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International Conference on Manipulation, Automation and robotics at Small Scales (MARSS)

作者： Yuzhu Liu Ruimin Li Jinyu Qiu Zuqi Wang Minghui Li Xin Zhao Qili Zhao National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Tianjin Key Laboratory of Intelligent Robotic (tikLIR) Institute of Robotics and Automatic Information System(IRAIS). Nankai University. Tianjin China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

ISBN: (数字)9798350376807

ISBN: (纸本)9798350376814

The inside-out patch clamp technique is widely used in brain science and neuroscience research because of its ability to detect the extremely weak currents flowing through a single ion channel. In the inside-out patch clamp operations, a micrometer-scale dimensions pipette is used to aspirate part of the cell membrane, creating a giga omega-scale seal (usually termed as gigaseal) between the cell membrane and the inner wall of the micropipette to suppress the environmental electromagnetic noise. The aspirated cell membrane is then gently pulled away from the cell to form a vesicle, which is subsequently exposed to low-calcium liquid or air to induce its rupture, forming an inside-out seal for signal detection on a single ion channel. Current manual operations of inside-out patch clamp are complex and require a high level of operator expertise. Moreover, existing robotic inside-out patch clamp technologies are usually applicable to suspension cells, limiting the widespread application of this technique. This paper proposed a robotic inside-out recording patch clamp technique based on vesicle rupture detection to address these limitations. Firstly, impedance models were established to detect the state of the vesicle before and after the rupture. Further, the detection of vesicle rupture was performed based on transmembrane current responses under step inputs. Based on the above work, a robotic inside-out recording patch clamp method based on vesicle rupture detection was established in this paper. Experimental results demonstrated the effectiveness of the proposed method in achieving successful inside-out recording patch clamp mode on adherent HEK-293 cells with a success rate of 60 %, laying the foundation for subsequent studies on single ion channel functionality.

关键词： Vesicles Neuroscience Liquids Limiting Seals Ions Recording

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Variation of sEMG with Muscle Fatigue for Lower Limb Stroke Patient During Walking

Variation of sEMG with Muscle Fatigue for Lower Limb Stroke ...

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IEEE International Conference on robotics and Biomimetics

作者： Wenze Shang Ruisen Huang Wei-Hsin Liao Xinyu Wu Fei Gao Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

sEMG signal is widely used and explored in control strategies of powered assistive human-robot interaction systems due to its non-invasive nature and ability to estimate motion intention well. However, prolonged use of the sEMG-based systems leads to muscle fatigue, contributing to unstable sEMG signals and regarding the performance of sEMG-based control strategies. In this study, three subjects, including two healthy subjects and one stroke subject, participated in our experiment, and the time domain, frequency domain, and complex features extracted from their $sEMG$ signals were utilized to evaluate muscle fatigue. The results indicate that $sEMG$ from the stroke side of the stroke subject contains more non-linear and chaotic parts, and the statistical differences between different trials are more significant, indicating that stroke has a more significant impact on the stroke side of the stroke subject. This study suggests that RMS in the stroke side of stroke subjects is less than that in others, whereas FuzzyEn in the stroke side of stroke subjects is greater than that in others, indicating that stroke leads to different motion patterns and more susceptibility to muscle fatigue.

关键词： Legged locomotion Limbs Frequency-domain analysis Biomimetics Human-robot interaction Muscles Fatigue Feature extraction Time-domain analysis Robots

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Natural Gas Load Forecasting Based on Multi-Stage Model Identification

Natural Gas Load Forecasting Based on Multi-Stage Model Iden...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Pengbin Zhang Hongrui Wang Kunpeng Xing Mingzhe Yuan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China GUANGZHOU INSTITUTE OF INDUSTRIAL INTELLIGENCE Guangzhou China China Nuclear Power Engineering Co. Ltd. Shenzhen China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

Natural gas load forecasting is crucial for the safe operation of urban natural gas. Based on the system identification method, the short-term natural gas load forecasting is studied in the paper. Employing a multi-stage identification framework, the initial values of the model parameters using the least squares and the instrumental variables method, while the model parameters are further optimized by the prediction error method to improve the accuracy of the model identification. The proposed method is validated by the simulation based on the public natural gas data in the U.S., demonstrating the algorithm's effectiveness in forecasting the daily natural gas load effectively.

