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24th COTA International Conference of Transportation Professionals: Resilient, intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wang, Yongsheng Xu, Bingrong Chu, Duanfeng Ding, Naikan Yin, Jianhua School of Information Engineering Wuhan Univ. of Technology Wuhan China School of Automation Wuhan Univ. of Technology Wuhan China Intelligent Transportation Systems Research Center Wuhan Univ. of Technology Wuhan China Chongqing Research Institute Wuhan Univ. of Technology Chongqing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Vehicle dynamics model is critical for autonomous vehicles since it provides certain rules of motion in response to the surrounding rapidly changing environment. However, current pure physics-based models and data-driven approaches are set against each other in constructing the dynamics model. In this work, we propose a vehicle dynamics restoration approach by integrating physics principles and data based on physics-informed machine learning. The vehicle dynamics model parameters are estimated by combining data and limited prior physical knowledge. The results of a case study demonstrate that (1) the proposed methods can accurately identify the dynamics model parameters and restore the vehicle dynamics model and (2) the neural network with physical knowledge can accurately predict the state of vehicle. The proposed method has the potential to be applied in quantifying the dynamics model uncertainty due to parameter uncertainty, and stochastic motion planning and control due to uncertainty, which will be further investigated in the future. © ASCE.

关键词： Stochastic programming

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MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing Dataset and Benchmark for Text-to-Image Generation 38

MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Luo, Jialin Wang, Yuanzhi Gu, Ziqi Qiu, Yide Yao, Shuaizhen Wang, Fuyun Xu, Chunyan Zhang, Wenhua Wang, Dan Cui, Zhen PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Beijing Institute of Spacecraft System Engineering Beijing China

Recently, the diffusion-based generative paradigm has achieved impressive general image generation capabilities with text prompts due to its accurate distribution modeling and stable training process. However, generating diverse remote sensing (RS) images that are tremendously different from general images in terms of scale and perspective remains a formidable challenge due to the lack of a comprehensive remote sensing image generation dataset with various modalities, ground sample distances (GSD), and scenes. In this paper, we propose a Multi-modal, MultiGSD, Multi-scene Remote Sensing (MMM-RS) dataset and benchmark for text-to-image generation in diverse remote sensing scenarios. Specifically, we first collect nine publicly available RS datasets and conduct standardization for all samples. To bridge RS images to textual semantic information, we utilize a large-scale pretrained vision-language model to automatically output text prompts and perform hand-crafted rectification, resulting in information-rich text-image pairs (including multi-modal images). In particular, we design some methods to obtain the images with different GSD and various environments (e.g., low-light, foggy) in a single sample. With extensive manual screening and refining annotations, we ultimately obtain a MMM-RS dataset that comprises approximately 2.1 million text-image pairs. Extensive experimental results verify that our proposed MMM-RS dataset allows off-the-shelf diffusion models to generate diverse RS images across various modalities, scenes, weather conditions, and GSD. The dataset is available at https://***/ljl5261/MMM-RS. © 2024 Neural information processing systems foundation. All rights reserved.

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LLM-BT: Performing Robotic Adaptive Tasks based on Large Language Models and Behavior Trees

arXiv

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arXiv 2024年

作者： Zhou, Haotian Lin, Yunhan Yan, Longwu Zhu, Jihong Min, Huasong Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China The School of Physics Engineering and Technology University of York United Kingdom

Large Language Models (LLMs) have been widely utilized to perform complex robotic tasks. However, handling external disturbances during tasks is still an open challenge. This paper proposes a novel method to achieve robotic adaptive tasks based on LLMs and Behavior Trees (BTs). It utilizes ChatGPT to reason the descriptive steps of tasks. In order to enable ChatGPT to understand the environment, semantic maps are constructed by an object recognition algorithm. Then, we design a Parser module based on Bidirectional Encoder Representations from Transformers (BERT) to parse these steps into initial BTs. Subsequently, a BTs Update algorithm is proposed to expand the initial BTs dynamically to control robots to perform adaptive tasks. Different from other LLM-based methods for complex robotic tasks, our method outputs variable BTs that can add and execute new actions according to environmental changes, which is robust to external disturbances. Our method is validated with simulation in different practical scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： robotics

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Study on Vehicle Recognition Algorithm Based on Convolutional Neural Network

Study on Vehicle Recognition Algorithm Based on Convolutiona...

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Communications technology and Computer Science (ACCTCS), Asia-Pacific Conference on

作者： Bi Wenkai Department of Intelligent Information Systems and Technology Moscow Institute of Physics and Technology Moscow Russia

As the application of vehicles expands, the types of vehicles have also become increasingly diverse. While vehicles bring convenience to daily life, they also pose certain traffic hazards due to their different types, such as large transport vehicles entering main roads and violations in prohibited areas and dedicated lanes. Therefore, it has become important to classify and manage actual vehicles. In the field of image processing, the key to vehicle classification is to design classifiers and classify images accordingly. Against this backdrop, relevant classifier algorithms are studied, and classifier design and optimization are completed. Currently, convolutional neural networks (CNN) have been widely applied in image processing and achieved good classification results. To address the issue of actual vehicle classification, this paper selects classic convolutional models, including LeNet5, VGG16, and ResNet50, for transfer learning. The image enhancement processing and optimizer selection are conducted to achieve the final classification. After multiple parameter iterations and comparative experiments, the optimal algorithm combination of VGG16 model, cross-entropy function, and Adam optimizer is selected. The validation accuracy rate of the last iteration under this algorithm combination is 93%, and the optimal accuracy rate throughout the entire training process is 94%.

