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Dual Autoencoder-Based Joint Learning: Enhancing Depth Classification of Hard Inclusions in Soft Tissue for robotic Palpation

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IEEE/ASME Transactions on Mechatronics 2025年

作者： Wang, Jingnan Zhou, Zhenning Cui, Yunduan Cui, Jian Qin, Longhui Xu, Tiantian Yi, Zhengkun Wu, Xinyu Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen 518055 China Shenzhen University of Advanced Technology Center for Neurocognition and Social Behavior Institute of Artificial Intelligence Shenzhen 518107 China Research Center for Augmented Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Zhejiang 310000 China Southeast University School of Mechanical Engineering Nanjing 211189 China City University of Macau Faculty of Data Science 999078 Macao Key Laboratory of Biomedical Imaging Science and System Chinese Academy of Sciences Beijing 100864 China Shenzhen University of Advanced Technology Shenzhen 518107 China

In robot-assisted minimally invasive surgery (RMIS), robotic palpation is vital for enhancing tissue assessment accuracy, especially for tumor depth detection, which is crucial for precise resections and improved treatment outcomes. By complementing and leveraging the strengths of data from multiple tactile sensors, the tumor detection task can realize significant improvements in accuracy and reliability, thereby enhancing overall performance in robotic palpation in RMIS. However, challenges arise due to differences in sensor modalities and the lack of unified data representation. To address this, we fabricate silicone-based phantom tissue to simulate soft tissue and embedded simulated tumors with hard inclusions at depths ranging from 0–11 mm, treating it as a 12-class classification problem. We conduct two robotic palpation experiments: one with the BarrettHand capacitive sensor and the other with the Digit sensor, collecting two tactile datasets. To explore the joint learnability of datasets collected from different tactile sensors, we design a dual autoencoder-based joint learning framework that integrates two recurrent autoencoders to process the two different tactile datasets. By applying a joint loss mechanism to connect their latent spaces, the autoencoders are trained jointly together, with the latent representation used for supervised classification. Extensive experiments show that joint learning enables sharing of features learned from different tactile datasets, thereby enhancing learning efficiency and classification accuracy for tactile datasets with different modalities and improving the performance of both autoencoders compared to independent training. © 1996-2012 IEEE.

关键词： Classification task dual autoencoder joint learning robotic palpation tactile sensor

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advanced Dual-Mode Optical Whisker Sensor for Enhancing Robot Perception

Advanced Dual-Mode Optical Whisker Sensor for Enhancing Robo...

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International Conference on advanced robotics and Mechatronics (ICARM)

作者： Chengcheng Jing Rui Xu Zhenlan Li Jiahao Yang Sijie Yin Zhiqiang Yu Beijing Advanced Innovation Center for Intelligent Robots and Systems the School of Mechatronical Engineering and Key Laboratory of Biomimetic Robots and System Ministry of Education Beijing Institute of Technology Beijing China Key Laboratory of Airborne Optical Imaging and Measurement Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China Department of Rehabilitation Medicine First Hospital of Jilin University Changchun China School of Automation Beijing institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology ZheJiang China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Whisker sensors play a significant role in the navigation of robots in dark environments. However, contemporary whisker sensors suffer from impractical sizes, underdeveloped space detection capabilities and robustness, hindering their integration into robotic systems. To overcome these issues, we propose a compact optical bionic whisker (OWH) capable of perceiving external stimuli in both contact and noncontact modes. The whisker sensor achieves deflection and direction solely by calculating the variation in light intensity collected from symmetrically distributed photodiodes. Additionally, fine element analysis (FEA) is employed to optimize the sensitivity of the whisker sensor. Experimental validation involving contact and noncontact multidirectional wind test results demonstrated the promising performance of the OWH sensor, affirming its potential for integration into robot perception systems and providing improved adaptability and sensing capabilities across diverse environments.

