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2022 International Conference on Electronic Information technology, EIT 2022

作者： Yu, Yajie Liu, Xianda Wei, Weixuan Chen, Hengfei Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China

ISBN: (纸本)9781510655096

Intrusion detection technology can effectively evaluate the security state of the system. However, the accuracy of traditional intrusion detection methods still needs to be improved. Therefore, this paper proposed a SVM model based on improved QPSO. QPSO is used to optimize the hyperparameters of SVM to improve its accuracy. In addition, the shrinkage expansion coefficient in QPSO is improved to make QPSO adaptive. Experiment shows that the proposed algorithm has good performance. © 2022 SPIE. All rights reserved.

关键词： Intrusion detection

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Pdm-Ssd: Single-Stage 3d Object Detector with Point Dilation Mechanism

SSRN

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SSRN 2024年

作者： Liang, Ao Hua, Haiyang Fang, Jian Chen, Wenyu Zhao, Huaici Wang, Guogang Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Optical Information and Simulation Technology Shenyang110016 China College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China

Current point-based detectors can only learn from the provided points, with limited receptive fields and insufficient global learning capabilities for such targets. In this paper, we present a novel Point Dilation Mechanism for single-stage 3D detection (PDM-SSD) that takes advantage of two representations. Specifically, we first use a PointNet-style 3D backbone for efficient feature encoding. Then, a neck with Point Dilation Mechanism (PDM) is used to expand the feature space, which involves two key steps: point dilation and feature filling. The former expands points to a certain size grid centered around the sampled points in Euclidean space. The latter fills the unoccupied grid with feature for backpropagation in terms of direction and scale. Next, we associate multiple dilation centers and fuse coefficients to obtain sparse grid features through height compression. Finally, we design a hybrid detection head for joint learning. The experiments confirm the effectiveness and efficiency of the proposed method. In particular, PDM-SSD achieves state-of-the-art results among single-modal methods on the challenging KITTI dataset, with an inference speed of 68 frames. We also demonstrate the advantages of PDM-SSD in detecting sparse and incomplete objects through numerous object-level instances. Additionally, PDM can serve as an auxiliary network to establish a connection between sampling points and object centers, thereby improving the accuracy of the model without sacrificing inference speed. Our code is available at \url{https://***/AlanLiangC/***}. © 2024, The Authors. All rights reserved.

关键词： Object detection

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Hybrid polynomial-based trajectory planning for lower limb exoskeleton robots

Hybrid polynomial-based trajectory planning for lower limb e...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Shupei Li Chi Zhang Feng Yi Pingping Lv Ting Yuan Meitong Zhang Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China School of Electrical Engineering Hebei University of Science & Technology Ningbo China

To ensure smooth, continuous, and precise motion of lower limb exoskeleton robots, this paper proposes a 6-5-6 polynomial trajectory planning method (6-5-6 PTPM) that combines 5 th and 6 th polynomials. By imposing corresponding kinematic constraints, this method achieves the trajectory plan of seven sub-phases of the gait cycle on the kinematic model of the lower limb exoskeleton robot. The simulation is conducted and compared to the 4-3-4 polynomial trajectory planning method (4-3-4 PTPM). The comparison results indicate that the 6-5-6 PTPM is shown to have a lower error between the planned trajectory and the actual trajectory. Additionally, it avoids spikes of the acceleration curve that existed in the 4-3-4 polynomial method. The proposed method enables the acceleration curve smoother and mechanical impact lessened, which demonstrates the feasibility and stability of the proposed method.

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Global Consensus for Heterogeneous Saturated Multi-Agent Systems via Sampled-Data Control

Global Consensus for Heterogeneous Saturated Multi-Agent Sys...

