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arXiv 2024年

作者： Ma, Xuanchao Jiang, Yanlin Liu, Hongyan Zhou, Chengxu Gu, Ke Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection China Beijing Key Laboratory of Computational Intelligence and Intelligent System China Beijing Artificial Intelligence Institute China School of Electronic & Information Engineering Liaoning University of Technology Liaoning China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology China

Recent years have witnessed a broader range of applications of image processing technologies in multiple industrial processes, such as smoke detection, security monitoring, and workpiece inspection. Different kinds of distortion types and levels must be introduced into an image during the processes of acquisition, compression, transmission, storage, and display, which might heavily degrade the image quality and thus strongly reduce the final display effect and clarity. To verify the reliability of existing image quality assessment methods, we establish a new industrial process image database (IPID), which contains 3000 distorted images generated by applying different levels of distortion types to each of the 50 source images. We conduct the subjective test on the aforementioned 3000 images to collect their subjective quality ratings in a well-suited laboratory environment. Finally, we perform comparison experiments on IPID database to investigate the performance of some objective image quality assessment algorithms. The experimental results show that the state-of-the-art image quality assessment methods have difficulty in predicting the quality of images that contain multiple distortion types. Copyright © 2024, The Authors. All rights reserved.

关键词： Database systems

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Gesture Recognition Based on Attention and Dilated Causal Convolution

Gesture Recognition Based on Attention and Dilated Causal Co...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Zhou, Xin Liu, Jiaqing Huai, Yaping Ye, Jiancong Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen518000 China University of Science and Technology of China China University of Chinese Academy of Sciences China South China University of Technology China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

In recent years, based on surface electromyography (sEMG), great progress has been made in gesture recognition tasks, which is significant to the study of computer interaction and prosthesis control. Prior to this, many researches have focused on feature selection and time series modeling of data. However, it is difficult and complicated to manually perform feature extraction and feature selection on data. Besides, most of the time series modeling is based on recurrent neural networks with memory cell, that is, Long Short-term Memory (LSTM). Even though LSTM has memory gate, but cannot completely records historical information. In this paper, to address the above problems, we propose a Temporal Convolutional Network based on Attention mechanism (ATCN), in which Attention is used to characterize temporal features, and dilated causal convolution to capture more historical information. ATCN achieves 95.26% accuracy with this method on the public dataset NinaPro DB2, which exceeds the state-of-the-art accuracy of 5%. The experiments show that this approach is feasible and effective for gesture recognition tasks. Our contributions are as follows: Firstly, Attention mechanism is introduced into the field of prosthetic control. Then, ATCN is proposed for gesture recognition. Finally, ATCN achieves better results than LSTM on multiple public datasets. © 2022 IEEE.

关键词： Long short-term memory

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Ultrasensitive Ammonia Gas Sensor Based on Ti3c2tx/Ti3alc2 Planar Composite at Room Temperature

SSRN

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SSRN 2022年

作者： Liu, Zhihua Han, Dan Liu, Lulu Li, Donghui Han, Xiaomei Chen, Yi Liu, Xiaoru Zhuo, Kai Cheng, Yongqiang Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of Technology Taiyuan030024 China

Two-dimensional (2D) transition metal carbides (MXene) have attracted great attention for device applications due to their unique structures and excellent properties, such as high electrical conductivity and abundant functional groups on the surface. However, gas sensors based on Ti3C2Tx MXene have the problems of high detection limit and poor long-term stability. Herein, we report a simple method to prepare Ti3C2Tx/Ti3AlC2 planar composite gas sensing materials using high molar ratio of HCl and LiF with Ti3AlC2 as a framework. The Ti3C2Tx/Ti3AlC2 planar composite gas sensor has a high sensitivity, good selectivity and low detection limit for NH3, which can detect 50 ppb (1.2%) at room temperature (25°C). Furthermore, the gas sensor has good repeatability and long-term stability. After 120 days, the response of the gas sensor to 500 ppb of NH3 fluctuated little due to the Ti3AlC2 as a framework. The superior performances can be attributed to the special heterojunction between Ti3C2Tx and Ti3AlC2 and the high proportion of -O and -OH functional groups on the surface of Ti3C2Tx/Ti3AlC2 composite. This study provides an important reference for the realization of high performance NH3 sensing by Ti3C2Tx. © 2022, The Authors. All rights reserved.