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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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Development of an in vitro perfusable neural interface model for sequential connection of nerve cells

Development of an in vitro perfusable neural interface model...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Menghua Liu Huaping Wang Qing Shi Yaozhen Hou Jiaxin Liu Tao Sun Qiang Huang Toshio Fukuda Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

Neural interface models based on microfluidic chips are widely used in the regeneration of neural interfaces in vitro for their low cost, simple structure and accurate simulation of complex interactions between cells and the extracellular environment. However, due to the lack of influence of the dynamic flow environment, it is difficult to realize the reduction of cell culture and the cell growth environment for a long time while forming sequential connections between cells. Here,we propose an in vitro perfusable culture chip as a neural interface for neuroblastoma cells (SH-SY5Y cells) to realize sequential cell connection under dynamic perfusion and physical guidance. A microfluidic chip with physical guidance was fabricated based on soft lithography. The microfluidic chip can realize long-term cell culture under dynamic perfusion conditions by an automatic perfusion system in vitro. The interior of the microfluidic chip is optimized to provide physical cues for SH-SY5Y cells to construct nerve conduction microunits. The microfluidic chip has great potential in the research of in vitro neural interface regeneration models in dynamic complex environments.

关键词： Soft lithography Costs Biological system modeling Neurons In vitro Extracellular Microfluidics

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A Comprehensive Evaluation on the Impact of Various Spoofing Scenarios on GPS Sensors in a Low-Cost UAV

TechRxiv

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TechRxiv 2023年

作者： Vaddhiparthy, SVSLN Surya Suhas Sreya, Garapati Turlapati, Prudhvi Raj Gangadharan, Deepak Kandath, Harikumar Computer Systems Group International Institute of Information Technology Hyderabad India Robotics Research Center International Institute of Information Technology Hyderabad India

Unmanned Aerial Vehicles (UAVs), particularly low-cost UAVs, have become increasingly important due to their wide range of applications and ease of use. However, with the rapid growth of the UAV market, the rising security concerns pose a greater risk. One such primary concern is location spoofing attacks which can compromise UAV’s navigation system, making it crucial to analyze various location-based attacks. In this paper, we identify 16 such GPS spoofing scenarios based on environmental conditions, attack type, and spoof signal propagation path. We evaluate these scenarios based on various GPS parameters like Horizontal Dilution Of Precision (HDOP), Vertical Dilution Of Precision (VDOP), GPS satellite count in view, and avg signal-to-noise power density (CN0). We then analyze the variations in GPS parameters for various such attack scenarios. Further, we analyze the impact of distance on average CN0 and the effect of satellite count on effective spoofable distance. We also discuss several critical insights which are empirically observed during our experimental trials. Our experiments revealed that the natural conditions within indoor and outdoor scenarios can vary considerably, and effective spoofable distance can be up to 100 meters when the satellite count is less than 10. © 2023, CC BY-SA.

关键词： Global positioning system

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Task-oriented and attractor regularized multi-task learning for environmental spatial–temporal time series prediction

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Knowledge and information systems 2025年 1-30页

作者： Lv, Fei Si, Shuaizong Ren, Weijie Beijing Key Laboratory of IOT Information Security Technology Institute of Information Engineering CAS Beijing China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China

With the development of sensor networks, the spatial–temporal relationships of multivariate time series also bring the challenge for multi-task spatial–temporal time series prediction in environmental datasets. In this paper, we propose the task-oriented and attractor regularized multi-task learning model (TOAR-MTL). In the model, random feature and Nyström approximation are adopted for hierarchical dynamics. Joint sparsity constraints are utilized for task-oriented feature selection, which could explore the dynamic-shared and dynamic-specific features. Simultaneously, the manifold regularization is adopted for attractor priori to constrain the predicted value back onto the attractor manifold. Furthermore, an efficient iterative algorithm based on the alternative direction multiplier method and accelerated proximal gradient method is proposed and a decoupling variable is introduced to handle the complex objective function. Simulations are conducted on the London, Beijing–Tianjin–Hebei, Lorenz and Beijing Air Quality datasets. The experiment results illustrate that the Nyström approximation could speed up the training process by 30–100 times and the task-oriented feature selection could adaptively obtain sparse interpretable weights. Therefore, the TOAR-MTL could effectively promote the multi-step prediction accuracy of environmental spatial–temporal time series.

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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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第42届中国控制会议

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

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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Dynamic Fluorescent Imaging for Robotic Patch Clamp in vivo: Modeling and Control

Dynamic Fluorescent Imaging for Robotic Patch Clamp in vivo:...

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第42届中国控制会议

作者： Ruimin Li Jinyu Qiu Minghui Li Mingzhu Sun Xin Zhao Qili Zhao Institute of Robotics and Automatic Information System (IRAIS) Nankai University Tianjin Key Laboratory of Intelligent Robotic (tjKLIR) Nankai University Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology

Fluorescent imaging through fluorescent dye injection is the key technique for lighting the in vivo environment and localizing the target cell in it for patch clamp operation in vivo. At present, manual fluorescent imaging is usually a labor-intensive high-skill requisite work, significantly limiting the application of patch clamp operation in vivo. In this paper, a computational modeling of the diffusion process of the injected fluorescent dye is established using the commercial ANSYS Fluent software. Based on this model, the distribution of the mass fraction, proven to be linearly related to fluorescence intensity,was calculated to simulate the fluorescent field formed by fluorescent dye injection. Further, an ADRC controller was developed to generate feedback-controlled fluorescent fields. With the above model and controller, a mobile fluorescent field, and varying fluorescent field with constant mass fraction at the target position are generated to fulfill the requirement of in vivo environment observation, search and localization of the target cell in patch clamp operation, respectively. The above results prove the feasibility of ADRC-controlled dynamic fluorescent imaging in the patch clamp operation in vivo.

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