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

作者： Jingyuan Xia Zecai Lin Xiaojie Ai Guangjun Yu Anzhu Gao Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai P. R. China Second Affiliated Hospital The Chinese University of Hong Kong Shenzhen P.R. China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Traditional interaction perception of parallel robots relies on a six-dimensional force sensor for contact sensing at their distal end. However, the sensor body occupies the space of moving platform and also increases the load on the robot actuations. To enable both minimization and embodied intelligence, this paper proposes an external interaction estimation method with embodied mechanical intelligence by embedding two single-axis force sensors in each leg of 6-PSS parallel robot. The method uses a backward propagation neural network optimized by sparrow search algorithm, and it can simultaneously estimate the external force and its position using information from multiple single-axis force sensors and the encoder of driving motor. The experimental platform is established to collect the data and train the network. The result shows that the force estimation mean error is 2.4% and the position estimation error is 2.9%. A demonstration with a virtual display interface showing the reconstructed parallel robot pose, and the interaction force and its pose using the proposed estimation method, indicates the effectiveness of the proposed interaction method with embodied mechanical intelligence for 6-PSS parallel robot

关键词： Robot motion Legged locomotion Parallel robots Force Neural networks Estimation Robot sensing systems Minimization Force sensors Payloads

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Design of Camera Equipment Arrangement and Attitude Calculation for Aircraft Attitude Measurement

Design of Camera Equipment Arrangement and Attitude Calculat...

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第40届中国控制会议

作者： Monan Zhang Wenbin Yu Key Laboratory of System Control and Information Processing Ministry of EducationShanghai Jiao Tong University

It is one of the important tasks for marine technology to track and observe,measure and calculate the attitude angle of aircraft landing in a certain range of sea area,measuring and calculating its attitude *** identification and tracking of the target and reasonable optimization of the surveillance camera placement are the key steps to complete the *** on a typical scene,this paper presents a technical scheme of aircraft tracking and shooting by three-spectrum cameras and constant-speed *** addition,the design of the camera placement scheme is optimized,and the calculation method of the aircraft attitude is *** simulation results confirm the effectiveness of the proposed approaches.

关键词： ocean monitoring binocular vision camera equipment arrangement attitude calculation

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Semantic Correspondence with Peripheral Position Coding

Semantic Correspondence with Peripheral Position Coding

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Jinjian Chen Zuoyong Li Taotao Lai Huosheng Xie College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

Establishing dense correspondences between semantically similar images is a challenging task. Cost aggregation is a crucial step in finding correct dense correspondences, with the goal of optimizing the initial correlation map thereby removing the ambiguity of the correspondences. Current approaches use transformer architectures for cost aggregation, which lack local priors to adequately capture the local information contained in the correlation map. We propose to incorporate peripheral position coding into the transformer to explore the local information to obtain the matching set and call it the Peripheral Transformer Matcher (PTM). This coding technique partitions the overall receptive field of the self-attention mechanism into diverse peripheral regions, each with its own set of weights. By doing this, the proposed PTM gets a specific local prior by adding an inductive bias to the transformer models and making the initial correlation map less confusing. In addition, a local self-attention module is used to enhance the image features and obtain an enhanced initial correlation map. Comparisons of the experimental results with baselines on public datasets demonstrate the effectiveness of the proposed PTM.

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Attention Residual Network for White Blood Cell Classification with WGAN Data Augmentation 11

Attention Residual Network for White Blood Cell Classificati...

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11th International Conference on information Technology in Medicine and Education, ITME 2021

作者： Zhao, Meng Jin, Lingmin Teng, Shenghua Li, Zuoyong College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9781665406796

In medicine, white blood cell (WBC) classification plays an important role in clinical diagnosis and treatment. Due to the similarity between classes and lack of training data, the precise classification of WBC is still challenging. To alleviate this problem, we propose an attention residual network for WBC image classification on the basis of data augmentation. Specifically, the attention residual network is composed of multiple attention residual blocks, an adaptive average pooling layer, and a full connection layer. The channel attention mechanism is introduced in each residual block to use the feature maps of WBC learned by a high layer to generate the attention map for a low layer. Each attention residual block also introduces depth separable convolution to extract the feature of WBC and decrease the training costs. The Wasserstein Generative adversarial network (WGAN) is used to create synthetic instances to enhance the size of training data. Experiments on two image datasets show the superiority of the proposed method over several state-of-the-art methods. © 2021 IEEE.

关键词： Generative adversarial networks

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On-orbit geometric rectification for micro-satellite based on Lightweight feature database

On-orbit geometric rectification for micro-satellite based o...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Linhui Wang Yuming Xiang Feng Wang Yuxin Hu Hongjian You Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences

On-orbit processing is becoming more prevalent due to its ability to efficiently exploit satellite resources. On-orbit geometric rectification improves positioning accuracy for follow-up tasks such as object detection or geometric calibration, while avoiding heavy burden on downlinking bandwidth and time delay. However, existing rectification methods faces some challenges. The hardware resources onboard satellites are restricted, and geographic positioning is often inaccurate. In this article, we propose a novel method designed for on-orbit rectification. The proposed method introduces a two-step registration framework to overcome large initial offsets and also a feature-compressing strategy to reduce the storage space of reference patches. Quantitative and practical experiments demonstrate that the proposed method performs well in terms of storage space, time efficiency as well as registration accuracy.

