Characterizing the wetting properties of fibers is crucial for many research and industry applications, including textiles for water-oil separation and composite materials. Those fibers are often soft, typically tens ...
Characterizing the wetting properties of fibers is crucial for many research and industry applications, including textiles for water-oil separation and composite materials. Those fibers are often soft, typically tens of micrometers in diameter but millimeters in length, making manipulation and characterization difficult. Contact angles of single fibers are usually determined by droplet shape analysis or force-based Wilhelmy method. However, these methods are unable to accurately measure contact angles above $60^{\circ}$ or ensure reliable control of the liquid-fiber interaction process, especially for soft fibers prone to bending. Consequently, reliable characterization of the advancing and receding contact angles of single fibers remains a challenge. Here we report a novel method for characterizing the advancing and receding contact angles of both soft and rigid single fibers using a millimeter-sized droplet probe affixed to a disk and a numerical model of the system. By analyzing side-view images, we extract key geometrical parameters of the disk-droplet-fiber system, which, when used in detailed simulations, allows estimating the contact angle of fibers ranging from $20^\circ$ to $140^\circ$ . We applied this method to characterize three distinct micro-fibers: a highly hydrophilic rigid borosilicate glass fiber, a mildly hydrophilic soft PET fiber, and a rigid hydrophobic tungsten wire coated with a commercial super-repellent coating.
Active voluntary participation in robot-assisted rehabilitation promote the recovery of stroke hemiplegia. However, different patients have personalized recovery states, which needs the adaptive control strategy to im...
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With widespread use in areas such as electric vehicles and portable electronic devices, lithium-ion batteries are favored for their high energy density and long cycle life. However, accurate prediction of battery life...
With widespread use in areas such as electric vehicles and portable electronic devices, lithium-ion batteries are favored for their high energy density and long cycle life. However, accurate prediction of battery life remains a challenging issue and is critical to the effective management and use of these energy storage devices. Battery degradation phenomena can lead to performance degradation, which in turn negatively impacts battery applications. Traditional research methods are often limited by time and resource constraints, making it difficult to conduct long-term life prediction studies. To overcome these problems, this paper proposes an optimization method based on Particle Swarm Optimization and Simulated Annealing algorithms. The method aims to search for the optimal parameter combinations by the SA algorithm and further finetune the parameters by the PSO algorithm to optimize the Least Squares Support Vector Machine (LS-SVM) model and achieve more accurate battery life prediction. The experimental results, based on real battery aging data sets, show that the proposed hybrid optimization method outperforms the traditional optimization method and the independent LS-SVM model in terms of accuracy and robustness. The method can be applied to a variety of Li-ion battery systems and provides a valuable tool for battery management and decision making. Future work will further validate the proposed method on a larger dataset and integrate it with a real-time battery monitoring system for online lifetime prediction.
Low-light image quality often suffers from noise, color distortion, and reduced contrast, challenging accurate object detection. Traditional enhancement methods can lead to over-processing and semantic information los...
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Artificial Intelligence (AI) is one of the most auspicious technologies in the mobile machine domain. It promises to optimize the machine operation to reduce energy consumption or provide an assistant function to supp...
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This study focused on jamming transition, one of the methods in soft robotics, and developed a robotic finger that imitates a human finger. By combining wire drive and jamming transition, we developed a highly versati...
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ISBN:
(数字)9788993215380
ISBN:
(纸本)9798331517939
This study focused on jamming transition, one of the methods in soft robotics, and developed a robotic finger that imitates a human finger. By combining wire drive and jamming transition, we developed a highly versatile robotic finger that can grasp objects with a wider range of shapes than can be grasped with the conventional gripper type. Wire drive has the property of deforming when an external force is applied, while jamming transition has the property of hardening. By combining these, it is possible to grasp objects without changing their shape even when an external force is applied. The developed jamming finger can hold thin objects such as pens with a single finger. Operation verification of movements such as pushing a small object against the fingertip and picking it up was also carried out, showing the developed finger to have sufficient grasping ability. Differences in retention between different film thicknesses and filling rates are also shown. To achieve improved versatility, several of these jamming fingers were combined to fabricate a multi-finger system. Experiments showed that the multi-finger system can hold objects of shapes that cannot be grasped by the general gripper type, which validates the versatility of the system.
This paper focuses on the fault-tolerant control (FTC) problem for unmanned aerial vehicles (UAVs) subject to possible multiple actuator failures, which is a tough problem to solve with traditional FTC methods since t...
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ISBN:
(数字)9798350379228
ISBN:
(纸本)9798350390780
This paper focuses on the fault-tolerant control (FTC) problem for unmanned aerial vehicles (UAVs) subject to possible multiple actuator failures, which is a tough problem to solve with traditional FTC methods since they usually require accurate mathematical models. To address the limitation of traditional FTC methods, a model-free FTC approach is proposed based on reinforcement learning (RL). Subsequently, the proposed approach is applied to construct fault-tolerant controller for UAVs without any knowledge of the quadrotor dynamic information. Then, an end-to-end control policy that can tolerant actuator failures is obtained, which can map the state of the UAVs directly to the control commands of the four rotors after learning. Finally, the effectiveness of the proposed fault-tolerant approach is demonstrated by using the flexible modular quadrotor simulator.
Remote center of motion (RCM) describes a robot with a rod-like end-effector operating through a hole in the interface separating the internal space from the external space. Considering that the control of RCM may be ...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Remote center of motion (RCM) describes a robot with a rod-like end-effector operating through a hole in the interface separating the internal space from the external space. Considering that the control of RCM may be influenced by perturbations (noises) and that the end-effector is frequently replaced to complete different tasks, the structural information related to the robot manipulator and its rod-like end-effector may contain errors. This paper proposes an acceleration-level remote center of cyclic motion (ARC
2
M) control scheme, which takes into account the cyclic motion index and the physical limitations of robot manipulators to achieve repetitive motion planning and RCM control at the acceleration level. Additionally, a parameter calculation method is proposed to compute unknown parameters of the end-effector under the influence of noise. Kalman filter and a neural dynamics-based method are employed to address noises effects, and related theoretical analyses are given. To validate the proposed ARC
2
M scheme, simulations and physical experiments are carried out. The source code is available at https://***/LongJin-lab/ARCM.
Attribute graph embedding algorithms based on graph neural networks aggregate information about the neighborhood of a target node to update the embedding of the target node, i.e. neighborhood aggregation. The shallow ...
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ISBN:
(数字)9798331533816
ISBN:
(纸本)9798331533823
Attribute graph embedding algorithms based on graph neural networks aggregate information about the neighborhood of a target node to update the embedding of the target node, i.e. neighborhood aggregation. The shallow network architecture of existing models limits the generalization ability of the models on large datasets, and the number of nodes increases exponentially with the number of graph neural network layers, resulting in gradient explosion and the need for faster model training methods. In addition, the multiple iteration process of neighborhood aggregation changes the original attribute information of the nodes, especially when the attributes of the target node and the attribute information of the neighborhood nodes are extremely different, which makes the target node attributes deviate significantly from the original attributes. Excessive neighborhood aggregation mixes different clustered nodes, causing all nodes to converge to the same embedding, resulting in excessive smoothing. In order to solve the above problems while considering the supervised learning of the model and the utility of neighborhood aggregation, this paper proposes a subgraph sampling method based on graph sampling for attribute graph data pre-processing, and an attribute graph embedding algorithm for improving neighborhood aggregation using an adaptive learning strategy.
Diabetes is a common chronic Metabolic disorder, and its treatment has always been a hot research topic in the medical field. Traditional Chinese medicine has a significant therapeutic effect on it and has become one ...
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