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IEEE Conference on Decision and Control

作者： Juan Qian Xiaoling Wang Daniele Astolfi Housheng Su Guo-Ping Jiang College of Automation and the College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Center for IOT Intelligent Robots(IOTRobot) Nanjing China CNRS LAGEPP UMR 5007 43 boulevard du 11 novembre 1918 Université Claude Bernard Lyon 1 Villeurbanne France Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper addresses the global consensus problem for multi-input multi-output saturated systems within a sampled-data framework, aiming to advance global consensus, manage heterogeneous actuator saturation across components, and preserve distributed characteristics by using sampled-data feedback. We propose a distributed control algorithm that incorporates a redesigned saturation function, represented as decentralized dynamic saturation levels. These levels for each agent’s dimensions are autonomously updated through an adaptive strategy, which mitigates heterogeneous saturation by carefully selecting constant and time-varying saturation parameters in it. Lyapunov analysis proves that global consensus can be achieved under the proposed control law, provided the sampling periods of all agents remain below a calculated threshold. An example is given to demonstrate the effectiveness of this approach.

关键词： Actuators Upper bound Heuristic algorithms Decentralized control Multi-agent systems

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_{2}$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-Databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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Tactile-Based Object Pose Estimation Employing Extended Kalman Filter

Tactile-Based Object Pose Estimation Employing Extended Kalm...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Qiguang Lin Chaojie Yan Qiang Li Yonggen Ling Yu Zheng Wangwei Lee Zhaoliang Wan Bidan Huang Xiaofeng Liu Jiangsu Key laboratory of Special Robotic Technology College of IoT Engineering Hohai University Changzhou Jiangsu P.R. China State Key Laboratory of Industrial Control and Technology Zhejiang University Institute of Cyber-System and Control Hangzhou P.R. China Neuroinformatics Group Center for Cognitive Interaction Technology (CITEC) Bielefeld University Bielefeld Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou P.R.China

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks.

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SLAM-based Joint Calibration of Multiple Asynchronous Microphone Arrays and Sound Source Localization

arXiv

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arXiv 2024年

作者： Wang, Jiang He, Yuanzheng Su, Daobilige Itoyama, Katsutoshi Nakadai, Kazuhiro Wu, Junfeng Huang, Shoudong Li, Youfu Kong, He The Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology No. 1088 Xueyuan Avenue Shenzhen China The College of Engineering China Agricultural University Beijing China The Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan The School of Data Science The Chinese University of Hong Kong Shenzhen China The Robotics Institute University of Technology Sydney Sydney Australia The Department of Mechanical Engineering City University of Hong Kong Hong Kong

Robot audition systems with multiple microphone arrays have many applications in practice. However, accurate calibration of multiple microphone arrays remains challenging because there are many unknown parameters to be identified, including the relative transforms (i.e., orientation, translation) and asynchronous factors (i.e., initial time offset and sampling clock difference) between microphone arrays. To tackle these challenges, in this paper, we adopt batch simultaneous localization and mapping (SLAM) for joint calibration of multiple asynchronous microphone arrays and sound source localization. Using the Fisher information matrix (FIM) approach, we first conduct the observability analysis (i.e., parameter identifiability) of the above-mentioned calibration problem and establish necessary/sufficient conditions under which the FIM and the Jacobian matrix have full column rank, which implies the identifiability of the unknown parameters. We also discover several scenarios where the unknown parameters are not uniquely identifiable. Subsequently, we propose an effective framework to initialize the unknown parameters, which is used as the initial guess in batch SLAM for multiple microphone arrays calibration, aiming to further enhance optimization accuracy and convergence. Extensive numerical simulations and real experiments have been conducted to verify the performance of the proposed method. The experiment results show that the proposed pipeline achieves higher accuracy with fast convergence in comparison to methods that use the noise-corrupted ground truth of the unknown parameters as the initial guess in the optimization and other existing frameworks. © 2024, CC BY.

关键词： Mapping

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Delay Optimization in Vehicular Edge Computing with Sensing Information Fusion and Heterogeneous Tasks

Delay Optimization in Vehicular Edge Computing with Sensing ...

