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A Seismic Phase-Picking Model Transfer Learning Approach Based on Maximum Mean Discrepancy 3

A Seismic Phase-Picking Model Transfer Learning Approach Bas...

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3rd international conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2023

作者： Zhu, Kun'ao Huang, Yongming Chen, Jun Sun, Yejun School of Automation Southeast University Nanjing China School of Electronic Information and Electrical Engineering Hefei Normal University Hefei China Jiangsu Earthquake Agency Nanjing China

ISBN: (纸本)9798350372052

Thanks to the abundance of earthquake data and the advancement of deep neural networks, many seismic phase-picking models have been proposed to detect the arrival time of P-waves and S-waves. Currently, the accuracy of these models exceeds that of traditional methods, such as short-term average/long-term average (STA/LTA). While many studies concentrate on phase picks on a single seismic dataset, there is limited research regarding the generalization of phase-picking models. Specifically, applying a model trained on one seismic dataset to an unseen dataset remains underexplored. A phase-picking model is designed in this paper, which consists of an encoder and a decoder. The self-attention is applied in the encoder module to focus seismic features. Then a seismic phase-picking model transfer learning approach based on maximum mean discrepancy (SPPTL-MMD) is proposed to train a universal seismic feature extraction module by utilizing diverse seismic datasets. The module will be transferred and a significantly improved phase-picking model is obtained through fine-tuning techniques. Experiments show that the model after fine-tuning can be generalized to other datasets. © 2023 IEEE.

关键词： Signal encoding

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Performance Analysis of Structural Defect Classification Applied to Concrete Cracks Using Light-Weight Machine Learning models

Performance Analysis of Structural Defect Classification App...

引用

robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： Shreyas Sanjeev Desai Shashank Hegde Vidya Kalyandurg Rohan V Shruthi M L J Department of ECE PES University Bengaluru Karnataka India

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Detection of concrete cracks plays a vital role in structural defect analysis. Classification using machine learning techniques is one of the ways to perform the process. This paper discusses the performance analysis of light-weight models used to detect concrete cracks. The Concrete Crack Images for Classification (CCIC) dataset is used in the training and testing phases of NASNetMobile., MobileNet V2., DenseNet121., and VGG-19. Modifications in the networks include layers for binary classification of the data. The quantitative analysis proves the ability of NASNetMobile as a light-weight model while MobileNet V2 can be opted if training time is the constraint. The experimental results validate a trade-off between the usage of light-weight models., especially., NASNetMobile as against regular networks. To prove the tradeoff., the performance of light-weight models is compared with VGG-19 which is a regular model. Analysis proves to waive off the usage of regular models in comparison with the NASNetMobile model.

关键词： Training Analytical models Accuracy Computational modeling Machine learning Real-time systems Performance analysis

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A Teaching Mechanism of Supernumerary Robotic Limbs for Natural Human-robot Collaboration

A Teaching Mechanism of Supernumerary Robotic Limbs for Natu...

引用

international conference on Advanced robotics and Mechatronics (ICARM)

作者： Shiyang Liu Jing Luo Chao Zeng Ting Lei Chenguang Yang School of Automation Wuhan University of Technology Wuhan China Department of Informatics University of Hamburg Hamburg Germany School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan China School of Automation Science and Engineering South China University of Technology Guangzhou China

Supernumerary robotic limb (SRL) is an intelligent platform for providing assistance to humans at extreme conditions in natural human-robot collaboration (HRC) tasks. However, it is still difficult to control the SRL by using a simple way and to eliminate the cognitive burden placed on the human operator. of the human operator. Learning that the burden on human operators can be reduced by using Learning from Demonstrations (LfD) which learns repetitive teleoperation tasks. Our study introduces a method for teaching SRL that utilizes Dynamic Time Warping in combination with Gaussian Mixture Model and Gaussian Mixture Regression. The proposed mechanism utilizes a DTW algorithm to align and compare motion patterns between the human teacher and the SRL. Then, the GMM algorithm was applied to encode the motion patterns into statistical models. Additionally, these models are used to reate a task model that is applicable in a broad range of situations through the GMR method, which can be utilized to regulate the motion of SRL in real-time. The proposed mechanism is tested in experiments, and the results show that the SRL can accurately replicate the motion patterns with an average error of less than 10% for the HRC tasks. The proposed teaching mechanism has potential applications in fields such as rehabilitation and industrial automation, where the use of SRL can strengthen human capabilities and improve task efficiency.

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Distributed Photovoltaic Power Prediction Based on Spatiotemporal Attention Convolutional Networks under Weather-free Conditions

Distributed Photovoltaic Power Prediction Based on Spatiotem...

