate. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Object detection

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Estimating Joint Kinematics and Muscles Forces During Robotic Rehabilitation to Detect and Counteract Reduced Ankle Mobility

Estimating Joint Kinematics and Muscles Forces During Roboti...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： Kim K. Peper Elisabeth R. Jensen Sami Haddadin Technical University of Munich Germany Department of Computer Engineering Chair of Robotics and Systems Intelligence TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI)

The paper presents a solution to detect active ankle joint movement while a patient undergoes therapy with a robotic lower limb rehabilitation device that neither restricts nor actively supports ankle dorsi- or plantarflexion. The presented method requires the addition of only two accelerometer sensors to the system as well as a musculoskeletal model of the lower limb. Using forward kinematics and inverse dynamics, it enables knee and ankle joint kinematic tracking in the sagittal plane and muscle force estimation. This is an extension of a previous work in which only hip joint tracking was possible and, thus, muscle force estimation was limited. The correlation results of the current validation study with 12 healthy subjects show high correlation $(R=0.88\pm 0.09)$ between the kinematics estimated with the proposed method and those calculated from a gold standard motion capture setup for all three joints (hip, knee, and ankle). The correlation results of the estimated m. tibialis anterior muscle force against electromyography measurements $(R\ =\ 0.62\pm 0.27)$ are promising and a first application to a patient data set shows potential for future clinical application.

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Rock-Climbing Fish Inspired Skeleton-Embedded Rigid-Flexible Coupling Suction Disc Design for Adhesion Enhancement 16th

Rock-Climbing Fish Inspired Skeleton-Embedded Rigid-Flexible...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Tan, Wenjun Qin, Hengshen Zhang, Chuang Wang, Ruiqian Zhang, Yiwei Yang, Lianchao Chen, Qin Wang, Feifei Xi, Ning Liu, Lianqing State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chengdu Institute of Biology Chinese Academy of Sciences Sichuan Chengdu610042 China Department of Electrical and Electronic Engineering University of Hong Kong 999077 Hong Kong Emerging Technologies Institute Department of Industrial and Manufacturing Systems Engineering University of Hong Kong Pokfulam999077 Hong Kong

ISBN: (纸本)9789819964888

Underwater high-performance biological adhesion systems have attracted increasing research interest to inspire engineered adhesion systems. Switchable underwater adhesion systems typically employ suction adhesion. However, achieving low pre-pressure negative pressure adhesion is still a challenge. An inspiring nature mode is provided by the rock-climbing fish, which needs little preload and can resist a pull-off force of 1000 times its own body weight. The pectoral and abdominal fins of rock-climbing fish have evolved to almost a flat suction cup. Through micro CT, we found that a unique skeletal network is embedded in the suction cup. This unique structure increases the stiffness of the suction cup and enhances its adhesion performance. Through numerical simulation, it was revealed that this rigid, flexible coupling structure enhances the ability of the suction cup to resist lateral pressure and increases the energy dissipation of the edge detachment of the suction cup. This work can shed light on the design of novel underwater adhesion systems and inspire underwater adhesion crawling robots. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Adhesion

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Performance Evaluation of High Gain 2×2 MIMO Microstrip Antenna Operating at 28 GHz 2

Performance Evaluation of High Gain 2×2 MIMO Microstrip Ant...

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Ismail, Alyani Binti Rasid, M.F.A. Hamidon, Mohd Nizar B Abdullah, Raja Syamsul. A. Raja Shebani, Nafaa Mohamad Universiti Putra Malaysia Wireless and Photonics Networks Research Centre Department of Computer and Communication Systems Engineering Faculty of Engineering Serdang Malaysia Universiti Putra Malaysia Institute of Nanoscience and Nanotechnology Department of Electrical and Electronic Engineering Faculty of Engineering Serdang Malaysia

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

In this paper, four configurations of 2×2 MIMO microstrip antenna operating at 28 GHz are proposed and simulated by computer Simulation Technology (CST) software. The antenna is designed on PTFE/Teflon substrate with dielectric constant of 2.1, loss tangent of 0.0002, and thickness of 1.58 mm. The performance of all proposed configurations of 2×2 MIMO antennas are evaluated based on the overall gain, envelope correlation coefficient (ECC), and diversity gain (DG). Moreover, the comparison between all cases in this work is done in order to choose the optimal configuration, and to prove the reliability of the design the best obtained case is compared with proposed antennas in published works. © 2022 IEEE.

关键词： Microstrip antennas

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Online Distributed Learning with Quantized Finite-Time Coordination

Online Distributed Learning with Quantized Finite-Time Coord...

