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8th International Conference on Electronic technology and Information science, ICETIS 2023

作者： Cao, ZhiPing Zhu, LiJun Department of Computer science and Technologyg Shenyang University of Chemical Technology Liaoning 110142 China Key Laboratory of Intelligent Technology for Chemical Process Industry Liaoning Shenyang110142 China

ISBN: (纸本)9781510666535

When using the non-contact method to collect the image of the back of the hand, in view of the problems that the different degrees of finger opening and closing affect the segmentation of the finger image, the area of interest of the knuckles of the knuckles is difficult to locate, and the image is easily affected by light, a knuckles of the knuckles of the knuckles based on morphological operations are proposed. Region of interest positioning method. In this method, a combination of opening and closing operations in morphological operations is used to construct an image mask of the back of the hand, and then the mask is used to shield the area of the back of the hand, and five images of the back of the finger are separated and angle-corrected. For the corrected image, the top-hat transformation is performed first, and then the method of combining the sliding window algorithm with image information entropy is used to locate the knuckle pattern area of the dorsum of fingers. Finally, LBP and SIFT are used for feature extraction, and support vector machine (SVM) is used as a classifier, and experimental verification is carried out on the self-built back of hand image database. The experimental results show that the proposed method can eliminate the influence of illumination and environmental noise, and can accurately and quickly locate and extract the region of interest of the dorsal knuckle pattern on the back of the hand image. © 2023 SPIE.

关键词： Support vector machines

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On tuning of ADRC with competing design indices:a quantitative study

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Control Theory and technology 2023年第1期21卷 16-33页

作者： Yutao Zhao Yi Huang Zhiqiang Gao Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Department of Electrical Engineering and Computer Science Center for Advanced Control TechnologiesCleveland State UniversityClevelandOH 44115USA

Controller optimization has mostly been done by minimizing a certain single cost *** practice,however,engineers must contend with multiple and conflicting considerations,denoted as design indices(DIs)in this *** to account for such complexity and nuances is detrimental to the applications of any advanced control *** paper addresses this challenge heads on,in the context of active disturbance rejection controller(ADRC)and with four competing DIs:stability margins,tracking,disturbance rejection,and noise *** this end,the lower bound for the bandwidth of the extended state observer is first established for guaranteed closed-loop ***,one by one,the mathematical formula is meticulously derived,connecting each DI to the set of controller *** our best knowledge,this has not been done in the context of *** formulas allow engineers to see quantitatively how the change of each tuning parameter would impact all of the DIs,thus making the guesswork *** example is given to show how such analytical methods can help engineers quickly determine controller parameters in a practical scenario.

关键词： Uncertain systems Active disturbance rejection control(ADRC) Design indices(DIs) Extended state observer(ESO)

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DisVAE: Disentangled Variational Autoencoder for High-Quality Facial Expression Features 17

DisVAE: Disentangled Variational Autoencoder for High-Qualit...

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17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023

作者： Wang, Tianhao Zhang, Mingyue Shang, Lin Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350345445

Facial expression feature extraction suffers from high inter-subject variations caused by identity-related personal attributes. The extracted expression features are consistently entangled with other identity-related features, which has an influence on related facial expression tasks such as recognition and editing. To achieve high-quality expression features, a Disentangled Variational Autoencoder (DisVAE) is proposed to disentangle expression and identity features. The identity features are removed from the facial features via facial image reconstruction firstly, and then the remaining features represent expression components. Extensive experiments on three public datasets have shown that the proposed DisVAE can effectively disentangle expression and identity features, and extract expression features without the interfere of identity attributes. The high-quality expression features improve the performance of facial expression recognition and can be well applied to facial expression editing. © 2023 IEEE.

关键词： Image reconstruction

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Parallel exploration via negatively correlated search

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Frontiers of computer science 2021年第5期15卷 123-135页

作者： Peng YANG Qi YANG Ke TANG Xin YAO Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

Effective exploration is key to a successful search *** recently proposed negatively correlated search(NCS)tries to achieve this by coordinated parallel exploration,where a set of search processes are driven to be negatively correlated so that different promising areas of the search space can be visited *** successful applications of NCS,the negatively correlated search behaviors were mostly devised by intuition,while deeper(e.g.,mathematical)understanding is *** this paper,a more principled NCS,namely NCNES,is presented,showing that the parallel exploration is equivalent to a process of seeking probabilistic models that both lead to solutions of high quality and are distant from previous obtained probabilistic *** learning,for which exploration is of particular importance,are considered for empirical *** proposed NCNES is applied to directly train a deep convolution network with 1.7 million connection weights for playing Atari *** results show that the significant advantages of NCNES,especially on games with uncertain and delayed rewards,can be highly owed to the effective parallel exploration ability.

