目的探究“非遗+AI”新模式下湖南土特产包装的创新设计形式,传承和发展非遗文化,提升土特产包装的文化内涵,提高传统包装的科技含量,以适应当代消费者不断提升的审美和消费需求。方法通过问卷调查、深度访谈等方法,分析“非遗+AI”模...目的探究“非遗+AI”新模式下湖南土特产包装的创新设计形式,传承和发展非遗文化,提升土特产包装的文化内涵,提高传统包装的科技含量,以适应当代消费者不断提升的审美和消费需求。方法通过问卷调查、深度访谈等方法,分析“非遗+AI”模式与当代湖南土特产包装的设计现状,了解消费者对包装设计的不同需求,总结设计策略,并基于“非遗+AI”新模式下机器学习和深度学习技术,借助SD(Stable Diffusion,稳定扩散模型)架构下的Lora模型(Low-RankAdaptation of Large Language Models,大语言模型的低秩适用方法),进行踏虎凿花在岳阳王鸽土特产包装设计中的创新应用实践。结果生成出紧跟新时代数字技术,驱动非遗文化活态传承的包装设计图案,从而实现湖南土特产包装的创新设计探索。结论基于“非遗+AI”模式下的SD模型技术,对泸溪踏虎凿花进行创新转化并应用在湖南土特产岳阳王鸽包装设计中,既活化了传统手工艺的表达形式,又创新了土特产的包装表现手法,为其他非遗文化元素和产品包装的有机融合提供了新路径。展开更多
Artificial intelligence(AI)models are promising to improve the accuracy of wireless positioning systems,particularly in indoor environments where unpredictable radio propagation channel is a great challenge.Although g...Artificial intelligence(AI)models are promising to improve the accuracy of wireless positioning systems,particularly in indoor environments where unpredictable radio propagation channel is a great challenge.Although great efforts have been made to explore the effectiveness of different AI models,it is still an open problem whether these models,trained with the data collected from all base stations(BSs),could work when some BSs are unavailable.In this paper,we make the first effort to enhance the generalization ability of AI wireless positioning model to adapt to the scenario where only partial BSs work.Particularly,a Siamese Network based Wireless Positioning Model(SNWPM)is proposed to predict the location of mobile user equipment from channel state information(CSI)collected from 5G BSs.Furthermore,a Feature Aware Attention Module(FAAM)is introduced to reinforce the capability of feature extraction from CSI data.Experiments are conducted on the 2022 Wireless Communication AI Competition(WAIC)dataset.The proposed SNWPM achieves decimeter-level positioning accuracy even if the data of partial BSs are unavailable.Compared with other AI models,the proposed SNWPM can reduce the positioning error by nearly 50%to more than 60%while using less parameters and lower computation resources.展开更多
Generative AI is rapidly employed by software developers to generate code or other software artifacts.However,the analysis and assessment of generative AI with respect to requirements analysis and modeling tasks,espec...Generative AI is rapidly employed by software developers to generate code or other software artifacts.However,the analysis and assessment of generative AI with respect to requirements analysis and modeling tasks,especially with UML,has received little attention.This paper investigates the capabilities of generative AI to aid in the creation of three types of UML models:UML use case models,class diagrams,and sequence diagrams.For this purpose,we designed an AI-aided UML modeling task in our course on software requirements modeling.50 undergraduates who majored in Software Engineering at Wuhan University completed the modeling task and the corresponding online survey.Our findings show that generative AI can help create these three types of UML models,but its performance is limited to identifying essential modeling elements of these UML models.展开更多
El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been develope...El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict it.In some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of research.This study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as ICM-UNet.The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.In the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO events.These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies.Furthermore,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.展开更多
2019年Metalearners for Estimating Heterogeneous Treatment Effects using Machine Learning的发表引发了世界因果推断理论的研究热情。目前,机器学习与因果推断论中的许多统计模型已被广泛应用。本文采用文献资料法、数理统计分析...2019年Metalearners for Estimating Heterogeneous Treatment Effects using Machine Learning的发表引发了世界因果推断理论的研究热情。目前,机器学习与因果推断论中的许多统计模型已被广泛应用。本文采用文献资料法、数理统计分析方法、案例研究法等研究方法,研究推演现代因果推断理论中较为知名的Uplift因果模型在体育中的应用场景,其中Uplift因果模型包括S-learner(单模型)、T-learner(双模型)、X-learner(交叉训练模型)。结果显示,在体育消费随机对照实验中应用Uplift因果模型,可以基于基本模型进一步推导出各变量因素之间的因果关系,验证并分析自变量对因变量变化的影响;率先在体育消费市场研究与实验中应用Uplift因果模型可以填补我国体育消费实验数据分析方法的空缺。展开更多
