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共 2056 条记录，以下是2011-2020 订阅

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Identification and control for an unknown robot in Intelligent Space

Identification and control for an unknown robot in Intellige...

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ieee international Workshop on Robot and Human Communication (ROMAN)

作者： Joo-Hyung Kim Jeong-Eom Lee Hyun-Gu Lee Gwi-Tae Park Department of Electrical Engineering Korea University Seoul South Korea

Intelligent Space is an area to perceive and understand what is happening in it, which provides diverse services to human and robots. We expect that Intelligent Space offers a lot of benefits as a good platform for robots. For example, robots can move by the guidance of Intelligent Space although they don't know about the environment. If a new robot comes in Intelligent Space, the robot wants to receive the guidance service from the space for its navigation. So, the space should perceive and control the robot, since it doesn't know anything about the space. In this paper, we propose a new algorithm for controlling an unknown robot in Intelligent Space. An unknown robot is an unidentified or new coming robot that an intelligent space has not yet known it. Objectives of the algorithm are to estimate the location of robot, to navigate it, and to identify it. The algorithm uses a color-based particle filter to estimate the location of robot and a predefined motion pattern to identify the robot. To evaluate the proposed algorithm, we command that a mobile robot moves according trajectory of robot.

关键词： Intelligent robots Orbital robotics Intelligent sensors Mobile robots Robot sensing systems Robot control computational intelligence Human robot interaction Simultaneous localization and mapping Intelligent networks

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Video Self-Supervised Cross-Pathway Training Based on Slow and Fast Pathways

Video Self-Supervised Cross-Pathway Training Based on Slow a...

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ieee international Conference on Systems, Man and Cybernetics

作者： Jie Li Jing Yang Zhou Jiang Yuehai Chen Shaoyi Du School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China School of Software Engineering Xi'an Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

In the field of video self-supervised learning, contrastive instance learning methods suffer from a lack of semantic information, resulting in inadequate generalization in downstream tasks. Although optical flow can provide some semantic information, it requires significant computational cost prior to training. To address this, we propose a Video self-supervised Cross-pathway training model based on Slow and Fast pathways (VCSF). This model separately extracts temporal and spatial features from pure RGB video frames, and uses the complementary representations of the two pathways to conduct cross-pathway training. Additionally, we propose a motion perception module in the low-frame-rate space to enhance the network's ability to perceive rapidly changing human motion. We conducted extensive experiments in downstream missions of UCF101 and HMDB51, and obtained state-of-the-art results in models using the UCF101 data set for self-supervised pre-training, including motion recognition and nearest neighbor retrieval.

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Enhancing Patient Care: Achieving Interoperability between General Practitioners’ and Stomatologists’ Information Systems Using FHIR Standard and Firebase Cloud

Enhancing Patient Care: Achieving Interoperability between G...

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international symposium on Applied computational intelligence and Informatics ( SACI)

作者： Oana Sorina Chirila Lăcrămioara Stoicu-Tivadar Denis Avram Mihaela Monica Szilagyi Ciprian-Bogdan Chirila Department of Automation and Applied Informatics University Politehnica Timișoara Timișoara Romania University Politehnica Timișoara Timișoara Romania Department of Computer and Information Technology University Politehnica Timișoara Timișoara Romania

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

Interoperability in the medical field is very important because it allows different healthcare providers, systems, and devices to exchange, interpret, and use patient health information efficiently and accurately. Interoperability between stomatology and general practitioners (GPs) information systems is still a work in progress but there are a lot of researchers that work to develop applications in this domain. This paper describes the implementation of interoperability between a GP's information system and a stomatology medical system, achieved using the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. Communication between the two systems was done using Google Firebase services. Using this type of system can significantly decrease medical errors, avoiding administration of substances to which the patient is allergic, during dental procedures. Additionally, it can help GPs prevent exacerbating or triggering other health issues when patients have certain dental problems.

关键词： Medical services Dentistry Internet Task analysis Informatics Interoperability Standards

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Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation

Generalized Correspondence Matching via Flexible Hierarchica...

