Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
Maize （Zea mays） is the most widely grown grain crop in the world, playing important roles in agriculture and industry. However, the functions of maize genes remain largely unknown. High-quality genome- wide transcr...Maize （Zea mays） is the most widely grown grain crop in the world, playing important roles in agriculture and industry. However, the functions of maize genes remain largely unknown. High-quality genome- wide transcriptome datasets provide important biological knowledge which has been widely and suc- cessfully used in plants not only by measuring gene expression levels but also by enabling co-expression analysis for predicting gene functions and modules related to agronomic traits. Recently, thousands of maize transcriptomic data are available across different inbred lines, development stages, tissues, and treatments, or even across different tissue sections and cell lines. Here, we integrated 701 transcriptomic and 108 epigenomic data and studied the different conditional networks with multi-dimensional omics levels. We constructed a searchable, integrative, one-stop online platform, the maize conditional co- expression network （MCENet） platform. MCENet provides 10 global/conditional co-expression net- works, 5 network accessional analysis toolkits （i.e., Network Search, Network Remodel, Module Finder, Network Comparison, and Dynamic Expression View） and multiple network functional support toolkits （e.g., motif and module enrichment analysis）. We hope that our database might help plant research communities to identify maize functional genes or modules that regulate important agronomic traits.展开更多
The aircraft condition monitoring network is responsible for collecting the status of each component in aircraft. The reliability of this network has a significant effect on safety of the aircraft. The aircraft condit...The aircraft condition monitoring network is responsible for collecting the status of each component in aircraft. The reliability of this network has a significant effect on safety of the aircraft. The aircraft condition monitoring network works in a real-time manner that all the data should be transmitted within the deadline to ensure that the control center makes proper decision in time. Only the connectedness between the source node and destination cannot guarantee the data to be transmitted in time. In this paper, we take the time deadline into account and build the task-based reliability model. The binary decision diagram （BDD）, which has the merit of efficiency in computing and storage space, is introduced when calculating the reliability of the network and addressing the essential variable. A case is analyzed using the algorithm proposed in this paper. The experimental results show that our method is efficient and proper for the reliability analysis of the real-time network.展开更多
In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only de...In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations.This tends to cause a large amount of calculation and low detection precision.To solve these problems,in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network(CDCGAN)was designed.In the traditional DCGAN,the image generated by the generator has a certain degree of randomness.Here,a small number of labeled belt images are taken as conditions and added them to the generator and discriminator,so the generator can generate images with the characteristics of belt damage under the aforementioned conditions.Moreover,because the discriminator cannot identify multiple types of damage,the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities,and it can accurately classify cracks,scratches,and tears.To avoid the features learned incompletely,skiplayer connection is adopted in the generator and discriminator.This not only can minimize the loss of features,but also improves the convergence speed.Compared with other algorithms,experimental results show that the loss value of the generator and discriminator is the least.Moreover,its convergence speed is faster,and the mean average precision of the proposed algorithm is up to 96.2%,which is at least 6%higher than that of other algorithms.展开更多
Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstru...Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial.Repeating unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of composites.This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs.The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading direction.Subsequently,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized stresses.The stresses predicted by cGAN are validated against the remaining true data not used for training,showing good agreement.This work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite RUCs.It can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.展开更多
Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challe...Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challenge,we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty(CGAN-GP).This innovative method allows for nearly instantaneous prediction of optimized structures.Given a specific boundary condition,the network can produce a unique optimized structure in a one-to-one manner.The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization(SIMP)method.Subsequently,we design a conditional generative adversarial network and train it to generate optimized structures.To further enhance the quality of the optimized structures produced by CGAN-GP,we incorporate Pix2pixGAN.This augmentation results in sharper topologies,yielding structures with enhanced clarity,de-blurring,and edge smoothing.Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms,all while maintaining an impressive accuracy rate of up to 85%,as demonstrated through numerical examples.展开更多
The research of removing rain from pictures or videos has always been an important topic in the field of computer vision and image processing. Most noise reduction methods more or less remove texture details in rain-f...The research of removing rain from pictures or videos has always been an important topic in the field of computer vision and image processing. Most noise reduction methods more or less remove texture details in rain-free areas, resulting in an over-smoothing effect in the restored background. The research on image noise removal is very meaningful. We exploit the powerful generative power of a modified generative adversarial network (CGAN) by enforcing an additional condition that makes the derained image indistinguishable from its corresponding ground-truth clean image. An efficient and lightweight attention machine mechanism NAM is introduced in the generator, and an IDN-CGAN model is proposed to capture image salient features through attention operations. Taking advantage of the mutual information in different dimensions of the features to further suppress insignificant channels or pixels to ensure better visual quality, we also introduce a new fine-grained loss function in the generator-discriminator pair, predicting and real data degree of disparity to achieve improved results.展开更多
In this paper,we apply adaptive coded modulation (ACM) schemes to a wireless networked control system (WNCS) to improve the energy efficiency and increase the data rate over a fading channel.To capture the characteris...In this paper,we apply adaptive coded modulation (ACM) schemes to a wireless networked control system (WNCS) to improve the energy efficiency and increase the data rate over a fading channel.To capture the characteristics of varying rate, interference,and routing in wireless transmission channels,the concepts of equivalent delay (ED) and networked condition index (NCI) are introduced.Also,the analytic lower and upper bounds of EDs are obtained.Furthermore,we model the WNCS as a multicontroller switched system (MSS) under consideration of EDs and loss index in the wireless transmission.Sufficient stability condition of the closed-loop WNCS and corresponding dynamic state feedback controllers are derived in terms of linear matrix inequality (LMI). Numerical results show the validity and advantage of our proposed control strategies.展开更多
A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless ...A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel 0Cr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.展开更多
Learning Bayesian network structure is one of the most exciting challenges in machine learning. Discovering a correct skeleton of a directed acyclic graph（DAG） is the foundation for dependency analysis algorithms fo...Learning Bayesian network structure is one of the most exciting challenges in machine learning. Discovering a correct skeleton of a directed acyclic graph（DAG） is the foundation for dependency analysis algorithms for this problem. Considering the unreliability of high order condition independence（CI） tests, and to improve the efficiency of a dependency analysis algorithm, the key steps are to use few numbers of CI tests and reduce the sizes of conditioning sets as much as possible. Based on these reasons and inspired by the algorithm PC, we present an algorithm, named fast and efficient PC（FEPC）, for learning the adjacent neighbourhood of every variable. FEPC implements the CI tests by three kinds of orders, which reduces the high order CI tests significantly. Compared with current algorithm proposals, the experiment results show that FEPC has better accuracy with fewer numbers of condition independence tests and smaller size of conditioning sets. The highest reduction percentage of CI test is 83.3% by EFPC compared with PC algorithm.展开更多
