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arXiv 2023年

作者： Zhang, Lu Zhang, Chenbo Zhao, Jiajia Guan, Jihong Zhou, Shuigeng Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China Department of Computer Science & Technology Tongji University China

Zero-shot object detection aims to localize and recognize objects of unseen classes. Most of existing works face two problems: the low recall of RPN in unseen classes and the confusion of unseen classes with background. In this paper, we present the first method that combines DETR and meta-learning to perform zero-shot object detection, named Meta-ZSDETR, where model training is formalized as an individual episode based meta-learning task. Different from Faster R-CNN based methods that firstly generate class-agnostic proposals, and then classify them with visual-semantic alignment module, Meta-ZSDETR directly predict class-specific boxes with class-specific queries and further filter them with the predicted accuracy from classification head. The model is optimized with meta-contrastive learning, which contains a regression head to generate the coordinates of class-specific boxes, a classification head to predict the accuracy of generated boxes, and a contrastive head that utilizes the proposed contrastive-reconstruction loss to further separate different classes in visual space. We conduct extensive experiments on two benchmark datasets MS COCO and PASCAL VOC. Experimental results show that our method outperforms the existing ZSD methods by a large margin. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Meta-ZSDETR: Zero-shot DETR with Meta-learning

Meta-ZSDETR: Zero-shot DETR with Meta-learning

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International Conference on Computer Vision (ICCV)

作者： Lu Zhang Chenbo Zhang Jiajia Zhao Jihong Guan Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China Department of Computer Science & Technology Tongji University China

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Impedance Modeling of PMSG Wind Farms from the Machine Side DC-Port with Outer Power Loop

Impedance Modeling of PMSG Wind Farms from the Machine Side ...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Jiajun Wang Xiaozhong Liao Bin Liu Xi Chen Xiangdong Liu Xiaoqin Lian Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing 100081 China GEIRI North America CA 95134 USA Beijing Technology and Business University Beijing 100048 China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Due to the advantages of large unit capacity, high efficiency and high reliability, direct-drive permanent magnet synchronous generators (PMSGs) wind turbines (WTs) have been widely used in the offshore wind power generation. The power loop controller aims to maintain the DC-Bus voltage of each PMSG-WT is equal to the others, guaranteeing that multiple WTs can be connected in DC serial parallel collection. In this paper, the DC-Port sequence impedance model of the PMSG-WT is established based on the harmonic linearization method, which takes the outer power loop into consideration. The impedance model is further verified by point-by-point scanning in MATLAB/Simulink. The proposed DC-Port impedance model of the PMSG-WT facilitates the establishment of offshore direct-drive wind farms, and is of great significance for analyzing and improving the system stability.

关键词： Impedance Mathematical model Harmonic analysis Wind farms Generators Power system stability Topology

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Malaria Cell Counting Diagnosis within Large Field of View

Malaria Cell Counting Diagnosis within Large Field of View

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Li-hui Zou Jie Chen Juan Zhang Narciso Garcia Education Ministry Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China Grupo de Tratamiento de Imágenes E.T.S.I. Telecomunicación Universidad Politécnica de Madrid Madrid Spain

Malaria is one of the most serious parasitic infections of human. The accurate and timely diagnosis of malaria infection is essential to control and cure the disease. Some image processing algorithms to automate the diagnosis of malaria on thin blood smears are developed, but the percentage of parasitaemia is often not as precise as manual count. One reason resulting in this error is ignoring the cells at the borders of images. In order to solve this problem, a kind of diagnosis scheme within large field of view (FOV) is proposed. It includes three steps. The first step is image mosaicing to obtain large FOV based on space-time manifolds. The second step is the segmentation of erythrocytes where an improved Hough Transform is used. The third step is the detection of nucleated components. At last, it is concluded that the counting accuracy of malaria infection within large FOV is finer than several regular FOVs.

关键词： Diseases Strips Brightness Blood Image color analysis Image motion analysis Computer vision

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

Deep Domain Adaptation Regression for Force Calibration of O...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Zhuo Chen Ni Ou Jiaqi Jiang Shan Luo Department of Engineering Robot Perception Lab Centre for Robotics Research King’s College London London United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.

关键词： Accuracy Force Tactile sensors Lighting Elastomers Data collection Optical imaging Adaptive optics Calibration Optical sensors

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Learning Hidden Markov Models for Linear Gaussian systems with Applications to Event-based State Estimation

arXiv

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arXiv 2020年

作者： Zheng, Kaikai Shi, Dawei Shi, Ling State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong

This work attempts to approximate a linear Gaussian system with a finite-state hidden Markov model (HMM), which is found useful in solving sophisticated event-based state estimation problems. An indirect modeling approach is developed, wherein a state space model (SSM) is firstly identified for a Gaussian system and the SSM is then used as an emulator for learning an HMM. In the proposed method, the training data for the HMM are obtained from the data generated by the SSM through building a quantization mapping. Parameter learning algorithms are designed to learn the parameters of the HMM, through exploiting the periodical structural characteristics of the HMM. The convergence and asymptotic properties of the proposed algorithms are analyzed. The HMM learned using the proposed algorithms is applied to event-triggered state estimation, and numerical results on model learning and state estimation demonstrate the validity of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

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Distributed Full Order Sliding Mode control for Finite-Time Attitude Synchronization and Tracking of Spacecraft

Distributed Full Order Sliding Mode Control for Finite-Time ...

