In this article we consider the asymptotic behavior of extreme distribution with the extreme value index γ>0 . The rates of uniform convergence for Fréchet distribution are constructed under the second-order ...In this article we consider the asymptotic behavior of extreme distribution with the extreme value index γ>0 . The rates of uniform convergence for Fréchet distribution are constructed under the second-order regular variation condition.展开更多
The Proton Exchange Membrane Fuel Cell(PEMFC)converts the chemical energy of hydrogen fuel directly into electrical energy with broad application prospects.Understanding how current density is distributed in the PEMFC...The Proton Exchange Membrane Fuel Cell(PEMFC)converts the chemical energy of hydrogen fuel directly into electrical energy with broad application prospects.Understanding how current density is distributed in the PEMFC systems is crucial as it is a key factor influencing system performance.However,direct modeling for current distribution may encounter the challenge of dimensional catastrophe owing to the high dimensionality of the data.This paper uses a high-resolution segmented measurement device with 396 points to conduct experimental tests on the current distribution of a PEMFC with reactive area of 406 cm^(2) during a stepwise increase in load current.The current distribution is modeled based on the test results to learn the mapping relationship between the experimental parameters and the current distribution.The proposed model utilizes a Conditional Variational Auto-Encoder(CVAE)to generate current distributions.The MSE(Mean-Square Error)of the trained CVAE model reaches 9.2×10^(-5),and the comparison results show that the 222.9A current distribution error has the largest MSE of 6.36×10^(-4) and a KL Divergence(Kullback-Leibler Divergence)of 9.55×10^(-4),both of which are at a low level.This model enables the direct determination of the current distribution based on the experimental parameters,thereby establishing a technical foundation for investigating the impact of experimental conditions on fuel cells.This model is also of great significance for research on fuel cell system control strategies and fault diagnosis.展开更多
Inverse design has long been an efficient and powerful design tool in the aircraft industry.In this paper,a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learnin...Inverse design has long been an efficient and powerful design tool in the aircraft industry.In this paper,a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learning.A Conditional Variational Auto Encoder(CVAE)and an integrated generative network CVAE-GAN that combines the CVAE with the Wasserstein Generative Adversarial Networks(WGAN),are conducted as generative models.They are used to generate target wall Mach distributions for the inverse design that matches specified features,such as locations of suction peak,shock and aft loading.Qualitative and quantitative results show that both adopted generative models can generate diverse and realistic wall Mach number distributions satisfying the given features.The CVAE-GAN model outperforms the CVAE model and achieves better reconstruction accuracies for all the samples in the dataset.Furthermore,a deep neural network for nonlinear mapping is adopted to obtain the airfoil shape corresponding to the target wall Mach number distribution.The performances of the designed deep neural network are fully demonstrated and a smoothness measurement is proposed to quantify small oscillations in the airfoil surface,proving the authenticity and accuracy of the generated airfoil shapes.展开更多
Invariant conditions for conformable fractional problems of the calculus of variations under the presence of external forces in the dynamics are studied. Depending on the type of transformations considered, different ...Invariant conditions for conformable fractional problems of the calculus of variations under the presence of external forces in the dynamics are studied. Depending on the type of transformations considered, different necessary conditions of invariance are obtained. As particular cases, we prove fractional versions of Noether's symmetry theorem. Invariant conditions for fractional optimal control problems, using the Hamiltonian formalism, are also investigated. As an example of potential application in Physics, we show that with conformable derivatives it is possible to formulate an Action Principle for particles under frictional forces that is far simpler than the one obtained with classical fractional derivatives.展开更多