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ieee international Conference on robotics and automation (ICRA)

作者： Yu Han Ziwei Long Yanting Zhang Jin Wu Zhijun Fang Rui Fan School of Computer Science and Technology Donghua University Shanghai P. R. China Shanghai Research Institute for Intelligent Autonomous Systems the State Key Laboratory of Intelligent Autonomous Systems and Frontiers Science Center for Intelligent Autonomous Systems Machine Intelligence & Autonomous Systems (MIAS) Group the College of Electronics & Information Engineering Tongji University Shanghai P. R. China Department of Electronics and Computer Engineering The Hong Kong University of Science and Technology P. R. China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Correspondence matching plays a crucial role in numerous robotics applications. In comparison to conventional hand-crafted methods and recent data-driven approaches, there is significant interest in plug-and-play algorithms that make full use of pre-trained backbone networks for multi-scale feature extraction and leverage hierarchical refinement strategies to generate matched correspondences. The primary focus of this paper is to address the limitations of deep feature matching (DFM), a state-of-the-art (SoTA) plug-and-play correspondence matching approach. First, we eliminate the pre-defined threshold employed in the hierarchical refinement process of DFM by leveraging a more flexible nearest neighbor search strategy, thereby preventing the exclusion of repetitive yet valid matches during the early stages. Our second technical contribution is the integration of a patch descriptor, which extends the applicability of DFM to accommodate a wide range of backbone networks pre-trained across diverse computer vision tasks, including image classification, semantic segmentation, and stereo matching. Taking into account the practical applicability of our method in real-world robotics applications, we also propose a novel patch descriptor distillation strategy to further reduce the computational complexity of correspondence matching. Extensive experiments conducted on three public datasets demonstrate the superior performance of our proposed method. Specifically, it achieves an overall performance in terms of mean matching accuracy of 0.68, 0.92, and 0.95 with respect to the tolerances of 1, 3, and 5 pixels, respectively, on the HPatches dataset, outperforming all other SoTA algorithms. Our source code, demo video, and supplement are publicly available at ***/GCM.

关键词： Performance evaluation Accuracy Semantic segmentation Source coding Image matching Supervised learning Pose estimation

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A comparison of neural network controllers for a mobile robot with an on-board manipulator

A comparison of neural network controllers for a mobile robo...

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ieee international symposium on Intelligent Control (ISIC)

作者： S. Jagannathan P.S. Shiakolas Systems and Controls Research Automated Analysis Corporation Peoria IL USA Mechanical and AeroSpace Engineering and The Automation & Robotics Research Institute University of Texas Arlington Arlington TX USA

A systematic approach for modeling and motion control of a mobile vehicle with on-board arm is presented. Two neural network based controllers which feedback linearize the composite system after the incorporation of non-holonomic constraints are considered. The feedback linearization provides an inner loop that accounts for possible motion of the on-board arm. These neural network controllers exhibit learning-while functioning features instead of the traditional learning-then-control training approach. Therefore, the control action is immediate with no off-line-learning phase needed. The case of maintaining a desired course and speed while the on-board arm is allowed to move to its desired orientation is considered. The two neural network algorithms used in designing the controller are backpropagation with e-mod and Hebbian learning with e-mod. computationally the Hebbian learning with e-mod outperforms the backpropagation with e-mod without any performance degradation. A computational comparison and simulation results are presented in order to justify the theoretical conclusion.

关键词： Neural networks Mobile robots Neurofeedback Backpropagation Hebbian theory Motion control Vehicles Linear feedback control systems Control systems Interconnected systems

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A Real Time Self-Generating Control for AI Platforms

A Real Time Self-Generating Control for AI Platforms

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international symposium on Applied computational intelligence and Informatics ( SACI)

作者： Mircea Trifan Bogdan Ionescu Dan Ionescu School of Electrical Engineering and Computer Science University of Ottawa Ottawa Ontario Canada

