Chaoshan drawnwork handkerchief design exhibits self-similarity and fractal characteristics due to their grid-based structure,overall symmetry,and the way local motifs reflect the whole pattern.To explore the potentia...Chaoshan drawnwork handkerchief design exhibits self-similarity and fractal characteristics due to their grid-based structure,overall symmetry,and the way local motifs reflect the whole pattern.To explore the potential of fractals in traditional textile design,a fractal-based generative framework was proposed for efficiently creating drawnwork patterns suitable for practical handicraft production.The research was initiated with an analysis of the structural composition of center,skeleton,and filler motifs extracted from a pattern sample library.Based on this hierarchical classification,the box-counting method was employed to calculate their respective fractal dimensions.Building on fractal art theory,generative algorithms,and studies on the application of Ultra Fractal,a Chaoshan drawnwork fractal design model was established.Using this model,51 drawnwork fractal patterns and 153 handkerchief patterns were generated.These patterns were subsequently applied in real-world production to validate the feasibility and value of fractal techniques in textile design.展开更多
Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific ...Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.展开更多
The method of describing deformation camouflage spots based on feature space has some shortcomings,such as inaccurate description and difficult reproduction.Depending on the strong fitting ability of the generative ad...The method of describing deformation camouflage spots based on feature space has some shortcomings,such as inaccurate description and difficult reproduction.Depending on the strong fitting ability of the generative adversarial network model,the distribution of deformation camouflage spot pattern can be directly fitted,thus simplifying the process of spot extraction and reproduction.The requirements of background spot extraction are analyzed theoretically.The calculation formula of limiting the range of image spot pixels is given and two kinds of spot data sets,forestland and snowfield,are established.Spot feature is decomposed into shape,size and color features,and a GAN(Generative Adversarial Network)framework is established.The effects of different loss functions on network training results are analyzed in the experiment.In the meantime,when the input dimension of generator network is 128,the balance between sample diversity and quality can be achieved.The effects of sample generation are investigated in two aspects.Subjectively,the probability of the generated spots being distinguished in the background is counted,and the results are all less than 20% and mostly close to zero.Objectively,the features of the spot shape are calculated and the independent sample T-test is applied to verify that the features are from the same distribution,and all the P-Values are much higher than 0.05.Both subjective and objective methods prove that the spots generated by this method are similar to the background spots.The proposed method can directly generate the desired camouflage pattern spots,which provides a new technical method for the deformation camouflage pattern design and camouflage effect evaluation.展开更多
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-ba...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.展开更多
Ground military target recognition plays a crucial role in unmanned equipment and grasping the battlefield dynamics for military applications, but is disturbed by low-resolution and noisyrepresentation. In this paper,...Ground military target recognition plays a crucial role in unmanned equipment and grasping the battlefield dynamics for military applications, but is disturbed by low-resolution and noisyrepresentation. In this paper, a recognition method, involving a novel visual attention mechanismbased Gabor region proposal sub-network(Gabor RPN) and improved refinement generative adversarial sub-network(GAN), is proposed. Novel central-peripheral rivalry 3D color Gabor filters are proposed to simulate retinal structures and taken as feature extraction convolutional kernels in low-level layer to improve the recognition accuracy and framework training efficiency in Gabor RPN. Improved refinement GAN is used to solve the problem of blurry target classification, involving a generator to directly generate large high-resolution images from small blurry ones and a discriminator to distinguish not only real images vs. fake images but also the class of targets. A special recognition dataset for ground military target, named Ground Military Target Dataset(GMTD), is constructed. Experiments performed on the GMTD dataset effectively demonstrate that our method can achieve better energy-saving and recognition results when low-resolution and noisy-representation targets are involved, thus ensuring this algorithm a good engineering application prospect.展开更多
The influence of irradiation by different doses(32)and(60)on microsporogenesis process andmale gametophyte development have beenstudied on different water supplementconditions.We have studied the pollination,fertiliza...The influence of irradiation by different doses(32)and(60)on microsporogenesis process andmale gametophyte development have beenstudied on different water supplementconditions.We have studied the pollination,fertilization,microsporogenesis and earlyembryogeny processes in Mo and FoMo.It展开更多