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2024 IEEE China international Youth conference on Electrical Engineering, CIYCEE 2024

作者： Peng, Xiaojin Luo, Bingyang Zhu, Guorong School of Automation Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9798331530174

With the massive integration of distributed photovoltaic (PV) systems into the power grid, accurate forecasting of distributed PV power generation is of great significance for the stable operation of power systems. Existing methods often neglect the spatiotemporal correlations among distributed PV systems, making it difficult to effectivelyutilize spatiotemporal feature information to improve power forecasting accuracy. Considering the strong spatiotemporal correlations among distributed PV outputs and the difficulty in obtaining meteorological data, this paper proposes a distributed PV power forecasting method based on Spatial- Time-Attention, Graph Convolutional Network (GCN), and Temporal Convolutional Network (TCN). This method achieves accurate PV power prediction under weather-free conditions based solely on historical power data. First, the spatial-time attention mechanism is used to extract the spatiotemporal dependencies of the distributed PV cluster output. Next, GCN and TCN are utilized to construct a spatiotemporal graph convolutional network for prediction training, obtaining the forecasting results. Finally, a simulation is conducted based on real data from a distributed PV power plant in a certain region of China. The case study results show that the proposed model has higher accuracy compared to traditional deep learning models. © 2024 IEEE.

关键词： Photovoltaics

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MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language models using 2D Priors 24

MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large ...

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32nd ACM international conference on Multimedia, MM 2024

作者： Tang, Yuan Han, Xu Li, Xianzhi Yu, Qiao Hao, Yixue Hu, Long Chen, Min Huazhong University of Science and Technology Wuhan China South China University of Technology Guangzhou China

ISBN: (纸本)9798400706868

Large 2D vision-language models (2D-LLMs) have gained significant attention by bridging Large Language models (LLMs) with images using a simple projector. Inspired by their success, large 3D point cloud-language models (3D-LLMs) also integrate point clouds into LLMs. However, directly aligning point clouds with LLM requires expensive training costs, typically in hundreds of GPU-hours on A100, which hinders the development of 3D-LLMs. In this paper, we introduce MiniGPT-3D, an efficient and powerful 3D-LLM that achieves multiple SOTA results while training for only 27 hours on one RTX 3090. Specifically, we propose to align 3D point clouds with LLMs using 2D priors from 2D-LLMs, which can leverage the similarity between 2D and 3D visual information. We introduce a novel four-stage training strategy for modality alignment in a cascaded way, and a mixture of query experts module to adaptively aggregate features with high efficiency. Moreover, we utilize parameter-efficient fine-tuning methods LoRA and Norm fine-tuning, resulting in only 47.8M learnable parameters, which is up to 260x fewer than existing methods. Extensive experiments show that MiniGPT-3D achieves SOTA on 3D object classification and captioning tasks, with significantly cheaper training costs. Notably, MiniGPT-3D gains an 8.12 increase on GPT-4 evaluation score for the challenging object captioning task compared to ShapeLLM-13B, while the latter costs 160 total GPU-hours on 8 A800. We are the first to explore the efficient 3D-LLM, offering new insights to the community. Code and weights are available at https://***/TangYuan96/MiniGPT-3D. © 2024 ACM.

关键词： Visual languages

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Constrained Passive Interaction Control: Leveraging Passivity and Safety for Robot Manipulators

Constrained Passive Interaction Control: Leveraging Passivit...

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IEEE international conference on robotics and automation (ICRA)

作者： Zhiquan Zhang Tianyu Li Nadia Figueroa School of Engineering and Applied Science University of Pennsylvania Pennsylvania PA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Passivity is necessary for robots to fluidly collaborate and interact with humans physically. Nevertheless, due to the unconstrained nature of passivity-based impedance control laws, the robot is vulnerable to infeasible and unsafe configurations upon physical perturbations. In this paper, we propose a novel control architecture that allows a torque-controlled robot to guarantee safety constraints such as kinematic limits, self-collisions, external collisions and singularities and is passive only when feasible. This is achieved by constraining a dynamical system based impedance control law with a relaxed hierarchical control barrier function quadratic program subject to multiple concurrent, possibly contradicting, constraints. Joint space constraints are formulated from efficient data-driven self- and external ${\mathcal{C}^2}$ collision boundary functions. We theoretically prove constraint satisfaction and show that the robot is passive when feasible. Our approach is validated in simulation and real robot experiments on a 7DoF Franka Research 3 manipulator.

关键词： Perturbation methods Kinematics Aerospace electronics Manipulators Control systems Safety Impedance

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An Innovative Approach to Glass Classification Using Deep Neural Network

An Innovative Approach to Glass Classification Using Deep Ne...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Md Shohanur Rahman Tania Sultana Wahid Tausif Islam Md. Rifat Ahmed Khan Department of CSE Daffodil International University Dhaka Bangladesh Department of CSE American International University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

A novel method for classifying glasses using deep neural networks is presented. The study uses data from the USA Forensic Science Service to classify six different varieties of glass according to their oxide level. The dataset is carefully cleaned up using preprocessing methods like Min-Max Scaling and Label Encoding to improve data quality and make it more neural network architecture compatible. The deep neural network model performs admirably in this difficult task, correctly categorizing glass samples with an accuracy of 81.40%. The analysis of the results shows that different varieties of glass behave differently, pointing out both areas of strength and improvement. In the future, the work may entail investigating more sophisticated deep learning techniques including transfer learning and attention processes, as well as broadening the dataset to include a greater range of glass samples. Moreover, validation tests conducted in the actual world and the model's implementation in industrial settings may offer important new perspectives on its usefulness. In general, this study advances automated glass categorization methods, which may have consequences for activities related to environmental sustainability, product creation, and quality assurance in a variety of sectors.