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IEEE Conference on Decision and Control

作者： Nicola Bastianello Apostolos I. Rikos Karl H. Johansson School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Sweden Department of Electrical and Computer Engineering Division of Systems Engineering Boston University Boston MA US

In this paper we consider online distributed learning problems. Online distributed learning refers to the process of training learning models on distributed data sources. In our setting a set of agents need to cooperatively train a learning model from streaming data. Differently from federated learning, the proposed approach does not rely on a central server but only on peer-to-peer communications among the agents. This approach is often used in scenarios where data cannot be moved to a centralized location due to privacy, security, or cost reasons. In order to overcome the absence of a central server, we propose a distributed algorithm that relies on a quantized, finite-time coordination protocol to aggregate the locally trained models. Furthermore, our algorithm allows for the use of stochastic gradients during local training. Stochastic gradients are computed using a randomly sampled subset of the local training data, which makes the proposed algorithm more efficient and scalable than traditional gradient descent. In our paper, we analyze the performance of the proposed algorithm in terms of the mean distance from the online solution. Finally, we present numerical results for a logistic regression task.

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An Optimizing Method for FMCW Chirp Linearity Based on Continuous Frequency Step Analysis

An Optimizing Method for FMCW Chirp Linearity Based on Conti...

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IEEE Annual Conference on Wireless and Microwave Technology (WAMICON)

作者： Sheng Sun Chunqi Shi Leilei Huang Jinghong Chen Runxi Zhang Institute of Microelectronic Circuits and Systems East China Normal University Shanghai China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9798331509606

ISBN: (纸本)9798331509613

This paper presents a method for optimizing chirp linearity in frequency-modulated continuous wave (FMCW) signal sources, utilizing continuous frequency step analysis. The study delves into the connection between the phase-locked loop (PLL) loop bandwidth and the chirp slope. Additionally, it examines how variations in frequency step granularity impact the root mean square (RMS) FM error across three distinct radar operational modes: long-range, medium-range, and short-range. Guided by the proposed optimization model, an FMCW signal source has been designed using a 55 nm CMOS process. This design occupies an area of 1.6 mm 2 and consuming 178.4 mW of power. The prototype demonstrates a chirp bandwidth of 5.4 GHz and achieves RMS FM errors of 152.7,167, and 201.3 kHz with the chirp slopes of 13.2,22, and 44 MHz/μs, respectively.

关键词： Semiconductor device modeling Wireless communication Frequency synthesizers Frequency modulation Chirp Software packages Linearity Bandwidth Root mean square Phase locked loops

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Reinforcement Learning Driven Smart Charging Algorithms to Enhance Battery Lifespan and Grid Sustainability

Reinforcement Learning Driven Smart Charging Algorithms to E...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Sivakumar, S. Yadav, Mali Ram Bharathi, A. Akila, D. Dineshkumar, P. Banupriya, V. Vel Tech Rangarajan Dr.Sagunthala R And D Institute Of Science And Technology Department Of Electrical And Electronics Engineering Chennai India Lovely Professional University Department Of Physical Education Punjab India Vels Institute Of Science Technology And Advanced Studies Department Of Information Technology Pallavaram India Saveetha College Of Liberal Arts And Sciences Simats Department Of Computer Applications Chennai India Sona College Of Technology Department Of Information Technology Tamil Nadu Salem India M. Kumarasamy College Of Engineering Department Of Computer Science And Business Systems Karur India

ISBN: (纸本)9798350371567

This study investigates the use of RL-driven smart charging algorithms, particularly the Deep Deterministic Policy Gradients (DDPG) model, to maximise grid sustainability and battery lifetime. The current state of this field's study is lacking in real-time adaptation to different settings and in handling the ever-changing nature of charging profiles. By comparison, the suggested approach demonstrates a significant efficiency boost through the use of RL principles, which allow the model to learn and adjust its charging tactics. In specifically, the strategy contributes to a 10% improvement in total grid efficiency while cutting peak demand by 25%, minimising charge-discharge cycles by 20%, and reducing battery degradation rates by 15%. Not only do these measurements show that the suggested method works, but they also show how unique it is in offering a smart charging solution that is both sensitive and adaptive. This research fills in the gaps left by other studies and proposes an optimisation technique that is more dynamic and works in real-time. Its influence on the health of individual batteries and the sustainability of the grid as a whole is noteworthy. © 2024 IEEE.

关键词： Reinforcement learning

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A Systematic Design Method Based on Vector Analysis for mmW DCML Divider

A Systematic Design Method Based on Vector Analysis for mmW ...