关键词： Load forecasting Simulation Load shedding Predictive models Prediction algorithms system identification Reliability Forecasting Natural gas Load modeling

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A Uniform Representation Model for Oct-Based Fingerprint Presentation Attack Detection and Reconstruction

SSRN

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SSRN 2023年

作者： Zhang, Wentian Liu, Haozhe Liu, Feng Ramachandra, Raghavendra The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Gjøvik2818 Norway

The technology of optical coherence tomography (OCT) to fingerprint imaging opens up a new research potential for fingerprint recognition owing to its ability to capture depth information of the skin layers. Developing robust and high security Automated Fingerprint Recognition systems (AFRSs) are possible if the depth information can be fully utilized. However, in existing studies, Presentation Attack Detection (PAD) and subsurface fingerprint reconstruction based on depth information are treated as two independent branches, resulting in high computation and complexity of AFRS building. Thus, this paper proposes a uniform representation model for OCT-based fingerprint PAD and subsurface fingerprint reconstruction. Firstly, we design a novel semantic segmentation network which only trained by real finger slices of OCT-based fingerprints to extract multiple subsurface structures from those slices (also known as B-scans). The latent codes derived from the network are directly used to effectively detect the PA since they contain abundant subsurface biological information, which is independent with PA materials and has strong robustness for unknown PAs. Meanwhile, the segmented subsurface structures are adopted to reconstruct multiple subsurface 2D fingerprints. Recognition can be easily achieved by using existing mature technologies based on traditional 2D fingerprints. Extensive experiments are carried on our own established database, which is the largest public OCT-based fingerprint database with 2449 volumes. In PAD task, our method can improve 0.33% Acc from the state-of-the-art method. For reconstruction performance, our method achieves the best performance with 0.834 mean Intersection of Union (mIOU) and 0.937 Pixel Accuracy (PA). By comparing with the recognition performance on surface 2D fingerprints (e.g. commercial and high resolution), the effectiveness of our proposed method on high quality subsurface fingerprint reconstruction is further proved. © 2023, The

关键词： Optical tomography

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A Toeplitz Prior-Based Deep Learning Framework for DOA Estimation with Unknown Mutual Coupling

A Toeplitz Prior-Based Deep Learning Framework for DOA Estim...

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European Signal Processing Conference (EUSIPCO)

作者： Zhuoqian Jiang Jingmin Xin Weiliang Zuo Nanning Zheng Akira Sano Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China National Engineering Laboratory for Visual Information Processing and Applications Xi'an Jiaotong University Xi'an China Shunan Academy of Artificial Intelligence Ningbo Zhejiang China Department of System Design Engineering Keio University Yokohama Japan

In this paper, we explore the problem of direction-of-arrival (DOA) estimation with unknown mutual coupling using a deep learning (DL) framework which is based on Toeplitz prior. First, for source number estimation, we model it as a multi- label classification task and build a source number detection network (SNDN) to learn relevant information in the real sample covariance matrix. Next, taking full advantage of the Toeplitz structure, an ideal covariance reconstruction network (ICRN) is proposed to recover the ideal covariance matrix free from mutual coupling and noise interference. Furthermore, we design a database to store the parameters of ICRN after training on different numbers of sources, and its role is to load the corresponding parameters for ICRN according to the detection results of SNDN. Finally, the DOAs can be easily estimated from the restored covariance matrix by the MUSIC. The simulation results show our proposed approach not only outperforms the existing classical methods, but in some cases its DOA estimation accuracy can even exceed the Cramer-Rao Lower Bound.

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Lateral Walking Gait Phase Detection with Data Optimization for a Hip Exoskeleton in Resistance Exercise

Lateral Walking Gait Phase Detection with Data Optimization ...

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IEEE Workshop on Advanced robotics and its Social Impacts, ARSO

作者： Wujing Cao Xinbo Zhu Rui Xu Changyu Li Hongliu Yu Mingming Zhang Haoyong Yu Worawarit Kobsiriphat Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society and SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems. College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jining Zhongke Intelligent Technology Co. Ltd. University of Shanghai for Science and Technology Southern University of Science and Technology Department of Biomedical Engineering National University of Singapore. National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The accurate detection of lateral walking gait phases is essential for the effective implementation of hip exoskeleton systems in lateral resistance walking exercises. However, limitations in hardware, such as memory and computing power, in the microcontrollers of wearable devices, significantly impact the size and training speed of the lateral walking gait phase detection model, thus affecting the exoskeleton system. This study proposes a data optimization algorithm that utilizes K-means clustering combined with commonly used machine learning algorithms, including Random Forests (RF), Support Vector Machines (SVM), and k-Nearest Neighbors (KNN), to reduce both the training time and size of the model. With the implementation of this algorithm, the training time and model size of RF, SVM, and KNN-based models are reduced by 89.6%, 99.8%, and 97.9%, and 89.6%, 92.7%, and 95.2% respectively. The corresponding gait phase prediction accuracy experiences only a slight decrease of 1.6%, 1.7%, and 2.8% respectively. This method ensures a sufficiently high accuracy in detecting lateral walking gait phases while simultaneously achieving higher efficiency and a smaller model size.

