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generative adversarial networks based motion learning towards robotic calligraphy synthesis

CAAI Transactions on Intelligence Technology 2024年第2期9卷 452-466页

作者： Xiaoming Wang Yilong Yang Weiru Wang Yuanhua Zhou Yongfeng Yin Zhiguo Gong Department of Computer and Information Science University of MacaoMacaoChina School of Software Beihang UniversityBeijingChina Department of Computer Science and Technology Faculty of Information TechnologyBeijing University of TechnologyBeijingChina School of Foreign Languages Guangzhou Huashang CollegeGuangzhouChina

Robot calligraphy visually reflects the motion capability of robotic *** traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters,this article presents a generative adversarial network(GAN)-based motion learning method for robotic calligraphy synthesis(Gan2CS)that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy *** key technologies in the proposed approach include:(1)adopting the GAN to learn the motion parameters from the robot writing operation;(2)converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration;(3)reproducing high-precision calligraphy works by synthesising the writing motion data *** this study,the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot *** robot performs the writing with motion planning,and the writing motion parameters of calligraphy strokes are learnt with *** the motion data of basic strokes is synthesised based on the hierarchical process of‘stroke-radicalpart-character’.And the robot re-writes the synthesised characters whose similarity with the original calligraphy characters is *** calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.

关键词： calligraphy synthesis generative adversarial networks Motion learning robot writing

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Fire resistance evaluation through synthetic fire tests and generative adversarial networks

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Frontiers of Structural and Civil Engineering 2024年第4期18卷 587-614页

作者： Aybike Özyüksel Çiftçioğlu M.Z.Naser Department of Civil Engineering Manisa Celal Bayar University45140Türkiye School of Civil&Environmental Engineering and Earth Sciences(SCEEES) Clemson UniversityClemsonSC 29631USA Artificial Intelligence Research Institute for Science and Engineering(AIRISE) Clemson UniversityClemsonSC 29631USA

This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete *** approach begins by creating deep learning models,namely generative adversarial networks and variational autoencoders,to learn the spatial distribution of real fire *** then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete *** generated data are employed to train state-of-the-art machine learning techniques,including Extreme Gradient Boost,Light Gradient Boosting Machine,Categorical Boosting Algorithm,Support Vector Regression,Random Forest,Decision Tree,Multiple Linear Regression,Polynomial Regression,Support Vector Machine,Kernel Support Vector Machine,Naive Bayes,and K-Nearest Neighbors,which can predict the fire resistance of the columns through regression and *** learning analyses achieved highly accurate predictions of fire resistance values,outperforming traditional models that relied solely on limited experimental *** study highlights the potential for using machine learning and deep learning analyses to revolutionize the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments.

关键词： deep learning fire resistance generative adversarial networks machine learning

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Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks

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Friction 2024年第6期12卷 1283-1298页

作者： Junhyeon SEO Prahalada RAO Bart RAEYMAEKERS Department of Mechanical Engineering Virginia TechBlacksburgVA 24061USA Grado Department of Industrial and Systems Engineering Virginia TechBlacksburgVA 24061USA

Numerically generating synthetic surface topography that closely resembles the features and characteristics of experimental surface topography measurements reduces the need to perform these intricate and costly ***,existing algorithms to numerically generated surface topography are not well-suited to create the specific characteristics and geometric features of as-built surfaces that result from laser powder bed fusion(LPBF),such as partially melted metal particles,porosity,laser scan lines,and ***,we present a method to generate synthetic as-built LPBF surface topography maps using a progressively growing generative adversarial *** qualitatively and quantitatively demonstrate good agreement between synthetic and experimental as-built LPBF surface topography maps using areal and deterministic surface topography parameters,radially averaged power spectral density,and material ratio *** ability to accurately generate synthetic as-built LPBF surface topography maps reduces the experimental burden of performing a large number of surface topography ***,it facilitates combining experimental measurements with synthetic surface topography maps to create large data-sets that facilitate,*** as-built surface topography to LPBF process parameters,or implementing digital surface twins to monitor complex end-use LPBF parts,amongst other applications.

