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Integer multiple quantum image scaling based on NEQR and bicubic interpolation

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Chinese Physics B 2024年第4期33卷 259-273页

作者：蔡硕周日贵罗佳陈思哲 College of Information Engineering Shanghai Maritime UniversityShanghai 201306China research center of intelligent Information Processing and Quantum intelligent Computing Shanghai 201306China College of Merchant Marine Shanghai Maritime UniversityShanghai 201306China School of Mathematics and Computational Science Shangrao Normal UniversityShangrao 334001China

As a branch of quantum image processing,quantum image scaling has been widely ***,most of the existing quantum image scaling algorithms are based on nearest-neighbor interpolation and bilinear interpolation,the quantum version of bicubic interpolation has not yet been *** this work,we present the first quantum image scaling scheme for bicubic interpolation based on the novel enhanced quantum representation(NEQR).Our scheme can realize synchronous enlargement and reduction of the image with the size of 2^(n)×2^(n) by integral ***,the image is represented by NEQR and the original image coordinates are obtained through multiple CNOT ***,16 neighborhood pixels are obtained by quantum operation circuits,and the corresponding weights of these pixels are calculated by quantum arithmetic ***,a quantum matrix operation,instead of a classical convolution operation,is used to realize the sum of convolution of these *** simulation experiments and complexity analysis,we demonstrate that our scheme achieves exponential speedup over the classical bicubic interpolation algorithm,and has better effect than the quantum version of bilinear interpolation.

关键词： quantum image processing image scaling bicubic interpolation quantum circuit

One-shot Face Reenactment with Dense Correspondence Estimation

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Machine Intelligence research 2024年第5期21卷 941-953页

作者： Yunfan Liu Qi Li Zhenan Sun School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100049China center for research on intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing100190China

One-shot face reenactment is a challenging task due to the identity mismatch between source and driving *** existing methods fail to completely eliminate the interference of driving subjects’identity information,which may lead to face shape distortion and undermine the realism of reenactment *** solve this problem,in this paper,we propose using a 3D morphable model(3DMM)for explicit facial semantic decomposition and identity *** of using 3D coefficients alone for reenactment control,we take advantage of the generative ability of 3DMM to render textured face *** proxies contain abundant yet compact geometric and semantic information of human faces,which enables us to compute the face motion field between source and driving images by estimating the dense *** this way,we can approximate reenactment results by warping source images according to the motion field,and a generative adversarial network(GAN)is adopted to further improve the visual quality of warping *** experiments on various datasets demonstrate the advantages of the proposed method over existing state-of-the-art benchmarks in both identity preservation and reenactment fulfillment.

关键词： Generative adversarial networks face image manipulation face image synthesis face reenactment 3D morphable model

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

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Computers, Materials & Continua 2024年第5期79卷 2063-2083页

作者： Xiaoqin Ma Jun Wang Wenchang Yu Qinli Zhang School of Big Data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big Data intelligent Perception and Application Engineering research center Chizhou247000China

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable *** available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal *** order to address the aforementioned issues,this paper delves into the study of attribute reduction *** of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute *** newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute ***,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute ***,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy *** effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information ***,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is *** experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction *** comprehensive analysis o

关键词： Hybrid decision information systems fuzzy conditional information entropy attribute reduction fuzzy relationship rough set theory(RST)

Context-Aware Augmented Reality Using Human-Computer Interaction Models

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Journal of Control and Decision 2024年第1期11卷 1-14页

作者： Ying Sun Qiongqiong Guo Shumei Zhao Karthik Chandran G.Fathima Henan intelligent education and intelligent technology application engineering technology research center Zhengzhou Railway Vocational and technical collegeZhengzhouHenanPeople’s Republic of China Henan Provincial research center of Wisdom Education and intelligent Technology Application Engineering Technology Zhengzhou Railway Vocational and technical collegeZhengzhouHenanPeople’s Republic of China Department of Mechatronics Engineering Jyothi Engineering CollegeKeralaIndia CSE Adhiyamaan College of Engineering HosurTamilnaduIndia

Augmented Reality is a technique that allows users to overlap digital information with their physical *** Augmented Reality(AR)displays have an exceptional characteristic from the Human–Computer Interaction(HCI)*** to its increasing popularity and application in diverse domains,increasing user-friendliness and AR usage are ***-aware is one approach since an AR application can adapt to the user,environment,needs and enhance ergonomic principles and *** paper proposes the intelligent Contextaware Augmented Reality Model(ICAARM)for Human–Computer Interaction *** study explores and reduces interaction uncertainty by semantically modeling user-specific interaction with context,allowing personalised *** information is captured from an AR device to understand user interactions and *** depictions carry semantics to Augmented Reality applications about the user’s intention to interact with a specific device ***,this study describes personalised gesture interaction in VR/AR applications for immersive/intelligent environments.

