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检索条件"机构=Institute of Robotics and Automation Information Systems"

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Machine Learning in Predicting the Impact of Filter Parameters on the Accuracy of Biosignal Measurement

Machine Learning in Predicting the Impact of Filter Paramete...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Yeldos A. Altay Raisa K. Uskenbayeva Abu-Alim E. Ayazbay Alexey V. Fedorov Department of Robotics and Technical Means of Automation Satbayev University Almaty Kazakhstan Institute of Automation and Information Technologies Vice-Rector Satbayev University Almaty Kazakhstan Electronic engineering Almaty University of Power Engineering and Telecomunications Almaty Kazakhstan Faculty of Control Systems and Robotics ITMO University Saint-Petersburg Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

In this scientific article under consideration, a machine learning model has been developed that allows predicting the impact of filter parameters on the accuracy of measuring a biosignal - an ECG (electrocardiography) signal. For the first time, quantitative results have shown the degree of influence and the error introduced into the measured biosignal and described through dependence. The results of assessing the obtained relationship of indicators demonstrate an inverse correlation relationship that characterizes a decrease in the accuracy of measurement at the output of a high-order digital filtering system. The machine learning model is based on the linear regression method the dependence of which is approximated by the method based on least squares and visualized using a scattergram. The adequacy of the developed machine learning model was confirmed by calculating the coefficient of determination and checking its statistical significance. The developed model can be used to create new or improve existing biomedical data processing systems to increase the reliability of the results.

关键词： Accuracy Shape Filtering Biological system modeling Machine learning Biomedical measurement Electrocardiography Distortion Distortion measurement Digital filters

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Motion Capture and Gait Analysis of Ants with Leg Injuries

Motion Capture and Gait Analysis of Ants with Leg Injuries

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Chinese Control Conference (CCC)

作者： Ming Zeng Yuanhao Li Chang Meng Shutong Zhong Zhijing Wang Feng Zhao Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Institute of Robotics and Automation Systems School of Electrical and Information Engineering Tianjin University Tianjin

Many insects in nature, such as ants, recover quickly after sustaining leg injuries. If this capability could be imparted to multi-legged robots, it would drastically enhance their ability to resist destruction and adapt to the environment. In this paper, the Camponotus Japonicus is used as the research object, and its locomotive patterns are studied under different leg-loss conditions. To facilitate this, a high-speed acquisition system, with a dual-view (bottom view and side view) setup, is designed to analyze the ant's linear gaits. Subsequently, the pivotal gait characteristics of the injured ants are extracted by keyframe annotation technology, including the gait phase diagram, duty factor, angle before support, angle before swing, and leg swing range. Lastly, according to the obtained gait characteristics, the general and specific laws of gait adjustment in the absence of one or two legs are systematically analyzed, which provides the basis for parametric settings and strategy optimization of robotic injured gait self-adjustment.

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Feature Space Augmentation and Old Class Space Preservation for Class Incremental Learning

Feature Space Augmentation and Old Class Space Preservation ...

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Chinese Control Conference (CCC)

作者： Ming Zeng Siying Li Jiazhe Liu Yuanhao Li Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Institute of Robotics and Automation Systems School of Electrical and Information Engineering Tianjin University Tianjin

The ability of continuously learning new knowledge without forgetting old ones is crucial to adapt to an ever-changing world. This scenario becomes more challenging when the previous data are not available. Current class incremental learning (CIL) approaches tend to incorporate new classes with backward compatibility, i.e., maintaining discriminability of old ones. By contrast, we focus on the extensibility and compatibility for future new classes in the early stage. Our proposed method achieves this by augmenting feature space. In detail, a large number of pseudo-new classes are generated via real example mixture and then train the initial spatially augmented model using pseudo-new classes and the base classes. Besides, considering the fact that it is impossible to maintain backward compatibility with only one prototype for each old class, the updated model also needs to preserve old class space in incremental stages. We employ a certain perturbation to the old class prototypes to effectively avoid old class space from being over-squeezed by the samples of new classes. The experiments on CIFAR-100 and ImageNet-Subset (100 classes) demonstrate that our method substantially reduces the overlap of old and new classes, outperforming state-of-the-art various baselines.

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The System of Coordinated Operation of Two Underwater Vehicles in a Complex Environment

The System of Coordinated Operation of Two Underwater Vehicl...