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

Recent advancements in hardware and software implementations of Artificial intelligence have sparked a multitude of revolutionary applications in the theory and implementation of AI algorithms and tools. Significant new developments have been driven by the application of the Transformer concept in the fields of Large Language Models (LLMs), Reinforcement Learning, and other areas. This in turn led to capturing long-range dependencies and contextual information based on data. More recently, strong positions in the AI research community, around the proper implementations and usage of certain Machine Learning (ML) applications, have been thoroughly debated. However, it is very much known, that ChatGPT and other like platforms, such as Llama, or large GNNs, suffer from a series of black-box drawbacks out of which the “factual accuracy”, “halucinations”, “overgeneralization” and others open loop LLMs were reported in the literature. This paper presents an Autonomic Computing (AC) closed-loop architecture that manages and gathers data from user prompts via a DOMifire module. The DOMifire acts as the sensor element of the LLM AC system, referred to as the Plant. This data is logically compared by an Expert System (ES), which serves as the core of the Autonomic Manager in the AC loop of the LLM, with the data obtained from the LLM's output―in this case, the responses generated by ChatGPT. After a reduced number of iterations, the results are evaluated using a Mean Absolute Scaled Error (MASE) metric. In the context of a time series, this process results in a stable set of sentences or rules produced by the Knowledge Base module. An example, in which the “Time Series” of AutoGluOn illustrates the AC - AI interactions for a complementary contributions to a more robust AI platform. An example of the interaction AC-AI is given in the Conclusion section of this paper.

关键词： Training automation computational modeling Architecture Time series analysis Computer architecture Chatbots

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Embodied Runtime Monitoring of Learning-enabled Robot Perception Components

Embodied Runtime Monitoring of Learning-enabled Robot Percep...

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ieee international Conference on automation Science and Engineering (CASE)

作者： Deebul Nair Sathwik Panchangam Miguel A. Olivares-Mendez Nico Hochgeschwender Institute for Artificial Intelligence and Autonomous Systems Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Space Robotics Research Group (SpaceR) Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg Department of Mathematics and Computer Science University of Bremen Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Monitoring during runtime, the estimated uncertainty from learning-enabled robot perception components, is a vital tool for assuring and evaluating model correctness. By utilizing different uncertainty estimation methods, monitors exploit the perceived sensor data and predictions generated by the perception model to make informed decisions, such as in safety-critical situations. In robotics, informed decisions should be aligned with embodied information regarding space and time. We argue and show that runtime monitors of learning-enabled components can be made more robust by including the embodiment information of the robot. However, it remains challenging to build uncertainty-based runtime monitors with embodiment information using only real-world datasets, which are tedious to collect. To this end, we propose a solution that leverages simulation-based datasets to characterize expected uncertainty estimates with robot embodiment. By constructing a nominal runtime monitor model based on simulated datasets, we establish a reference for the expected uncertainty along with the embodiment information that can be subsequently used to monitor real-world deployed perception components. We demonstrate the zero-shot sim-to-real transfer of our approach on a Kinova Gen3 manipulator picking task, for which the runtime monitor reliability model was trained using simulation-based data containing embodiment information of distance from the object being selected.

关键词： Runtime Uncertainty computational modeling Estimation Predictive models Robot sensing systems Manipulators Reliability engineering Data models Monitoring

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A Dynamic-Static Weighted Network with Diverse-Token Fusion Attention for Rhexis Margin Recognition in Microscopic Surgery

A Dynamic-Static Weighted Network with Diverse-Token Fusion ...

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ieee international Conference on robotics and Biomimetics

作者： Zhen Li Wen-Qian Yue Gui-Bin Bian Shuai Zhang Wei-Peng Liu Shuo Li School of Electronic and Information Engineering Tongji University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China Departments Biomedical Engineering and Computer and Data Science Case Western Reserve University