关键词： Augmented Reality Context-aware Human–Computer Interaction

Probabilistic-Ellipsoid Hybrid Reliability Multi-Material Topology Optimization Method Based on Stress Constraint

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 757-792页

作者： Zibin Mao Qinghai Zhao Liang Zhang College of Mechanical and Electrical Engineering Qingdao UniversityQingdao266071China National Engineering research center for intelligent Electrical Vehicle Power System Qingdao UniversityQingdao266071China

This paper proposes a multi-material topology optimization method based on the hybrid reliability of the probability-ellipsoid model with stress constraint for the stochastic uncertainty and epistemic uncertainty of mechanical loads in optimization *** probabilistic model is combined with the ellipsoidal model to describe the uncertainty of mechanical *** topology optimization formula is combined with the ordered solid isotropic material with penalization(ordered-SIMP)multi-material interpolation *** stresses of all elements are integrated into a global stress measurement that approximates the maximum stress using the normalized p-norm ***,the sequential optimization and reliability assessment(SORA)is applied to transform the original uncertainty optimization problem into an equivalent deterministic topology optimization(DTO)*** response surface and sparse grid technique are combined with SORA to get accurate information on the most probable failure point(MPP).In each cycle,the equivalent topology optimization formula is updated according to the MPP information obtained in the previous *** adjoint variable method is used for deriving the sensitivity of the stress constraint and the moving asymptote method(MMA)is used to update design ***,the validity and feasibility of the method are verified by the numerical example of L-shape beam design,T-shape structure design,steering knuckle,and 3D T-shaped beam.

关键词： Stress constraint probabilistic-ellipsoid hybrid topology optimization reliability analysis multi-material design

Low-Rank Optimal Transport for Robust Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1667-1680页

作者： Bingrong Xu Jianhua Yin Cheng Lian Yixin Su Zhigang Zeng IEEE the School of Automation Wuhan University of TechnologyWuhan 430070China intelligent Transportation Systems research center Wuhan University of TechnologyWuhan 430063 Chongqing research Institute Wuhan University of TechnologyChongqingChina the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 the Key Laboratory of Image Processing and intelligent Control of Education Ministry of China Wuhan 430074China

When encountering the distribution shift between the source(training) and target(test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are welllabeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation:distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.

关键词： Domain adaptation low-rank constraint noise corruption optimal transport

An effective nonlinear dynamic formulation to analyze grasping capability of soft pneumatic robotic gripper

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International Journal of Smart and Nano Materials 2024年第3期15卷 405-431页

作者： Qiping Xu Chenhang Ying Kehang Zhang Huiyu Xie Shiju E Department of robotics Engineering Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology&Equipment of Zhejiang ProvinceCollege of EngineeringZhejiang Normal UniversityJinhuaP.R.China

Soft pneumatic robotic grippers have found extensive applica-tions across various engineering domains,which prompts active research due to their splendid compliance,high flex-ibility,and safe human-robot interaction over conventional stiff *** simplified rod-based models prin-cipally focused on clarifying overall large deformation and bending postures of soft grippers from static or quasi-static perspectives,whereas it is challenging to elaborate grasping characteristics of soft grippers without considering contact interaction and nonlinear large deformation *** address this,based on absolute nodal coordinate formulation(ANCF),comprehensively allowing for structural complexity,geometric,material and boundary nonlinearities,and incorpor-ating Coulomb’friction law with a multiple-point contact method,we put forward an effective nonlinear dynamic mod-eling approach for exploring grasping capability of soft ***,we solved the established dynamic equations using Generalized-αscheme,and conducted thorough numer-ical simulation analysis on a three-jaw soft pneumatic gripper(SPG)in terms of grasping configurations,displacements and contact *** proposed dynamic approach can accurately both describe complicated deformed configurations along with stress distribution and provide a feasible solution to simulate grasping targets,whose effectiveness and precision were analyzed theoretically and verified experimentally,which may shed new light on devising and optimizing other multi-functional SPGs.

关键词： Nonlinear dynamic formulation soft pneumatic robotic grippers contact interaction grasping capability configuration variations

Sparse convolutional model with semantic expression for waste electrical appliances recognition

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Science China(Technological Sciences) 2024年第9期67卷 2881-2893页

作者： HAN HongGui LIU YiMing LI FangYu DU YongPing Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and intelligent System Beijing 100124China Engineering research center of Digital Community Ministry of EducationBeijing 100124China Beijing Artificial Intelligence Institute and Beijing Laboratory for intelligent Environmental Protection Beijing 100124China

Deep neural networks play an important role in the recognition of waste electrical appliances. However, deep neural network components still lack reliability in decision-making features. To address this problem, a sparse convolutional model with semantic expression(SCMSE) is proposed. First, a low-rank sparse semantic expression component, combining the benefits of residual networks and sparse representation, is adapted to enhance sparse feature extraction and semantic expression. Second, a reliable network architecture is obtained by iterating the optimal sparse solution, enhancing semantic expression. Finally, the results of visualization experiments on the waste electrical appliances dataset demonstrate that the proposed SCMSE can obtain excellent semantic performance.

关键词： sparse convolutional model deep neural network semantic expression visualization computer vision

intelligent Small Sample Defect Detection of Concrete Surface Using Novel Deep Learning Integrating Improved YOLOv5

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 545-547页

作者： Yongming Han Lei Wang Youqing Wang Zhiqiang Geng IEEE College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029 Engineering research center of intelligent PSE Ministry of Education in ChinaBeijing 100029China

Dear Editor,This letter presents an intelligent small sample defect detection of concrete surface using novel deep learning integrating the improved YOLOv5 based on the Wasserstein GAN(WGAN)enhancement *** proposed method is capable of producing top-notch data sets to address the issues of insufficient samples and substandard quality.

关键词： concrete integrating algorithm