关键词： evolutionary computation reinforcement learning exploration

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AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network 39

AutoAC: Towards Automated Attribute Completion for Heterogen...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Zhu, Guanghui Zhu, Zhennan Wang, Wenjie Xu, Zhuoer Yuan, Chunfeng Huang, Yihua Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350322279

Many real-world data can be modeled as heterogeneous graphs that contain multiple types of nodes and edges. Meanwhile, due to excellent performance, heterogeneous graph neural networks (GNNs) have received more and more attention. However, the existing work mainly focuses on the design of novel GNN models, while ignoring another important issue that also has a large impact on the model performance, namely the missing attributes of some node types. The handcrafted attribute completion requires huge expert experience and domain knowledge. Also, considering the differences in semantic characteristics between nodes, the attribute completion should be fine-grained, i.e., the attribute completion operation should be node-specific. Moreover, to improve the performance of the downstream graph learning task, attribute completion and the training of the heterogeneous GNN should be jointly optimized rather than viewed as two separate processes. To address the above challenges, we propose a differentiable attribute completion framework called AutoAC for automated completion operation search in heterogeneous GNNs. We first propose an expressive completion operation search space, including topology-dependent and topology-independent completion operations. Then, we propose a continuous relaxation schema and further propose a differentiable completion algorithm where the completion operation search is formulated as a bi-level joint optimization problem. To improve the search efficiency, we leverage two optimization techniques: discrete constraints and auxiliary unsupervised graph node clustering. Extensive experimental results on real-world datasets reveal that AutoAC outperforms the SOTA handcrafted heterogeneous GNNs and the existing attribute completion method. © 2023 IEEE.

关键词： Graph neural networks

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UAV-Aided Localization and Communication: Joint Frame Structure, Beamwidth, and Power Allocation

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 154-165页

作者： Liang, Tianhao Zhang, Tingting Zhou, Sheng Liu, Wentao Li, Dong Zhang, Qinyu School of Electronics and Information Engineering Shenzhen518055 China Guangdong Provincial Key Laboratory of Space-Aerial Networking and Intelligent Sensing 518055 China Pengcheng Laboratory Shenzhen150001 China Tsinghua University Department of Electronic Engineering Beijing100084 China Macau University of Science and Technology School of Computer Science and Engineering 999078 China

In wireless sensors networks, integrating localization and communication technique is crucial for efficient spectrum and hardware utilizations. In this article, we present a novel framework of the unmanned aerial vehicle (UAV)-aided localization and communication for ground node (GN), where the average spectral efficiency (SE) is used to reveal the intricate relationship among the frame structure, channel estimation error, and localization accuracy. In particular, we first derive the lower bounds for channel estimation error and the 3-D location prediction error, respectively. Leveraging these comprehensive analysis, we formulate a problem to maximize the average SE in the UAV-GN communication, where the frame structure, beamwidth, and power allocation can be jointly optimized. Subsequently, we propose an efficient iterative algorithm to address this nonconvex problem with closed-form expressions for beamwidth design and power allocation. Numerical results demonstrate that the performance of our proposed method can approach the upper bound with low complexity, and achieve over 70% performance gain compared with communication-only benchmarks. In addition, the analysis highlights the dominated impacts of the Doppler effect on the average SE. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A lightweight distillation CNN-transformer architecture for remote sensing image super-resolution

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International Journal of Digital Earth 2023年第1期16卷 3560-3579页

作者： Yu Wang Zhenfeng Shao Tao Lu Lifeng Liu Xiao Huang Jiaming Wang Kui Jiang Kangli Zeng State Key Laboratory for Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China School of General Aviation Jingchu University of TechnologyJingmenPeople’s Republic of China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of TechnologyWuhanPeople’s Republic of China Department of Engineering University of DurhamDurhamUK Department of Environmental Sciences Emory UniversityAtlant1GAUSA School of Computer Science and Technology Harbin Institute of TechnologyHarbinPeople’s Republic of China NERCMS School of Computer ScienceWuhan UniversityWuhanPeople’s Republic of China

Remote sensing images exhibit rich texture features and strong *** the super-resolution(SR)method of remote sensing images based on convolutional neural networks(CNN)can capture rich local information,the limited perceptual field prevents it from establishing long-distance dependence on global information,leading to the low accuracy of remote sensing image ***,it is difficult for existing SR methods to be deployed in mobile devices due to their large network parameters and high computational *** this study,we propose a lightweight distillation CNN-Transformer SR architecture,named DCTA,for remote sensing SR,addressing the aforementioned ***,the proposed DCTA first extracts the coarse features through the coarse feature extraction layer and then learns the deep features of remote sensing at different scales by fusing the feature distillation extraction module of CNN and *** addition,we introduce the feature fusion module at the end of the feature distillation extraction module to control the information propagation,aiming to select the informative components for better feature *** extracted low-resolution(LR)feature maps are reorganized through the up-sampling module to obtain high-resolution(HR)feature maps with high accuracy to generate highquality HR remote sensing *** experiments comparing different methods demonstrate that the proposed approach performs well on multiple datasets,including NWPU-RESISC45,Draper,and UC *** is achieved by balancing reconstruction performance and network complexity,resulting in both competitive subjective and objective results.