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Chinese control Conference (CCC)

作者： Nsubuga Latifu Zhuoyue Song Chao Duan Zhen Li Housheng Su Qinghe Wu Xiangdong Liu State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing P. R. China School of Automation Huazhong University of Science and Technology Wuhan People's Republic of China

This paper investigates a distributed finite-time attitude synchronization and tracking problem of multiple spacecraft when a time varying reference signal is available to only a subset of group members. Modified Rodrigues parameters are used for attitude representation. A two layer sliding mode surface with fast non-singular terminal sliding mode surface (FNTSMS) as the inner layer and terminal sliding mode surface (TSMS) as the outer layer is used to create a full order sliding mode surface. The proposed distributed attitude coordination control law guarantees attitude synchronization and tracking as long as their exists atleast one spacecraft that has a direct access to the virtual leader's time varying attitude. Finite-time stability of the closed loop system is guaranteed by Lyapunov-based stability method. Simulation results are presented to validate the performance of the proposed control strategy.

关键词： Space vehicles Attitude control Synchronization Sliding mode control Decentralized control Symmetric matrices Stability analysis

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Crowdsensing game with demand uncertainties: A deep reinforcement learning approach

arXiv

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arXiv 2018年

作者： Zhan, Yufeng Xia, Yuanqing Zhang, Jiang Li, Ting Wang, Yu School of Automation Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Lab Department of Computer Science University of North Carolina at Charlotte CharlotteNC28223 United States

Currently, explosive increase of smartphones with powerful built-in sensors such as GPS, accelerometers, gyroscopes and cameras has made the design of crowdsensing applications possible, which create a new interface between human beings and life environment. Until now, various mobile crowdsensing applications have been designed, where the crowdsourcers can employ mobile users (MUs) to complete the required sensing tasks. In this paper, emerging learning-based techniques are leveraged to address crowdsensing game with demand uncertainties and private information protection of MUs. Firstly, a novel economic model for mobile crowdsensing is designed, which takes MUs' resources constraints and demand uncertainties into consideration. Secondly, an incentive mechanism based on Stackelberg game is provided, where the sensing-platform (SP) is the leader and the MUs are the followers. Then, the existence and uniqueness of the Stackelberg Equilibrium (SE) is proven and the procedure for computing the SE is given. Furthermore, a dynamic incentive mechanism (DIM) based on deep reinforcement learning (DRL) approach is investigated without knowing the private information of the MUs. It enables the SP to learn the optimal pricing strategy directly from game experience without any prior knowledge about MUs' information. Finally, numerical simulations are implemented to evaluate the performance and theoretical properties of the proposed mechanism and approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Autocorrelation Invariance Property of Chaos for Wireless Communication

arXiv

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arXiv 2022年

作者： Yin, Hui-Ping Ren, Hai-Peng Grebogi, Celso Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'An710048 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AB24 3UE United Kingdom

A new feature of the chaotic signal generated by chaotic shape-forming filter (CSF) is uncovered in this work. We find that, the autocorrelation function (ACF) of the transmitting signal generated by CSF keeps the same as that of the base function of CSF, no matter what information is encoded. We derive the analytical equation to describe the relation between the ACF of the received signal and the wireless channel parameters using the ACF of the transmitted signal as prior knowledge revealed by the finding in this work. This new property can be utilized together with different wireless communication systems to improve the system performance. Specially, to demonstrate the improvement, channel state information (CSI) is identified using the chaotic baseband wireless communication as a paradigm. Two significant benefits by using the new property are 1) the CSI can be identified without the probe information known to the receiver as done in the conventional wireless communication systems, which improves the bandwidth efficiency, especially in the time-varying channel;2) the correlation operation is insensitive to the channel noise, which improves the identification accuracy as compared to the commonly used methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Channel state information

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Artificial Neural Network Based Maximum Power Point Tracking for PV system

Artificial Neural Network Based Maximum Power Point Tracking...

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第三十八届中国控制会议

作者： Al-wesabi Ibrahim Min Ding Xiaoling Jin Xianzhong Dai Mohammad Abu Sarhan Muhammed Badeaa Shafik Hao Zhou School of Automation China University of Geoscience Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System State Grid Energy Research Institute School of electrical engineering and automation Wuhan university

Nowadays PV systems is suffering from two main problems: high production cost and low efficiency especially under variable weather conditions. Therefore, to reduce such associated problems, PV systems are being connected with various optimization controllers such as maximum power point tracking. In this paper, an artificial neural network maximum power point method has been designed to be linked between a PV system and DC-DC buck converter, and each part of the system is fully explained. The whole system was modeled under MATLAB/Simulink environment, simulation results demonstrated the efficiency of the proposed method. The proposed method has been examined to track the maximum power point under different weather conditions which shows a rapid and accurate tracking of the maximum power point(MPP) of PV system. The whole system which consists of PV arrays, Buck converter, ANN tracker, and load has been modeled using MATLAB/Simulink environment. This Simulink model has been examined using different levels of temperature and irradiation and the output results of both P-V and I-V characteristics, output power, and the system efficiency clearly demonstrate the validation of the modeled system. This demonstration has been done by comparing between the result of experimental platform and the results of simulated model which approves that ANN is highly recommended to be used in the field of maximum power point tracking of PV system.

关键词： Artificial neural network(ANN) Photovoltaic(PV) Maximum power point tracking(MPPT) Buck converter

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