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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24757 条记录，以下是4541-4550 订阅

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

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

引用

IEEE international Joint conference on Biometrics (IJCB)

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

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

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.

关键词： Adaptation models Analytical models Face recognition Biological system modeling Transfer learning Natural languages Benchmark testing Robustness Security Visual perception

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Global Localization of Mobile Robots Based on Dynamic Partitioning Global Descriptor*

Global Localization of Mobile Robots Based on Dynamic Partit...

引用

IEEE international conference on Real-time Computing and robotics (RCAR)

作者： Shiyun Xue Jingchuan Wang Peng Xiao Rui Guo Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University China State Grid Intelligence Technology CO. LTD

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Global localization is a challenging problem as it is difficult for a mobile robot to obtain the current pose without the prior pose. It relies on prior environmental maps and sensor information. When the robot is in an environment with similar features or sparse features, using existing global localization methods may cause localization failure. In order to solve the localization failure of mobile robots in both indoor and outdoor environments, this paper proposes a global localization system based on dynamic partition global descriptors for 3D point cloud maps. The system is evaluated in indoor corridors and outdoor campus environments. The results show that dynamic partition global descriptors can improve the overall success rate of localization, which reaches more than 90% in different scenarios. The method effectively balances the accuracy and speed of global localization, optimizing the global localization system.

关键词： Location awareness Point cloud compression Three-dimensional displays Accuracy Wheelchairs Robot sensing systems Real-time systems

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FKA-Owl: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs 24

FKA-Owl: Advancing Multimodal Fake News Detection through Kn...

引用

32nd ACM international conference on Multimedia, MM 2024

作者： Liu, Xuannan Li, Peipei Huang, Huaibo Li, Zekun Cui, Xing Liang, Jiahao Qin, Lixiong Deng, Weihong He, Zhaofeng Beijing University of Posts and Telecommunications Beijing China Institute of Automation Chinese Academy of Sciences Beijing China University of California Santa Barbara Goleta United States

ISBN: (纸本)9798400706868

The massive generation of multimodal fake news involving both text and images exhibits substantial distribution discrepancies, prompting the need for generalized detectors. However, the insulated nature of training restricts the capability of classical detectors to obtain open-world facts. While Large Vision-Language models (LVLMs) have encoded rich world knowledge, they are not inherently tailored for combating fake news and struggle to comprehend local forgery details. In this paper, we propose FKA-Owl, a novel framework that leverages forgery-specific knowledge to augment LVLMs, enabling them to reason about manipulations effectively. The augmented forgery-specific knowledge includes semantic correlation between text and images, and artifact trace in image manipulation. To inject these two kinds of knowledge into the LVLM, we design two specialized modules to establish their representations, respectively. The encoded knowledge embeddings are then incorporated into LVLMs. Extensive experiments on the public benchmark demonstrate that FKA-Owl achieves superior cross-domain performance compared to previous methods. Code is publicly available at https://***/***/. © 2024 ACM.

关键词： Visual languages

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Hot Water Load Prediction Based on a Combined Model of CNN-GRU and TCN 4

Hot Water Load Prediction Based on a Combined Model of CNN-G...

引用

4th international conference on Advanced Electrical and Energy Systems, AEES 2023

作者： Cheng, Yongyuan Qian, Ziwen Bao, Keqin Shanghai University of Electric Power School of Automation Engineering Shanghai China

ISBN: (纸本)9798350342659

The imbalance between water supply and demand in the hot water systems of universities has become increasingly prominent, leading to long-term resource wastage. Traditional methods struggle to capture the dynamic changes in hot water usage, making it challenging to accurately predict hot water demand in universities. This poses significant importance for water management and design in educational *** learning methods are well-suited for handling nonlinear load data and have shown significant effectiveness in load forecasting. In this paper, we propose a combined approach using Temporal Convolutional Networks (TCN) and Convolutional Neural Networks with Gated Recurrent Units (CNN-GRU) for predicting the hot water demand in university campuses. Collect historical hot water load data and combine it with relevant influencing factors such as daily maximum temperature, meteorological information, and corresponding holiday schedules to construct a rich set of input samples. We consider time features like periodicity and trends. Train the neural network model using several years of hot water load data from the campus and validate its predictive *** results indicate that this model can effectively capture the temporal variations in hot water usage and achieve high prediction accuracy. This research offers a new perspective on water demand forecasting for universities. © 2023 IEEE.

