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2nd International Conference on Signal Image Processing and Communication, ICSIPC 2022

作者： Yan, Wenwei Chen, Shuai Mu, Baoyan Gao, Liang Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory on Intelligent Detection and Equipment Technology of Liaoning Province Liaoning Shenyang110179 China

ISBN: (纸本)9781510657694

In line structured light vision measurement, an adaptive image enhancement algorithm is proposed to address the problem that the conventional image enhancement algorithm is ineffective in enhancing the laser stripe features in the unevenly illuminated images acquired by the system. The algorithm separates the channels in HSV color space, processes the separated S and V channels separately using an iterative adaptive histogram equalization algorithm to enhance the image saturation and brightness, and merges the equalized S1 and V1 channels with the original H channel to convert the image to RGB color space to complete the image enhancement. The experimental result shows that the information entropy index value S of the proposed algorithm is higher than those of other comparative algorithms, and the algorithm performs better in laser stripe image enhancement with better robustness. © 2022 SPIE.

关键词： Image enhancement

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A deep learning-based approach for motion-induced error reduction in fringe projection profilometry 8

A deep learning-based approach for motion-induced error redu...

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8th Symposium on Novel Photoelectronic detection Technology and Applications

作者： Su, Wenqing Tan, Ji He, Zhaoshui School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong University of Technology Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Fringe projection profilometry (FPP) is one of popular 3D measurement techniques, which can be divided into two categories: Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP). Compared to FTP, PSP has been increasingly appealing to researchers due to its merits of higher accuracy and sensitivity. Although PSP works well with the assumption that the object stays quasi-static, it is sensitive to the object motion and causes the motion-induced error in dynamic measurement due to the multi-frame measurement mechanism. However, the multi-frequency phase unwrapping is always utilized to solve the problem of surface discontinuities, which limits the reduction of numbers of projected patterns. Besides, using the high-speed camera will increase the cost of the hardware. Recently, researchers have demonstrated that the phase unwrapping, fringe denoising and dynamic range can be improved with the assistance of deep learning technique. Therefore, in this paper, a deep learning-based method is proposed for motion-induced error reduction. With the aid of strong fitting capability of the neural network, the motion-induced errors can be significantly reduced even under low capture frame rate. The proposed method is experimentally verified on its applicability for dynamic 3D measurement. © 2022 SPIE

关键词： Errors

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Unsupervised Multi-Target Domain Adaptation Incremental Method Based on Contrastive Learning

Unsupervised Multi-Target Domain Adaptation Incremental Meth...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xing Wei Zhaoxin Ji Bin Wen Fan Yang Chong Zhao Yang Lu School of Computer and Information Hefei University of Technology Hefei Intelligent Manufacturing Technology Research Institute of Hefei University of Technology Hefei Anhui Mine IOT and Security Monitoring Technology Key Laboratory Anhui P.R. China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Unsupervised domain adaptation (UDA) aims to alleviate the problem of distribution differences between unlabeled target domain and labeled source domain. Multi-target domain adaptation (MTDA) requires simultaneous transfer of knowledge from a single source domain to multiple target domains. Although the mixed-domain methods and some solutions that integrate single-target domain adaptation (STDA) optimization strategies have been proposed, the problem of knowledge forgetting has not been well addressed. To solve this problem, this paper proposes a multi-target domain adaptation incremental method based on contrastive learning. We construct a contrastive adaptation network and an incremental container network, which are connected by a feature discriminator. Contrastive learning ensures the adaptation effect of each source-target domain pair. Incremental module saves the weights of contrastive module, and the both networks combine feature discriminator to align the source features, alleviating the knowledge forgetting proplem. Extensive experiments on public datasets demonstrate that our method achieves excellent classification performance.

关键词： Knowledge engineering Adaptation models Semantics Contrastive learning Containers Optimization

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An Accurate Handheld Device to Measure Log Diameter and Volume Using Machine Vision Technique

SSRN

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SSRN 2023年

作者： Xu, Min Chen, Shuai Xu, Shoushuai Mu, Baoyan Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Key Laboratory on Intelligent Detection and Equipment Technology of Liaoning Province Shenyang China

Log diameter and volume measurements are crucial processes in the wood supply chain. Conventionally, the measurement mainly focuses on diameters and lengths. Despite the clear definition of conventional measurement methods, obtaining exact diameters is challenging due to the complexity of the measuring process and variability in the organic nature of logs. In this study, a smart handheld device based on machine vision is designed, and a measuring method which combines instance segmentation with pose estimation is proposed to automatically measure the diameters and volumes of cut-to-length logs. Subsequently, several groups of comparative experiments are conducted with manual measurements as reference. The experimental results indicate that approximately 97.33% of diameter measurements can be expected to fall within a range of 0.54 cm lower and 0.61 cm higher than manual measurements, with mean absolute deviation (MAD) and mean relative deviation (MRD) values of 0.25 cm and 0.78%, respectively. In terms of volume estimations, the MAD and MRD values are found to be 0.21m3 and 0.56%, respectively, with conventional formula estimations as reference. The results imply that the device contributes to substantial improvement in accuracy, reliability, and traceability and provides a promising potential to replace manual measurement. © 2023, The Authors. All rights reserved.