关键词： Legged locomotion Training Support vector machines Solid modeling Machine learning algorithms Accuracy Exoskeletons

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Design of an experimental setup for delivering intracortical microstimulation in vivo via Spiking Neural Network

Design of an experimental setup for delivering intracortical...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： M. Di Florio M. Carè R. Beaubois V. R. Cota F. Barban T. Levi M. Chiappalone Department of Informatics Bioengineering Robotics System Engineering (DIBRIS) University of Genova Genova Italy Rehab Technologies IIT-INAIL Lab Istituto Italiano di Tecnologia Genova Italy IMS Laboratory CNRS UMR 5218 University of Bordeaux Talence France

Electroceutical approaches for the treatment of neurological disorders, such as stroke, can take advantage of neuromorphic engineering, to develop devices able to achieve a seamless interaction with the neural system. This paper illustrates the development and test of a hardware-based Spiking Neural Network (SNN) to deliver neural-like stimulation patterns in an open-loop fashion. Neurons in the SNN have been designed by following the Hodgkin-Huxley formalism, with parameters taken from neuroscientific literature. We then built the set-up to deliver the SNN-driven stimulation in vivo. We used deeply anesthetized healthy rats to test the potential effect of the SNN-driven stimulation. We analyzed the neuronal firing activity pre- and post-stimulation in both the primary somatosensory and the rostral forelimb area. Our results showed that the SNN-based neurostimulation was able increase the spontaneous level of neuronal firing at both monitored locations, as found in the literature only for closed-loop stimulation. This study represents the first step towards translating the use of neuromorphic-based devices into clinical *** Relevance— Stroke represents one of the leading causes of long-term disability and death worldwide. Intracortical microstimulation is an effective approach for restoring lost sensory motor integration by promoting plasticity among the affected brain areas. Stimulation delivered via neuromorphic-based open-loop systems (i.e. neuromorphic prostheses) can pave the way to novel electroceutical strategies for brain repair.

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Efficient Extended Ant Colony Optimization for Capacitated Electric Vehicle Routing

Efficient Extended Ant Colony Optimization for Capacitated E...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Bo-Cheng Lin Xiao-Fang Liu Yi Mei Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9781665487696

With the rapid development of electric vehicle (EV) freight industry, the capacitated electric vehicle routing problem (CEVRP) becomes a challenging and significant prob-lem. CEVRP considers both the cargo capacity of EVs and the battery capacity of EVs, resulting in the difficulty of the construction of feasible solutions. In this paper, we propose an extended ant colony optimization (EACO) algorithm, which uses a pheromone matrix to record the routing information of customers and charging stations for solution construction. Two route construction methods: coarse route construction method and fine route construction method, are developed to generate a customer service sequence with recharging stations. In the coarse route construction method, cargo capacity constraints are ignored and the selection of recharging stations depends on the remaining battery power after the routing of customers only. In the fine routing construction method, the cargo capacities are considered to better evaluate the remaining battery power of vehicles if a return to depots is necessary in the current construction step. After the construction of routing, the giant tour is split and local search is applied to improve solution quality. Experiments show that the proposed EACO is moderate effective. The fine routing construction shows higher exploration ability and performs better than coarse routine construction.

关键词： Industries Ant colony optimization Customer services Charging stations Routing Electric vehicles Batteries

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A Sample Repetitive Manipulation Mechanism (SRMM) for Lunar Regolith In-Situ Analysis: Design and Validation

A Sample Repetitive Manipulation Mechanism (SRMM) for Lunar ...

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IEEE International Conference on robotics and Biomimetics

作者： Yi Liu Junyue Tang Yafang Liu Gongbo Ma Feng Sun Ye Li Shengyuan Jiang Shenyang University of Technology Shenyang P. R. China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin P. R. China Beijing Institute of Spacecraft System Engineering Beijing P. R. China Beijing P. R. China

To exactly detect the water ice of the South Pole of the moon, a lunar regolith in-situ analysis payload deploying a mass spectrometer is proposed for China future lunar exploration missions. In order to receive the lunar regolith sample from a robotic arm with a soil sampler and transfer it into a furnace for further analysis, a sample manipulation mechanism is required during the above work flow. To solve the problems of adapting the sampler’s docking accuracy, receiving and transferring two different types of lunar soil sample under times of in-situ analysis, etc., a sample repetitive manipulation mechanism (SRMM) is proposed in this paper. By using a floating adjustable docking components and a flexible hopper, two types of encapsulated regolith sample and bulk material sample can be received with minimal sample loss, respectively. In order to receive and transfer two types of samples multiple times, two sample receiving methods have been designed that can be repeatedly transferred. A worm and worm wheel combined with a ball screw is designed in SRMM. To verify the above mechanism design, validation experiments were conducted. It indicates that this novel SRMM can be deployed in the future mission after further environmental tests.

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CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

arXiv

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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