关键词： Integrated optics Biomedical optical imaging Sensitivity Optical design Sensor phenomena and characterization Robot sensing systems Sensor systems Sensors Optical sensors Sensor arrays

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Research on Computer Network technology system Based on Artificial Intelligence technology 7

Research on Computer Network Technology System Based on Arti...

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2020 7th International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation, CDMMS 2020

作者： Yao, Jinling Liu, Jie College of Electronic Information and Engineering Tianjin Vocational Institute Tianjin300000 China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China

This article introduces computer network intrusion detection systems and their classification based on artificial intelligence technology, and points out the challenges that intrusion detection systems (IDS) are facing in wireless sensor computer networks. On this basis, a hierarchical multi-layer wireless sensor network intrusion detection system and technology based on artificial intelligence technology Agent is proposed, and its network structure, working principle and performance are analysed. © Published under licence by IOP Publishing Ltd.

关键词： Intrusion detection

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A moving target detection and localization strategy based on optical flow and pin-hole imaging methods using monocular vision

A moving target detection and localization strategy based on...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Wang, Shun Guo, Qingqiang Xu, Sheng Su, Dan School of Control Science and Engineering Shandong University Shandong Jinan250061 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Orbbec Inc. Shenzhen518057 China

ISBN: (纸本)9781665436786

This paper proposes a new strategy for moving target detection and localization based on monocular vision. Firstly, to detect a moving target with large displacement and high speed accurately, two consecutive video images captured by a monocular camera are preprocessed using the enhancement and denoising methods. Then, the optical flow representing motion information is calculated iteratively by the modified Lucas-Kanade optical flow method. Secondly, a new interest region extraction method is developed to overcome the negative impacts caused by the noises in the background. Specifically, this proposed method combines a two-level image segmentation strategy from coarse to fine, including median filtering, two-direction dynamic threshold segmentation, the Otsu method, and morphological processing. Thirdly, a low computational cost target localization algorithm is proposed based on pin-hole imaging theory. Besides, it only uses two-dimensional image and camera parameters to obtain the moving target's position in the three-dimensional space. Finally, experimental results show that the proposed strategy can effectively eliminate noise interferences and realize moving target detection, extraction, and localization. © 2021 IEEE.

关键词： Image segmentation

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Modeling and analysis of reverse-fixed piezoelectric tube fiber scanner for two-photon endomicroscopy

Modeling and analysis of reverse-fixed piezoelectric tube fi...

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2022 Applied Optics and Photonics China: Biomedical Optics, AOPC 2022

作者： Wang, Conghao Zhang, Xiulei Liu, Huilan Ma, Jianrui Tian, Jingquan Fu, Qiang Wu, Runlong Wang, Aimin Feng, Lishuang School of Instrumentation and Optoelectronic Engineering Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing100191 China Beijing Transcend Vivoscope Biotech Co. Ltd Beijing100049 China State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences Peking University Beijing100871 China State Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Peking University Beijing100871 China Laboratory of Intelligent Sensing Materials and Chip Integration Technology of Zhejiang Province Hangzhou innovation institute of Beihang University Beijing100191 China

ISBN: (纸本)9781510662346

Miniature two-photon microscopy combines the two-photon excitation principle and is more lightweight and flexible, allowing it to be applied to freely behaving animals. The piezoelectric tube (PZT) fiber scanner is the key actuated component in miniature fiber-scanning two-photon endomicroscopy (TPEM). In this paper, we use multi-physics field finite element simulation to model and analyze a reverse-fixed PZT fiber scanner for TPEM. The simulation results show that the first two resonant frequencies of the PZT fiber scanner are 163.6 Hz and 757.9 Hz, respectively. At the first two resonant points, the PZT fiber scanner scan range are 0.078 mm and 0.68 mm, respectively. Theoretical guidance for frequency selection of the reverse-fixed PZT fiber scanner is provided by these simulation results. © 2023 SPIE.