关键词： Chemical detection

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Bearing Fault Diagnosis Based on Dual-Data Input Convolutional Neural Network

SSRN

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SSRN 2022年

作者： He, Chaoqian Hao, Runfang Yang, Kun Yuan, Zhongyun Shang, Shengbo Wang, Xiaorui Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China

Previous bearing fault diagnosis models show low accuracy and unstable performance in strong noise environment, which are not suitable for the actual complex industrial scenarios. In this paper, the dual-channel convolution model (DDCNN) based on dual-data inputs is established. The model used the reconstructed two-dimensional data as inputs. In one channel, convolution with small kernel is used to extract details of FE data, and large kernel obtain global information of DE data in the other. The output is classified through the full connection layer after the data features of both channels are fused. Besides, data are mixed from different loads to avoid cross-load effects as much as possible. The results demonstrate that the proposed model has good accuracy and anti-noise performance. The accuracy is as high as 92.65% when the SNR is -10. © 2022, The Authors. All rights reserved.

关键词： Failure analysis

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An insulation diagnosis method for battery pack based on battery model

An insulation diagnosis method for battery pack based on bat...

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2019能源动力与机械工程研讨会

作者： Yanhui Zhang Qingpeng Chen Xiao Liu Zhile Yang Shifeng Guo Wei Feng Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences

The collision and components aging of electric vehicles may lead to dramatic decrease of insulation performance in the complex application environment,which will reduce the insulation between battery pack and chassis,and will affect the performance of on-board equipment and electronic control unit,resulting in heat accumulation effect of leakage circuit and even vehicle ***,it is critical to develop a real-time,accurate and reliable detection device to monitor the insulation resistance of the high-voltage system of electric vehicles,which is beneficial to the normal operation of electrical equipment,the safe operation of vehicles and the personal safety of *** this work,an online estimation method of insulation resistance based on battery model is proposed.

关键词： An insulation diagnosis method for battery pack based on battery model

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Learning Compliant Assembly Strategy From Demonstration

Learning Compliant Assembly Strategy From Demonstration

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Sheng Liu Juyi Sheng Yongsheng Ou Southern University of Science and Technology (SUSTech) Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems (#2019B121205007)

Compared with robots, humans can complete the different assembly tasks of parts flexibly and quickly. By teaching robots with human experiences, not only the industrial assembling tasks can be resolved, but also many other robot applications can be realized. The pose adjustment stage is the most critical part of the peg-in-hole assembly process. This paper analyzes the contact force in the pose adjustment stage and calculates the control variables that affect the assembly motion. Based on the Gaussian mixture model (GMM), the nonlinear mapping relationship between the control variables and the state variables during the assembly is established using the human demonstration data, and the parameters of the model are solved by the expectation maximization (EM) algorithm, and thus the human-like compliant assembly is completed by a robot. In order to verify the effectiveness of the demonstration-learning algorithm, a peg-in-hole assembly experiment was carried out using KUKA manipulator. Finally, the experiment shows that the proposed learning-based method not only improves the efficiency of the robot peg-in-hole assembly but also makes the manipulator have a satisfied adaptive ability to the complex environments in the assembly process.

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Ultrasensitive Ammonia Gas Sensor Based on Ti3c2tx/Ti3alc2 Planar Composite at Room Temperature

SSRN

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SSRN 2022年

作者： Liu, Zhihua Han, Dan Liu, Lulu Li, Donghui Han, Xiaomei Chen, Yi Liu, Xiaoru Zhuo, Kai Cheng, Yongqiang Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System the Ministry of Education Taiyuan University of Technology Taiyuan030024 China

Two-dimensional (2D) transition metal carbides (MXene) have attracted great attention for device applications due to their unique structures and excellent properties, such as high electrical conductivity and abundant functional groups on the surface. However, gas sensors based on Ti3C2Tx MXene have the problems of high detection limit and poor long-term stability. Herein, we report a simple method to prepare Ti3C2Tx/Ti3AlC2 planar composite gas sensing materials using high molar ratio of HCl and LiF with Ti3AlC2 as a framework. The Ti3C2Tx/Ti3AlC2 planar composite gas sensor has a high sensitivity, good selectivity and low detection limit for NH3, which can detect 50 ppb (1.2%) at room temperature ((25 ± 2) °C). Furthermore, the gas sensor has good repeatability and long-term stability. After 120 days, the response of the gas sensor to 500 ppb of NH3 fluctuated little due to the Ti3AlC2 as a framework. The superior performances can be attributed to the special heterojunction between Ti3C2Tx and Ti3AlC2 and the high proportion of -O and -OH functional groups on the surface of Ti3C2Tx/Ti3AlC2 composite. This study provides an important reference for the realization of high performance NH3 sensing by Ti3C2Tx. © 2022, The Authors. All rights reserved.