A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent(STD). The methodology of relative robust optimization was applied as mea...A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent(STD). The methodology of relative robust optimization was applied as measures for comparing time-varying, random path travel times for a priori optimization. In accordance with the situation in real world, a stochastic consistent condition was provided for the STD networks and under this condition, a mathematical proof was given that the STD robust optimal path problem can be simplified into a minimum problem in specific time-dependent networks. A label setting algorithm was designed and tested to find travelers' robust optimal path in a sampled STD network with computation complexity of O(n2+n·m). The validity of the robust approach and the designed algorithm were confirmed in the computational tests. Compared with conventional probability approach, the proposed approach is simple and efficient, and also has a good application prospect in navigation system.展开更多
The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from...The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.展开更多
The Unified Power Quality Conditioner (UPQC) plays an important role in the constrained delivery of electrical power from the source to an isolated pool of load or from a source to the grid. The proposed system can co...The Unified Power Quality Conditioner (UPQC) plays an important role in the constrained delivery of electrical power from the source to an isolated pool of load or from a source to the grid. The proposed system can compensate voltage sag/swell, reactive power compensation and harmonics in the linear and nonlinear loads. In this work, the off line drained data from conventional fuzzy logic controller. A novel control system with a Combined Neural Network (CNN) is used instead of the traditionally four fuzzy logic controllers. The performance of combined neural network controller compared with Proportional Integral (PI) controller and Fuzzy Logic Controller (FLC). The system performance is also verified experimentally.展开更多
The generalized kt-connectivity K(k)(G)and k-edge-connectivityλ_(k)(G)of a graph G are a natural generalization of traditional connectivity K(G)and edge connectivityλ(G),respectively,which for K(G)=K_(2)(G)andλ(G)=...The generalized kt-connectivity K(k)(G)and k-edge-connectivityλ_(k)(G)of a graph G are a natural generalization of traditional connectivity K(G)and edge connectivityλ(G),respectively,which for K(G)=K_(2)(G)andλ(G)=λ_(2)(G).They are important parameters which can often be used to measure the reliability and fault tolerance of interconnection networks.CRNs is a new family of composite networks based on the complete graph,which contain common networks and have the same structural properties as alter-nating group network,and may also include some unknown networks.In this paper,we investigate the generalized 3-connectivity and 3-edge-connectivity of CRNs,and show that K_(3)(G_(l),m)=λ_(3)(G_(l)m)=m-2.展开更多
Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power gr...Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations.This enhances the efficiency of wind power integration into the grid.It allows grid operators to anticipate and mitigate the impact of wind power fluctuations,significantly improving the resilience of wind farms and the overall power grid.Furthermore,it assists wind farm operators in optimizing the management of power generation facilities and reducing maintenance costs.Despite these benefits,accurate wind power prediction especially in extreme scenarios remains a significant challenge.To address this issue,a novel wind power prediction model based on learning approach is proposed by integrating wavelet transform and Transformer.First,a conditional generative adversarial network(CGAN)generates dynamic extreme scenarios guided by physical constraints and expert rules to ensure realism and capture critical features of wind power fluctuations under extremeconditions.Next,thewavelet transformconvolutional layer is applied to enhance sensitivity to frequency domain characteristics,enabling effective feature extraction fromextreme scenarios for a deeper understanding of input data.The model then leverages the Transformer’s self-attention mechanism to capture global dependencies between features,strengthening its sequence modelling capabilities.Case analyses verify themodel’s superior performance in extreme scenario prediction by effectively capturing local fluctuation featureswhile maintaining a grasp of global trends.Compared to other models,it achieves R-squared(R^(2))as high as 0.95,and the mean absolute error(MAE)and rootmean square error(RMSE)are also significantly lower than those of othermodels,proving its high accuracy and effectiveness in managing complex wind power generation conditions.展开更多
