检索结果-内蒙古大学图书馆

Chinese Automation Congress (CAC)

作者： Yilong Liu Cong Zhang Sihang Zhang Jiaxing Li Beijing Institute of Control Engineering Beijing China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing China

The autonomous mission planning problem of space targets tracking with multi-satellite is studied in this paper. For effective evaluation of different candidate tracking resource combinations, a tracking performance evaluation model based on GDOP is proposed firstly, then a tracking priorities evaluation model for maneuverable dynamic targets is designed to autonomous evaluate and update the planning priorities during the tracking. To solve the problem of conflict resolution of tracking resources scheduling, the reinforcement learning algorithm is applied, considering of restricted computation resources onboard. A pointer network is utilized as the strategy model, with the tracking targets and corresponding priorities, the tracking performance evaluation of each potential candidate tracking resources combinations as input tensors, and the tracking tasks allocation of multi-satellites will be inferenced by the network accordingly. The actor-critic reinforcement learning algorithm is selected for training. It is shown that the algorithm proposed can formulate better planning strategies than the heuristic method, which reduces the mean tracking error by 21% during the whole tracking process.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on Dataset Preparation and Registration Methods for Multi-Temporal Airport SAR Images 2

Research on Dataset Preparation and Registration Methods for...

引用

2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Wu, Guanghui Liu, Lujiao Li, Jianhao Pan, Hongxin Shi, Hao Beijing Institute of Technology Chongqing Innovation Center Chongqing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Synthetic Aperture Radar (SAR) offers high-resolution imaging capabilities that are unaffected by weather and lighting conditions, making it ideal for complex environments. Recent advancements in spaceborne SAR technology have provided diverse high-resolution, multi-temporal SAR data, crucial for applications like airport monitoring. Despite these advancements, there is a notable lack of multi-temporal airport SAR datasets for research. This study addresses this gap by presenting a method to create a multi-temporal airport SAR image dataset. Utilizing known latitude and longitude information, the method converts geographic coordinates to image projection coordinates to locate airports in SAR images, followed by image conversion and alignment to construct the dataset. Additionally, this study introduces a registration method named SAR-SURF for multi-temporal SAR images, employing a SURF-based Feature Points Detector and an improved Fast Sample Consensus (FSC) algorithm. The experimental results demonstrate that this method effectively reduces speckle noise, ensures matching accuracy, and minimizes processing time, enabling rapid and precise registration of multi-temporal airport SAR images. © 2024 IEEE.

关键词： Image registration

来源：评论

学校读者我要写书评

暂无评论

Visual Instruction Tuning for Holistic and Regional Remote Sensing Imagery Comprehension 2

Visual Instruction Tuning for Holistic and Regional Remote S...

引用

2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Zhang, Wei Cai, Miaoxin Zhang, Tong Yin, Zhuang Mao, Xuerui National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing China Advanced Research Institute of Multidisciplinary Sciences China School of Mechatronical Engineering Yangtze Delta Region Academy of Beijing Institute of Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331515669

Recently, the visual instruction multimodal large language models (MLLMs) have been extensively studied in the nature scenario. However, current remote sensing (RS) MLLMs mainly focus on image-level understanding and typically allow interaction only through text instructions, resulting in limited accuracy and efficiency. To address those limitations, a visual instruction model is proposed in this article to extend MLLM's fine-grained perception ability by incorporating bounding boxes with language instruction, aiming at achieving region-level visual understanding. To achieve this goal, a visual instruction dataset featuring multi-modal box-based region-text pairs is constructed. Furthermore, the visual instructions and images are encoded by the different encoders and subsequently fed into a large language model (LLM) along with text instruction tokens for tuning. Finally, experimental results demonstrate the proposed model's promising performance in region-level image comprehension. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Resilient Time-Varying Formation-Tracking of Multi-UAV Systems Against Composite Attacks: A Two-Layered Framework

引用

IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 969-984页

作者： Xin Gong Michael V.Basin Zhiguang Feng Tingwen Huang Yukang Cui School of Cyber Science and Engineering Southeast UniversityNanjing 210096the School of AstronauticsNorthwestern Polytechnical UniversityXi’an 710072 Department of Mechanical Engineering The University of Hong KongHong Kong 999077China School of Physical and Mathematical Sciences the Autonomous University of Nuevo LeonNaevo Leon 66455Mexico Ningbo Institute of Intelligent Equipment Technology Ningbo 315299China College of Intelligent Systems Science and Engineering Harbin Engineering UniversityHarbin 150001China Texas A&M University at Qatar Doha 23874Qatar Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots the College of Mechatronics and Control EngineeringShenzhen UniversityShenzhen 518060 Peng Cheng Laboratory Shenzhen 518000China

