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Optimal Resource Allocation and Feasible Hexagonal Topology for Cyber-Physical systems

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Journal of systems Science & Complexity 2023年第4期36卷 1583-1608页

作者： LIU Yifa CHENG Long state key laboratory of multimodal artificial intelligence systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of artificial intelligence University of Chinese Academy of SciencesBeijing 100049China

Networked cyber-physical systems are facing serious security threats from malicious *** is noted that the networked cyber-physical system should take defense measures into account at the beginning of its *** the conservative defensive perspective,this paper proposes a robust optimal defense resource allocation strategy to reduce the maximum possible losses of the networked cyber-physical system caused by potential ***,based on the robust optimal allocation strategy,it can be proved that the topology of the networked cyber-physical system has a great influence on the loss *** order to further improve security,the effects of adding redundant connections are ***,by taking geographical knowledge into account,a hexagonal construction scheme is proposed for providing a geographically-feasible and economically-viable solution for building networked cyber-physical systems,where the loss function has a cubic decay.

关键词： Cyber-physical system hexagonal city planning optimization resource allocation

RC-Net:Row and Column Network with Text Feature for Parsing Floor Plan Images

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Journal of Computer Science & Technology 2023年第3期38卷 526-539页

作者：王腾孟维亮卢政达郭建伟肖俊张晓鹏 state key laboratory of multimodal artificial intelligence systems Institute of AutomationChinese Academy of Sciences Beijing 100190China School of artificial intelligence University of Chinese Academy of SciencesBeijing 100049China

The popularity of online home design and floor plan customization has been steadily increasing. However, the manual conversion of floor plan images from books or paper materials into electronic resources can be a challenging task due to the vast amount of historical data available. By leveraging neural networks to identify and parse floor plans, the process of converting these images into electronic materials can be significantly streamlined. In this paper, we present a novel learning framework for automatically parsing floor plan images. Our key insight is that the room type text is very common and crucial in floor plan images as it identifies the important semantic information of the corresponding room. However, this clue is rarely considered in previous learning-based methods. In contrast, we propose the Row and Column network (RC-Net) for recognizing floor plan elements by integrating the text feature. Specifically, we add the text feature branch in the network to extract text features corresponding to the room type for the guidance of room type predictions. More importantly, we formulate the Row and Column constraint module (RC constraint module) to share and constrain features across the entire row and column of the feature maps to ensure that only one type is predicted in each room as much as possible, making the segmentation boundaries between different rooms more regular and cleaner. Extensive experiments on three benchmark datasets validate that our framework substantially outperforms other state-of-the-art approaches in terms of the metrics of FWIoU, mACC and mIoU.

关键词： floor plan understanding text feature Row and Column(RC)constraint module Row and Column network(RC-Net)

PuzzleNet:Boundary-Aware Feature Matching for Non-Overlapping 3D Point Clouds Assembly

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Journal of Computer Science & Technology 2023年第3期38卷 492-509页

作者：刘昊宇郭建伟姜海勇刘彦超张晓鹏严冬明 state key laboratory of multimodal artificial intelligence systems Institute of AutomationChinese Academy of Sciences Beijing 100190China School of artificial intelligence University of Chinese Academy of SciencesBeijing 100049China

We address the 3D shape assembly of multiple geometric pieces without overlaps, a scenario often encountered in 3D shape design, field archeology, and robotics. Existing methods depend on strong assumptions on the number of shape pieces and coherent geometry or semantics of shape pieces. Despite raising attention to 3D registration with complex or low overlapping patterns, few methods consider shape assembly with rare overlaps. To address this problem, we present a novel framework inspired by solving puzzles, named PuzzleNet, which conducts multi-task learning by leveraging both 3D alignment and boundary information. Specifically, we design an end-to-end neural network based on a point cloud transformer with two-way branches for estimating rigid transformation and predicting boundaries simultaneously. The framework is then naturally extended to reassemble multiple pieces into a full shape by using an iterative greedy approach based on the distance between each pair of candidate-matched pieces. To train and evaluate PuzzleNet, we construct two datasets, named ModelPuzzle and DublinPuzzle, based on a real-world urban scan dataset (DublinCity) and a synthetic CAD dataset (ModelNet40) respectively. Experiments demonstrate our effectiveness in solving 3D shape assembly for multiple pieces with arbitrary geometry and inconsistent semantics. Our method surpasses state-of-the-art algorithms by more than 10 times in rotation metrics and four times in translation metrics.