Under complex flight conditions,such as obstacle avoidance and extreme sea state,wing-in-ground(WIG)effect aircraft need to ascend to higher altitudes,resulting in the disappearance of the ground effect.A design of hi...Under complex flight conditions,such as obstacle avoidance and extreme sea state,wing-in-ground(WIG)effect aircraft need to ascend to higher altitudes,resulting in the disappearance of the ground effect.A design of high-speed WIG airfoil considering non-ground effect is carried out by a novel two-step inverse airfoil design method that combines conditional generative adversarial network(CGAN)and artificial neural network(ANN).The CGAN model is employed to generate a variety of airfoil designs that satisfy the desired lift-drag ratios in both ground effect and non-ground effect conditions.Subsequently,the ANN model is utilized to forecast aerodynamic parameters of the generated airfoils.The results indicate that the CGAN model contributes to a high accuracy rate for airfoil design and enables the creation of novel airfoil designs.Furthermore,it demonstrates high accuracy in predicting aerodynamic parameters of these airfoils due to the ANN model.This method eliminates the necessity for numerical simulations and experimental testing through the design procedure,showcasing notable efficiency.The analysis of airfoils generated by the CGAN model shows that airfoils exhibiting high lift-drag ratios under both flight conditions typically have cambers of among[0.08c,0.105c],with the positions of maximum camber occurring among[0.35c,0.5c]of the chord length,and the leading-edge radiuses of these airfoils primarily cluster among[0.008c,0.025c]展开更多
为实现集成智能楼宇(intelligent building,IBs)的主动配电网(active distribution network,ADN)灵活运行,该文提出一种基于机会约束规划的含IBs的ADN分布式能量管理策略。首先,基于建筑物的热惯性,构建含空调柔性负荷的IBs数学模型;其...为实现集成智能楼宇(intelligent building,IBs)的主动配电网(active distribution network,ADN)灵活运行,该文提出一种基于机会约束规划的含IBs的ADN分布式能量管理策略。首先,基于建筑物的热惯性,构建含空调柔性负荷的IBs数学模型;其次,综合考虑楼宇侧与网络侧的运行约束,建立基于Dist Flow的集成IBs的ADN数学模型;然后,考虑到光伏(photovoltaic,PV)出力与外界温度的不确定性,利用机会约束规划将集成IBs的ADN优化问题转化为混合整数二阶锥规划(mixed integer second-order cone programming,MISOCP)问题;最后,为了保护配电网运营商与用户的隐私性,利用交替方向乘子法(alternating direction method of multipliers,ADMM)实现了集成IBs的ADN的分布式能量管理。基于ADMM的解耦机制,原MISOCP问题可以被分解为楼宇侧的混合整数线性规划(mixed-integer linear programming,MILP)子问题以及网络侧的二阶锥规划(second-order cone programming,SOCP)子问题进行求解。结果表明,在保障各主体信息隐私性的前提下,所提策略利用IBs灵活性实现了集成IBs的ADN全局最优能量管理。展开更多
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
基金supported by the National Natural Science Foundation of China (Nos. 31771467, 31571360 and 31371291)
文摘Maize （Zea mays） is the most widely grown grain crop in the world, playing important roles in agriculture and industry. However, the functions of maize genes remain largely unknown. High-quality genome- wide transcriptome datasets provide important biological knowledge which has been widely and suc- cessfully used in plants not only by measuring gene expression levels but also by enabling co-expression analysis for predicting gene functions and modules related to agronomic traits. Recently, thousands of maize transcriptomic data are available across different inbred lines, development stages, tissues, and treatments, or even across different tissue sections and cell lines. Here, we integrated 701 transcriptomic and 108 epigenomic data and studied the different conditional networks with multi-dimensional omics levels. We constructed a searchable, integrative, one-stop online platform, the maize conditional co- expression network （MCENet） platform. MCENet provides 10 global/conditional co-expression net- works, 5 network accessional analysis toolkits （i.e., Network Search, Network Remodel, Module Finder, Network Comparison, and Dynamic Expression View） and multiple network functional support toolkits （e.g., motif and module enrichment analysis）. We hope that our database might help plant research communities to identify maize functional genes or modules that regulate important agronomic traits.
基金National Natural Science Foundation of China (60879024)
文摘The aircraft condition monitoring network is responsible for collecting the status of each component in aircraft. The reliability of this network has a significant effect on safety of the aircraft. The aircraft condition monitoring network works in a real-time manner that all the data should be transmitted within the deadline to ensure that the control center makes proper decision in time. Only the connectedness between the source node and destination cannot guarantee the data to be transmitted in time. In this paper, we take the time deadline into account and build the task-based reliability model. The binary decision diagram （BDD）, which has the merit of efficiency in computing and storage space, is introduced when calculating the reliability of the network and addressing the essential variable. A case is analyzed using the algorithm proposed in this paper. The experimental results show that our method is efficient and proper for the reliability analysis of the real-time network.