This paper studies the countermeasure design problems of distributed resilient time-varying formation-tracking control for multi-UAV systems with single-way communications against composite attacks,including denial-of-services(DoS)attacks,false-data injection attacks,camouflage attacks,and actuation attacks(AAs).Inspired by the concept of digital twin,a new two-layered protocol equipped with a safe and private twin layer(TL)is proposed,which decouples the above problems into the defense scheme against DoS attacks on the TL and the defense scheme against AAs on the cyber-physical ***,a topologyrepairing strategy against frequency-constrained DoS attacks is implemented via a Zeno-free event-triggered estimation scheme,which saves communication resources *** upper bound of the reaction time needed to launch the repaired topology after the occurrence of DoS attacks is ***,a decentralized adaptive and chattering-relief controller against potentially unbounded AAs is ***,this novel adaptive controller can achieve uniformly ultimately bounded convergence,whose error bound can be given *** practicability and validity of this new two-layered protocol are shown via a simulation example and a UAV swarm experiment equipped with both Ultra-WideBand and WiFi communication channels.

关键词： Composite attacks multi-UAV systems resilient control time-varying formation-tracking

来源：评论

学校读者我要写书评

暂无评论

Evolution 6.0: Evolving Robotic Capabilities Through Generative Design

arXiv

引用

arXiv 2025年

作者： Khan, Muhammad Haris Myshlyaev, Artyom Lykov, Artem Cabrera, Miguel Altamirano Tsetserukou, Dzmitry Intelligent Space Robotics Laboratory Center for Digital Engineering Skolkovo Institute of Science and Technology Russia

We propose a new concept, Evolution 6.0, which represents the evolution of robotics driven by Generative AI. When a robot lacks the necessary tools to accomplish a task requested by a human, it autonomously designs the required instruments and learns how to use them to achieve the goal. Evolution 6.0 is an autonomous robotic system powered by Vision-Language Models (VLMs), Vision-Language Action (VLA) models, and Text-to-3D generative models for tool design and task execution. The system comprises two key modules: the Tool Generation Module, which fabricates task-specific tools from visual and textual data, and the Action Generation Module, which converts natural language instructions into robotic actions. It integrates QwenVLM for environmental understanding, OpenVLA for task execution, and Llama-Mesh for 3D tool generation. Evaluation results demonstrate a 90% success rate for tool generation with a 10-second inference time and action generation achieving 83.5% in physical and visual generalization, 70% in motion generalization, and 37% in semantic generalization. Future improvements will focus on bimanual manipulation, expanded task capabilities, and enhanced environmental interpretation to improve real-world adaptability. © 2025, CC BY-NC-SA.

关键词： Design

来源：评论

学校读者我要写书评

暂无评论

One-step Framework for RF-Based Drone Identification

One-step Framework for RF-Based Drone Identification

引用

Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Jiaxin Ge Hanshuo Zhang Jinhui Li Zhijin Wen Laboratory of Electromagnetic Space Cognition and Intelligent Control Technology Beijing China Xidian University Xian China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

With the popularity of drones, incidents that threaten public safety caused by drones occur more frequently. In drone control and countermeasures scenarios, it is needed to further identify drones of the same type based on type recognition in order to determine the specific source of the signal. Existing schemes are multistep, which increases the complexity and overhead of post-maintenance. In this work, we propose a joint scheme based on RE fingerprinting where the type and individual belonging to the same type are identified in one step. Specifically, we adopt short-time Fourier transform to calculate the spectrogram of the received signal and label the types and individuals of the drone signals on the spectrogram to develop a data set. Based on the data set, we train a YOLOv5 neural network to extract detailed RF characteristics to identify which type and individual the signal belongs to. To further improve the identification performance, we introduce grayscale image colorization. Furthermore, we use median filtering to reduce noise and protect image edge information. Results on data set composed of real signals from 18 drones of 9 types show the classification accuracy of our proposed framework reaches over $98\%$ at saturation and $3\%$ higher than the method without grayscale image colorization.

关键词： YOLO Radio frequency RF signals Noise Neural networks Gray-scale Fingerprint recognition

来源：评论

学校读者我要写书评

暂无评论

LLM-Glasses: GenAI-driven Glasses with Haptic Feedback for Navigation of Visually Impaired People

arXiv

引用

arXiv 2025年

作者： Tokmurziyev, Issatay Cabrera, Miguel Altamirano Khan, Muhammad Haris Mahmoud, Yara Moreno, Luis Tsetserukou, Dzmitry Intelligent Space Robotics Laboratory Center for Digital Engineering Skolkovo Institute of Science and Technology Russia

We present LLM-Glasses, a wearable navigation system designed to assist visually impaired individuals by combining haptic feedback, YOLO-World object detection, and GPT-4o-driven reasoning. The system delivers real-time tactile guidance via temple-mounted actuators, enabling intuitive and independent navigation. Three user studies were conducted to evaluate its effectiveness: (1) a haptic pattern recognition study achieving an 81.3% average recognition rate across 13 distinct patterns, (2) a VICON-based navigation study in which participants successfully followed predefined paths in open spaces, and (3) an LLM-guided video evaluation demonstrating 91.8% accuracy in open scenarios, 84.6% with static obstacles, and 81.5% with dynamic obstacles. These results demonstrate the system’s reliability in controlled environments, with ongoing work focusing on refining its responsiveness and adaptability to diverse real-world scenarios. LLM-Glasses showcases the potential of combining generative AI with haptic interfaces to empower visually impaired individuals with intuitive and effective mobility solutions. © 2025, CC BY-NC-ND.

