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13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023

作者： Kastner, Steffen Ebner, Markus Bullmann, Markus Fetzer, Toni Deinzer, Frank Grzegorzek, Marcin Technical University of Applied Sciences Würzburg-Schweinfurt Center for Artificial Intelligence and Robotics Würzburg Germany Technical University of Applied Sciences Würzburg-Schweinfurt Faculty of Computer Science Würzburg Germany University of Lübeck Institute of Medical Informatics Lübeck Germany

ISBN: (纸本)9798350320114

This work presents SIMUL, a new dataset consisting of 550 minutes of annotated motion data from six synchronized IMUs placed consistently at the same strategically chosen positions on the bodies of 32 participants. With a focus on indoor localization, the selection fell on hand, feet, and trouser pockets. Due to the sensor's synchronization, this selection allows, for example, to label the data recorded in the hand based on the data captured at the feet. For a better generalizability, the dataset was recorded freestyle under many different environmental conditions and walking speeds. Thus, indoors, numerous floor coverings such as stone, wood, carpet and PVC, as well as stairs are included in the data. The outdoor recordings additionally contain uneven surfaces such as paving stones and slopes. The annotation of the participant's currently performed activity additionally allows this dataset to be used for activity recognition. © 2023 IEEE.

关键词： Activity Recognition Dataset IMU PDR Stairs Step Counting Step Identification Walking

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FIRE-3DV: Framework-Independent Rendering Engine for 3D Graphics Using Vulkan

FIRE-3DV: Framework-Independent Rendering Engine for 3D Grap...

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IEEE International Conference on Robotic Computing (IRC)

作者： Christopher John Allison Haoying Zhou Adnan Munawar Peter Kazanzides Juan Antonio Barragan Khoury College of Computer Sciences Northeastern University Boston MA USA Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Computer Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

Interactive dynamic simulators are an accelerator for developing novel robotic control algorithms and complex systems involving humans and robots. In user training and synthetic data generation applications, high-fidelity visualizations from the simulation are essential. Yet, robotic simulators often limit their rendering algorithms to preserve real-time interaction with the simulation. Advancements in Graphics Processing Units (GPU) enable improved visualization without compromising performance. However, these advancements cannot be fully leveraged in simulation frameworks that use legacy graphics application programming interfaces (API) to interface with the GPU. This paper presents a performance-focused and lightweight rendering engine supporting the modern Vulkan graphics API that can be easily integrated with other simulation frameworks to enhance visualizations. To illustrate the proposed method, our engine is used to modernize the legacy rendering pipeline of the Asynchronous Multi-Body Framework (AMBF), a dynamic simulation framework used extensively for interactive robotics simulation development. This new rendering engine implements graphical features such as physically based rendering (PBR), anti-aliasing, and ray-traced shadows, significantly improving the image fidelity of AMBF. Computational experiments show that the engine can render a simulated scene with over seven million triangles while maintaining GPU computation times within two milliseconds.

关键词： Training Three-dimensional displays Heuristic algorithms Graphics processing units Data visualization Rendering (computer graphics) Robots Engines Application programming interfaces Synthetic data

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Simultaneous Estimation of Shape and Force along Highly Deformable Surgical Manipulators Using Sparse FBG Measurement

arXiv

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arXiv 2024年

作者： Lu, Yiang Li, Bin Chen, Wei Yan, Junyan Cheng, Shing Shin Wang, Jiangliu Zhou, Jianshu Dou, Qi Liu, Yun-Hui T Stone Robotics Institute Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong

Recently, fiber optic sensors such as fiber Bragg gratings (FBGs) have been widely investigated for shape reconstruction and force estimation of flexible surgical robots. However, most existing approaches need precise model parameters of FBGs inside the fiber and their alignments with the flexible robots for accurate sensing results. Another challenge lies in online acquiring external forces at arbitrary locations along the flexible robots, which is highly required when with large deflections in robotic surgery. In this paper, we propose a novel data-driven paradigm for simultaneous estimation of shape and force along highly deformable flexible robots by using sparse strain measurement from a single-core FBG fiber. A thin-walled soft sensing tube helically embedded with FBG sensors is designed for a robotic-assisted flexible ureteroscope with large deflection up to 270◦ and a bend radius under 10 mm. We introduce and study three learning models by incorporating spatial strain encoders, and compare their performances in both free space without interactions as well as constrained environments with contact forces at different locations. The experimental results in terms of dynamic shape-force sensing accuracy demonstrate the effectiveness and superiority of the proposed methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Fiber Bragg gratings