基金This work was supported by the Shanxi Province Applied Basic Research Project,China(Grant No.201901D111100).Xiaoli Hao received the grant,and the URL of the sponsors’website is http://kjt.shanxi.gov.cn/.
文摘In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations.This tends to cause a large amount of calculation and low detection precision.To solve these problems,in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network(CDCGAN)was designed.In the traditional DCGAN,the image generated by the generator has a certain degree of randomness.Here,a small number of labeled belt images are taken as conditions and added them to the generator and discriminator,so the generator can generate images with the characteristics of belt damage under the aforementioned conditions.Moreover,because the discriminator cannot identify multiple types of damage,the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities,and it can accurately classify cracks,scratches,and tears.To avoid the features learned incompletely,skiplayer connection is adopted in the generator and discriminator.This not only can minimize the loss of features,but also improves the convergence speed.Compared with other algorithms,experimental results show that the loss value of the generator and discriminator is the least.Moreover,its convergence speed is faster,and the mean average precision of the proposed algorithm is up to 96.2%,which is at least 6%higher than that of other algorithms.
基金the support from the National Key R&D Program of China underGrant(Grant No.2020YFA0711700)the National Natural Science Foundation of China(Grant Nos.52122801,11925206,51978609,U22A20254,and U23A20659)G.W.is supported by the National Natural Science Foundation of China(Nos.12002303,12192210 and 12192214).
文摘Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial.Repeating unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of composites.This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs.The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading direction.Subsequently,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized stresses.The stresses predicted by cGAN are validated against the remaining true data not used for training,showing good agreement.This work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite RUCs.It can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.
基金supported by the National Key Research and Development Projects (Grant Nos.2021YFB3300601,2021YFB3300603,2021YFB3300604)Fundamental Research Funds for the Central Universities (No.DUT22QN241).
文摘Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challenge,we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty(CGAN-GP).This innovative method allows for nearly instantaneous prediction of optimized structures.Given a specific boundary condition,the network can produce a unique optimized structure in a one-to-one manner.The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization(SIMP)method.Subsequently,we design a conditional generative adversarial network and train it to generate optimized structures.To further enhance the quality of the optimized structures produced by CGAN-GP,we incorporate Pix2pixGAN.This augmentation results in sharper topologies,yielding structures with enhanced clarity,de-blurring,and edge smoothing.Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms,all while maintaining an impressive accuracy rate of up to 85%,as demonstrated through numerical examples.
文摘The research of removing rain from pictures or videos has always been an important topic in the field of computer vision and image processing. Most noise reduction methods more or less remove texture details in rain-free areas, resulting in an over-smoothing effect in the restored background. The research on image noise removal is very meaningful. We exploit the powerful generative power of a modified generative adversarial network (CGAN) by enforcing an additional condition that makes the derained image indistinguishable from its corresponding ground-truth clean image. An efficient and lightweight attention machine mechanism NAM is introduced in the generator, and an IDN-CGAN model is proposed to capture image salient features through attention operations. Taking advantage of the mutual information in different dimensions of the features to further suppress insignificant channels or pixels to ensure better visual quality, we also introduce a new fine-grained loss function in the generator-discriminator pair, predicting and real data degree of disparity to achieve improved results.
基金National Outstanding Youth Founda-tion (No.60525303)National Natural Science Foundation of China(No.60404022,60704009)Natural Science Foundation of Hebei Province (No.F2005000390,F2006000270).
文摘In this paper,we apply adaptive coded modulation (ACM) schemes to a wireless networked control system (WNCS) to improve the energy efficiency and increase the data rate over a fading channel.To capture the characteristics of varying rate, interference,and routing in wireless transmission channels,the concepts of equivalent delay (ED) and networked condition index (NCI) are introduced.Also,the analytic lower and upper bounds of EDs are obtained.Furthermore,we model the WNCS as a multicontroller switched system (MSS) under consideration of EDs and loss index in the wireless transmission.Sufficient stability condition of the closed-loop WNCS and corresponding dynamic state feedback controllers are derived in terms of linear matrix inequality (LMI). Numerical results show the validity and advantage of our proposed control strategies.