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Ocean Studies (ICOS), International Conference on

作者： Vladimir Filaretov Alexey Katsurin Laboratory of Robotic Systems Laboratory of Automation and Robotics Institute of Automation and Control Processes FEB RAS Vladivostok State University Vladivostok Russia Department of Automation and Robotics Laboratory of Intelligent Information Systems for Marine Robots Far Eastern Federal University M.D. Ageev Institute for Marine Technology Problems FEB RAS Vladivostok Russia

The paper discusses methods of coordinated operation of two autonomous underwater vehicles, one of which is equipped with a multi-link manipulator and a vision system, and the second - auxiliary - only a vision system. These methods ensure the joint operation of two autonomous underwater vehicles both in the mode of semi-automatic positional remote control, and in the mode of automatic execution of operations with various objects. At the same time, work is considered in situations when the vision system of the main underwater vehicle does not allow observing the object of work, which is nevertheless located in the working area of the manipulator. In this case, the auxiliary inspection underwater vehicle with its own vision system is used to monitor the object. In the semi-automatic remote control mode, the manipulator automatically takes into account the current spatial orientation of the optical axis of the vision system of the inspection underwater vehicle, as well as the spatial orientations of both underwater vehicles at yaw angles in the absolute coordinate system. And in automatic mode, an accurate transfer of the spatial location and orientation of the object, which are determined using the vision system of the inspection underwater vehicle, is additionally provided to the associated coordinate system of the main underwater vehicle. It is possible to identify and eliminate errors that occur during the operation of the vision system and navigation systems of both underwater vehicles. The results of experimental studies have confirmed the operability of the proposed methods.

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2D-3D Object Shape Alignment for Camera-Object Pose Compensation in Object-Visual SLAM

2D-3D Object Shape Alignment for Camera-Object Pose Compensa...

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IEEE International Conference on robotics and automation (ICRA)

作者： Hanyeol Lee Jae Hyung Jung Chan Gook Park Department of Aerospace Engineering and Automation and Systems Research Institute Seoul Natioinal University Seoul South Korea Smart Robotics Lab School of Computation Information and Technology Technical University of Munich Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this study, we propose an object shape alignment method through a robust optimization scheme for 6-degrees-of-freedom (DOF) object pose compensation. Although the pose estimation of the 3D object by the camera has been rapidly improved in recent years with the development of deep learning, the estimate still contains errors due to several factors. To compensate for this, we perform a shape alignment between the 2D segmentation of the object and the projection of the 3D object in the image plane. To avoid convergence to a local minimum in nonlinear optimization, we separate the pose into translation and rotation. This approach derives the optimization of a linear form in terms of a translation with reduced computational cost. For the rotation, the parallel optimization is performed with multiple initial values, reflecting to the uncertainty of an initial value. We formulate an invariant extended Kalman filter (EKF)-based object-visual simultaneous localization and mapping (SLAM) with a camera-object relative pose as the measurement model. To verify the performance of the proposed algorithm, we present the improved results of camera-object relative pose accuracy and localization and mapping accuracy in the several sequences of YCB-video dataset.

关键词： Location awareness Image segmentation Three-dimensional displays Simultaneous localization and mapping Uncertainty Accuracy Shape

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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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Usability Evaluation of FURS Robot Control Panel Interface Design Based on SUS 16th

Usability Evaluation of FURS Robot Control Panel Interface D...

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

作者： You, Yong Wei, Lai Zhu, Shiqiang Yu, Yongwu Jiang, Yunxiang Liu, Xiaolei Han, Jianda Chen, Lingkai Song, Wei Design Innovation Center China Academy of Art Hangzhou310024 China L’École de Design Nantes Atlantique China Academy of Art Hangzhou310024 China Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Hangzhou Yunphant Network Technology Co. Ltd. Hangzhou China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 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)9789819964949

The objective of this study is to compare the interfaces of two FURS (Flexible Ureterorenoscopy) robotic operation consoles across generations. Analyzing functional requirements during surgery, the first-generation interface was designed. Evaluation of the first-generation interface, combined with user needs and design principles, informed the design of the second-generation interface, which aimed to improve the usability of the console during surgery. Both interface generations were assessed for usability and user experience using the System Usability Scale (SUS).The results indicate improvement in usability and user satisfaction following interface iteration. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Surgery

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BLIP-Adapter: Bridging Vision-Language Models with Adapters for Generalizable Face Anti-spoofing 18

