检索结果-内蒙古大学图书馆

Model Predictive control for Human Following Rehabilitation Robot

学校读者我要写书评

暂无评论

Model Predictive Control for Human Following Rehabilitation ...

IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

作者： Shihao Yan Jing Tao Jian Huang Anshun Xue the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Ministry of Civil Affairs of the People's Republic of China National Research Center for Rehabilitation Technical Aids Beijing China the Beijing Advanced Innovation Center of Intelligent Robots and Systems Beijing China

A new type of rehabilitation robot that can follow the user side-by-side is designed for aiding the elderly and disabled. The Human Following Rehabilitation Robot (HFRR) consists of a metal armrest, a set of sensors and a differential mobile base. By predicting the user's speed in the future through the user's current walking speed and the preset path, the problem of human following side-by-side is converted to a problem of keeping a certain lateral distance from the predicted trajectory. Linearizing the tracking error model, the nonholonomic mobile robot (NMR) can maintain a certain lateral distance from the trajectory by using Model Predictive control (MPC) and then follow the user side-by-side. The simulation and experimental results show the proposed controller can obtain good performance in this problem.

关键词：

Improving magnetic nanothermometry accuracy through mixing-frequency excitation

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Guo, Silin Liu, Jay Du, ZhongZhou Liu, Wenzhong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China Ningbo Chuanshanjia Electrical and Mechanical Co. Ltd. Ningbo315400 China

In this study, we proposed a temperature model of magnetic nanoparticle relaxation and a phase measurement method under a mixing-frequency excitation field, which can improve the temperature accuracy of magnetic nanothermometry. According to the Debye-based magnetization model for magnetic nanoparticles, the phases at the mixing frequencies are used to solve the relaxation phase delay to the magnetic field with the higher frequency. The method could improve the signal-to-noise ratio of the magnetic response signal, and also weaken the phase shift of the detection coils caused by temperature changes. Experimental results show that the method can achieve static temperature measurement error less than 0.1K and dynamic temperature measurement error less than 0.2K. © 2020, CC-BY.

关键词： Temperature measurement

New intra prediction mode for interlaced video in AVS

学校读者我要写书评

暂无评论

Gaojishu Tongxin/Chinese High Technology Letters 2007年第3期17卷 221-225页

作者： Chen, Liang Tan, Yihua Tian, Jinwen State Key Laboratory of Education Ministry for Image Processing and Intelligent Control Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

The technologies of intra prediction and MBAFF were introduced, and a new intra prediction mode based on the characteristics of spatial distribution in interlaced video was proposed. The spatial correlation of five luma intra prediction modes in AVS-P2 and the new mode were analyzed. From the analysis result, it can be concluded that the new mode can exploit the spatial correlation better and predict the samples more precisely than the existed ones. The experimental results showed that the average gain in peak signal to noise ratio was above 0.12dB and the average reduction in bit-rate was above 1.77%, so the proposed mode is an effective prediction mode for improvement of coding performance.

关键词： AVS Interlaced video Intra prediction Video coding

BoostTree and BoostForest for Ensemble Learning

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Zhao, Changming Wu, Dongrui Huang, Jian Yuan, Ye Zhang, Hai-Tao Peng, Ruimin Shi, Zhenhua Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China The Autonomous Intelligence Unmanned Systems Engineering Research Center of Ministry of Education of China The State Key Lab of Digital Manufacturing Equipment and Technology Wuhan China

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This paper proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It increases the randomness (diversity) by drawing the cut-points randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest generally outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 35 classification and regression datasets. Remarkably, BoostForest tunes its parameters by simply sampling them randomly from a parameter pool, which can be easily specified, and its ensemble learning framework can also be used to combine many other base learners. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Hu, Bin-Bin Zhang, Hai-Tao Yao, Weijia Sun, Zhiyong Cao, Ming The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Robotics Hunan University Hunan410082 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands Department of Electrical Engineering Eindhoven University of Technology Netherlands

We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is characterized by three terms, i.e., a convergence term to drive the robots to converge to the desired surface, a propagation term to provide a traversing direction for maneuvering on the desired surface, and a coordinated term to achieve the surface motion coordination with an arbitrary ordering of the robotic group. By setting the surface parameters as additional virtual coordinates, the proposed approach eliminates potential singularity of the CGVF and enables both the global convergence to the desired surface and the maneuvering on the surface from all possible initial conditions. The ordering-flexible surface motion coordination is realized by each robot to share with its neighbors only two virtual coordinates, i.e. that of a given target and that of its own, which reduces the communication and computation cost in multi-robot surface navigation. Finally, the effectiveness of the CGVF is substantiated by extensive numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

Study on the Method of image Restoration in the Environment of Dust

学校读者我要写书评

暂无评论

Study on the Method of Image Restoration in the Environment ...