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Representation Learning of Multivariate Time Series using Attention and Adversarial Training

arXiv

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arXiv 2024年

作者： Scharwächter, Leon Otte, Sebastian Neuro-Cognitive Modeling Group Department of Computer Science University of Tübingen Germany Adaptive AI Lab Institute of Robotics and Cognitive Systems University of Lübeck Germany

A critical factor in trustworthy machine learning is to develop robust representations of the training data. Only under this guarantee methods are legitimate to artificially generate data, for example, to counteract imbalanced datasets or provide counterfactual explanations for blackbox decision-making systems. In recent years, Generative Adversarial Networks (GANs) have shown considerable results in forming stable representations and generating realistic data. While many applications focus on generating image data, less effort has been made in generating time series data, especially multivariate signals. In this work, a Transformer-based autoencoder is proposed that is regularized using an adversarial training scheme to generate artificial multivariate time series signals. The representation is evaluated using t-SNE visualizations, Dynamic Time Warping (DTW) and Entropy scores. Our results indicate that the generated signals exhibit higher similarity to an exemplary dataset than using a convolutional network approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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From news to forecast: integrating event analysis in LLM-based time series forecasting with reflection 24

From news to forecast: integrating event analysis in LLM-bas...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xinlei Wang Maike Feng Jing Qiu Jinjin Gu Junhua Zhao School of Electrical and Computer Engineering The University of Sydney School of Science and Engineering The Chinese University of Hong Kong Shenzhen School of Science and Engineering The Chinese University of Hong Kong Shenzhen and Shenzhen Institute of Artificial Intelligence and Robotics for Society

ISBN: (纸本)9798331314385

This paper introduces a novel approach that leverages Large Language Models (LLMs) and Generative Agents to enhance time series forecasting by reasoning across both text and time series data. With language as a medium, our method adaptively integrates social events into forecasting models, aligning news content with time series fluctuations to provide richer insights. Specifically, we utilize LLM-based agents to iteratively filter out irrelevant news and employ human-like reasoning to evaluate predictions. This enables the model to analyze complex events, such as unexpected incidents and shifts in social behavior, and continuously refine the selection logic of news and the robustness of the agent's output. By integrating selected news events with time series data, we fine-tune a pre-trained LLM to predict sequences of digits in time series. The results demonstrate significant improvements in forecasting accuracy, suggesting a potential paradigm shift in time series forecasting through the effective utilization of unstructured news data.

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A study on obtaining accurate height information of red blood cells in digital holographic microscopy (DHM)

A study on obtaining accurate height information of red bloo...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Kosei Nakamura Hyun-Woo Kim Myungjin Cho Min-Chul Lee Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyoung National University Anseong Republic of Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Digital holographic microscopy (DHM) is used in the fields of microbiological research and disease diagnosis. Especially in the medical field, it is required to obtain the precise shape information of red blood cells and living cells for disease diagnosis. To obtain the highly accurate three-dimensional profile of an object, it is necessary to set parameters accurately on the system and perform the analysis. In DHM, it is especially important to set the refractive index of the medium surrounding the object accurately. However, the refractive index varies depending on the temperature and the humidity of the experimental environment, as well as on the sample preparation method, and it is difficult to derive the appropriate refractive index. This is a problem in the medical field, which requires the accurate three-dimensional information. Therefore, in this paper, we propose a method to derive the optimum refractive index and to obtain the accurate height information of red blood cells by conducting experiments using two types of lasers with different wavelengths and comparing the height information of red blood cells obtained with each laser. By using the proposed method, the optimu m refractive index of the surrounding medium and the accurate height information of the red blood cells are obtained.

