Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weath...
Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver's behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds.
Over the past decade, cubic boron arsenide (BAs) has emerged as a highly promising semiconductor owing to its extraordinary thermal conductivity (1,200 W/m·K) and high ambipolar mobility (1,600 cm2/V·s). Thi...
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Hospital-acquired infections (HAIs) pose a significant challenge to healthcare systems worldwide, exacerbated by the COVID-19 pandemic. Current disinfection methods often fall short in ensuring comprehensive steriliza...
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ISBN:
(数字)9798331542559
ISBN:
(纸本)9798331542566
Hospital-acquired infections (HAIs) pose a significant challenge to healthcare systems worldwide, exacerbated by the COVID-19 pandemic. Current disinfection methods often fall short in ensuring comprehensive sterilization of high-contact areas, leading to persistent risks. This study addresses this gap by leveraging 5G biomedical internet of things (IoT) robots equipped with ultra-violet light emitting diodes (UV-LEDs) for effective disinfection. Our approach focuses on destroying the genetic material of pathogens, thereby preventing their spread within medical facilities. By integrating autonomous IoT 5G disinfection robots, we aim to enhance hospital sanitation and continuous infectious disease surveillance. A thorough review of nine biomedical studies underscores the efficacy of UV-based disinfection methods. We demonstrate that advancements in 5G infrastructure will significantly boost IoT and 5G medical robotic innovations, facilitating their application in both hospital settings and industrial healthcare automation. Thus, this paper presents a robust framework for utilizing 5G and IoT technologies in medical robotics to improve healthcare safety and efficacy.
Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing method...
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In this paper, we present a novel and generic data-driven method to servo-control the 3-D shape of continuum and soft robots embedded with fiber Bragg grating (FBG) sensors. Developments of 3-D shape perception and co...
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Environmental selection is an important process in multi-objective evolutionary algorithms (MOEAs). As the evolution progresses, the number of non-dominated solutions increases. This paper is focused on selecting a su...
Environmental selection is an important process in multi-objective evolutionary algorithms (MOEAs). As the evolution progresses, the number of non-dominated solutions increases. This paper is focused on selecting a subset from excess non-dominated solutions for evolutionary or the final output. However, traditional selection methods in classical MOEAs encounter difficulties when dealing with candidate solutions that possess irregular topologies. Although the distance-based subset selection methods are not sensitive to the topologies of the candidate points, they have significant room for reducing computational complexity. In order to address the above issues, a subspace selection method is proposed in this paper. It partitions the objective space into multiple subspaces that have comparable volumes and shapes. The maximal minimum distance of each solution is considered to ensure that the sparsest solution is always chosen first. To save computational costs, only the solutions in the neighboring subspaces are taken into account. The experimental results demonstrate that the proposed subspace selection method outperforms classical selection methods in solving problems with various shapes of the Pareto front.
This paper presents the development of a Physicsrealistic and Photo-realistic humanoid robot testbed, PR2, to facilitate collaborative research between Embodied Artificial Intelligence (Embodied AI) and robotics. PR2 ...
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Hair direction is an important external feature of hair, and recognising hair direction is a prerequisite for processing hair. In this paper, a new algorithm is proposed and systematically verified experimentally for ...
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ISBN:
(数字)9798331506100
ISBN:
(纸本)9798331506117
Hair direction is an important external feature of hair, and recognising hair direction is a prerequisite for processing hair. In this paper, a new algorithm is proposed and systematically verified experimentally for the problem of recognising hair direction. The main goal of this paper is to develop an algorithm that can identify hair direction in a complex image environment. A curve segment analysis method based on image skeletonisation is adopted, which is based on skeleton extraction, intersection identification, curve segmentation and direction prediction. In addition, this paper combines the technique of non-maximal value suppression and PCA analysis to improve the accuracy and stability of the estimation. In the experimental design, this paper chooses a representative image dataset to verify the effectiveness of this paper's algorithm. The experimental process includes the steps of image preprocessing, skeletonisation processing, intersection detection and merging, and direction prediction. The experimental results show that the method in this paper can accurately and effectively identify the hair direction. The main contribution of this paper is to propose a new hair direction recognition method and experimentally verify its effectiveness and accuracy in complex backgrounds.
With extensive pretrained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects, such as multitask learning, sample ...
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With extensive pretrained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects, such as multitask learning, sample efficiency, and high-level task planning. In this survey, we provide a comprehensive review of the existing literature in LLM-enhanced RL and summarize its characteristics compared with conventional RL methods, aiming to clarify the research scope and directions for future studies. Utilizing the classical agent-environment interaction paradigm, we propose a structured taxonomy to systematically categorize LLMs’ functionalities in RL, including four roles: information processor, reward designer, decision-maker, and generator. For each role, we summarize the methodologies, analyze the specific RL challenges that are mitigated and provide insights into future directions. Finally, the comparative analysis of each role, potential applications, prospective opportunities, and challenges of the LLM-enhanced RL are discussed. By proposing this taxonomy, we aim to provide a framework for researchers to effectively leverage LLMs in the RL field, potentially accelerating RL applications in complex applications, such as robotics, autonomous driving, and energy systems.
Magnetic Resonance Imaging(MRI)is now a widely used modality for providing multimodal,high-quality soft tissue contrast images with good spatiotemporal resolution but without subjecting patients to ionizing *** additi...
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Magnetic Resonance Imaging(MRI)is now a widely used modality for providing multimodal,high-quality soft tissue contrast images with good spatiotemporal resolution but without subjecting patients to ionizing *** addition to its diagnostic potential,its future theranostic value lies in its ability to provide MRI-guided robot intervention with combined structural and functional mapping,as well as integrated instrument localization,target recognition,and in situ,in vivo monitoring of the therapeutic *** of current applications include neurosurgery,breast biopsy,cardiovascular intervention,prostate biopsy and *** applications in targeted drug delivery and MRI-guided chemoembolization are also being *** promising progress has been made in recent years,there are still significant basic science research and engineering *** paper provides a comprehensive review of the current state-of-the-art in MRI-guided robot intervention and allied technologies in actuation,sensing,new materials,interventional instruments,and interactive/real-time *** future research directions and new clinical developments are also discussed.
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