关键词： Super-resolution remote sensing lightweight network CNN-Transformer

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Development of a Universal RNA Dual-Terminal Labeling Method for Sensing RNA-Ligand Interactions

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CCS Chemistry 2023年第1期5卷 221-233页

作者： Longhuai Cheng Dejun Ma Jie Zhang Xueying Kang Yang Wu Yi Zhao Long Yi Zhen Xi State Key Laboratory of Elemento-Organic Chemistry Department of Chemical BiologyCollege of ChemistryNational Pesticide Engineering Research CenterCollaborative Innovation Center of Chemical Science and EngineeringNankai UniversityTianjin 300071 State Key Laboratory of Organic-Inorganic Composites and Beijing Key Laboratory of Bioprocess Beijing University of Chemical Technology(BUCT)Beijing 100029 Key Laboratory of Intelligent Information Processing Research Center for Ubiquitous Computing SystemsInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190

Dual labeling of an RNA can provide Förster resonance energy transfer(FRET)sensors for studying RNA folding,miRNA maturation,and RNA-protein ***,we report the development of a highly efficient strategy for direct dual-terminal labeling of any RNA of *** explored new Michael cycloaddition for facile labeling of 5′-terminal RNA with improved *** chemical tetrazinylation of RNA at the 3′-terminus was achieved with the highly efficient and catalysis-free tetrazine-cycloalkyne *** single-terminal labeling methods were combined for dual-terminal labeling of an RNA including short hairpin RNA,pre-miRNA,riboswitch,and noncoding ***,these dual-labeled RNA-based FRET sensors were used to monitor RNA-ligand interactions in vitro and in live *** is anticipated that these universal RNA labeling strategies will be useful to study RNA structures and functions.

关键词： dual labeling Förster resonance energy transfer sensor Michael cycloaddition tetrazinecycloalkyne ligation RNA-ligand interaction

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Image segmentation with texture-oriented superpixel 2

Image segmentation with texture-oriented superpixel

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2nd International Symposium on computer Applications and Information systems, ISCAIS 2023

作者： Song, Dongke Huang, Heming Zhang, Huiyun School of computer science and technology Qinghai Normal University Xining810008 China State Key Laboratory of Tibetan intelligent information processing and application Xining810008 China

ISBN: (纸本)9781510666665

Superpixel segmentation is an image preprocessing technique that uses pixel blocks instead of pixels to improve the efficiency of subsequent image tasks. Existing methods are not sensitive to image texture. To solve this problem, a texture-oriented superpixel (TOS) segmentation method is proposed. Firstly, an adaptive parameter function based on pixel boundary probability is used to calculate the distance. Secondly, a gradual merging strategy is used for merging. And finally, a loop iterative framework is used to optimize the seeds. The experimental results show that TOS can effectively preserve the smaller texture information, generate seeds that are more consistent with image texture, and better detect image texture. © 2023 SPIE.

关键词： Superpixels

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SmokerViT: A Transformer-Based Method for Smoker Recognition

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computers, Materials & Continua 2023年第10期77卷 403-424页

作者： Ali Khan Somaiya Khan Bilal Hassan Rizwan Khan Zhonglong Zheng College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua321004China School of Electronics Engineering Beijing University of Posts and TelecommunicationsBeijing100876China Department of Electrical Engineering and Computer Science Khalifa University of Science and TechnologyAbu Dhabi127788United Arab Emirates Key Laboratory of Intelligent Education of Zhejiang Province Zhejiang Normal UniversityJinhua321004China

Smoking has an economic and environmental impact on society due to the toxic substances it *** Neural Networks(CNNs)need help describing low-level features and can miss important ***,accurate smoker detection is vital with minimum false *** answer the issue,the researchers of this paper have turned to a self-attention mechanism inspired by the ViT,which has displayed state-of-the-art performance in the classification *** effectively enforce the smoking prohibition in non-smoking locations,this work presents a Vision Transformer-inspired model called SmokerViT for detecting ***,this research utilizes a locally curated dataset of 1120 images evenly distributed among the two classes(Smoking and NotSmoking).Further,this research performs augmentations on the smoker detection dataset to have many images with various representations to overcome the dataset size *** convolutional operations used in most existing works,the proposed SmokerViT model employs a self-attention mechanism in the Transformer block,making it suitable for the smoker classification ***,this work integrates the multi-layer perceptron head block in the SmokerViT model,which contains dense layers with rectified linear activation and linear kernel regularizer with L2 for the recognition *** work presents an exhaustive analysis to prove the efficiency of the proposed SmokerViT *** performance of the proposed SmokerViT performance is evaluated and compared with the existing methods,where it achieves an overall classification accuracy of 97.77%,with 98.21%recall and 97.35%precision,outperforming the state-of-the-art deep learning models,including convolutional neural networks(CNNs)and other vision transformer-based models.

关键词： Smoker recognition SmokerViT deep learning transformer for vision

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