关键词： Temperature dependence Accuracy Red blood cells Shape Microscopy Lasers Refractive index Process control Medical diagnosis Medical diagnostic imaging

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A Generative AI Technique for Synthesizing a Digital Twin for U.S. Residential Solar Adoption and Generation

arXiv

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arXiv 2024年

作者： Kishore, Aparna Thorve, Swapna Marathe, Madhav Department of Computer Science University of Virginia CharlottesvilleVA United States Biocomplexity Institute University of Virginia CharlottesvilleVA United States Amazon Robotics MA United States

Residential rooftop solar adoption is considered crucial for reducing carbon emissions. The lack of photovoltaic (PV) data at a finer resolution (e.g., household, hourly levels) poses a significant roadblock to informed decision-making. We discuss a novel methodology to generate a highly granular, residential-scale realistic dataset for rooftop solar adoption across the contiguous United States. The data-driven methodology consists of: (i) integrated machine learning models to identify PV adopters, (ii) methods to augment the data using explainable AI techniques to glean insights about key features and their interactions, and (iii) methods to generate household-level hourly solar energy output using an analytical model. The resulting synthetic datasets are validated using real-world data and can serve as a digital twin for modeling downstream tasks. Finally, a policy-based case study utilizing the digital twin for Virginia demonstrated increased rooftop solar adoption with the 30% Federal Solar Investment Tax Credit, especially in Low-to-Moderate-Income communities. © 2024, CC BY.

关键词： Zero-carbon

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Simultaneous Estimation of Shape and Force along Highly Deformable Surgical Manipulators Using Sparse FBG Measurement

Simultaneous Estimation of Shape and Force along Highly Defo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yiang Lu Bin Li Wei Chen Junyan Yan Shing Shin Cheng Jiangliu Wang Jianshu Zhou Qi Dou Yun-Hui Liu Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Recently, fiber optic sensors such as fiber Bragg gratings (FBGs) have been widely investigated for shape reconstruction and force estimation of flexible surgical robots. However, most existing approaches need precise model parameters of FBGs inside the fiber and their alignments with the flexible robots for accurate sensing results. Another challenge lies in online acquiring external forces at arbitrary locations along the flexible robots, which is highly required when with large deflections in robotic surgery. In this paper, we propose a novel data-driven paradigm for simultaneous estimation of shape and force along highly deformable flexible robots by using sparse strain measurement from a single-core FBG fiber. A thin-walled soft sensing tube helically embedded with FBG sensors is designed for a robotic-assisted flexible ureteroscope with large deflection up to 270° and a bend radius under 10 mm. We introduce and study three learning models by incorporating spatial strain encoders, and compare their performances in both free space without interactions as well as constrained environments with contact forces at different locations. The experimental results in terms of dynamic shape-force sensing accuracy demonstrate the effectiveness and superiority of the proposed methods.

关键词： Optical fiber sensors Shape Soft sensors Force Estimation Robot sensing systems Fiber gratings

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Two-stream Dynamic Heterogeneous Graph Recurrent Neural Network for Multi-label Multi-modal Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Wang, Jing Feng, Zhiyang Ning, Xiaojun Lin, Youfang Chen, Badong Jia, Ziyu Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence School of Computer Science and Technology Beijing China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Chinese Academy of Sciences Brainnetome Center Institute of Automation Beijing100190 China

The study of the relationship between emotions and physiological signals of subjects under multimedia stimulation is an emerging field, and many important advances are made. However, there are still some challenges: 1) How to effectively utilize the complementarity among spatial-spectral-temporal domain information. 2) How to employ the heterogeneity and the correlation among multi-modal physiological signals simultaneously. 3) How to improve the robustness of the model dealing with missing channels. 4) How to model the dependency among different emotions. In this paper, we propose a novel two-stream Dynamic Heterogeneous Graph Recurrent Neural Network called DHGRNN. Specifically, DHGRNN consists of a spatial-temporal stream, a spatial-spectral stream, a fusion layer, and a multi-label classifier. Each stream is composed of a graph transformer network, evolved graph convolutional neural network, and gated recurrent units. We propose a graph-based two-stream structure to fuse the information of the spatial-spectral-temporal domain simultaneously. Graph transformer network and evolved graph convolutional neural network are used to model the heterogeneity and correlation of multi-modal physiological signals, respectively. To deal with the problem of robustness in the face of missing channel data, we transform it into the problem of dynamic graphs and use a dynamic graph neural network to improve the robustness. In addition, we propose a multi-label classifier to model the dependency among different emotion dimensions. Experiments on three public datasets demonstrate that our proposed model outperforms existing state-of-the-art methods. © 2010-2012 IEEE.

关键词： Graph neural networks

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