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Pneumatic soft bionic actuators inspired by elephant trunk for multidisciplinary robotics applications

Physica Scripta 2025年第7期100卷 075013-075013页

作者： Xiaohui Guo Deli Kong Zihan Lin Xianghui Li Yupeng Shao Zhaobin Li Yinuo Chen Xinyu Wu Weiqiang Hong Tianxu Zhang Xiaowen Zhu Congguang He Junjie Qiu Xiangchen Zhao Qi Hong Yunong Zhao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Integrated Circuits Anhui University Hefei 230601 People’s Republic of China State Key Laboratory of High-Performance Precision Manufacturing Dalian University of Technology Dalian 116024 People’s Republic of China Key Laboratory for Micro/Nano Technology and System of Liaoning Province Dalian University of Technology Dalian 116024 People’s Republic of China School of Physics University of Science and Technology of China Hefei 230000 People’s Republic of China

Soft robots in technology makes rapid development in the field of robotics, so that robots are widely used in all aspects of life. This paper proposed a bionic elephant trunk structure of pneumatic actuator (SPA). To solve the insufficient tip force and bending angle of SPA, mathematical model and Finite Element Analysis (FEA) are used to determine the optimal parameters of the SPA. Based on optimized SPAs, robots for multidisciplinary applications are developed. These applications include soft gripper with high gripping force of up to 480 N, a quadruped soft robot capable of adapting complex amphibious environments, and a robot designed to assist with sign language communication. These demonstrate the versatility of soft robots in addressing challenges across various domains, such as healthcare, human-machine interaction, and environmental exploration.

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RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks

arXiv

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arXiv 2024年

作者： Li, Xiaojie Zhang, Songyang Li, Hang Li, Xiaoyang Xu, Lexi Xu, Haigao Mei, Hui Zhu, Guangxu Qi, Nan Xiao, Ming Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Guangdong China College of Physics Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70503 United States Research Institute China United Network Communications Corporation Beijing100048 China China Mobile Communications Group Jiangxi Co. Ltd 330038 China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing210016 China State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China Department of Information Science and Engineering School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MBRMR within a single area, eliminating the need for multiregion datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multiband data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework's adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT's enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT's effectiveness and practicality for MBRMR in environments with limited data availability. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Neural network detection of shilling attack based on user rating history and latent features

Neural network detection of shilling attack based on user ra...

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2019 International Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2019

作者： Yuan, Wanqiao Xiao, Yingyuan Jiao, Xu Ming, Yidong Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781728150949

The recommendation system is a significant field to solve the problem of information overload today. However, the recommendation system relies heavily on the user's behavior information, which is easy to suffer attacks. Therefore, how to prevent the recommendation system from effecting by the attacks is a major issue in the research field of protection recommendation system. In response to the above questions, we found that the shilling attack has the features of short rating history, large number of rating and other potential features compared with normal user profiles. Therefore, we propose a neural network structure to learn the latent features between users and items and we construct shilling attack detection model based on neural network. The experimental results show that the detection method of this paper has achieved the expected goal. © 2019 IEEE.

关键词： Recommender systems

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Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition

arXiv

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arXiv 2022年

作者： Chen, Huayu He, Huanhuan Zhu, Jing Sun, Shuting Li, Jianxiu Shao, Xuexiao Li, Junxiang Li, Xiaowei Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China Brain Health Engineering Laboratory School of Medical Technology Beijing Institute of Technology Beijing China School of Information Science and Engineering Inner Mongolia University Inner Mongolia China Shandong Academy of Intelligent Computing Technology Jinan China Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Open Source Software and Real-Time System Lanzhou University Ministry of Education Lanzhou China

Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results. Facing the situation that lacks EEG information decoding research, we first analyzed the impact of different EEG information(individual, session, emotion and trial) for emotion recognition by sample space visualization, sample aggregation phenomena quantification, and energy pattern analysis on five public datasets. Based on these phenomena and patterns, we provided the processing methods and interpretable work of various EEG differences. Through the analysis of emotional feature distribution patterns, the Individual Emotional Feature Distribution Difference(IEFDD) was found, which was also considered as the main factor of the stability for emotion recognition. After analyzing the limitations of traditional modeling approach suffering from IEFDD, the Weight-based Channel-model Matrix Framework(WCMF) was proposed. To reasonably characterize emotional feature distribution patterns, four weight extraction methods were designed, and the optimal was the correction T-test(CT) weight extraction method. Finally, the performance of WCMF was validated on cross-dataset tasks in two kinds of experiments that simulated different practical scenarios, and the results showed that WCMF had more stable and better emotion recognition ability. Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

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New Method of Traffic Flow Forecasting Based on QPSO Strategy for Internet of Vehicles

New Method of Traffic Flow Forecasting Based on QPSO Strateg...

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IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： De-gan Zhang Jing-yu Du Ting Zhang Hong-rui Fan The Tianjin Key Lab of Intelligent Computing & Novel software Technology Tianjin University of Technology Tianjin China The Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education Tianjin

ISBN: (数字)9781728165141

ISBN: (纸本)9781728165158

We propose a new method of traffic flow forecasting based on quantum particle swarm optimization strategy (QPSO) for Internet of Vehicles (IOV). Establish a corresponding model based on the characteristics of the traffic flow data. The genetic simulated annealing method is applied to the quantum particle swarm method to obtain the optimized initial cluster center, and is applied to the parameter optimization of the radial basis neural network prediction model. The function approximation of radial basis neural network can be used to obtain the required data. In addition, in order to compare the performance of the methods, a comparison study with other related methods such as QPSO-RBF is also performed. Our method can reduce prediction errors and get better and more stable prediction results.