Owing to the wide range of applications in various fields,generative models have become increasingly popular.However,they do not handle spatio-temporal features well.Inspired by the recent advances in these models,thi...Owing to the wide range of applications in various fields,generative models have become increasingly popular.However,they do not handle spatio-temporal features well.Inspired by the recent advances in these models,this paper designs a distributed spatio-temporal generative adversarial network(STGAN-D)that,given some initial data and random noise,generates a consecutive sequence of spatio-temporal samples which have a logical relationship.This paper builds a spatio-temporal discriminator to distinguish whether the samples generated by the generator meet the requirements for time and space coherence,and builds a controller for distributed training of the network gradient updated to separate the model training and parameter updating,to improve the network training rate.The model is trained on the skeletal dataset and the traffic dataset.In contrast to traditional generative adversarial networks(GANs),the proposed STGAN-D can generate logically coherent samples with the corresponding spatial and temporal features while avoiding mode collapse.In addition,this paper shows that the proposed model can generate different styles of spatio-temporal samples given different random noise inputs,and the controller can improve the network training rate.This model will extend the potential range of applications of GANs to areas such as traffic information simulation and multiagent adversarial simulation.展开更多
Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual...Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual perception,significantly increasing the utility of low-resolution images.In this study,an improved image superresolution reconstruction model based on Generative Adversarial Networks(SRGAN)was proposed.This model introduced a channel and spatial attention mechanism(CSAB)in the generator,allowing it to effectively leverage the information from the input image to enhance feature representations and capture important details.The discriminator was designed with an improved PatchGAN architecture,which more accurately captured local details and texture information of the image.With these enhanced generator and discriminator architectures and an optimized loss function design,this method demonstrated superior performance in image quality assessment metrics.Experimental results showed that this model outperforms traditional methods,presenting more detailed and realistic image details in the visual effects.展开更多
The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade tempe...The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade temperature regulation performance.To address these challenges,we propose a composite control scheme combining fuzzy logic and a variable-gain generalized supertwisting algorithm(VG-GSTA).Firstly,a one-dimensional(1D)fuzzy logic controler(FLC)for the pump ensures stable coolant flow,while a two-dimensional(2D)FLC for the fan regulates the stack temperature near the reference value.The VG-GSTA is then introduced to eliminate steady-state errors,offering resistance to disturbances and minimizing control oscillations.The equilibrium optimizer is used to fine-tune VG-GSTA parameters.Co-simulation verifies the effectiveness of our method,demonstrating its advantages in terms of disturbance immunity,overshoot suppression,tracking accuracy and response speed.展开更多
Given two graphs G and H,the Ramsey number R(G,H)is the smallest positive integer N such that every 2-coloring of the edges of K_(N)contains either a red G or a blue H.Let K_(N-1)■K_(1,k)be the graph obtained from K_...Given two graphs G and H,the Ramsey number R(G,H)is the smallest positive integer N such that every 2-coloring of the edges of K_(N)contains either a red G or a blue H.Let K_(N-1)■K_(1,k)be the graph obtained from K_(N-1)by adding anew vertexνconnecting k vertices of K_(N-1).A graph G withχ(G)=k+1 is called edge-critical if G contains an edge e such thatχ(G-e)=k.A considerable amount of research has been conducted by previous scholars on Ramsey numbers ofgraphs.In this study,we show that for an edge-critical graph G with x(G)=k+1,when k≥2,1≥2,and n is sufficiently large,R(G,K_(1)+nK_(t))=knt+1 and r,(G,K_(1)+nK_(t))=(k-1)nt+1.展开更多
The Type-2 generalized Feistel structure is widely used in block cipher design.This work conducts a quantum key recovery attack on TWINE-80,a lightweight block cipher based on the improved Type-2 generalized Feistel s...The Type-2 generalized Feistel structure is widely used in block cipher design.This work conducts a quantum key recovery attack on TWINE-80,a lightweight block cipher based on the improved Type-2 generalized Feistel structure.By constructing a round function,a new 7-round quantum distinguisher for TWINE-80 is identified.Leveraging the reuse characteristics of round keys in the algorithm,three pairs of repeated round keys are discovered during the 5-round transformation process.Using Grover’s algorithm to search for partial round keys,a 17-round quantum key recovery attack on TWINE-80 is successfully implemented,with a time complexity of 296 and requiring 327 qubits.Compared to similar studies,this work reduces the time complexity by 26 and slightly decreases the required quantum resources by 12 qubits.展开更多