In this study, we developed a general method to analytically tackle a kind of movable boundary problem from the viewpoint of energy variation. Having grouped the adhesion of a micro-beam, droplet and carbon nanotube ...In this study, we developed a general method to analytically tackle a kind of movable boundary problem from the viewpoint of energy variation. Having grouped the adhesion of a micro-beam, droplet and carbon nanotube （CNT） ring on a substrate into one framework, we used the developed line of reasoning to investigate the adhesion behaviors of these systems. Based upon the derived governing equations and transversality conditions, explicit solutions involving the critical parameters and morphologies for the three systems are successfully obtained, and then the parameter analogies and common characteristics of them are thor- oughly investigated. The presented method has been verified via the concept of energy release rate in fracture mechanics. Our analyses provide a new approach for exploring the mechanism of different systems with similarities as well as for understanding the unity of nature. The analysis results may be beneficial for the design of nano-structured materi- als, and hold potential for enhancing their mechanical, chemical, optical and electronic properties.展开更多
Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies...Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies.展开更多
As the popularity and dependence on the Internet increase,DDoS(distributed denial of service)attacks seriously threaten network security.By accurately distinguishing between different types of DDoS attacks,targeted de...As the popularity and dependence on the Internet increase,DDoS(distributed denial of service)attacks seriously threaten network security.By accurately distinguishing between different types of DDoS attacks,targeted defense strategies can be formulated,significantly improving network protection efficiency.DDoS attacks usually manifest as an abnormal increase in network traffic,and their diverse types of attacks,along with a severe data imbalance,make it difficult for traditional classification methods to effectively identify a small number of attack types.To solve this problem,this paper proposes a DDoS recognition method CVWGG(Conditional Variational Autoencoder-Wasserstein Generative Adversarial Network-gradient penalty-Gated Recurrent Unit)for unbalanced data,which generates less noisy data and high data quality compared with existing methods.CVWGG mainly includes unbalanced data processing for CVWG,feature extraction,and classification.CVWGG uses the CVAE(Conditional Variational Autoencoder)to improve the WGAN(Wasserstein Generative Adversarial Network)and introduces a GP(gradient penalty)term to design the loss function to generate balanced data,which enhances the learning ability and stability of the data.Subsequently,the GRU(Gated Recurrent Units)are used to capture the temporal features and patterns of the data.Finally,the logsoftmax function is used to differentiate DDoS attack categories.Using PyCharm and Python 3.10 for programming and evaluating performance with metrics such as accuracy and precision,the results show that the method achieved accuracy rates of 96.0%and 97.3%on two datasets,respectively.Additionally,comparison and ablation experiment results demonstrate that CVWGG effectively mitigates the imbalance between DDoS attack categories,significantly improves the classification accuracy of different types of attacks and provides a valuable reference for network security defense.展开更多
With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human interaction.Traditional emotional dialogue systems usually use an e...With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human interaction.Traditional emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate responses.However,selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the response.We propose a semantic and emotion-based dual latent variable generation model(Dual-LVG)for dialogue systems,which is able to generate appropriate emotional responses without an emotional dictionary.Different from previous work,the conditional variational autoencoder(CVAE)adopts the standard transformer structure.Then,Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and emotion.The content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features respectively.Moreover,the average attention mechanism is adopted to better extract semantic features at the sequence level,and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the model.Experimental results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors.展开更多