关键词： shape assembly 3D registration geometric learning boundary feature point cloud

A Bio-Inspired Integration Model of Basal Ganglia and Cerebellum for Motion Learning of a Musculoskeletal Robot

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Journal of systems Science & Complexity 2024年第1期37卷 82-113页

作者： ZHANG Jinhan CHEN Jiahao ZHONG Shanlin QIAO Hong state key laboratory of multimodal artificial intelligence systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of artificial intelligence University of Chinese Academy of SciencesBeijing 100049China.

It is a significant research direction for highly complex musculoskeletal robots that how to develop the ability of motion learning and *** cooperations of multiple brain regions are crucial to improving motion *** by the neural mechanisms of structures,functions,and interconnections of basal ganglia and cerebellum,a biologically inspired integration model for motor learning of musculoskeletal robots is *** on the neural characteristics of the basal ganglia,the basal ganglia actor network,which mainly simulates the dorsal striatum,outputs motion commands,and the basal ganglia critic network,which simulates the ventral striatum,estimates actionstate *** network parameters are updated using the soft actor-critic *** on the sensorimotor prediction mechanism of the cerebellum,the cerebellum network evaluates the state feature extraction quality of the basal ganglia actor network and then updates the weights of its feature *** learning method is proven to converge to the optimal ***,drawing on the mechanism of dopaminergic dynamic regulation in the basal ganglia,the adaptive adjustment of target entropy and the dopaminergic experience replay are proposed to further improve the integration model,which contributes to the exploration-exploitation trade-off of motor *** bio-inspired integration model is validated on a musculoskeletal *** results indicate that this model can effectively control the musculoskeletal robot to accomplish the motion task from random starting locations to random target positions with high precision and robustness.

关键词： Basal ganglia and cerebellum bio-inspired integration model motion learning musculoskeletal robot reinforcement learning.

Unified Classification and Rejection:A One-versus-all Framework

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Machine intelligence Research 2024年第5期21卷 870-887页

作者： Zhen Cheng Xu-Yao Zhang Cheng-Lin Liu state key laboratory of multimodal artificial intelligence systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of artificial intelligence University of Chinese Academy of SciencesBeijing100049China

Classifying patterns of known classes and rejecting ambiguous and novel(also called as out-of-distribution(OOD))inputs are involved in open world pattern *** neural network models usually excel in closed-set classification while perform poorly in rejecting OOD *** tackle this problem,numerous methods have been designed to perform open set recognition(OSR)or OOD rejection/detection *** methods mostly take post-training score transformation or hybrid models to ensure low scores on OOD inputs while separating known *** this paper,we attempt to build a unified framework for building open set classifiers for both classification and OOD *** formulate the open set recognition of K-known-class as a(K+1)-class classification problem with model trained on known-class samples *** decomposing the K-class problem into K one-versus-all(OVA)binary classification tasks and binding some parameters,we show that combining the scores of OVA classifiers can give(K+1)-class posterior probabilities,which enables classification and OOD rejection in a unified *** maintain the closed-set classification accuracy of the OVA trained classifier,we propose a hybrid training strategy combining OVA loss and multi-class cross-entropy *** implement the OVA framework and hybrid training strategy on the recently proposed convolutional prototype network and prototype classifier on vision transformer(ViT)*** on popular OSR and OOD detection datasets demonstrate that the proposed framework,using a single multi-class classifier,yields competitive performance in closed-set classification,OOD detection,and misclassification *** code is available at https://***/zhen-cheng121/CPN_OVA_unified.

关键词： Open set recognition out-of-distribution detection misclassification detection convolutional prototype network oneversus-all Dempster-Shafer theory of evidence

Outlier Robust Feature Correspondence by Learning Based Matching Process

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Journal of systems Science & Complexity 2024年第4期37卷 1351-1367页

作者： YANG Xu ZENG Shaofeng LIU Zhiyong state key laboratory of multimodal artificial intelligence systems Institute of Automation of Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

Feature correspondence is a crucial aspect of various computer vision and robot vision *** traditional optimization-based matching techniques,researchers have recently adopted a learning-based approach for matching,but these methods face challenges in dealing with outlier *** paper presents an outlier robust feature correspondence method that employs a pruned attentional graph neural network and a matching layer to address the outlier ***,the authors introduce a modified cross-entropy matching loss to handle the outlier *** a result,the proposed method significantly enhances the performance of learning-based matching algorithms in the presence of outlier *** experiments confirm the effectiveness of the proposed approach.