关键词： Neural network models

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Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation

Learning Risk-Aware Costmaps via Inverse Reinforcement Learn...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Samuel Triest Mateo Guaman Castro Parv Maheshwari Matthew Sivaprakasam Wenshan Wang Sebastian Scherer Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Mathematics Indian Institute of Technology Kharagpur

The process of designing costmaps for off-road driving tasks is often a challenging and engineering-intensive task. Recent work in costmap design for off-road driving focuses on training deep neural networks to predict costmaps from sensory observations using corpora of expert driving data. However, such approaches are generally subject to over-confident mis-predictions and are rarely evaluated in-the-loop on physical hardware. We present an inverse reinforcement learning-based method of efficiently training deep cost functions that are uncertainty-aware. We do so by leveraging recent advances in highly parallel model-predictive control and robotic risk estimation. In addition to demonstrating improvement at reproducing expert trajectories, we also evaluate the efficacy of these methods in challenging off-road navigation scenarios. We observe that our method significantly outperforms a geometric baseline, resulting in 44% improvement in expert path reconstruction and 57% fewer interventions in practice. We also observe that varying the risk tolerance of the vehicle results in qualitatively different navigation behaviors, especially with respect to higher-risk scenarios such as slopes and tall grass. 3 3 More detailed algorithms and additional visualizations are provided in the appendix Appendix (appendix link: ***/mtkj63e8)

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Efficient RRT*-based Safety-Constrained Motion Planning for Continuum Robots in Dynamic Environments

Efficient RRT*-based Safety-Constrained Motion Planning for ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Peiyu Luo Shilong Yao Yiyao Yue Jiankun Wang Hong Yan Max Q.-H. Meng Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Department of Electrical Engineering City University of Hong Kong Hong Kong China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Continuum robots, characterized by their high flexibility and infinite degrees of freedom (DoFs), have gained prominence in applications such as minimally invasive surgery and hazardous environment exploration. However, the intrinsic complexity of continuum robots requires a significant amount of time for their motion planning, posing a hurdle to their practical implementation. To tackle these challenges, efficient motion planning methods such as Rapidly Exploring Random Trees (RRT) and its variant, RRT*, have been employed. This paper introduces a unique RRT*-based motion control method tailored for continuum robots. Our approach embeds safety constraints derived from the robots’ posture states, facilitating autonomous navigation and obstacle avoidance in rapidly changing environments. Simulation results show efficient trajectory planning amidst multiple dynamic obstacles and provide a robust performance evaluation based on the generated postures. Finally, preliminary tests were conducted on a two-segment cable-driven continuum robot prototype, confirming the effectiveness of the proposed planning approach. This method is versatile and can be adapted and deployed for various types of continuum robots through parameter adjustments.

关键词： Performance evaluation Minimally invasive surgery Trajectory planning Simulation Dynamics Prototypes Planning

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Accurate tracking of multirotor UAVs based on acceleration feedback control and smooth anti-disturbance 1

Accurate tracking of multirotor UAVs based on acceleration f...

引用

1st Aerospace Frontiers conference, AFC 2024

作者： Bowen, Zhang Hao, Zhang Qijun, Zhou Jianchuan, Ye Tao, Jiang School of Automation Chongqing University Chongqing401331 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9781510681613

Conventional design methods of multirotor controllers are based on the inverse of kinematic and dynamical models, and their open-loop structure makes acceleration control defective. Therefore, this thesis designs an acceleration feedback-based controller to refine the thrust control structure and ensure strong robustness in the presence of inaccurate model and aerodynamic parameter estimates. In addition, to compensate for external disturbances and suppress the effect of high-frequency noise on the conventional observer, this thesis develops an adaptive dead-zone extended state observer, which ensures accurate and smooth disturbance estimation. The experimental results of the numerical simulations show that: 1) during hovering, when subjected to a sudden disturbance of 5 N, the AFBC converges faster, with a smaller maximal positional error and a stabilization error is less than 1 cm;2) the maximum error of the proposed control strategy is less than 3 cm when tracking a dynamic circle with a radius of 2 m and a maximum speed of 2 m/s under a wind disturbance environment of 5 m/s. In summary, the proposed control strategy significantly improves the robustness, disturbance resistance, and tracking accuracy of the multirotor with model uncertainty in a complex environment. © 2024 SPIE.

关键词： Feedback control

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A Finite Element Approach for Forearm Neuromodulation: Impact of Electrode Size on the Current Density 12

A Finite Element Approach for Forearm Neuromodulation: Impac...