关键词： additive manufacturing surface topography synthetic surface topography generative adversarial networks

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Correcting Climate Model Sea Surface Temperature Simulations with generative adversarial networks:Climatology,Interannual Variability,and Extremes

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Advances in Atmospheric Sciences 2024年第7期41卷 1299-1312页

作者： Ya WANG Gang HUANG Baoxiang PAN Pengfei LIN Niklas BOERS Weichen TAO Yutong CHEN BO LIU Haijie LI State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China Earth System Modelling School of Engineering and DesignTechnical University of MunichOttobrunn 85521Germany Potsdam Institute for Climate Impact Research Potsdam 14412Germany Department of Mathematics and Global Systems Institute University of ExeterExeter EX44QEUK CMA Earth System Modeling and Prediction Centre(CEMC) Chinese Academy of Meteorological SciencesBeijing 100081China

Climate models are vital for understanding and projecting global climate change and its associated ***,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future *** these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning ***,we employ an unsupervised Cycle-consistent generative adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST ***,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to ***,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to *** enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales *** study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

关键词： generative adversarial networks model bias deep learning El Niño-Southern Oscillation marine heatwaves

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Training generative adversarial networks with Adaptive Composite Gradient

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Data Intelligence 2024年第1期6卷 120-157页

作者： Huiqing Qi Fang Li Shengli Tan Xiangyun Zhang School of Mathematical Sciences East China Normal UniversityShanghai 200241China

The wide applications of generative adversarial networks benefit from the successful training methods,guaranteeing that an object function converges to the local ***,designing an efficient and competitive training method is still a challenging task due to the cyclic behaviors of some gradient-based ways and the expensive computational cost of acquiring the Hessian *** address this problem,we proposed the Adaptive Composite Gradients(ACG)method,linearly convergent in bilinear games under suitable *** analysis and toy-function experiments both suggest that our approach alleviates the cyclic behaviors and converges faster than recently proposed SOTA *** convergence speed of the ACG is improved by 33%than other *** ACG method is a novel Semi-Gradient-Free algorithm that can reduce the computational cost of gradient and Hessian by utilizing the predictive information in future *** mixture of Gaussians experiments and real-world digital image generative experiments show that our ACG method outperforms several existing technologies,illustrating the superiority and efficacy of our method.

关键词： generative adversarial networks adaptive composite gradient semi-gradient free game theory bilinear game

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Design and Parameter Optimization of Zero Position Code Considering Diffraction Based on Deep Learning generative adversarial networks

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Nanomanufacturing and Metrology 2024年第1期7卷 15-27页

作者： Shengtong Wang Linbin Luo Xinghui Li Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China Tsinghua-Berkeley Shenzhen Institute Tsinghua UniversityShenzhen 518055China

Absolute measurement has consistently been the primary focus in the development of precision linear and angular displace-ment *** scheme design of binary zero position codes is an important factor for absolute *** and optimizing high-bit zero position codes with over 100 bits face considerable ***,the working parameters of zero position codes[unit code width(b),distance(d),and yaw angle(α)]remarkably affect their post-installation performance,particularly in absolute positioning and limit code application in multi-degree-of-freedom measurement *** study addresses these challenges by proposing a design method for zero position codes that considers diffraction based on generative adversarial networks and aims to explore a design with increased efficiency and accuracy as well as optimization for high-bit zero position ***,the tolerance range of zero positioning per-formance for each working parameter is *** leveraging the adversarial network structure,this study generates the optimization of a 150-bit code and processes the tests of the zero position code by using simulation *** following working parameter ranges for code design are recommended on the basis of theoretical and experimental results:b greater than 10μm,d andαwithin 1000μm and 3490μrad,and avoidance of intervals with sharp changes in the full width at half *** proposed code design and parameter optimization lay a solid foundation for research and engineering appli-cations in absolute measurement field and have considerable potential for generalization and wide applicability.

关键词： Absolute measurement Zero position code Deep learning generative adversarial networks Tolerance range Parameter optimization

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generative adversarial networks Based Digital Twin Channel Modeling for Intelligent Communication networks

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China Communications 2023年第8期20卷 32-43页

作者： Yuxin Zhang Ruisi He Bo Ai Mi Yang Ruifeng Chen Chenlong Wang Zhengyu Zhang Zhangdui Zhong State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China Institute of Computing Technologies China Academy of Railway Sciences Co.Ltd.Beijing 100081China

Integration of digital twin(DT)and wireless channel provides new solution of channel modeling and simulation,and can assist to design,optimize and evaluate intelligent wireless communication system and *** DT channel modeling,the generated channel data can be closer to realistic channel measurements without requiring a prior channel model,and amount of channel data can be significantly *** intelligence(AI)based modeling approach shows outstanding performance to solve such *** this work,a channel modeling method based on generative adversarial networks is proposed for DT channel,which can generate identical statistical distribution with measured *** validation is conducted by comparing DT channel characteristics with measurements,and results show that DT channel leads to fairly good agreement with measured ***,a link-layer simulation is implemented based on DT *** is found that the proposed DT channel model can be well used to conduct link-layer simulation and its performance is comparable to using measurement *** observations and results can facilitate the development of DT channel modeling and provide new thoughts for DT channel applications,as well as improving the performance and reliability of intelligent communication networking.