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IEEE International Conference on Communications Workshops, ICC

作者： Yunzhi Zhao Fen Hou Jianwei Huang Bin Lin Hangguan Shan Department of ECE State Key Laboratory of IoT for Smart City University of Macau Macao China Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities Macao China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China School of Information Science and Technology Dalian Maritime University Dalian China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

Integrated Sensing and Communication (ISAC) technology has attracted extensive attention and been widely studied in automatic drive. However, most of existing works related to automatic drive focused on the single-vehicle intelligence, i.e., each vehicle processes its radar sensing data independently. With the single-vehicle intelligence, the sensing results may not satisfy the accuracy requirements in application scenarios such as obstacle detection and congestion detection. In this work, we consider information fusion within a vehicular mobile edge computing system. Multiple vehicles transmit their unique sensing data to an edge server, which fuses and processes the data to enhance sensing accuracy. Then, we formulate a joint resource allocation and beamforming design problem to minimize the system delay with heterogeneous tasks while satisfying the sensing quality requirements. Due to the non-convexity of the problem, we decouple the problem into two sub-problems and propose an efficient method based on alternate optimization and successive convex approximation to approximate the optimal solution. Simulation results demonstrate that the proposed method improves the performance by 38.1 %, 43.8% and 65.7% in system delay compared with the existing algorithms, i.e., rate maximization, MEC computing and equal allocation.

关键词： Accuracy Array signal processing Simulation Conferences Integrated sensing and communication Delays Resource management

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Analysis motion imagination EEG signal in spatiotemporal-energy domain 3

Analysis motion imagination EEG signal in spatiotemporal-ene...

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3rd International Conference on Computer Science and Artificial Intelligence, CSAI 2019 and its Workshop - The 11th International Conference on Information and Multimedia technology, ICIMT 2019

作者： Sheng, Zhengyan Yao, Xiao Chen, Ruixuan Diao, Wenyu Li, Weiliang Li, Yao College of IoT Engineering Hohai University China Changzhou Key Laboratory of Robotics and Intelligent Technology China

ISBN: (纸本)9781450376273

Brain is the most advanced part of the nervous system. As an emerging neighborhood exploring the brain, the brain-computer interface (BCI) may completely influence people's current communication and lifestyle in the future. We propose an architecture for feature extraction and classification model in brain-computer interface (BCI). Three features of EEG signal are extracted and combined in an attempt to describe the EEG signal more comprehensively including Wavelet packet decomposition, Information entropy and Co-space pattern (CSP). We construct the C-LSTM model combining the Convolutional Neural Networks (CNN) and the Long Short-Term Memory (LSTM) to perform the classification. Comparison was made with traditional approaches, and results show the method has a better performance for classification of motor imagery. © 2019 Association for Computing Machinery.

关键词： Brain computer interface

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Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning

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Fundamental Research 2023年第6期3卷 951-959页

作者： Renhai Zhong Yue Zhu Xuhui Wang Haifeng Li Bin Wang Fengqi You Luis F.Rodríguez Jingfeng Huang K.C.Ting Yibin Ying Tao Lin College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhouZhejiang 310058China International Campus Zhejiang UniversityHainingZhejiang 314400China Sino-French Institute for Earth System Science College of Urban and Environmental SciencesPeking UniversityBeijing 100871China School of Geosciences and Info-Physics Central South UniversitySouth Lushan RoadChangsha 410000China NSW Department of Primary Industries Wagga Wagga Agricultural InstitutePine Gully Road Wagga WaggaNSW 2650Australia Robert Frederick Smith School of Chemical and Biomolecular Engineering Cornell UniversityIthacaNY 14853USA Department of Agricultural and Biological Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Institute of Applied Remote Sensing and Information Technology Zhejiang UniversityHangzhouZhejiang 310058China Key Laboratory of On Site Processing Equipment for Agricultural Products Ministry of Agriculture and Rural AffairsHangzhouZhejiang 310058China Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province HangzhouZhejiang 310058China

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food *** the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains *** this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in *** found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in *** results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.

关键词： Crop yield estimation Deep Learning Long short-term memory Multi-task learning Extreme yield loss Attribution analysis

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