关键词： Computer vision

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Galileo Multi-frequency Observation Combination Method Based on Minimum Noise Coefficients

Galileo Multi-frequency Observation Combination Method Based...

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International Conference on Control and Robots (ICCR)

作者： Rong Yuan Shengli Xie Feng Gao Zhenni Li School of Automation Guangdong University of Technology Guangzhou China Guangdong KeyLaboratory of IoT Information Technology Guangzhou China Key Laboratory of Intelligent Information Processing and System Integration of IoT (GDUT) Guangzhou China Laboratory of In-telligent Detection and The Internet of Things in Manufacturing (GDUT) Ministry of Education Guangzhou China Techtotop Microelectronics Technology Co. Ltd Guangzhou China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology (GDUT) Guangzhou China

Classic multi-frequency combination is mainly to build a long wavelength combination and eliminate the ionospheric delay combination, and the combined noise factor is greater than 1. On the basis of un-differenced and un-combining observations, this paper proposes a general real coefficient combination based on the principle of minimum noise coefficients. The combined noise coefficient is less than 1, which can be applied to any multi-frequency observation combination. According to the frequency of each signal frequency of Galileo, we give the optimal combination of Galileo multi-frequency noise coefficients and the effect of theoretical improving accuracy. Compared with single frequency observation, the optimal combination of dual frequency triple frequency, four frequency and five frequency improve the observation accuracy by 40%, 49%,54% and 59% respectively. It is verified by the observation of the actual Galileo three frequency E1, E5A and E5b signals, the optimal combination of dual frequency and triple frequency improves the observation accuracy by 52% and 62% respectively compared with single frequency observation. According to the actual measurements, the observation accuracy improvement of minimum noise coefficients combination is basically consistent with the theoretical analysis. Finally, we analyze the equivalence between the minimum noise combination and the un-differenced un-combining observations, the results show that the minimum noise combination is an optimal weight model of un-differenced un-combining observations, which the optimization criterion is the minimum observation noise.

关键词： Analytical models Satellites Frequency measurement Delays Noise measurement Optimization Robots

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Blind Separation of Heart Sound Convolutive Mixtures Utilizing Independent Vector Analysis

Blind Separation of Heart Sound Convolutive Mixtures Utilizi...

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第40届中国控制会议

作者： Yuan Xie Kan Xie Shengli Xie School of Automation Guangdong University of Technology and Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong Key Laboratory of IoT Information Processing and Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education and 111 Center for Intelligent Batch Manufacturing Based on IoT Technology

Separation of heart sound signals from the recorded mixtures has become a hot research topic in the clinical diagnosis of heart diseases. To better understand the current health status of patients, it is of great significance to develop intelligent auscultation methods to improve the effectiveness of hearing and assist clinicians in practice. In this paper, an optimization convolutive blind source separation algorithm utilizing independent vector analysis is proposed for separation of the heart sound mixtures. In the algorithm, a denoising approach is firstly used to reduce the impact of the additive white Gaussian noise. Then,using the short time Fourier transform, the mixing signals in time domain are transmitted into the frequency domain. Afterwards,the unmixng matrix is updated using Newton’s method, and the heart sound sources are reconstructed based on the estimated unmixing matrix. Experimental results show that the algorithm obtains better separation performance than the baseline ***, it has better superiority in the strong reverberant environment.

关键词： Blind source separation（BSS） Convolutive mixtures Heart sound Independent vector analysis

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Latent Matrices for Tensor Network Decomposition and to Tensor Completion

arXiv

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arXiv 2022年

作者： Yang, Peilin Sun, Weijun Zhao, Qinbin Zhou, Guoxu School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China RIKEN Tokyo103-0027 Japan Key Laboratory of Intelligent Detection The Internet of Things in Manufacturing Ministry of Education Guangzhou510006 China

The prevalent fully-connected tensor network (FCTN) has achieved excellent success to compress data. However, the FCTN decomposition suffers from slow computational speed when facing higher-order and large-scale data. Naturally, there arises an interesting question: can a new model be proposed that decomposes the tensor into smaller ones and speeds up the computation of the algorithm? This work gives a positive answer by formulating a novel higher-order tensor decomposition model that utilizes latent matrices based on the tensor network structure, which can decompose a tensor into smaller-scale data than the FCTN decomposition, hence we named it Latent Matrices for Tensor Network Decomposition (LMTN). Furthermore, three optimization algorithms, LMTN-PAM, LMTN-SVD and LMTN-AR, have been developed and applied to the tensor-completion task. In addition, we provide proofs of theoretical convergence and complexity analysis for these algorithms. Experimental results show that our algorithm has the effectiveness in both deep learning dataset compression and higher-order tensor completion, and that our LMTN-SVD algorithm is 3-6 times faster than the FCTN-PAM algorithm and only a 1.8 points accuracy drop. Copyright © 2022, The Authors. All rights reserved.