引用

12th international conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2023

作者： Salkim, Enver London United Kingdom Department of Electronic and Automation Muş Alparslan University Muş Turkey

Electrical impedance myography is a transcutaneous neuromodulation method for assessing muscle conditions through the application of a high-frequency, low-intensity current to the muscle region of interest (ROI). It has been shown that the mechanisms underpinning these findings are controversial as studies showed that the current reaching the target structure may not be enough to activate tissue due to various factors. It has been shown that anatomical properties as well as non-anatomical factors including electrode shape and size, inter-electrode distance may affect the outcome. This study was conducted to investigate the impact of the different sizes of the electrodes on the current density of the ROI. It may not be feasible to investigate these parameters impact on the outcome using experimental procedures. Alternatively, the computational methos have been used as a tool to study electrical stimulation of bio-computational models. The neuromodulators can be designed and developed using such advanced methods. This study investigates the impact of the electrode size on the current distributions. The fundamental anatomical layers of the human forearm were generated based on standard dimensions using concentric shapes. A sinusoidal bipolar current pulse was applied on the different sizes of electrodes to simulate current distribution within the associated anatomical layers. It was shown that the electrode size has a significant impact on the induced current density of the target anatomical layer. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Muscle

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Updating the Content of an AI Course for Students from the robotics and automation 31

Updating the Content of an AI Course for Students from the R...

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31st Annual conference of the European-Association-for-Education-in-Electrical-and-Information-Engineering (EAEEIE)

作者： Cojocaru, Dorian Manta, Liviu Florin Abagiu, Marian Dragomir, Andrei Mariniuc, Alexandru Marin Univ Craiova Mechatron & Robot Dept Craiova Romania

ISBN: (纸本)9781665484428

The impact of artificial intelligence applications in today's society is constantly growing. In this context, it is important to adapt the curricula of artificial intelligence courses with an emphasis on the practical applicability of new concepts and methods. At the University of Craiova, Romania, such a course is offered to undergraduate students in robotics and automation. The course includes chapters dedicated to problem solving, fuzzy logic, neural networks, and expert systems, with the identification of techniques related to the representation of knowledge and deep learning, respectively. This paper will present how to update the chapter on deep learning, referring to practical applications developed by groups of teachers, researchers and students from the faculty. The following applications are considered. An application is related to the management of a mobile robot that helps people with special needs in terms of control of the lower and upper limbs but who also have specific problems related to their visual system. Another application refers to the use of artificial intelligence techniques in applications for detecting casting defects in car engines. Another chapter that is to be introduced in the course refers to bots - cooperative software robots. The introduction of this chapter is also related to the international presence of an important number of highly successful companies that have been established by Romanian entrepreneurs. The three applications are demonstrated during the practical classes of the artificial intelligence course. Latest hardware platforms (i.e. nVidia Jetson Nano development boards) are used, and trending development environments, languages (Python, C++), and libraries (Numpy, Pandas, Matplotlib, SciKits) are implied. The inclusion of the presentation of these practical applications aims to increase the students' confidence that the knowledge received increases not only their level of theoretical knowledge, but also their chances

关键词： artificial intelligence engineering application course curriculum

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Data Augmentation Method for Meter Instance Segmentation under Data Balancing Strategy 16

Data Augmentation Method for Meter Instance Segmentation und...

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16th IEEE international conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Wang, Zhaolin Tian, Lianfang Du, Qiliang Zhao, Zhiyao An, Yi Sun, Zhengzheng School of Automation Science and Engineering South China University of Technology Guangzhou China School of Future Technology South China University of Technology Guangzhou China

ISBN: (纸本)9798350327144

With the development of smart grids, the methods of using cameras or robots for image acquisition and recognition of meters are gradually replacing traditional manual inspections. To enable more efficient meter recognition by deep learning models, the quality of training data is of paramount importance, with data imbalance being a major challenge in rough data. We propose a data augmentation method for meter instance segmentation under a data balancing strategy, called meter balanced object paste (MeterBOP), which pastes meter objects into images of different scenes according to predefined rules, thereby optimizing the data distribution. The approach selects the pasting meter types by class-balanced sampling and scales them to various sizes for scale balance. The pasting regions are generated according to the distribution of existing objects in the image, thus achieving spatial balance while avoiding severe occlusion caused by introducing new objects. Experiments demonstrate that our method improves the performance of Mask R-CNN by 3.1 Box mAP and 1.0 Mask mAP on power meter image-mask (PMI-M) dataset. Furthermore, the method can provide stable performance improvements under various detectors and conditions, exhibiting superior performance enhancement compared to other data augmentation methods. © 2023 IEEE.

关键词： Image enhancement

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