The feasibility of rooftop rainwater harvesting （RRWH） as an alternative source of water to meet the outdoor water demand in nine states of the U.S. was evaluated using a system dynamics model developed in Systems T...The feasibility of rooftop rainwater harvesting （RRWH） as an alternative source of water to meet the outdoor water demand in nine states of the U.S. was evaluated using a system dynamics model developed in Systems Thinking, Experimental Learning Laboratory with Animation. The state of Arizona was selected to evaluate the effects of the selected model parameters on the efficacy of RRWH since among the nine states the arid region of Arizona showed the least potential of meeting the outdoor water demand with rain harvested water. The analyses were conducted on a monthly basis across a 10-year projected period from 2015 to 2024. The results showed that RRWH as a potential source of water was highly sensitive to certain model parameters such as the outdoor water demand, the use of desert landscaping, and the percentage of existing houses with RRWH. A significant difference （as high as 37.5%） in rainwater potential was observed between the projected wet and dry climate conditions in Arizona. The analysis of the dynamics of the storage tanks suggested that a 1.0-2.0 m3 rainwater barrel, on an average, can store approximately 80% of the monthly rainwater generated from the rooftops in Arizona, even across the high seasonal variation. This interactive model can be used as a quick estimator of the amount of water that could be generated, stored, and utilized through RRWH systems in the U.S. under different climate conditions. The findings of such comprehensive analyses may help regional policymakers, especially in arid regions, to develop a sustainable water management infrastructure.展开更多
Data-driven garment animation is a current topic of interest in the computer graphics industry.Existing approaches generally establish the mapping between a single human pose or a temporal pose sequence,and garment de...Data-driven garment animation is a current topic of interest in the computer graphics industry.Existing approaches generally establish the mapping between a single human pose or a temporal pose sequence,and garment deformation,but it is difficult to quickly generate diverse clothed human animations.We address this problem with a method to automatically synthesize dressed human animations with temporal consistency from a specified human motion label.At the heart of our method is a twostage strategy.Specifically,we first learn a latent space encoding the sequence-level distribution of human motions utilizing a transformer-based conditional variational autoencoder(Transformer-CVAE).Then a garment simulator synthesizes dynamic garment shapes using a transformer encoder-decoder architecture.Since the learned latent space comes from varied human motions,our method can generate a variety of styles of motions given a specific motion label.By means of a novel beginning of sequence(BOS)learning strategy and a self-supervised refinement procedure,our garment simulator is capable of efficiently synthesizing garment deformation sequences corresponding to the generated human motions while maintaining temporal and spatial consistency.We verify our ideas experimentally.This is the first generative model that directly dresses human animation.展开更多
In this paper we study tree martingales and proved that if 1≤α,β〈∞,1≤p〈∞ then for every predictable tree martingale f=（ft,t∞T）and E[σ^（P）（f）]〈∞,E[S^（P）（f）]〈∞,it holds that ‖（St^（p）（f）,t∈...In this paper we study tree martingales and proved that if 1≤α,β〈∞,1≤p〈∞ then for every predictable tree martingale f=（ft,t∞T）and E[σ^（P）（f）]〈∞,E[S^（P）（f）]〈∞,it holds that ‖（St^（p）（f）,t∈T）‖M^α∞≤Cαβ‖f‖p^αβ,‖（σt^（p）（f）,t∈T）‖M^α,β‖f‖P^αβ,where Cαβ depends only on α and β.展开更多
文摘In this article we consider the asymptotic behavior of extreme distribution with the extreme value index γ>0 . The rates of uniform convergence for Fréchet distribution are constructed under the second-order regular variation condition.
基金sponsored by Science and Technology Program of Sichuan Province(2024ZDZX0035 and 2024ZHCG0072)。
文摘The Proton Exchange Membrane Fuel Cell(PEMFC)converts the chemical energy of hydrogen fuel directly into electrical energy with broad application prospects.Understanding how current density is distributed in the PEMFC systems is crucial as it is a key factor influencing system performance.However,direct modeling for current distribution may encounter the challenge of dimensional catastrophe owing to the high dimensionality of the data.This paper uses a high-resolution segmented measurement device with 396 points to conduct experimental tests on the current distribution of a PEMFC with reactive area of 406 cm^(2) during a stepwise increase in load current.The current distribution is modeled based on the test results to learn the mapping relationship between the experimental parameters and the current distribution.The proposed model utilizes a Conditional Variational Auto-Encoder(CVAE)to generate current distributions.The MSE(Mean-Square Error)of the trained CVAE model reaches 9.2×10^(-5),and the comparison results show that the 222.9A current distribution error has the largest MSE of 6.36×10^(-4) and a KL Divergence(Kullback-Leibler Divergence)of 9.55×10^(-4),both of which are at a low level.This model enables the direct determination of the current distribution based on the experimental parameters,thereby establishing a technical foundation for investigating the impact of experimental conditions on fuel cells.This model is also of great significance for research on fuel cell system control strategies and fault diagnosis.