关键词： Haptic interfaces

来源：评论

学校读者我要写书评

暂无评论

Negative self-feedback induced enhancement and transition of spiking activity for class-3 excitability

引用

Chinese Physics B 2022年第7期31卷 203-213页

作者： Li Li Zhiguo Zhao Huaguang Gu Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent ManufacturingGuangdong Academy of SciencesGuangzhou 510070China School of Science Henan Institute of TechnologyXinxiang 453003China School of Aerospace Engineering and Applied Mechanics Tongji UniversityShanghai 200092China

Post-inhibitory rebound(PIR)spike,which has been widely observed in diverse nervous systems with different physiological functions and simulated in theoretical models with class-2 excitability,presents a counterintuitive nonlinear phenomenon in that the inhibitory effect can facilitate neural firing *** this study,a PIR spike induced by inhibitory stimulation from the resting state corresponding to class-3 excitability that is not related to bifurcation is simulated in the Morris–Lecar ***,the inhibitory self-feedback mediated by an autapse with time delay can evoke tonic/repetitive spiking from phasic/transient *** dynamical mechanism for the PIR spike and the tonic/repetitive spiking is acquired with the phase plane analysis and the shape of the quasi-separatrix *** result extends the counterintuitive phenomenon induced by inhibition to class-3 excitability,which presents a potential function of inhibitory autapse and class-3 neuron in many neuronal systems such as the auditory system.

关键词： post-inhibitory rebound spike class 3 excitability inhibitory autapse neuronal firing

来源：评论

学校读者我要写书评

暂无评论

***: DRL-based Autonomous Drone Landing on Moving 3D Surface in the Presence of Aerodynamic Disturbances

***: DRL-based Autonomous Drone Landing on Moving 3D Surface...

引用

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Robinroy Peter Lavanya Ratnabala Demetros Aschu Aleksey Fedoseev Dzmitry Tsetserukou Intelligent Space Robotics Laboratory CDE Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Autonomous drone landing on dynamic platforms presents formidable challenges due to unpredictable velocities and aerodynamic disturbances. This study introduces an advanced Deep Reinforcement Learning (DRL) agent, ***, designed to navigate and land on 3D moving platforms in the presence of aerodynamic disturbance and sudden velocity changes, thereby enhancing drone autonomy and safety. *** is trained in the gym-pybullet-drone simulation, an environment that mirrors real-world complexities, including wind disturbance, to ensure the agent's robustness and adaptability. The agent's capabilities were empirically validated with Crazyflie 2.1 drones across various test scenarios, encompassing both simulated environments and real-world conditions. The experimental results showcased ***'s high-precision landing and its ability to adapt to moving platforms, even under wind-induced disturbances. Furthermore, our system performance was bench-marked against a baseline Proportional-integral-derivative (PID) controller augmented with an Extended Kalman Filter (EKF), illustrating significant improvements in landing precision and error recovery. This research not only advances drone landing technologies, essential for inspection and emergency applications, but also highlights the potential of DRL in addressing intricate aerodynamic challenges.

关键词： Three-dimensional displays Navigation System performance Aerodynamics Robustness Safety PD control

来源：评论

学校读者我要写书评

暂无评论

Musinger: Communication of Music over a Distance with Wearable Haptic Display and Touch Sensitive Surface

Musinger: Communication of Music over a Distance with Wearab...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Miguel Altamirano Cabrera Muhammad Haris Khan Ali Alabbas Luis Moreno Issatay Tokmurziyev Dzmitry Tsetserukou Intelligent Space Robotics Laboratory Skolkovo Institute of Science and Technology (Skoltech) Moscow Russia

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

This study explores the integration of auditory and tactile experiences in musical haptics, focusing on enhancing sensory dimensions of music through touch. Addressing the gap in translating auditory signals to meaningful tactile feedback, our research introduces a novel method involving a touch-sensitive recorder and a wearable haptic display that captures musical interactions via Force Sensitive Resistor (FSR) and converts these into tactile sensations. Previous studies have shown the potential of haptic feedback to enhance musical expressivity, yet challenges remain in conveying complex musical nuances. Our method aims to expand music accessibility for individuals with hearing impairments and deepen digital musical interactions. Experimental results reveal high user melodies recognition through tactile feedback (93% without noise cancellation and 98% without noise cancelation), demonstrating effective transmission and perception of musical information. The findings highlight the potential of haptic technology to bridge sensory gaps, offering significant implications for music therapy, education, and remote musical collaboration, advancing the field of musical haptics and multi-sensory technology applications.

关键词： Resistors Bridges Force Music Tactile sensors Medical treatment Focusing Collaboration Noise cancellation Haptic interfaces

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：