关键词： Tensors

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In-Situ Measurement Method for Solid-Liquid Coupling Based on Multi-Wavelength Collaboration and Improved Graph Neural Network

SSRN

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SSRN 2024年

作者： Zhou, Wenhao Li, Xingqiang Liu, Chuanxing Du, Jinsong Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory on Intelligent Detection and Equipment Technology of Liaoning Province Shenyang110179 China

In the seamless integration of machining and measurement processes, the cutting fluid remaining on the workpiece surface would significantly impair the accuracy of optical in-situ measurement systems. Existing solid-liquid coupled measurement methods typically necessitate prior information of the medium and struggle to accommodate the complexities of real measurement scenarios, thus hindering the attainment of precise displacement measurements. To tackle the aforementioned challenge, this paper proposes a novel method for achieving precise displacement measurement on surfaces with residual cutting fluid, utilizing multi-wavelength collaboration. This innovative approach exploits the variations in optical path length induced by differences in refractive indices across multiple wavelengths to determine the measured displacement values. Moreover, to mitigate the inherent limitations of geometric optics models, an improved graph attention network is incorporated into the optimization process of the measurement model. This enhances the accuracy and robustness of the system. Based on this methodology, a measurement system is designed and constructed, and rigorous experiments are conducted to validate the efficacy of the proposed method. The experimental data demonstrate that the multi-wavelength collaboration method boosts measurement accuracy by at least 91%. Furthermore, after integrating improved graph attention network, the measurement accuracy is augmented by an additional at least 45%, culminating in a final measurement accuracy surpassing 10 μm. © 2024, The Authors. All rights reserved.

关键词： Displacement measurement

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Multi-Robot Cross-Station 3D Perception System for Automatic Assembly

Multi-Robot Cross-Station 3D Perception System for Automatic...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Guangchao Tan Jinsong Du Shiliang Shao Rui Guo Rui Su Kai Chang Chinese Academy of Sciences Shenyang Institute of Automation ShenYang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing ShenYang China University of Chinese Academy of Sciences Beijing China Key Laboratory on Intelligent Detection and Equipment Technology of Liaoning Province ShenYang China

This paper presents a robot automatic perception and assembly method for the situation that the measurement and assembly processes are not conducted at the same workstation. This method allows the assembly unit and the subassembly unit can be measured at different workstation. The key point can be transformed between different station with reflective markers and unified to the coordinate system of the assembly robot. The assembly process can be performed at any workstation. The method has been testified both in simulated experiments and in the real industrial field.

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Optical fiber surface plasmon resonance sensor using electroless-plated gold film for thrombin detection

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Analytical and Bioanalytical Chemistry 2024年第6期416卷 1469-1483页

作者： Huang, Yu Pu, Xiaoyin Qian, Husun Chuang, Chin-Jung Dong, Shanshan Wu, Jiangling Xue, Jianjiang Cheng, Wei Ding, Shijia Li, Shengqiang Chongqing Key Laboratory of Multi-scale Manufacturing Technology Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China Chongqing School University of Chinese Academy of Sciences Chongqing400714 China Department of Laboratory Medicine The Affiliated Hospital of Guizhou Medical University Guizhou Guiyang550004 China Department of Opto-Electronic Engineering National Dong Hwa University Hualien97401 Taiwan Department of Clinical Laboratory Medical Sciences Research Center University-Town Hospital of Chongqing Medical University Chongqing401331 China The Center for Clinical Molecular Medical Detection The First Affiliated Hospital of Chongqing Medical University Chongqing400016 China College of Laboratory Medicine Chongqing Medical University Chongqing400016 China Clinical Laboratory First Teaching Hospital of Tianjin University of Traditional Chinese Medicine Tianjin300000 China National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion Tianjin300000 China

This paper describes the simple and label-free detection of thrombin using optical fiber surface plasmon resonance (SPR) sensors based on gold films prepared by the cost-effective method of electroless plating. The plating conditions for simultaneously obtaining gold film on cylindrical core and end surfaces of an optical fiber suitable for measurement were optimized. The fabricated sensor exhibited a linear refractive index sensitivity of 2150 nm/RIU and 7.136 (a.u.)/RIU in the refractive index of 1.3329–1.3605 interrogated by resonance wavelength and amplitude methods respectively and a single wavelength monitoring method was proposed to investigate the sensing performance of this sensor. Polyadenine diblock and thiolated thrombin aptamers were immobilized on gold nanoparticles and gold films respectively to implement a sandwich optical fiber assay for thrombin. The developed optical fiber SPR sensors were successfully used in the determination of thrombin down to 0.56 nM over a wide range from 2 to 100 nM and showed good selectivity for thrombin, which indicated their potential clinical applications for biomedical samples. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2024.

关键词： Surface plasmon resonance

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