BLIP-Adapter: Bridging Vision-Language Models with Adapters ...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Zhang, Bin Zhu, Xiangyu Gao, Li Zhang, Xiaoyu Lei, Zhen Chinese Academy of Sciences Institute of Information Engineering China University of Chinese Academy of Sciences School of Cyber Security China China Mobile Financial Technology Co. Ltd. China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation China University of Chinese Academy of Sciences School of Artificial Intelligence China Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Hong Kong

ISBN: (纸本)9798350364132

Face anti-spoofing is essential for ensuring the security of facial recognition systems against spoofing attacks. Recent methods have transferred Vision-Language models to face anti-spoofing (e.g., FLIP and CLIPC8), demonstrating that learning perception from supervision in natural language can enhance the model's detection performance. However, such methods exhibit limited depth in the interaction between images and texts, resulting in poor performance on fine-grained understanding tasks such as face anti-spoofing. Besides, the lack of diversity in image-text pairs for face anti-spoofing further hinders such methods from playing their best. To address these issues, we propose a novel fine-tuning strategy for Vision-Language models in face anti-spoofing. This strategy introduces the Bootstrapping Language-Image Pre-training model (BLIP), known for its novel interaction mechanisms and superior image-text comprehension, to construct a more generalized feature representation for face anti-spoofing. Furthermore, we propose an Adapter module for the text branch to reduce the negative impact of insufficient data diversity and catastrophic forgetting. Extensive experiments conducted on various cross-domain testing benchmarks demonstrate the significant superiority of our method over the state-of-the-art, highlighting its effectiveness and robustness. © 2024 IEEE.

关键词： Visual languages

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AGV State Monitoring Based on Global and First-person View Fusion in Digital Workshops 12

AGV State Monitoring Based on Global and First-person View F...

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12th International Conference on CYBER Technology in automation, Control, and Intelligent systems, CYBER 2022

作者： Zhang, Sichao Liang, Wei Li, Gengyu Yuan, Xudong An, Haibo Zhang, Yinlong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems 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 Science Beijing100049 China School of Information and Control Engineering Shenyang Jianzhu University Shenyang110168 China Science and Technology on Information Systems Engineering Laboratory The 28th Research Institute of Cetc Jiangsu Nanjing210007 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

AGV state monitoring is becoming increasingly important for production system safety in digital workshops. This paper develops a global and first-person view fusion to monitor the AGV (Automated Guided Vehicle) running state in the complex digital workshops. Based on the affine transformation model, the AGV running states, i.e., position, velocity and orientation are estimated by the global camera with the assistance of ArUco markers, which are pasted on the front side of the AGV. Besides, the first-person view camera is used to implement pedestrian detection and distance measurement based on the YOLOv3 model. It is noticeable that the pitch angle is considered for distance measurement, and an improved triangular model for distance measurement is proposed. The effectiveness of the proposed method has been verified in our built platform. © 2022 IEEE.

关键词： Cameras

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A Novel Gas Classification Method Based on The Improved GasNet

A Novel Gas Classification Method Based on The Improved GasN...

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第43届中国控制会议

作者： Xiaolin Qin Lei Cheng Xiang Tao Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology School of Information Science and Engineering Wuhan University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Accurately and promptly identifying harmful gases in the environment can effectively prevent many accidents. Most existing pattern recognition methods are focused on addressing gas classification problems in static scenarios, but the real world is a dynamic and changing environment. Therefore, researching gas classification in continuous learning scenarios will better meet the needs of suddenly emerging gas classification tasks in real-world environments. To address this issue, an incremental learning model was introduced into gas classification, and an improved deep convolutional network was proposed for gas image feature extraction. The proposed model and other baseline methods experimented on an open-source gas dataset. The proposed model reduces knowledge loss due to catastrophic forgetting and effectively classifies the 10 gas samples in the dataset in continuous learning scenarios compared to non-incremental methods. It outperforms other baseline incremental methods by optimizing the network's shortcut connections, making the feature extraction network easier to train and improving gas classification performance. In dynamic learning scenarios, the proposed model achieved a classification accuracy of 86.30% for 10 gas *** experimental results demonstrate that the proposed method effectively mitigates catastrophic forgetting interference and completes gas classification tasks in continuous learning scenarios.

关键词： Gas classification Incremental learning Shortcut connection Catastrophic forgetting

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