文摘A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel 0Cr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.
基金Supported by the National Natural Science Foundation of China(61403290,11301408,11401454)the Foundation for Youths of Shaanxi Province(2014JQ1020)+1 种基金the Foundation of Baoji City(2013R7-3)the Foundation of Baoji University of Arts and Sciences(ZK15081)
文摘Learning Bayesian network structure is one of the most exciting challenges in machine learning. Discovering a correct skeleton of a directed acyclic graph（DAG） is the foundation for dependency analysis algorithms for this problem. Considering the unreliability of high order condition independence（CI） tests, and to improve the efficiency of a dependency analysis algorithm, the key steps are to use few numbers of CI tests and reduce the sizes of conditioning sets as much as possible. Based on these reasons and inspired by the algorithm PC, we present an algorithm, named fast and efficient PC（FEPC）, for learning the adjacent neighbourhood of every variable. FEPC implements the CI tests by three kinds of orders, which reduces the high order CI tests significantly. Compared with current algorithm proposals, the experiment results show that FEPC has better accuracy with fewer numbers of condition independence tests and smaller size of conditioning sets. The highest reduction percentage of CI test is 83.3% by EFPC compared with PC algorithm.
基金Project(71001079)supported by the National Natural Science Foundation of China
文摘A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent(STD). The methodology of relative robust optimization was applied as measures for comparing time-varying, random path travel times for a priori optimization. In accordance with the situation in real world, a stochastic consistent condition was provided for the STD networks and under this condition, a mathematical proof was given that the STD robust optimal path problem can be simplified into a minimum problem in specific time-dependent networks. A label setting algorithm was designed and tested to find travelers' robust optimal path in a sampled STD network with computation complexity of O(n2+n·m). The validity of the robust approach and the designed algorithm were confirmed in the computational tests. Compared with conventional probability approach, the proposed approach is simple and efficient, and also has a good application prospect in navigation system.
基金Supported by the National Natural Science Foundation of China under Grant Nos.7110317971102129+1 种基金11121403by Program for Young Innovative Research Team in China University of Political Science and Law
文摘The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.
文摘The Unified Power Quality Conditioner (UPQC) plays an important role in the constrained delivery of electrical power from the source to an isolated pool of load or from a source to the grid. The proposed system can compensate voltage sag/swell, reactive power compensation and harmonics in the linear and nonlinear loads. In this work, the off line drained data from conventional fuzzy logic controller. A novel control system with a Combined Neural Network (CNN) is used instead of the traditionally four fuzzy logic controllers. The performance of combined neural network controller compared with Proportional Integral (PI) controller and Fuzzy Logic Controller (FLC). The system performance is also verified experimentally.
基金supported by the Innovation Projects of Qinghai Minzu University(No.07M2024008)AFSFQH(No.2022-ZJ-753).
文摘The generalized kt-connectivity K(k)(G)and k-edge-connectivityλ_(k)(G)of a graph G are a natural generalization of traditional connectivity K(G)and edge connectivityλ(G),respectively,which for K(G)=K_(2)(G)andλ(G)=λ_(2)(G).They are important parameters which can often be used to measure the reliability and fault tolerance of interconnection networks.CRNs is a new family of composite networks based on the complete graph,which contain common networks and have the same structural properties as alter-nating group network,and may also include some unknown networks.In this paper,we investigate the generalized 3-connectivity and 3-edge-connectivity of CRNs,and show that K_(3)(G_(l),m)=λ_(3)(G_(l)m)=m-2.
基金funded by the Science and Technology Project of State Grid Corporation of China under Grant No.5108-202218280A-2-299-XG.