关键词： digital twin channel modeling generative adversarial networks intelligent communication networking

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generative adversarial networks with Joint Distribution Moment Matching

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Journal of the Operations Research Society of China 2019年第4期7卷 579-597页

作者： Yi-Ying Zhang Chao-Min Shen Hao Feng Preston Thomas Fletcher Gui-Xu Zhang Department of Computer Science East China Normal UniversityShanghai 200062China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal UniversityShanghai 200062China Westlake Institute for Brain-Like Science and Technology Hangzhou 310027China Didi Chuxing Science and Technology Co.Ltd. Beijing 100193China Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904USA

generative adversarial networks(GANs)have shown impressive power in the field of machine *** GANs have focused on unsupervised learning *** recent years,conditional GANs that can generate data with labels have been proposed in semi-supervised learning and have achieved better image quality than traditional *** GANs,however,generally only minimize the difference between marginal distributions of real and generated data,neglecting the difference with respect to each class of the *** address this challenge,we propose the GAN with joint distribution moment matching(JDMM-GAN)for matching the joint distribution based on maximum mean discrepancy,which minimizes the differences of both the marginal and conditional *** learning procedure is iteratively conducted by the stochastic gradient descent and *** evaluate JDMM-GAN on several benchmark datasets,including MNIST,CIFAR-10 and the Extended Yale *** with the state-of-the-art GANs,JDMM-GAN generates more realistic images and achieves the best inception score for CIFAR-10 dataset.

关键词： generative adversarial networks Joint Distribution Moment Matching Maximum mean discrepancy

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Renewable Scenario Generation Using Controllable generative adversarial networks with Transparent Latent Space

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CSEE Journal of Power and Energy Systems 2021年第1期7卷 66-77页

作者： Ji Qiao Tianjiao Pu Xinying Wang the China Electric Power Research Institute Haidian DistrictBeijing 100192China

With the growing penetration of renewable energysources in power systems, it becomes increasingly important tocharacterize their inherent variability and uncertainty. Scenariogeneration is a key approach to provide a series of possible powerscenarios in the future for the system planner and operator tomake decisions. In this paper, a data-driven method is presentedfor renewable scenario generation using stable and controllablegenerative adversarial networks with transparent latent space(ctrl-GANs). The machine learning based algorithm can capturethe nonlinear and dynamic renewable patterns without the needfor modeling assumptions and complicated sampling *** orthogonal regularization and spectral normalization areadopted to improve the training stabilization of the GAN *** control the generation process, a relationship is built betweenfeatures of the generated scenarios and latent vectors on themanifold. Moreover, several new metrics for GANs are used toevaluate the quality of the scenarios. The proposed approachis applied to generate realistic time series data of wind andphotovoltaic power. The results demonstrate that our methodhas a better performance on numerical stabilization and is ableto control the generation process with latent space.

关键词： Renewable scenario generation deep learning generative adversarial networks neural network transparent latent spacer

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Ballistic response of armour plates using generative adversarial networks

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Defence Technology（防务技术） 2022年第9期18卷 1513-1522页

作者： S.Thompson F.Teixeira-Dias M.Paulino A.Hamilton Institute for Infrastructure and Environment(IIE) School of EngineeringThe University of Edinburgh Alexander Graham Bell BuildingThe King's BuildingsEdinburghEH93FGUnited Kingdom School of Engineering Faculty of ScienceEngineering and Built EnvironmentDeakin University Waurn PondsVictoria3220Australia Data Science and AI EngineeringGroup Chief Information O CeLloyds Banking Group69 Morrison StEdinburghEH38BWUnited Kingdom

It is important to understand how ballistic materials respond to impact from projectiles such that informed decisions can be made in the design process of protective armour systems. Ballistic testing is a standards-based process where materials are tested to determine whether they meet protection, safety and performance criteria. For the V50ballistic test, projectiles are fired at different velocities to determine a key design parameter known as the ballistic limit velocity(BLV), the velocity above which projectiles perforate the target. These tests, however, are destructive by nature and as such there can be considerable associated costs, especially when studying complex armour materials and systems. This study proposes a unique solution to the problem using a recent class of machine learning system known as the generative adversarial Network(GAN). The GAN can be used to generate new ballistic samples as opposed to performing additional destructive experiments. A GAN network architecture is tested and trained on three different ballistic data sets, and their performance is compared. The trained networks were able to successfully produce ballistic curves with an overall RMSE of between 10 and 20 % and predicted the V50BLV in each case with an error of less than 5 %. The results demonstrate that it is possible to train generative networks on a limited number of ballistic samples and use the trained network to generate many new samples representative of the data that it was trained on. The paper spotlights the benefits that generative networks can bring to ballistic applications and provides an alternative to expensive testing during the early stages of the design process.

关键词： Machine learning generative adversarial networks GAN Terminal ballistics Armour systems

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