In microscopic cataract surgery, capsulorhexis is the first, most critical step. However, recognizing the boundary of the rhexis during capsulorhexis is challenging and has rarely been studied due to its intraoperative invisibility, variable shape, and lack of a proprietary labeled dataset, especially in the robot-sssisted surgery. To solve the problems, this paper proposes a creative dynamic boundary recognition solution, which includes a novel segmentation network, DCTNet, and a compensation block. First, the dilated HRNet is designed to constrain the over-compression while maintaining high-resolution representation for better detail preservation. Second, the cascaded transformer block is proposed: The decoupling transformer (DET) is developed to assign dynamic-static weights thus learning the variation pattern for segmented targets decoupling. The coupling transformer(COT) with redistributed embeddings is then developed to fuse diverse-resolution tokens for multi-scale prediction coupling. Moreover, the compensation block compensates for inter-frame movement by key-point registration and recovers a more realistic boundary by a smoothing process. Creatively, we mathematically estimate the uncertainty of framework to eliminate the misidentifications due to aleatoric uncertainty in data. Finally, to evaluate the proposed framework, we establish the first known capsulorhexis dataset, CapSeg. Our proposed DCTNet achieves superior performance to state-of-the-art networks with a lower computational cost, and the feasibility experiment shows its great potential in medical surgical robot.

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Calibrating a Soft ERT-Based Tactile Sensor with a Multiphysics Model and Sim-to-real Transfer Learning

Calibrating a Soft ERT-Based Tactile Sensor with a Multiphys...

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ieee international Conference on robotics and automation (ICRA)

作者： Hyosang Lee Hyunkyu Park Gokhan Serhat Huanbo Sun Katherine J. Kuchenbecker Haptic Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart Germany Autonomous Learning Group Max Planck Institute for Intelligent Systems Tübingen Germany

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Tactile sensors based on electrical resistance tomography (ERT) have shown many advantages for implementing a soft and scalable whole-body robotic skin; however, calibration is challenging because pressure reconstruction is an ill-posed inverse problem. This paper introduces a method for calibrating soft ERT-based tactile sensors using sim-to-real transfer learning with a finite element multiphysics model. The model is composed of three simple models that together map contact pressure distributions to voltage measurements. We optimized the model parameters to reduce the gap between the simulation and reality. As a preliminary study, we discretized the sensing points into a 6 by 6 grid and synthesized single- and two-point contact datasets from the multiphysics model. We obtained another single-point dataset using the real sensor with the same contact location and force used in the simulation. Our new deep neural network architecture uses a de-noising network to capture the simulation-to-real gap and a reconstruction network to estimate contact force from voltage measurements. The proposed approach showed 82% hit rate for localization and 0.51 N of force estimation error performance in singlecontact tests and 78.5% hit rate for localization and 5.0 N of force estimation error in two-point contact tests. We believe this new calibration method has the possibility to improve the sensing performance of ERT-based tactile sensors.

关键词： Fabrics computational modeling Tactile sensors Mathematical model Electrodes Force Conductivity

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Face Recognition by Global Optimal Discriminant Features and Ensemble Artificial Neural Networks

Face Recognition by Global Optimal Discriminant Features and...

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ieee international symposium on Computer Network and Multimedia Technology, CNMT

作者： Jingjing Wang Jianqin Yin College of Physics and Electronics Shandong Normal University Jinan China School of Information Science and Engineering University of Ji''nan Jinan China

Good feature extraction scheme and classifiers are the key to face recognition algorithms. A general and efficient face feature extraction approach is presented which utilizes linear discriminant information and global search strategy. In order to get rid of redundant information and meanwhile reduce computational burden, we first compute the nonzero feature space of scatter matrix of the training set, and then perform a global search on it to seek out the most valuable discriminant information of faces. Genetic algorithm is used for searching because of its well-known global search ability. Also, good classifiers are designed by using ensemble artificial neural networks. Based on the individual classifiers, appropriate combinations are selected to construct the classification committee using EDAs (estimation of distribution algorithms). Experiments are designed to test the performance of the feature extraction method and the classifiers separately. Results show that the proposed method produces good recognition rates on three benchmark databases.

关键词： Face recognition Artificial neural networks Feature extraction Humans Face detection Scattering Competitive intelligence Electronic design automation and methodology Linear discriminant analysis Image recognition

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