基金co-supported by the National Key Project of China(No.GJXM92579)the National Natural Science Foundation of China(Nos.92052203,61903178 and61906081)。
文摘Inverse design has long been an efficient and powerful design tool in the aircraft industry.In this paper,a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learning.A Conditional Variational Auto Encoder(CVAE)and an integrated generative network CVAE-GAN that combines the CVAE with the Wasserstein Generative Adversarial Networks(WGAN),are conducted as generative models.They are used to generate target wall Mach distributions for the inverse design that matches specified features,such as locations of suction peak,shock and aft loading.Qualitative and quantitative results show that both adopted generative models can generate diverse and realistic wall Mach number distributions satisfying the given features.The CVAE-GAN model outperforms the CVAE model and achieves better reconstruction accuracies for all the samples in the dataset.Furthermore,a deep neural network for nonlinear mapping is adopted to obtain the airfoil shape corresponding to the target wall Mach number distribution.The performances of the designed deep neural network are fully demonstrated and a smoothness measurement is proposed to quantify small oscillations in the airfoil surface,proving the authenticity and accuracy of the generated airfoil shapes.
基金supported by CNPq and CAPES(Brazilian research funding agencies)Portuguese funds through the Center for Research and Development in Mathematics and Applications(CIDMA)the Portuguese Foundation for Science and Technology(FCT),within project UID/MAT/04106/2013
文摘Invariant conditions for conformable fractional problems of the calculus of variations under the presence of external forces in the dynamics are studied. Depending on the type of transformations considered, different necessary conditions of invariance are obtained. As particular cases, we prove fractional versions of Noether's symmetry theorem. Invariant conditions for fractional optimal control problems, using the Hamiltonian formalism, are also investigated. As an example of potential application in Physics, we show that with conformable derivatives it is possible to formulate an Action Principle for particles under frictional forces that is far simpler than the one obtained with classical fractional derivatives.
基金supported by the National Natural Science Foundation of China (11272357 and 11102140)Doctoral Fund of Ministry of Education of China (200804251520 and 20110141120024)Natural Science Foundation of Shandong Province (ZR2009AQ006)
文摘In this study, we developed a general method to analytically tackle a kind of movable boundary problem from the viewpoint of energy variation. Having grouped the adhesion of a micro-beam, droplet and carbon nanotube （CNT） ring on a substrate into one framework, we used the developed line of reasoning to investigate the adhesion behaviors of these systems. Based upon the derived governing equations and transversality conditions, explicit solutions involving the critical parameters and morphologies for the three systems are successfully obtained, and then the parameter analogies and common characteristics of them are thor- oughly investigated. The presented method has been verified via the concept of energy release rate in fracture mechanics. Our analyses provide a new approach for exploring the mechanism of different systems with similarities as well as for understanding the unity of nature. The analysis results may be beneficial for the design of nano-structured materi- als, and hold potential for enhancing their mechanical, chemical, optical and electronic properties.
文摘Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies.
基金The financial support from the Fundamental Research Funds for Higher Education Institutions of Heilongjiang Province(Grant No.145209126)the Research and Innovation Platform Project(Grant No.145309314)is acknowledged.
文摘As the popularity and dependence on the Internet increase,DDoS(distributed denial of service)attacks seriously threaten network security.By accurately distinguishing between different types of DDoS attacks,targeted defense strategies can be formulated,significantly improving network protection efficiency.DDoS attacks usually manifest as an abnormal increase in network traffic,and their diverse types of attacks,along with a severe data imbalance,make it difficult for traditional classification methods to effectively identify a small number of attack types.To solve this problem,this paper proposes a DDoS recognition method CVWGG(Conditional Variational Autoencoder-Wasserstein Generative Adversarial Network-gradient penalty-Gated Recurrent Unit)for unbalanced data,which generates less noisy data and high data quality compared with existing methods.CVWGG mainly includes unbalanced data processing for CVWG,feature extraction,and classification.CVWGG uses the CVAE(Conditional Variational Autoencoder)to improve the WGAN(Wasserstein Generative Adversarial Network)and introduces a GP(gradient penalty)term to design the loss function to generate balanced data,which enhances the learning ability and stability of the data.Subsequently,the GRU(Gated Recurrent Units)are used to capture the temporal features and patterns of the data.Finally,the logsoftmax function is used to differentiate DDoS attack categories.Using PyCharm and Python 3.10 for programming and evaluating performance with metrics such as accuracy and precision,the results show that the method achieved accuracy rates of 96.0%and 97.3%on two datasets,respectively.Additionally,comparison and ablation experiment results demonstrate that CVWGG effectively mitigates the imbalance between DDoS attack categories,significantly improves the classification accuracy of different types of attacks and provides a valuable reference for network security defense.
