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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Song, Ha-Yoon Chung, Hyun-Joon Jeon, Kwang-Woo Kim, Tae-Hwan Kim, Junyoung Yoo, Jin-Yeol Ryu, Jae-Kwan Korea Institute of Robotics Technology Convergence AI Robotics RD Division Seoul06372 Korea Republic of LIG Nex1 Unmanned/Intelligent Robotic Systems RD Seongnam13488 Korea Republic of

ISBN: (纸本)9798350344349

This study proposes an optimization method for sensor placement in lower-limb exoskeleton robots, aiming to improve the accuracy of the Center of Pressure (CoP) estimation during various human activities. Utilizing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, the proposed method identifies an optimal sensor count, as the Root Mean Square Error (RMSE) decelerates when the sensor count exceeds 6. Application of this method resulted in a reduction of the Mean Absolute Error (MAE) to 4.13 mm and 8.92 mm on the mediolateral and anteroposterior axes respectively, a 22.8% improvement in CoP estimation accuracy compared to traditional anatomical methods. Further analysis revealed that weight parameters influence the CoP estimation accuracy, suggesting an enhancement in sensor placement efficiency through the adjustment of individual objectives' significance. The proposed sensor placement optimization method is expected to significantly enhance the performance of lower-limb exoskeleton robots and increase user satisfaction. Moreover, it could substantially contribute to the enhancement of human-computer interaction in robot control by providing a more accurate reflection of the user's intentions. These findings highlight the importance of continuing research in the field of lower-limb exoskeleton robots. © 2024 IEEE.

关键词： Mean square error

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Emotion Classification based on Multi Physiological Signals Using Hybrid Fusion Strategy 30

Emotion Classification based on Multi Physiological Signals ...

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30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Wan, Jiacheng Fang, Yinfeng Guo, Chunsheng Ju, Zhaojie Zhou, Dalin Hangzhou Dianzi University Pinghu Digital Technology Innovation Research Institute School of Communication Engineering Hangzhou China University of Portsmouth Intelligent Systems and Biomedical Robotics Group School of Computing PortsmouthPO1 3HE United Kingdom

ISBN: (纸本)9798350391916

The demand for emotion recognition is increasing not only in daily life but also in professional domains such as disease diagnosis and clinical rehabilitation. By monitoring non-invasive physiological signals of patients, providing timely feedback on their emotional state can significantly aid in patient recovery and subsequent expert treatment evaluation. In this study, we propose a mutual fusion approach utilizing both feature fusion and decision fusion techniques for emotion classification. Firstly, statistical knowledge-based methods were employed to select physiological signals and extract highly correlated and complementary features for feature-level fusion. Subsequently, a similarity weight, based on feature selection and statistical analysis, was incorporated into the decision fusion process. Our proposed method yielded promising results, achieving 82.5% accuracy and 75.2% macro f1 score for three-class classification (neutral vs. stress vs. amusement), 95.4% accuracy and 94.6% macro f1 score for two-class classification (stress vs. non-stress) using publicly available data from the WESAD dataset. These results demonstrate that employing a mutual fusion approach outperforms single fusion methods in emotion classification tasks. © 2024 IEEE.

关键词： Patient treatment

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Characteristics of laser-induced breakdown spectroscopy of liquid slag

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Plasma Science and Technology 2024年第2期26卷 86-93页

作者：董长言于洪霞孙兰香李洋刘修业周平黄少文 State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016People's Republic of China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016People's RepublicofChina Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169People's Republic of China Shenyang University of Technology Shenyang 110870People's Republic of China Shenyang Ligong University Shenyang 1i0142People's Republic of China Shandong Iron and Steel Company Ltd. Jinan 271105People's Republic of China

Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production. To investigate the key factors that affect the online measurement of refined slag using laser-induced breakdown spectroscopy(LIBS), this study examined the effects of slag composition and temperature on the intensity and stability of the LIBS spectra. The experimental temperature was controlled at three levels: 1350℃, 1400℃, and 1450℃. The results showed that slag composition and temperature significantly affected the intensity and stability of the LIBS spectra. Increasing the Fe content and temperature in the slag reduces its viscosity, resulting in an enhanced intensity and stability of the LIBS spectra. Additionally, 42 refined slag samples were quantitatively analyzed for Fe, Si, Ca, Mg, Al, and Mn at 1350℃, 1400℃, and 1450℃.The normalized full spectrum combined with partial least squares(PLS) quantification modeling was used, using the Ca Ⅱ 317.91 nm spectral line as an internal standard. The results show that using the internal standard normalization method can significantly reduce the influence of spectral fluctuations. Meanwhile, a temperature of 1450℃ has been found to yield superior results compared to both 1350℃ and 1400℃, and it is advantageous to conduct a quantitative analysis of the slag when it is in a “water-like” state with low viscosity.

关键词： laser-induced breakdown spectroscopy(LIBS) slag temperature composition viscosity internal standard normalization partial least squares(PLS)

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Zero-Shot Kidney Stone Segmentation Based on Segmentation Anything Model for Robotic-Assisted Endoscope Navigation 16th

Zero-Shot Kidney Stone Segmentation Based on Segmentation A...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Meng, Hui Chen, Lingkai Zhu, Shiqiang Fei, Chentao Zhang, Yong Zheng, Miaojuan Han, Jianda Song, Wei School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences 1 Sub-lane Xiangshan Hangzhou310024 China Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Hangzhou ElephantChain Network Technology Co. Ltd. Hangzhou China Faculty of Mechanical Engineering and Automation Zhejiang Sci-Tech University Hangzhou310018 China College of Artificial Intelligence and Tianjin Key Laboratory of Intelligent Robotics Institute of Robotics and Automatic Information Systems Nankai University Tianjin300350 China

ISBN: (纸本)9789819964888

Biomedical robotics has had a significant impact on flexible ureteroscopy (FURS) for the management of nephrolithiasis. The biomedical robots provide a suitable and safe platform for FURS. However, operating the robots expertly still represents a challenge for surgeons. Vision-based navigation is an effective means to simplify the operations of robots by surgeons, which has great potential to improve surgical efficiency. To achieve vision-based navigation, the localization of kidney stones in endoscopic images is the first step. In this paper, we propose a stone segmentation method (SSM) based on zero-shot learning, which consists of segmentation anything model (SAM), an image preprocessing module, and a mask postprocessing module. The SAM segments all objects in endoscopic images, which lack semantic information. The preprocessing module is designed to suppress interference of reflective area in endoscopic images, and the mask postprocessing module is used to filter noise masks. The experimental results on our collected endoscopic images indicate that our method achieves close accuracy on stone segmentation compared with supervised U-Net and VGG_U-Net, which significantly outperforms the Ostu and the SAM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Endoscopy

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A Novel Deep Learning Representation for Industrial Control System Data

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intelligent Automation & Soft Computing 2023年第6期36卷 2703-2717页

作者： Bowen Zhang Yanbo Shi Jianming Zhao Tianyu Wang Kaidi Wang Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang110016China Shenyang Institute of Automation Chinese Academy of SciencesShenyang110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110169China Shenyang Aircraft Corporation Shenyang110850China Molarray Research TorontoL4B3K1Canada

Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensional features,data heterogeneity,and data noise due to the diversity of data dimensions,formats and noise of sensors,controllers and ***,a novel unsupervised learn-ing autoencoder model is proposed for ICS data in this *** traditional methods only capture the linear correlations of ICS features,our deep industrial representation learning model(DIRL)based on a convolutional neural network can mine high-order features,thus solving the problem of high-dimensional and heterogeneous ICS *** addition,an unsupervised denoising autoencoder is introduced for noisy ICS data in *** the denoising autoencoder allows the model to better mitigate the sensor noise *** this way,the represen-tative features learned by DIRL could help to evaluate the safety state of ICSs more *** tested our model with absolute and relative accuracy experi-ments on two large-scale ICS *** with other popular methods,DIRL showed advantages in four common indicators of AI algorithms:accuracy,precision,recall,and *** study contributes to the effective analysis of large-scale ICS data,which promotes the stable operation of ICSs.