关键词： Neural networks Simulated annealing Clustering algorithms Neurons Biological system modeling Genetics Linear programming

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New Algorithm of QoS Constrained Routing for Node Energy Optimization of Edge computing

New Algorithm of QoS Constrained Routing for Node Energy Opt...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor systems (MASS)

作者： Degan Zhang Jinyu Du Ting Zhang Peng Yang Hongrui Fan Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin China

ISBN: (数字)9781728198668

ISBN: (纸本)9781728198675

We propose an oriented edge computing nodes energy optimized QoS constrained routing algorithm. The mechanism of this algorithm is to optimize the network energy consumption and extend the network life cycle by creatively combining the edge computing technology to preprocess the original data of the node, accelerating the transmission and processing of effective data, accelerating the algorithm convergence by using the way of automata interacting with the environment and controlling the dormant activation state of the node. Through experimental testing and comparison, this paper gave the Multi-QoS constrained routing algorithm for Edge computing and Node energy optimization algorithm (MQEN), which can meet the requirements of end-to-end delay and reliability service with multi-QoS constraints when significantly reducing network energy consumption.

关键词： Energy consumption Learning automata Quality of service Routing Acceleration Optimization Edge computing

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Advancing low-light image enhancement through deep learning: A comprehensive experimental study

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Knowledge-Based systems 2025年

作者： Muhammad Tahir Rasheed Hufsa Khan Junsong Wang Yan Kang College of Big Data and Internet Shenzhen Technology University Shenzhen 518118 China Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound National Engineering Laboratory for Big Data System Computing Technology School of Biomedical Engineering Shenzhen University Shenzhen 518060 China College of Electronics and Information Engineering Shenzhen University Shenzhen 518060 China College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen 518118 China

Low-light photography severely degrades the perceptual quality of images, which adversely affects the performance of computer vision algorithms. Deep learning-based low-light image enhancement (LLIE) methods are dominating in improving the quality of degraded and corrupted images taken in non-optimal lighting conditions. Either the designed methods are evaluated on a limited set of test datasets or they are not evaluated for machine vision applications. A detailed examination of the recent developments, their generalization, and their application to computer vision tasks is required. This experimental review highlights the future trend of recent learning-based LLIE methods through statistical analysis, experimentally analyzing their generalization capability on a wide spectrum of test datasets, examining the effectiveness of LLIE in computer vision applications, and discussing a correlation between them. The test data used for the generality of these methods covers diversified scenes/contents as well as complex degradation in real scenarios. Rich variety of full-reference and no-reference metrics are applied to compare the relative performances. Furthermore, the application of enhancement methods in low-light face detection is also validated to examine the effectiveness of these LLIE methods as a preprocessing step in machine vision tasks. The discussion on correlation of experimental results from the perspective of both human and machine vision in the subsequent part provides broader view of the field. This systematic review concludes with the limitations of enhancement methodologies and unresolved issues.

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Adaptive finite time stability of delayed systems via aperiodically intermittent control and quantized control

arXiv

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arXiv 2020年

作者： Liu, Xiwei Ma, Hailian Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China

In this brief, we set up the finite time stability (FnTSta) theory for dynamical systems with bounded timevarying delays via aperiodically intermittent control (AIC) and quantized control (QC). A more general QC is designed in this brief. The bound of time-varying delay is required to be less than the infimum in AIC. Two-phases-method (2PM) is applied to solve the FnTSta for delayed system, i.e., it divides the whole proof process into two phases, one phase is that the process for the norm of system error evolving from initial values to 1, and the other is the process for the norm of system error evolving from 1 to 0. By proving that these two phases both use FnT to realize, the whole FnTSta for system is proved. We also design the adaptive rules and prove its validity rigorously. Furthermore, the obtained theories are used to discuss the finite time synchronization (FnTSyn) of neural networks as an application. Finally, some simulations are given to illustrate the effectiveness of our theoretical result. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamical systems

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RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation

RCGA-Net: An Improved Multi-hybrid Attention Mechanism Netwo...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xiao, Feng Shen, Cong Chen, Yu Yang, Tian Chen, Shengyong Liao, Zhijun Tang, Jijun Tianjin University of Technology School of Computer Science and Engineering Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China School of Basic Medical Sciences Fujian Medical University Fujian Fuzhou China College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Systems Bioengineering Ministry of Education Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department of Computer Science and Engineering University of South Carolina ColumbiaSC United States

ISBN: (纸本)9781665401265

Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score. © 2021 IEEE.

关键词： Diagnosis

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New Method of MSCA for Edge computing

New Method of MSCA for Edge Computing

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IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： De-gan Zhang Hao-li Zhu Ting Zhang Jia-xu Wang Tianjin Key Lab of Intelligent Computing & Novel software Technology Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System (Tianjin University of Technology) Ministry of Education Tianjin

ISBN: (数字)9781728165141

ISBN: (纸本)9781728165158

By innovative use of edge computing technology to construct a node data cache model and step-by-step calculation of node channel separation, a multi-strategy channel allocation algorithm (MSCA) for edge computing is proposed. The mechanism of this algorithm is to use edge computing technology to pre-store the data in the nodes in the multicast tree and calculate the channel separation between the nodes, and then select the transmission channel number of the node with the least interference to avoid mutual interference between node information transmissions. Through experimental tests and comparisons, the MSCA algorithm for edge computing presented in this paper can minimize channel interference and overall network energy consumption while satisfying throughput and end-to-end delay.

关键词： Edge computing Interference Computational modeling Delays Peer-to-peer computing Channel allocation Protocols

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