关键词： Natural frequencies

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Adversarial Learning Network for Recovering Rppg Signals from Low-Resolution Images for Remote Heart Rate Measurement

SSRN

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SSRN 2024年

作者： Zhai, Dezhao Chen, Wei Li, Qinwei Yu, Ming Wu, Hang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Systems Engineering Institute Academy of Military Sciences People's Liberation Army Tianjin300161 China Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin300300 China School of Artificial Intelligence Nankai University Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, which has significant research value and application potential. However, in long-distance vital sign measurement tasks, the low resolution of facial images and insufficient pixels in the facial region severely degrade the quality of the rPPG signals, leading to a sharp decline in HR accuracy. To address this issue, this paper proposes an adversarial learning network for recovering rPPG signals from low-resolution images (ALRSL-HR). First, arbitrary resolution face images are fed into multi-channel network to extract rPPG signal features capturing rich rPPG information, generating rPPG feature blocks, and temporally aggregating for richer feature representation. These features are then inputted into the backbone network to generate adversarial examples of rPPG signals of varying qualities. Finally, a two-stage adversarial loss function is employed to enable the low-quality rPPG signals to learn the periodic characteristics of high-quality rPPG signals, recover the low-quality signals, and narrow the quality gap between rPPG signals from low-resolution images. Extensive experiments were conducted in this paper to validate the effectiveness of our model on the public COHFACE, UBFC-Phys and UBFC-rPPG datasets. The results of the experiments demonstrate that the proposed ALRSL-HR model can effectively recover degraded rPPG signals from low-resolution images. © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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Superior Solar Water Purification Achieved by the Excellent Photothermal Conversion and Thermal Management of Porous Plasmonic Tin Nanoparticles

SSRN

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SSRN 2023年

作者： Gao, Yaping Zhou, Xiaojie Fu, Nuo Su, Sihua Ma, Baopeng Ruan, Qifeng Wu, Di Zhang, Nan Deng, Ziwei Jiang, Ruibin Key Laboratory of Applied Surface and Colloid Chemistry National Ministry of Education Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Engineering Lab for Advanced Energy Technology School of Materials Science and Engineering Shaanxi Normal University Xi’an710119 China Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Harbin Institute of Technology Shenzhen518055 China

Solar photothermal steam generation promises decentralized water purification, but the lack of materials that have strong sunlight absorption in wide spectral range, superior thermal management, large specific surface area as well as good water wettability limits the overall solar water purification efficiency. Herein, porous TiN nanospheres are prepared for solar water purification. The monodispersed porous TiN nanospheres are prepared through the wet-chemistry synthesis of TiO2 and followed nitridation. The prepared porous TiN nanospheres have relatively large specific surface area and very good hydrophilicity. In comparison with solid TiN nanospheres, the porous counterparts have stronger light absorption and better thermal management. The optimum solar evaporation film prepared with the porous TiN nanospheres and filter membrane (TiN/FM) exhibit a reflectance smaller than ~19% in the entire solar spectrum. All these merits of the porous TiN nanospheres give rise to a water evaporation rate of 1.68 kg m-2 h-1 and a solar-steam conversion efficiency of 93.4% under AM 1.5 irradiation. Both seawater and polluted water can be purified to potable water with TiN/FM. Our work not only provides a preparation approach for monodispersed porous TiN nanospheres but also demonstrates the great potential of porous plasmonic nanoparticles for the future development of solar water purification. © 2023, The Authors. All rights reserved.