文摘Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations.This enhances the efficiency of wind power integration into the grid.It allows grid operators to anticipate and mitigate the impact of wind power fluctuations,significantly improving the resilience of wind farms and the overall power grid.Furthermore,it assists wind farm operators in optimizing the management of power generation facilities and reducing maintenance costs.Despite these benefits,accurate wind power prediction especially in extreme scenarios remains a significant challenge.To address this issue,a novel wind power prediction model based on learning approach is proposed by integrating wavelet transform and Transformer.First,a conditional generative adversarial network(CGAN)generates dynamic extreme scenarios guided by physical constraints and expert rules to ensure realism and capture critical features of wind power fluctuations under extremeconditions.Next,thewavelet transformconvolutional layer is applied to enhance sensitivity to frequency domain characteristics,enabling effective feature extraction fromextreme scenarios for a deeper understanding of input data.The model then leverages the Transformer’s self-attention mechanism to capture global dependencies between features,strengthening its sequence modelling capabilities.Case analyses verify themodel’s superior performance in extreme scenario prediction by effectively capturing local fluctuation featureswhile maintaining a grasp of global trends.Compared to other models,it achieves R-squared(R^(2))as high as 0.95,and the mean absolute error(MAE)and rootmean square error(RMSE)are also significantly lower than those of othermodels,proving its high accuracy and effectiveness in managing complex wind power generation conditions.
基金supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,the Fundamental Research Funds for the Central Universities(No.ILA220101A23)CARDC Fundamental and Frontier Technology Research Fund(No.PJD20200210)the Aeronautical Science Foundation of China(No.20200023052002).
文摘Under complex flight conditions,such as obstacle avoidance and extreme sea state,wing-in-ground(WIG)effect aircraft need to ascend to higher altitudes,resulting in the disappearance of the ground effect.A design of high-speed WIG airfoil considering non-ground effect is carried out by a novel two-step inverse airfoil design method that combines conditional generative adversarial network(CGAN)and artificial neural network(ANN).The CGAN model is employed to generate a variety of airfoil designs that satisfy the desired lift-drag ratios in both ground effect and non-ground effect conditions.Subsequently,the ANN model is utilized to forecast aerodynamic parameters of the generated airfoils.The results indicate that the CGAN model contributes to a high accuracy rate for airfoil design and enables the creation of novel airfoil designs.Furthermore,it demonstrates high accuracy in predicting aerodynamic parameters of these airfoils due to the ANN model.This method eliminates the necessity for numerical simulations and experimental testing through the design procedure,showcasing notable efficiency.The analysis of airfoils generated by the CGAN model shows that airfoils exhibiting high lift-drag ratios under both flight conditions typically have cambers of among[0.08c,0.105c],with the positions of maximum camber occurring among[0.35c,0.5c]of the chord length,and the leading-edge radiuses of these airfoils primarily cluster among[0.008c,0.025c]
文摘为实现集成智能楼宇(intelligent building,IBs)的主动配电网(active distribution network,ADN)灵活运行,该文提出一种基于机会约束规划的含IBs的ADN分布式能量管理策略。首先,基于建筑物的热惯性,构建含空调柔性负荷的IBs数学模型;其次,综合考虑楼宇侧与网络侧的运行约束,建立基于Dist Flow的集成IBs的ADN数学模型;然后,考虑到光伏(photovoltaic,PV)出力与外界温度的不确定性,利用机会约束规划将集成IBs的ADN优化问题转化为混合整数二阶锥规划(mixed integer second-order cone programming,MISOCP)问题;最后,为了保护配电网运营商与用户的隐私性,利用交替方向乘子法(alternating direction method of multipliers,ADMM)实现了集成IBs的ADN的分布式能量管理。基于ADMM的解耦机制,原MISOCP问题可以被分解为楼宇侧的混合整数线性规划(mixed-integer linear programming,MILP)子问题以及网络侧的二阶锥规划(second-order cone programming,SOCP)子问题进行求解。结果表明,在保障各主体信息隐私性的前提下,所提策略利用IBs灵活性实现了集成IBs的ADN全局最优能量管理。