基金Fundamental Research Funds for the Central Universities of China,Grant/Award Number:CUC220B009National Natural Science Foundation of China,Grant/Award Numbers:62207029,62271454,72274182。
文摘With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human interaction.Traditional emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate responses.However,selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the response.We propose a semantic and emotion-based dual latent variable generation model(Dual-LVG)for dialogue systems,which is able to generate appropriate emotional responses without an emotional dictionary.Different from previous work,the conditional variational autoencoder(CVAE)adopts the standard transformer structure.Then,Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and emotion.The content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features respectively.Moreover,the average attention mechanism is adopted to better extract semantic features at the sequence level,and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the model.Experimental results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors.
文摘The feasibility of rooftop rainwater harvesting （RRWH） as an alternative source of water to meet the outdoor water demand in nine states of the U.S. was evaluated using a system dynamics model developed in Systems Thinking, Experimental Learning Laboratory with Animation. The state of Arizona was selected to evaluate the effects of the selected model parameters on the efficacy of RRWH since among the nine states the arid region of Arizona showed the least potential of meeting the outdoor water demand with rain harvested water. The analyses were conducted on a monthly basis across a 10-year projected period from 2015 to 2024. The results showed that RRWH as a potential source of water was highly sensitive to certain model parameters such as the outdoor water demand, the use of desert landscaping, and the percentage of existing houses with RRWH. A significant difference （as high as 37.5%） in rainwater potential was observed between the projected wet and dry climate conditions in Arizona. The analysis of the dynamics of the storage tanks suggested that a 1.0-2.0 m3 rainwater barrel, on an average, can store approximately 80% of the monthly rainwater generated from the rooftops in Arizona, even across the high seasonal variation. This interactive model can be used as a quick estimator of the amount of water that could be generated, stored, and utilized through RRWH systems in the U.S. under different climate conditions. The findings of such comprehensive analyses may help regional policymakers, especially in arid regions, to develop a sustainable water management infrastructure.
基金supported by the National Natural Science Foundation of China(Grant No.61972379).
文摘Data-driven garment animation is a current topic of interest in the computer graphics industry.Existing approaches generally establish the mapping between a single human pose or a temporal pose sequence,and garment deformation,but it is difficult to quickly generate diverse clothed human animations.We address this problem with a method to automatically synthesize dressed human animations with temporal consistency from a specified human motion label.At the heart of our method is a twostage strategy.Specifically,we first learn a latent space encoding the sequence-level distribution of human motions utilizing a transformer-based conditional variational autoencoder(Transformer-CVAE).Then a garment simulator synthesizes dynamic garment shapes using a transformer encoder-decoder architecture.Since the learned latent space comes from varied human motions,our method can generate a variety of styles of motions given a specific motion label.By means of a novel beginning of sequence(BOS)learning strategy and a self-supervised refinement procedure,our garment simulator is capable of efficiently synthesizing garment deformation sequences corresponding to the generated human motions while maintaining temporal and spatial consistency.We verify our ideas experimentally.This is the first generative model that directly dresses human animation.
基金Supported by The National Natural Science Foundation of China （No.10371093）Acknowledgements. The authors would like to thank Professor Pei-De Liu for his valuable suggestions and express their gratitude to the referee for his very helpful and detailed comments.
文摘In this paper we study tree martingales and proved that if 1≤α,β〈∞,1≤p〈∞ then for every predictable tree martingale f=（ft,t∞T）and E[σ^（P）（f）]〈∞,E[S^（P）（f）]〈∞,it holds that ‖（St^（p）（f）,t∈T）‖M^α∞≤Cαβ‖f‖p^αβ,‖（σt^（p）（f）,t∈T）‖M^α,β‖f‖P^αβ,where Cαβ depends only on α and β.