关键词： Gases

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Biocompatible chitosan/polyethylene glycol/multi-walled carbon nanotube composite scaffolds for neural tissue engineering

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2022年第1期23卷 58-73页

作者： Shengbo SANG Rong CHENG Yanyan CAO Yayun YAN Zhizhong SHEN Yajing ZHAO Yanqing HAN Shanxi Key Laboratory of Micro Nano Sensors&Artificial Intelligence Perception College of Information and ComputerTaiyuan University of TechnologyTaiyuan030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan030024China College of Information Science and Engineering Hebei North UniversityZhangjiakou075000China Shanxi Six-Dimensional Artificial Intelligence Biomedical Research Institute Taiyuan030031China Department of Neurology Shanxi Provincial Cardiovascular HospitalTaiyuan030024China

Carbon nanotube(CNT)composite materials are very attractive for use in neural tissue engineering and biosensor *** scaffolds are excellent mimics of extracellular matrix due to their hydrophilicity,viscosity,and *** can also impart conductivity to other insulating materials improve mechanical stability guide neuronal cell behavior and trigger axon *** performance of chitosan(CS)/polyethylene glycol(PEG)composite scaffolds could be optimized by introducing multi-walled CNTs(MWCNTs).CS/PEG/CNT composite scaffolds with CNT content of 1%,3%,and 5%(1%=0.01 g/mL)were prepared by *** physical and chemical properties and biocompatibility were *** electron microscopy(SEM)showed that the composite scaffolds had a highly connected porous *** electron microscope(TEM)and Raman spectroscopy proved that the CNTs were well dispersed in the CS/PEG matrix and combined with the CS/PEG nanofiber *** enhanced the elastic modulus of the *** porosity of the scaffolds ranged from 83%to 96%.They reached a stable water swelling state within 24 h,and swelling decreased with increasing MWCNT *** electrical conductivity and cell adhesion rate of the scaffolds increased with increasing MWCNT *** showed that rat pheochromocytoma(PC12)cells grown in the scaffolds had characteristics similar to nerve *** measured changes in the expression of nerve cell markers by quantitative real-time polymerase chain reaction(qRT-PCR),and found that PC12 cells cultured in the scaffolds expressed growth-associated protein 43(GAP43),nerve growth factor receptor(NGFR),and class IIIβ-tubulin(TUBB3)*** research showed that the prepared CS/PEG/CNT scaffold has good biocompatibility and can be further applied to neural tissue engineering research.

关键词： Multi-walled carbon nanotube(MWCNT) Cell-scaffold PC12 cells Biocompatibility

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RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration

RegFormer: An Efficient Projection-Aware Transformer Network...

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International Conference on Computer Vision (ICCV)

作者： Jiuming Liu Guangming Wang Zhe Liu Chaokang Jiang Marc Pollefeys Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University ETH Zürich MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China University of Mining and Technology Microsoft

Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise from the huge point number, complex distribution, and outliers of outdoor LiDAR scans. In addition, most existing registration works generally adopt a two-stage paradigm: They first find correspondences by extracting discriminative local features and then leverage estimators (eg. RANSAC) to filter outliers, which are highly dependent on well-designed descriptors and post-processing choices. To address these problems, we propose an end-to-end transformer network (RegFormer) for large-scale point cloud alignment without any further post-processing. Specifically, a projection-aware hierarchical transformer is proposed to capture long-range dependencies and filter outliers by extracting point features globally. Our transformer has linear complexity, which guarantees high efficiency even for large-scale scenes. Furthermore, to effectively reduce mismatches, a bijective association transformer is designed for regressing the initial transformation. Extensive experiments on KITTI and NuScenes datasets demonstrate that our RegFormer achieves competitive performance in terms of both accuracy and efficiency. Codes are available at https://***/IRMVLab/RegFormer.

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Cogging Force Identification Based on Self-Adaptive Hybrid Self-Learning TLBO Trained RBF Neural Network for Linear Motors 13

Cogging Force Identification Based on Self-Adaptive Hybrid S...

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13th International Symposium on Linear Drives for Industry Applications, LDIA 2021

作者： Fu, Xuewei Ding, Chenyang Zanchetta, Pericle Yang, Xiaofeng Tang, Mi Liu, Yang State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Department of Electrical University of Pavia Pavia Italy Power Electronics Machine and Control Group University of Nottingham Nottingham United Kingdom Center of Ultra-Precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9781728172101

The cogging force arising due to the strong attraction forces between the iron core and the permanent magnets, is a common inherent property of the linear motors, which significantly affects the control performance. Therefore, significant research efforts have been devoted to the compensation of the cogging force. In this paper, an identification approach based on the radial basis function neural network (RBFNN) is proposed to obtain an accurate model of the cogging force. A self-adaptive hybrid self-learning teaching-learning-based optimization (SHSLTLBO) method is utilized to train the neural network. Finally, the experimental results confirm the effectiveness and the superiority of the proposed cogging force identification method. © 2021 IEEE

关键词： Radial basis function networks

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