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IEEE Annual Conference on Wireless and Microwave Technology (WAMICON)

作者： Wangming Liu Wenbo Guo Chunqi Shi Jinghong Chen Runxi Zhang Institute of Microelectronic Circuits and Systems East China Normal University Shanghai China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9798331509606

ISBN: (纸本)9798331509613

The traditional RC delay model and the small-signal model do not suffice for a precise analysis of the dynamic current mode logic (DCML) divider in the millimeter-wave (mmW) frequency band. This paper explores the current vector model and examines the prerequisites for the proper functioning of the DCML, including phase, gain, and amplitude aspects, using expressions and vector diagrams as references. To fulfill the frequency division requirements of mmW ultra-wideband phaselocked loops, a broadband DCML4 divider has been designed based on the aforementioned analysis. Simulation results show that with an input power of -4 dBm, the maximum frequency division range for a single sub-band can reach up to 137.5% (from 5 to 27 GHz). The center frequency is adjusted via biasing, enabling the total frequency coverage to achieve 150% (ranging from 5 to 35 GHz). At a supply voltage of 1.2 V, the average power consumption is only 6.72 mW.

关键词： Wireless communication Analytical models Power demand Systematics Simulation Millimeter wave technology Voltage Frequency conversion Vectors Ultra wideband technology

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Analysis of A Mixed Neural Network Based on CNN and RNN for Computational Model of Sensory Cortex

Analysis of A Mixed Neural Network Based on CNN and RNN for ...

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2021 International Conference on Electronics, Circuits and Information engineering, ECIE 2021

作者： Yan, Haoyue Wu, Chenwei University of Toronto Department of Electrical and Computer Engineering Toronto Canada Institute of Ai and Robotics Fudan University Academy for Engineering and Technology Shanghai China

ISBN: (纸本)9781665418690

Under the limitation of modern science, Anatomy is not able to discover the extremely complicated organ - brain's working activities. Along with the development of machine learning and its subset - neural network, named by nearly implementing neurons' connection, scientists found an efficient way to represent brain as a visible approach. Therefore, a new discipline has been created under biology, named as computational neuroscience. Most scientists focus on finding computational models that are closed to fit the specific or general areas of the cortex. Convolutional neural network (CNN) and Recurrent neural network (RNN) are two of them. By taking advantages of those two most popular networks, in this study, a mixed computational model with CNN and RNN might be the best computational model for analogizing brain's activities so far. © 2021 IEEE.

关键词： Recurrent neural networks

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Portable comprehensive two-dimensional microgas chromatography using an integrated flowrestricted pneumatic modulator

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Microsystems & Nanoengineering 2022年第6期8卷 61-72页

作者： Xiaheng Huang Maxwell Wei-hao Li Wenzhe Zang Xiaolu Huang Anjali Devi Sivakumar Ruchi Sharma Xudong Fan Department of Biomedical Engineering University of MichiganAnn ArborMI 48109USA Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA Center for Wireless Integrated MicroSensing and Systems(WIMS2) University of MichiganAnn ArborMI 48109USA Max Harry Weil Institute for Critical Care Research and InnovationUniversity of Michigan Ann ArborMI 48109USA

Two-dimensional(2D)gas chromatography(GC)provides enhanced vapor separation capabilities in contrast to conventional one-dimensional GC and is useful for the analysis of highly complex chemical *** developed a microfabricated flow-restricted pneumatic modulator(FRPM)for portable comprehensive 2D micro-GC(μGC),which enables rapid ^(2)D injection and separation without compromising the ^(1)D separation speed and eluent peak profiles.^(2)D injection characteristics such as injection peak width and peak height were fully characterized by using flow-through micro-photoionization detectors(μPIDs)at the FRPM inlet and outlet.A ^(2)D injection peak width of~25 ms could be achieved with a ^(2)D/^(1)D flow rate ratio over *** FRPM was further integrated with a 0.5-m long ^(2)D μcolumn on the same chip,and its performance was ***,we developed an automated portable comprehensive 2D μGC consisting of a 10m OV-11D μcolumn,an integrated FRPM with a built-in 0.5m polyethylene glycol ^(2)D μcolumn,and two μ*** separation of 40 volatile organic compounds in~5 min was demonstrated.A hybrid 2D contour plot was constructed by using both ^(1)D and ^(2)D chromatograms obtained with the two μPIDs at the end of the ^(1)D and ^(2)D μcolumns,which was enabled by the presence of the flow resistor in the FRPM.

关键词： flow restricted comprehensive

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