关键词： Accuracy Quality assurance Service robots Transfer learning Refining Glass Artificial neural networks Recycling Manufacturing Sustainable development

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Generation of Indian Sign Language Letters, Numbers, and Words

Generation of Indian Sign Language Letters, Numbers, and Wor...

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2024 international conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Yadav, Ajeet Kumar Kumar, Nishant Rathna, G.N. Indian Institute of Science Electrical Communication Engineering Banglore Bengaluru India Indian Institute of Science Computer Science and Automation Banglore Bengaluru India Indian Institute of Science Electrical Engineering Banglore Bengaluru India

ISBN: (纸本)9798350360660

Sign language, which contains hand movements, facial expressions and bodily gestures, is a significant medium for communicating with hard-of-hearing people. A well-trained sign language community communicates easily, but those who don't know sign language face significant challenges. Recognition and generation are basic communication methods between hearing and hard-of-hearing individuals. Despite progress in recognition, sign language generation still needs to be explored. The Progressive Growing of Generative Adversarial Network (ProGAN) excels at producing high-quality images, while the Self-Attention Generative Adversarial Network (SAGAN) generates feature-rich images at medium resolutions. Balancing resolution and detail is crucial for sign language image generation. We are developing a Generative Adversarial Network (GAN) variant that combines both models to generate feature-rich, high-resolution, and class-conditional sign language images. Our modified Attention-based model generates high-quality images of Indian Sign Language letters, numbers, and words, outperforming the traditional ProGAN in Inception Score (IS) and Frechet Inception Distance (FID), with improvements of 3.2 and 30.12, respectively. Additionally, we are publishing a large dataset incorporating high-quality images of Indian Sign Language alphabets, numbers, and 129 words. © 2024 IEEE.

关键词： Generative adversarial networks

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SPOTLIGHT: MOBILE UI UNDERSTANDING USING VISION-LANGUAGE models WITH A FOCUS 11

SPOTLIGHT: MOBILE UI UNDERSTANDING USING VISION-LANGUAGE MOD...

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11th international conference on Learning Representations, ICLR 2023

作者： Li, Gang Li, Yang Google Research Mountain ViewCA United States

Mobile UI understanding is important for enabling various interaction tasks such as UI automation and accessibility. Previous mobile UI modeling often depends on the view hierarchy information of a screen, which directly provides the structural data of the UI, with the hope to bypass challenging tasks of visual modeling from screen pixels. However, view hierarchies are not always available, and are often corrupted with missing object descriptions or misaligned structure information. As a result, despite the use of view hierarchies could offer short-term gains, it may ultimately hinder the applicability and performance of the model. In this paper, we propose Spotlight, a vision-only approach for mobile UI understanding. Specifically, we enhance a vision-language model that only takes the screenshot of the UI and a region of interest on the screen-the focus-as the input. This general architecture of Spotlight is easily scalable and capable of performing a range of UI modeling tasks. Our experiments show that our model establishes SoTA results on several representative UI tasks and outperforms previous methods that use both screenshots and view hierarchies as inputs. Furthermore, we explore multi-task learning and few-shot prompting capacities of the proposed models, demonstrating promising results in the multi-task learning direction. © 2023 11th international conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Image segmentation

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Integrating Visual Reasoning Localization with Reinforcement Learning for 3C Assembly

Integrating Visual Reasoning Localization with Reinforcement...

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IEEE international conference on robotics and Biomimetics

作者： Baolin Huang Huaidong Zhou Jiaqi Zhang Fuchun Sun Jiahui Guo Department of Computer Science and Technology Tsinghua University Beijing China School of Automation Beijing Information Science and Technology University Beijing China School of Automation Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In 3C (Computer, Communication, and Consumer Electronics) flexible assembly tasks, due to the frequent updates and the increasing demand for intelligence, traditional automated assembly methods struggle to meet the requirements for high precision and adaptability. To address these challenges, this paper proposes an assembly method that combines visual reasoning localization with deep reinforcement learning. The method improves the reward function by integrating visual reasoning results with reinforcement learning and trains the model in a virtual environment. Through sim-to-real transfer, the assembly strategies learned in the virtual environment are applied to real-world scenarios, significantly enhancing the assembly accuracy and success rate in real-world settings. Validation on a 3C front camera assembly task demonstrates an assembly accuracy of 0.15 mm and a success rate of 97%.

关键词： Location awareness Training Visualization Accuracy Virtual environments Cameras Cognition Assembly Smart phones Testing

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