International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Yuanyu Wang Yuanzong Li Tianxu Zhang Department of Computer Fundamental Education Taiyuan University Of Technology Taiyuan China College of Mechanical Engineering Taiyuan University Of Technology Taiyuan China Institute of Pattern Recognition and A.I State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology Wuhan China

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, GA optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with automatic threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments.

关键词： image edge detection image restoration Scattering Degradation Gallium Atmospheric modeling Genetic algorithms

Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Liu, Tianying Zhang, Lu Wang, Yang Guan, Jihong Fu, Yanwei Zhao, Jiajia Zhou, Shuigeng Department of Computer Science and Technology Tongji University Shanghai201804 China The Project Management Office of China National Scientific Seafloor Observatory Tongji University Shanghai200092 China Shanghai Key Lab of Intelligent Information Processing The School of Computer Science Fudan University Shanghai200438 China Shanghai Key Lab of Intelligent Information Processing The School of Data Science Fudan University Shanghai200433 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. Especially, the research in this emerging field has been flourishing in recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [58, 59, 74, 78] that systematically study and compare them as the groups of fine-tuning/transfer learning, and meta-learning methods. In contrast, we review the existing FSOD algorithms from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Specifically, we give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols. The taxonomy is then proposed that groups FSOD methods into three types. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

A support value transform-based multi focus image fusion method

学校读者我要写书评

暂无评论

A support value transform-based multi focus image fusion met...

International Conference on Signal processing Proceedings (ICSP)

作者： Sheng Zheng Jian Liu Guangxi Zhu Jinwen Tian Electronic & Information Engineering Department Huazhong University of Science and Technology Wuhan China College of Electrical Engineering & Information Technology China Three Gorges University Yichang China State Edu Commission Key Laboratory for Image Processing & Intelligent Control HUST Wuhan China Chinese Key laboratory of optic & electronics HUST Wuhan China

The image fusion is an important approach to produce a single complete image which preserves all relevant information from different sensors. In this paper, we proposed a support value transform-based multi focus image fusion method, where the fused saliency features are represented by support values. Based on the mapped least squares support vector machine, the support value transform is developed as a multi-scale analysis tool. The fusing results on the multi focus images demonstrate that the proposed image fusion method is effective and efficient

关键词： Focusing image fusion Support vector machines Discrete wavelet transforms Optical sensors image sensors Sensor fusion Support vector machine classification image processing Least squares methods

Distributed observer for LTI systems under stochastic impulsive sequential attacks

学校读者我要写书评

暂无评论

AUTOMATICA 2024年 159卷

作者： Su, Housheng Wu, Yali Zheng, Wei Xing Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Mini Educ Minist China Luoyu Rd 1037 Wuhan 430074 Peoples R China Western Sydney Univ Sch Comp Data & Math Sci Sydney NSW 2751 Australia

In this paper, we consider the distributed state estimation problem of continuous-time linear time -invariant (LTI) systems, where communication channels are attacked by stochastic impulsive se-quences. In a networked system, the communication channel between an agent and its neighbor is vulnerable to being attacked. Continuous attacks have been widely studied so far, but with the development of attack techniques, intermittent attacks have been emerging, which are more difficult to be identified and detected, and have not been well portrayed and studied. Therefore, this paper investigates a random intermittent injection attack, which leads to abrupt and random changes of the state information transmitted by the neighbors. First, this attack is modeled as a stochastic impulsive sequential attack, where both its impulsive time and impulsive intensity are random. Then a distributed observer under stochastic impulsive sequential attack is proposed. Since the impulsive attack destroys the stability of the system, in order to attenuate the impact of the impulsive attack, a large feedback gain is necessary. By using the graph theory, matrix theory and stochastic analysis, sufficient conditions for the existence of the distributed observer in the sense of mean square and in the sense of almost sure are presented, respectively. Finally, the effectiveness of the proposed distributed observer is verified by numerical simulations.(c) 2023 The Author(s). Published by Elsevier *** is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Distributed state estimation Stochastic impulsive sequential attack Communication perturbations