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Analyzing Behavior Data of Students in the Classroom with Association Rule Mining

Analyzing Behavior Data of Students in the Classroom with As...

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Chinese Automation Congress (CAC)

作者： Qingbo Zeng Zhezhuang Xu Song Zheng Chi Liu Qinqin Chai Ying Zhang Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Fuzhou University Fuzhou China

Students behavior in the classroom has a close relation with the academic performance of students. With the widespread use of mobile phones, the students are easily distracted by mobile phones during the class. Therefore, analyzing the students behavior in the classroom becomes important to improve the quality of teaching. However, the existing methods for analyzing students behavior in the classroom, such as facial recognition, has low efficiency with large amounts of irrelevant data. And it also raises concerns about privacy and ethical issues. To solve this problem, in this paper, a novel mobile software called EasyClass has been developed to collect and quantify students behaviors. Based on the data collected by EasyClass, we propose to utilize user profiling to study students behavior in the classroom. The clustering algorithm is firstly employed to classify the data and generate semantically tags which describe students behavior in the classroom. Then a sequence and association rule analysis method is used to analyze the behavior tags and generated association rules. The results enable educators and non-expert users to gain a deeper understanding of students behavior in the classroom, and further improve the quality of the classroom teaching.

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Achieving high-security and massive-capacity optical communications based on orbital angular momentum configured chaotic laser

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Advanced Photonics Nexus 2024年第4期3卷 92-100页

作者： Yanwei Cui Jianguo Zhang Zhongquan Nie Anbang Wang Yuncai Wang Taiyuan University of Technology Ministry of EducationCollege of Electronic Information and Optical EngineeringKey Laboratory of Advanced Transducers and Intelligent Control SystemTaiyuanChina National University of Defense Technology College of Advanced Interdisciplinary StudiesChangshaChina Guangdong University of Technology School of Information EngineeringGuangzhouChina Guangdong Provincial Key Laboratory of Photonics Information Technology GuangzhouChina

Secure and high-speed optical communications are of primary focus in information *** it is widely accepted that chaotic secure communication can provide superior physical layer security,it is challenging to meet the demand for high-speed increasing communication *** theoretically propose and experimentally demonstrate a conceptual paradigm for orbital angular momentum(OAM)configured chaotic laser(OAM-CCL)that allows access to high-security and massivecapacity optical *** 11 OAM modes and an all-optical feedback chaotic laser,we are able to theoretically empower a well-defined optical communication system with a total transmission capacity of 100 Gb∕s and a bit error rate below the forward error correction threshold 3.8×10^(-3).Furthermore,the OAM-CCL-based communication system is robust to 3D misalignment by resorting to appropriate mode spacing and beam ***,the conceptual paradigm of the OAM-CCL-based communication system is *** contrast to existing systems(traditional free-space optical communication or chaotic optical communication),the OAM-CCL-based communication system has threein-one characteristics of high security,massive capacity,and *** findings demonstrate that this will promote the applicable settings of chaotic laser and provide an alternative promising route to guide high-security and massive-capacity optical communications.

关键词： chaotic secure communication orbital angular momentum configured chaotic laser high security massive capacity misalignment

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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Short-Term Traffic Flow Prediction of Highway Tunnels Based on SSA and Improved TSK Fuzzy system

Short-Term Traffic Flow Prediction of Highway Tunnels Based ...

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Chinese Automation Congress (CAC)

作者： Zhijing Huang Kang Chen Zhezhuang Xu Qinbo Zeng Zhibin Xu Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Fuzhou University Fuzhou China

Aiming at the problem that the traffic flow has an low prediction accuracy due to varying randomness and high nonlinearity, an improved TSK fuzzy neural network prediction model by singular spectrum analysis is ***, the original traffic flow time series can be decomposed into several subseries by singular spectrum analysis (SSA). Then, the Takagi-Sugeno-Kang (TSK) fuzzy system was trained using simulated annealing genetic algorithm (SAGA) and minibatch gradient descent method (MBGD) combined with three techniques (RDA) of regularization, DropRule and *** prediction model of TSK fuzzy neural network is established to predict each subseries independently, and the forecasted traffic flow is obtained by superimposing the prediction results of each subseries. Finally, the traffic flow dataset of expressway tunnels in a city is used to verify the effectiveness of the model. Experimental results can show high traffic flow prediction accuracy of the model, and the model can accurately predict multiple different highway tunnels.

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Nonmonotonic propositional logic

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Frontiers of Computer Science 2021年第3期15卷 215-216页

作者： Wei LI Yuefei SUI Yuhui WANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100049China Information Technology Center China Reinsurance(Group)CorporationBeijing 100033China

Propositional logic[1]is basic,based on which other logics are *** deduction system for propositional logic is *** logics are a class of logics which deduction systems are *** ones are default logic[2,3],R-calculus[4]... 详细信息

关键词： default propositional Nonmonotonic

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