关键词： Nanospheres

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Automated Assembly by Two-Fingered Microhand for Fabrication of Soft Magnetic Microrobots

Automated Assembly by Two-Fingered Microhand for Fabrication...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yue Zhao Xiaoming Liu Ruixi Wang Dan Liu Masaru Kojima Qiang Huang Tatsuo Arai Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Intelligent Manufacturing Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Materials Engineering Science Osaka University Osaka Japan Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Micro-assembly is an emerging method to fabricate microrobots with multiple modules or particles. However, there is always a lack of a flexible and efficient method to freely create the desired magnetic soft microrobots. In this paper, an automated assembly system based on a two-fingered microhand is presented for fabricating magnetic soft microrobots. Our proposed system can automatically pick and place components to assemble microrobots with a two-fingered micromanipulator, and orient these components through an external magnetic field. The automated assembly has the advantages of high accuracy, high speed, and high success rate. It can endow magnetic microrobots with flexible material selection, arbitrary geometry design, and programable magnetization profile. We can make full use of this system to fabricate multiple magnetic soft microrobots. The experiment results demonstrate that this system can efficiently fabricate microrobots with excellent mechanical properties, which have application potential in robotics, biomedical engineering, and environmental governance.

关键词： Three-dimensional displays Shape Micromanipulators Magnetization Manuals Three-dimensional printing Soft magnetic materials

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The measuring ZMP of self-balancing exoskeleton robot is calibrated by using the neural network

The measuring ZMP of self-balancing exoskeleton robot is cal...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Xu, Yang Xiao, Yang Ma, Yue Zheng, Liangsheng He, Yong Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665436786

The exoskeleton robot is an auxiliary device to help the disabled people walk, and the self-balancing exoskeleton robot is one which is to keep balance without the assistance of external crutches. In order to keep the balance of the self-balancing exoskeleton robot, it is necessary to get the position of the Zero Moment Point by measuring the pressure of the footplate, and make the position of ZMP in range of supporting area. In this experiment, the footplate is used with the double-deck structure, this structure is compared with the single-deck structure, the double-dack structure will not lose the information of the collected ZMP without direct touch with the sensor, and it is lighter than another structure with dozens of sensors. But there is an inevitable structural coupling in the double-deck structure, which makes the ZMP have a large measurement error. In order to solve this problem, a novel idea is proposed, with the help of the powerful processing and learning capabilities of the neural network, four kinds of neural networks are used to calibrate measured position of ZMP so that reducing error of the measured ZMP. By comparing position of the actual ZMP before and after the calibration with the ideal position of ZMP and computing the errors to judge the effect of the calibration. Through experimental comparison, it is concluded that the different neural networks eliminate error of the measured ZMP in different extent. When the GRNN neural network is used to calibrate position of ZMP, the effect is the most ideal. © 2021 IEEE.

关键词： Errors

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Research on Intrusion Detection Based on SVM Fusion Anomaly Data

Research on Intrusion Detection Based on SVM Fusion Anomaly ...

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Sensing, Measurement, Communication and Internet of Things Technologies (SMC-IoT), International Conference on

作者： Tie Tian Yujie Tao Chao Wu Chengjun Feng Yong Luo Dongyang Dai Haoyu Li Key Laboratory of Advanced Manufacturing Technology for Automobile Parts Ministry of Education Chongqing University of Technology Chongqing China Chongqing Engineering Research Center of Research and Testing for Automated Driving System and Intelligent Connected Vehicle China Merchants Testing Vehicle Technology Research Institute Co.Ltd. Chong Qing China

ISBN: (数字)9798350373257

ISBN: (纸本)9798350373264

With the wide application of on-board Ethernet technology, security has become a major challenge that hinders its development. This paper proposes a support vector machine (SVM) based on-board Ethernet intrusion detection method, and uses cross-validation strategy to optimize and verify the model. Therefore, this effective detection method is researched and designed to protect on-board networks from potential network attacks. In this method, the attack data is first constructed by Kali-linux and Brupsuite platform, and the SVM intrusion detection algorithm data set is established by combining the normal data. The SVM is used to classify the training data, and the model robustness and accuracy are improved by cross-validation. The experimental results show that the model shows good results on multiple data sets, and can effectively identify and detect even complex attack types.

关键词： Support vector machines Intrusion detection Training data Ethernet Data models Robustness Sensors

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