关键词： Industrialcontrolsystem machinelearning deeplearning autoencoder

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Optimal Placement of Foot Pressure Sensors for Lower-Limb Exoskeletons Based on Multi-Objective Particle Swarm Optimization Algorithm

Optimal Placement of Foot Pressure Sensors for Lower-Limb Ex...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Ha-Yoon Song Hyun-Joon Chung Kwang-Woo Jeon Tae-Hwan Kim Junyoung Kim Jin-Yeol Yoo Jae-Kwan Ryu AI Robotics R&D Division Korea Institute of Robotics & Technology Convergence Seoul Republic of Korea Unmanned/Intelligent Robotic Systems R&D LIG Nex1 Seongnam Republic of Korea

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A Sensing Platform to Monitor Sleep Efficiency 11th

A Sensing Platform to Monitor Sleep Efficiency

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11th Italian Forum on Ambient Assisted Living, ForItAAL 2020

作者： Crivello, Antonino La Rosa, Davide Wilhelm, Elisabeth Palumbo, Filippo 1 Via G. Moruzzi Pisa56124 Italy Department of Health Sciences and Technology Istitute of Robotics and Intelligent Systems Sensory Motor Systems Lab ETH Zurich Zurich8092 Switzerland Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands

ISBN: (纸本)9783031088377

Sleep plays a fundamental role in the human life. Sleep research is mainly focused on the understanding of the sleep patterns, stages and duration. An accurate sleep monitoring can detect early signs of sleep deprivation and insomnia consequentially implementing mechanisms for preventing and overcoming these problems. Recently, sleep monitoring has been achieved using wearable technologies, able to analyse also the body movements, but old people can encounter some difficulties in using and maintaining these devices. In this paper, we propose an unobtrusive sensing platform able to analyze body movements, infer sleep duration and awakenings occurred along the night, and evaluating the sleep efficiency index. To prove the feasibility of the suggested method we did a pilot trial in which several healthy users have been involved. The sensors were installed within the bed and, on each day, each user was administered with the Groningen Sleep Quality Scale questionnaire to evaluate the user’s perceived sleep quality. Finally, we show potential correlation between a perceived evaluation with an objective index as the sleep efficiency. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sleep research

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CS-UNet: A LightWeight UNet Model Based On Context Information 17th

CS-UNet: A LightWeight UNet Model Based On Context Informati...

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17th International Conference on intelligent robotics and Applications, ICIRA 2024

作者： Shen, Chenxin Fang, Yinfeng Yu, Xixia Guo, Chunsheng Ju, Zhaojie Pinghu Digital Technology Innovation Research Institute Hangzhou Dianzi University Zhejiang Pinghu China School of Communication Engineering Hangzhou Dianzi University Zhejiang Hangzhou China Department of Internal Medicine Xinhua Hospital of Zhejiang Province The Second Affiliated Hospital Zhejiang Chinese Medical University Zhejiang Hangzhou310003 China Intelligent Systems and Biomedical Robotics Group School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom

ISBN: (纸本)9789819607761

Since the introduction of UNet in 2015,U-shaped architecture has become an important paradigm in the field of medical image segmentation. However, due to the inherent local limitations of convolutions, a fully convolutional segmentation networks with U-shaped architecture cannot effectively utilize global context information. Although the combination of transformers and CNNs can address these issues, it also brings larger computational cost. At the same time, Due to the specificity of medical scene, scarce medical resources will be an obstacle to the application of transformers in the medical field, which is well adapted by the inductive bias in lightweight networks. For this reason we propose CS-UNet, a lightweight fully convolutional model that enables fast image segmentation in real medical scenarios. In this model, we reconstruct the encoder and decoder to generate a dense receptive domain to extract contextual information. We evaluate CS-UNet on five different types of medical image datasets, and found that CS-UNet excelled in segmentation performance. While ensuring high accuracy, CS-UNet has faster computation speed and smaller model parameters, which achieves a better balance between performance and computation cost, and is less constrained by the environment as well as the equipment in practical application scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Parameter-Sharing-Based Average-Consensus Time Synchronization in IoT Networks

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IEEE Internet of Things Journal 2023年第9期10卷 8215-8227页

作者： Shi, Fanrong Yang, Simon X. Mukherjee, Mithun Jiang, Hong Da Costa, Daniel Benevides Wong, Wing-Kwong Southwest University of Science and Technology Robot Technology Used for Special Environment Key Laboratory of Sichuan Province School of Information Engineering Mianyang621010 China School of Engineering University of Guelph Advanced Robotics and Intelligent Systems Laboratory GuelphONN1G 2W1 Canada Nanjing University of Information Science and Technology School of Artificial Intelligence School of Future Technology Nanjing210044 China Technology Innovation Institute Abu Dhabi United Arab Emirates National Yunlin University of Science and Technology Future Technology Research Center Douliu64002 Taiwan

Average-consensus protocol is one of the ways to develop distributed time-synchronization algorithms in Internet of Things (IoT) networks. However, the large number of iteration leads to a common time notion issue in nodes. This poses a critical challenge in the convergence of the time-synchronization algorithm and resulting asymptotic convergence in the average consensus protocol. In this article, a parameter-sharing-based average-consensus time-synchronization (PACTS) algorithm is proposed. For fast convergence, the proposed PACTS quickly forwards the time information to multihop nodes and employs multihop average-consensus instead of single-hop average consensus. Specifically, a node asynchronously and periodically broadcasts the relative clock offset estimation of neighbors with its local time information. Meanwhile, the relative clock offset estimation of the multihop node is calculated and used to estimate the average value. Consequently, an average consensus among local multihop nodes is obtained. As a result, the iteration number and convergence time are significantly reduced over the network. Finally, the experimental results indicate that the proposed PACTS algorithm has low complexity, high accuracy, and quick convergence. © 2023 IEEE.

关键词： Topology

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An Improved Method for Small Target Recognition Based on Faster RCNN 17th

An Improved Method for Small Target Recognition Based on Fas...

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17th Chinese intelligent systems Conference, CISC 2021

作者： Liu, Qun-po Wang, Qi-jing Hanajima, Naohiko Su, Bo Department of Robotics Engineering Henan Polytechnic University 2001 Century Avenue Jiaozuo Henan454003 China Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment 2001 Century Avenue Jiaozuo Henan454003 China College of Information and Systems Muroran Institute of Technology 27-1 Mizumoto-cho Muroran-shi Hokkaido050-8585 Japan

ISBN: (纸本)9789811663239

As one of the most challenging tasks in the field of deep learning, target detection has achieved remarkable results. The existing algorithms have achieved excellent results in the detection of large and medium-sized targets, but the detection accuracy of small targets is not ideal because of their small size and weak features. A small target recognition method based on improved Faster_Rcnn that aims at the problem of low accuracy in small target detection is proposed in this paper. Since the ROI Pooling method in Faster Rcnn might cause quantization errors in the operation process, resulting in inaccurate positioning and other problems during detection, an improved ROI Align method is adopted to eliminate the quantization errors. The cell public data set is used for the experiment, the results show that the average recognition accuracy of the recognition model with improved algorithm is 92.11%, which is an increase of 7%. The method also obtains good effect on NWPU VHR-10 data set, which indicates that it has good universality. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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