关键词： Feature correspondence graph neural network optimal transport problem pointmatching

GRAMO:geometric resampling augmentation for monocular 3D object detection

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Frontiers of Computer Science 2024年第5期18卷 161-169页

作者： He GUAN Chunfeng SONG Zhaoxiang ZHANG School of artificial intelligence University of Chinese Academy of SciencesBeijing 100049China Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence SystemsInstitute of Automation Chinese Academy of SciencesBeijing 100190China

Data augmentation is widely recognized as an effective means of bolstering model ***,when applied to monocular 3D object detection,non-geometric image augmentation neglects the critical link between the image and physical space,resulting in the semantic collapse of the extended *** address this issue,we propose two geometric-level data augmentation operators named Geometric-Copy-Paste(Geo-CP)and Geometric-Crop-Shrink(Geo-CS).Both operators introduce geometric consistency based on the principle of perspective projection,complementing the options available for data augmentation in monocular ***,Geo-CP replicates local patches by reordering object depths to mitigate perspective occlusion conflicts,and Geo-CS re-crops local patches for simultaneous scaling of distance and scale to unify appearance and *** operations ameliorate the problem of class imbalance in the monocular paradigm by increasing the quantity and distribution of geometrically consistent *** demonstrate that our geometric-level augmentation operators effectively improve robustness and performance in the KITTI and Waymo monocular 3D detection benchmarks.

关键词： 3D detection monocular augmentation geometry

Scene text recognition via dual character counting-aware visual and semantic modeling network

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Science China(Information Sciences) 2024年第3期67卷 313-314页

作者： Ke XIAO Anna ZHU Brian Kenji IWANA Cheng-Lin LIU School of Computer Science and artificial intelligence Wuhan University of Technology Human Interface laboratory Kyushu University state key laboratory of multimodal artificial intelligence systems Institute of AutomationChinese Academy of Sciences School of artificial intelligence University of Chinese Academy of Sciences

Scene text recognition(STR) is drawing increasing attention nowadays due to its wide application in real life. Character counting information, as auxiliary information, has been shown to be effective in boosting text ... 详细信息

关键词： Decoding

Flexible Graphene Field‑Effect Transistors and Their Application in Flexible Biomedical Sensing

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Nano-Micro Letters 2025年第2期17卷 252-313页

作者： Mingyuan Sun Shuai Wang Yanbo Liang Chao Wang Yunhong Zhang Hong Liu Yu Zhang Lin Han Institute of Marine Science and Technology Shandong UniversityQingdao 266237ShandongPeople’s Republic of China state key laboratory of Crystal Materials Shandong UniversityJinan 250100ShandongPeople’s Republic of China School of Integrated Circuits Shandong UniversityJinan 250100ShandongPeople’s Republic of China Shandong Engineering Research Center of Biomarker and artificial intelligence Application Jinan250100ShandongPeople’s Republic of China

Flexible electronics are transforming our lives by making daily activities more *** to this innovation are field-effect transistors(FETs),valued for their efficient signal processing,nanoscale fabrication,low-power consumption,fast response times,and ***,known for its exceptional mechanical properties,high electron mobility,and biocompatibility,is an ideal material for FET channels and *** combination of graphene and FETs has given rise to flexible graphene field-effect transistors(FGFETs),driving significant advances in flexible electronics and sparked a strong interest in flexible biomedical ***,we first provide a brief overview of the basic structure,operating mechanism,and evaluation parameters of FGFETs,and delve into their material selection and patterning *** ability of FGFETs to sense strains and biomolecular charges opens up diverse application *** specifically analyze the latest strategies for integrating FGFETs into wearable and implantable flexible biomedical sensors,focusing on the key aspects of constructing high-quality flexible biomedical ***,we discuss the current challenges and prospects of FGFETs and their applications in biomedical *** review will provide valuable insights and inspiration for ongoing research to improve the quality of FGFETs and broaden their application prospects in flexible biomedical sensing.

关键词： Flexible Graphene Field-effect transistor Wearable Implantable Biosensor