Underwater acoustic target recognition is a widely investigated issue in the field of underwater acoustics. Many good results have been reported for underwater acoustic target recognition. However, in practical applic...
Underwater acoustic target recognition is a widely investigated issue in the field of underwater acoustics. Many good results have been reported for underwater acoustic target recognition. However, in practical applications, the strong demand for labeled data for underwater acoustic target recognition is a big obstacle. In order to solve this problem, researchers have explored few-shot learning and unsupervised methods in various papers. A Siamese network is proposed which is composed of one-dimensional convolution and Long Short-Term Memory (LSTM) neural networks, called 1DCLSN. A structure for 1DCLSN is designed which combines contrastive information with label information and obtains satisfactory recognition results. In addition, the contrastive loss function with a different clustering term is modified to improve the performance. With only few labeled training samples, the performance of the proposed approach is better than those of other deep learning methods. The experiment shows the great potential of our method.
Haptic feedback is critical for teleoperation in surgical robots, particularly in Natural Orifice Transluminal Endoscopic Surgery (NOTES). This paper introduces a haptic controller designed to enhance force generation...
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Soft robots have the characteristics of high flexibility and strong environmental adaptability compared with traditional robots, and the peristaltic soft robot is a critical class for this field because of the perista...
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With the rapid development of the logistics industry, the path planning problem for automated guided vehicles (AGVs) in warehousing systems has become a prominent research topic. Traditional multi-robot path planning ...
With the rapid development of the logistics industry, the path planning problem for automated guided vehicles (AGVs) in warehousing systems has become a prominent research topic. Traditional multi-robot path planning algorithms often perform poorly in narrow-lane environments found in warehouses. This paper proposes an improved dynamic multi-robot path planning algorithm specifically tailored for narrow-lane environments. By constraining the traversal directions within narrow lanes and dynamically determining the optimal directions, this algorithm achieves efficient multi-robot path planning and enhances the quality of robot paths. The effectiveness of the algorithm is theoretically proven in this paper. Extensive simulation data demonstrates that our algorithm efficiently generates high-quality paths in high-density narrow-lane warehouse environments.
This paper presents a centimeter-scale robotic manipulator fabricated using 3D printing technology. It consists of three mechanisms: a grasping mechanism, an RCM (remote center of motion) function mechanism, and a rot...
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ISBN:
(数字)9798350376807
ISBN:
(纸本)9798350376814
This paper presents a centimeter-scale robotic manipulator fabricated using 3D printing technology. It consists of three mechanisms: a grasping mechanism, an RCM (remote center of motion) function mechanism, and a rotational mechanism, actuated by bellows-driven soft pneumatic actuators. Thus, the robotic manipulator enables the grasping of tools for micro-manipulations and the 2-DOF orientation adjustment of the tools, including the rotation around the axis of the rotational mechanism (0° - 45°) and the rotation around an RCM (−10° - 10°). Finally, one 5-DOF robotic manipulation system consisting of the 2-DOF rotational centimeter-scale robotic manipulator and one 3-DOF translational external manipulator (moving speed ~ 500 μm/s) was used to conduct micro-manipulations, in which a printed needle was grasped to carve the desired trajectory on the agar (gel strength ~ 650 g/cm
2
) resembling biological tissues. the external manipulator controlled the displacement of the needle while the robotic manipulator adjusted the orientation of the needle insertion into the agar. Results showed that the 5-DOF robotic manipulation system had mechanical stability while achieving micron-level accuracy (200 μm) in the micro-manipulation task.
We develop the resilient observer-based event-triggered control update strategy in this *** is utilized to achieve the input-to-state stability（ISS） of cyber-physical systems（CPSs） when there is an asynchronous de...
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We develop the resilient observer-based event-triggered control update strategy in this *** is utilized to achieve the input-to-state stability（ISS） of cyber-physical systems（CPSs） when there is an asynchronous denial-of-service（DoS） attack, which is launched by malicious adversaries both on measurement channel and control channel in a random attack strategy. To estimate the unmeasurable states while saving the limited networked bandwidth, an H∞observer-based event-triggered control scheme is first designed to guarantee the ISS of CPSs with economic communication. Moreover, the occurrence of Zeno behavior can be eliminated by providing the existence of a lower positive bound of any two inter-event times. Then, a recursive model of asynchronous DoS attacks is introduced. A resilient control update strategy is proposed and further analyzed to derive the stability criterion of CPSs. At last, a numerical simulation example is given to demonstrate the effectiveness of the introduced control update policy.
Single-cell RNA sequencing technology captures gene expression at the whole-genome scale and single-cell level in many cells. Thus, the cellular states during a physiological process would be captured in detail. These...
Single-cell RNA sequencing technology captures gene expression at the whole-genome scale and single-cell level in many cells. Thus, the cellular states during a physiological process would be captured in detail. These data provide new opportunities and challenges for systems biology. Previously, the models in systems biology dealt with the dynamics of specific signaling pathways. However, it is almost impossible to directly model the dynamics of the whole genome due to the ultrahigh dimensionality of the data. Here, to model its gene regulatory dynamics, the dimensionality of the whole genome was reduced significantly using the latent space of a variational autoencoder, a generative deep learning model. In the low-dimensional latent space, an ordinary differentiation equation (ODE) system has been established to analyze the dynamic characteristics of the whole genome system. To demonstrate the effectiveness of this method, we generated a low-dimensional representation of a systems biology model we previously developed. The variational autoencoder reduced the dimension from 13 to 2 in latent space. An ODE model was developed to analyze the dynamics of this two-dimensional system. The results indicated that the 2-dimensional model captured the essence of the original systems biology model.
Intra-operative cardiac ultrasound robots are emerging as a new tool to assist in cardiac interventional procedures to improve the efficiency of the procedure and reduce the operator's experience. These robots are...
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Parallel-mechanism-based ultrasound robots bring the benefits of convenient robot-assisted remote ultrasound or automated scanning. During the use of this type of robot, accurate tracking and feedback of 6-DOF posture...
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The quadrotor is popularly used in challenging environments due to its superior agility and flexibility. In these scenarios, trajectory planning plays a vital role in generating safe motions to avoid obstacles while e...
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