Compliant interaction control is a key technology for robots performing contact-rich manipulation *** design of the compliant controller needs to consider the robot hardware because complex control algorithms may not ...
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Compliant interaction control is a key technology for robots performing contact-rich manipulation *** design of the compliant controller needs to consider the robot hardware because complex control algorithms may not be compatible with the hardware performance,especially for some industrial robots with low bandwidth *** paper focuses on effective and easy-to-use compliant control algorithms for position/velocity-controlled *** by human arm stiffness adaptation behavior,a novel variable target stiffness(NVTS)admittance control strategy is proposed for adaptive force tracking,in which a proportional integral derivative(PID)variable stiffness law is designed to update the stiffness coefficient of the admittance function by the force and position ***,its stability and force-tracking capability are theoretically *** addition,an impact compensator(Impc)is integrated into the NVTS controller to enhance its disturbance-suppression capability when the robot is subjected to strong vibration disturbances in complicated surface polishing *** proposed controllers are validated through four groups of experimental tests using different robots and the corresponding results demonstrate that they have high-accuracy tracking capability and strong adaptability in unknown environments.
With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data ***-view ...
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With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data ***-view image compression aims to improve compression efficiency by leveraging correlations between ***,the requirement of synchronization and inter-image communication at the encoder side poses significant challenges,especially for constrained *** this study,we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during *** model integrates an encoder network,a quantization module,and a decoder network,to ensure both high compression performance and high-quality image *** encoder uses a deep Convolutional Neural Network(CNN)to extract high-level features from the input image,which then pass through the quantization module for further compression before undergoing lossless entropy *** decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder ***,we first introduce a channel-spatial attention module to capture and refine information within individual image feature ***,we employ a semi-coupled convolution module to extract both shared and specific information in ***,a cross-attention module is employed to fuse mutual information extracted from side *** effectiveness of our model is validated on various datasets,including KITTI Stereo and *** results highlight the superior compression capabilities of our method,surpassing state-of-the-art techniques.
The coloring theory of graphs is a very important direction in graph theory. The graph coloring problem has a strong application background. Many practical problems such as timetabling problems, frequency allocation p...
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Transformers designed for natural language processing have originally been explored for computer vision in recent research. Various Vision Transformers(ViTs) play an increasingly important role in the field of image t...
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Transformers designed for natural language processing have originally been explored for computer vision in recent research. Various Vision Transformers(ViTs) play an increasingly important role in the field of image tasks such as computer vision, multimodal fusion and multimedia analysis. However, to obtain promising performance, most existing ViTs usually rely on artificially filtered high-quality images, which may suffer from inherent noise ***, such well-constructed images are not always available in every situation. To this end,we propose a Robust ViT(RViT) to focus on the relevant and robust representation learning for image classification tasks. Specifically, we first develop a novel Denoising VTUnet module,where we conceptualize the nonrobust noise as the uncertainty under the variational ***, we design a fusion transformer backbone with a tailored fusion attention mechanism to perform image classification based on the extracted robust representations effectively. To demonstrate the superiority of our model, the compared experiments are conducted on several popular datasets. Benefiting from the sequence regularity of the Transformer and captured robust feature,the proposed method exceeds compared Transformer-based models with superior performance in visual tasks.
Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are d...
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Entity alignment plays a crucial role in the integration of knowledge graphs. However, current knowledge graphs often suffer from inconsistent construction standards, structural heterogeneity, and imprecise semantic r...
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Vehicular edge computing (VEC) enables vehicles to offload their tasks to idle vehicles for processing. To process tasks, the needed service model should be stored ahead. Considering the storage of vehicles is limited...
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Cellular traffic prediction is crucial to the network efficiency as it provides prior knowledge to guide the wireless resource allocation. The artificial intelligence (AI) based traffic prediction can provide better p...
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In recent years, a variety of rolling bearing fault diagnosis methods based on deep learning has become an emerging research orientation. However, there is still a gap between the existing diagnostic model and the pra...
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作者：
Tian, YePan, JingwenYang, ShangshangZhang, XingyiHe, ShupingJin, YaochuAnhui University
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center
Institute of Artificial Intelligence Hefei230088 China Anhui University
School of Computer Science and Technology Hefei230601 China Anhui University
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University
Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University
Faculty of Technology Bielefeld33619 Germany
The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision s...
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