This paper introduces an innovative data-driven approach for replicating behaviors in interconnected and heterogeneous dynamic systems. The core concept involves real-time control of dynamic systems to closely mimic r...
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Robot-assisted laparoscopic radical prostatectomy(RARP)is widely used to treat prostate *** rigid instruments primarily used in RARP cannot overcome the problem of blind areas in surgery and lead to more trauma such a...
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Robot-assisted laparoscopic radical prostatectomy(RARP)is widely used to treat prostate *** rigid instruments primarily used in RARP cannot overcome the problem of blind areas in surgery and lead to more trauma such as more incision for the passage of the instrument and additional tissue damage caused by rigid *** robots are relatively fexible and theoretically have infinite degrees of freedom which can overcome the problem of the rigid instrument.A soft robot system for single-port transvesical robot-assisted radical prostatectomy(STvRARP)is developed in this *** soft manipulator with 10 mm in diameter and a maximum bending angle of 270°has good fexibility and *** design and mechanical structure of the soft robot are *** kinematics of the soft manipulator is established and the inverse kinematics is compensated based on the characteristics of the designed soft *** master-slave control system of soft robot for surgery is built and the feasibility of the designed soft robot is verified.
Human action recognition and posture prediction aim to recognize and predict respectively the action and postures of persons in *** are both active research topics in computer vision community,which have attracted con...
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Human action recognition and posture prediction aim to recognize and predict respectively the action and postures of persons in *** are both active research topics in computer vision community,which have attracted considerable attention from academia and *** are also the precondition for intelligent interaction and human-computer cooperation,and they help the machine perceive the external *** the past decade,tremendous progress has been made in the field,especially after the emergence of deep learning ***,it is necessary to make a comprehensive review of recent *** this paper,firstly,we attempt to present the background,and then discuss research ***,we introduce datasets,various typical feature representation methods,and explore advanced human action recognition and posture prediction ***,facing the challenges in the field,this paper puts forward the research focus,and introduces the importance of action recognition and posture prediction by taking interactive cognition in self-driving vehicle as an example.
Natural swarms have inspired various controlling algorithms for swarm robotics, while few of them were programmed in a similar way as the natural swarms. Defining a reliable programming method is still a daunting chal...
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The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective...
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The idea of a dazzling metropolis has drawn interest from all across the world. New innovations like blockchain, IoT, artificial intelligence, robots, and many other things were added to it. Security is one of the top...
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Accurately predicting the future motions of traffic agents is essential for autonomous systems. Despite the significant success of existing motion forecasting methods based on supervised learning, they still exhibit t...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Accurately predicting the future motions of traffic agents is essential for autonomous systems. Despite the significant success of existing motion forecasting methods based on supervised learning, they still exhibit two main limitations. First, when annotated data for a scene is limited, these methods often fail to achieve the expected accuracy. Second, they typically rely on complex architectures and extensive prior knowledge to improve performance. To overcome these challenges, we propose MF-BERT, a novel framework that adapts the concept of BERT to motion forecasting, inspired by advancements in the self-supervised pre-training paradigm. During pre-training, we design a siamese sequence modeling task with an asymmetric mask strategy to capture complex behavior patterns of agents. During fine-tuning, the pre-trained representation module initializes the feature encoder of the motion forecasting model, and a multimodal trajectory decoder generates all possible predictions. Experimental results demonstrate the superiority of MF-BERT over state-of-the-art methods.
Sustainable power sources for outdoor wearable electronics are essential for the continuous operation of wearable ***,the current lack of engineering design that can harvest energy regardless of weather conditions pre...
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Sustainable power sources for outdoor wearable electronics are essential for the continuous operation of wearable ***,the current lack of engineering design that can harvest energy regardless of weather conditions presents a significant *** this regard,this study introduces a wearable,breathable all-weather usable dual energy harvester(AWuDEH)that can seamlessly generate electrical energy regardless of weather *** this study,the AWuDEH integrated with the thermoelectric generator and the droplet-based electricity generator is *** AWuDEH,especially engineered with a bi-functional top substrate for radiative cooling and electrification,achieves sustainable energy harvesting outdoors,thereby addressing the conventional challenge associated with the necessity for separate energy harvesters tailored to outdoor usage contingent on weather *** device reaches a maximum power output of 14.6μW cm^(-2)under simulated sunny conditions and generates a much more enhanced thermoelectric power of 74.78μW cm^(-2)and a droplet-based electric power of 256.25 mW m^(-2)in rainy *** proof,this study developed self-powered wearable electronics capable of acquiring physiological signals in simulated outdoor *** study presents a promising advancement in wearable technology,offering a potent solution for sustainable energy harvesting independent of weather conditions.
In a blockchain-based energy trading system, prosumers engage in energy trading and simultaneously participate in blockchain mining to process trading transactions. However, as blockchain mining consumes energy, prosu...
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Centralized machine learning algorithms in vehicular networks face privacy and resource constraints. Federated Learning (FL) addresses these by enabling collaborative model training without sharing raw data. To incent...
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