文摘目的探究“非遗+AI”新模式下湖南土特产包装的创新设计形式,传承和发展非遗文化,提升土特产包装的文化内涵,提高传统包装的科技含量,以适应当代消费者不断提升的审美和消费需求。方法通过问卷调查、深度访谈等方法,分析“非遗+AI”模式与当代湖南土特产包装的设计现状,了解消费者对包装设计的不同需求,总结设计策略,并基于“非遗+AI”新模式下机器学习和深度学习技术,借助SD(Stable Diffusion,稳定扩散模型)架构下的Lora模型(Low-RankAdaptation of Large Language Models,大语言模型的低秩适用方法),进行踏虎凿花在岳阳王鸽土特产包装设计中的创新应用实践。结果生成出紧跟新时代数字技术,驱动非遗文化活态传承的包装设计图案,从而实现湖南土特产包装的创新设计探索。结论基于“非遗+AI”模式下的SD模型技术,对泸溪踏虎凿花进行创新转化并应用在湖南土特产岳阳王鸽包装设计中,既活化了传统手工艺的表达形式,又创新了土特产的包装表现手法,为其他非遗文化元素和产品包装的有机融合提供了新路径。
基金supported by National Natural Science Foundation of China (No. 62076251)sponsored by IMT-2020(5G) Promotion Group 5G+AI Work Group+3 种基金jointly sponsored by China Academy of Information and Communications TechnologyGuangdong OPPO Mobile Telecommunications Corp., Ltdvivo Mobile Communication Co., LtdHuawei Technologies Co., Ltd
文摘Artificial intelligence(AI)models are promising to improve the accuracy of wireless positioning systems,particularly in indoor environments where unpredictable radio propagation channel is a great challenge.Although great efforts have been made to explore the effectiveness of different AI models,it is still an open problem whether these models,trained with the data collected from all base stations(BSs),could work when some BSs are unavailable.In this paper,we make the first effort to enhance the generalization ability of AI wireless positioning model to adapt to the scenario where only partial BSs work.Particularly,a Siamese Network based Wireless Positioning Model(SNWPM)is proposed to predict the location of mobile user equipment from channel state information(CSI)collected from 5G BSs.Furthermore,a Feature Aware Attention Module(FAAM)is introduced to reinforce the capability of feature extraction from CSI data.Experiments are conducted on the 2022 Wireless Communication AI Competition(WAIC)dataset.The proposed SNWPM achieves decimeter-level positioning accuracy even if the data of partial BSs are unavailable.Compared with other AI models,the proposed SNWPM can reduce the positioning error by nearly 50%to more than 60%while using less parameters and lower computation resources.
文摘Generative AI is rapidly employed by software developers to generate code or other software artifacts.However,the analysis and assessment of generative AI with respect to requirements analysis and modeling tasks,especially with UML,has received little attention.This paper investigates the capabilities of generative AI to aid in the creation of three types of UML models:UML use case models,class diagrams,and sequence diagrams.For this purpose,we designed an AI-aided UML modeling task in our course on software requirements modeling.50 undergraduates who majored in Software Engineering at Wuhan University completed the modeling task and the corresponding online survey.Our findings show that generative AI can help create these three types of UML models,but its performance is limited to identifying essential modeling elements of these UML models.
基金supported by the National Natural Science Foundation of China(NFSCGrant No.42030410)+2 种基金Laoshan Laboratory(No.LSKJ202202402)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB40000000)the Startup Foundation for Introducing Talent of NUIST.
文摘El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict it.In some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of research.This study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as ICM-UNet.The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.In the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO events.These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies.Furthermore,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.
文摘2019年Metalearners for Estimating Heterogeneous Treatment Effects using Machine Learning的发表引发了世界因果推断理论的研究热情。目前,机器学习与因果推断论中的许多统计模型已被广泛应用。本文采用文献资料法、数理统计分析方法、案例研究法等研究方法,研究推演现代因果推断理论中较为知名的Uplift因果模型在体育中的应用场景,其中Uplift因果模型包括S-learner(单模型)、T-learner(双模型)、X-learner(交叉训练模型)。结果显示,在体育消费随机对照实验中应用Uplift因果模型,可以基于基本模型进一步推导出各变量因素之间的因果关系,验证并分析自变量对因变量变化的影响;率先在体育消费市场研究与实验中应用Uplift因果模型可以填补我国体育消费实验数据分析方法的空缺。