The existing analytical models for umbrella arch method(UAM)based on elastic foundation beams often overlook the influence of the surrounding soil beyond the beam edges on the shear stresses acting on the beam.Consequ...The existing analytical models for umbrella arch method(UAM)based on elastic foundation beams often overlook the influence of the surrounding soil beyond the beam edges on the shear stresses acting on the beam.Consequently,such models fail to adequately reflect the continuity characteristics of soil deformation.Leveraging the Pasternak foundation-Euler beam model,this study considers the generalized shear force on the beam to account for the influence of soil outside the beam ends on the shear stress.An analytical model for the deformation and internal forces of finite-length beams subjected to arbitrary loads is derived based on the initial parameter method under various conditions.The mechanical model of the elastic foundation beam for advanced umbrella arch under typical tunnel excavation cycles is established,yielding analytical solutions for the longitudinal response of the umbrella arch.The reliability of the analytical model is verified with the existing test data.The improved model addresses anomalies in existing models,such as abnormal upward deformation in the loosened segment and maximum deflection occurring within the soil mass.Additionally,dimensionless characteristic parameters reflecting the relative stiffness between the umbrella arch structure and the foundation soil are proposed.Results indicate that the magnitude of soil characteristic parameters significantly influences the deformation and internal forces of the umbrella arch.Within common ranges of soil values,the maximum deformation and internal forces of the umbrella arch under semi-logarithmic coordinates exhibit nearly linear decay with decreasing soil characteristic parameters.The impact of tunnel excavation height on the stress of unsupported sections of the umbrella arch is minor,but it is more significant for umbrella arch buried within the soil mass.Conversely,the influence of tunnel excavation advance on the umbrella arch is opposite.展开更多
A generalized multiple-mode prolate spherical wave functions (PSWFs) multi-carrier with index modulation approach is proposed with the purpose of improving the spectral efficiency of PSWFs multi-carrier systems. The p...A generalized multiple-mode prolate spherical wave functions (PSWFs) multi-carrier with index modulation approach is proposed with the purpose of improving the spectral efficiency of PSWFs multi-carrier systems. The proposed method,based on the optimized multi-index modulation, does not limit the number of signals in the first and second constellations and abandons the concept of limiting the number of signals in different constellations. It successfully increases the spectrum efficiency of the system while expanding the number of modulation symbol combinations and the index dimension of PSWFs signals. The proposed method outperforms the PSWFs multi-carrier index modulation method based on optimized multiple indexes in terms of spectrum efficiency, but at the expense of system computational complexity and bit error performance. For example, with n=10 subcarriers and a bit error rate of 1×10^(-5),spectral efficiency can be raised by roughly 12.4%.展开更多
Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research si...Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.展开更多
The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time...The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.展开更多
Special Issue Guest Editors·Michael Gofman,Senior Lecturer in Finance at the Hebrew University of Jerusalem·Zhao Jin,Assistant Professor of Finance,CKGSB Special Issue Information Artificial intelligence(AI)...Special Issue Guest Editors·Michael Gofman,Senior Lecturer in Finance at the Hebrew University of Jerusalem·Zhao Jin,Assistant Professor of Finance,CKGSB Special Issue Information Artificial intelligence(AI)is becoming an increasingly important tool for fund managers,CFOs,regulators,traders,investors,and entrepreneurs.The generative AI revolution that started with the ChatGPT,has spurred a gale of creative destruction that poses risks and opportunities to most firms in the world.展开更多
In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of ea...In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.展开更多
In this paper,a dynamic modeling method of motor driven electromechanical system is presented,and the uncertainty quantification of mechanism motion is investigated based on this method.The main contribution is to pro...In this paper,a dynamic modeling method of motor driven electromechanical system is presented,and the uncertainty quantification of mechanism motion is investigated based on this method.The main contribution is to propose a novel mechanism-motor coupling dynamic modeling method,in which the relationship between mechanism motion and motor rotation is established according to the geometric coordination of the system.The advantages of this include establishing intuitive coupling between the mechanism and motor,facilitating the discussion for the influence of both mechanical and electrical parameters on the mechanism,and enabling dynamic simulation with controller to take the randomness of the electric load into account.Dynamic simulation considering feedback control of ammunition delivery system is carried out,and the feasibility of the model is verified experimentally.Based on probability density evolution theory,we comprehensively discuss the effects of system parameters on mechanism motion from the perspective of uncertainty quantization.Our work can not only provide guidance for engineering design of ammunition delivery mechanism,but also provide theoretical support for modeling and uncertainty quantification research of mechatronics system.展开更多
A generalized labeled multi-Bernoulli(GLMB)filter with motion mode label based on the track-before-detect(TBD)strategy for maneuvering targets in sea clutter with heavy tail,in which the transitions of the mode of tar...A generalized labeled multi-Bernoulli(GLMB)filter with motion mode label based on the track-before-detect(TBD)strategy for maneuvering targets in sea clutter with heavy tail,in which the transitions of the mode of target motions are modeled by using jump Markovian system(JMS),is presented in this paper.The close-form solution is derived for sequential Monte Carlo implementation of the GLMB filter based on the TBD model.In update,we derive a tractable GLMB density,which preserves the cardinality distribution and first-order moment of the labeled multi-target distribution of interest as well as minimizes the Kullback-Leibler divergence(KLD),to enable the next recursive cycle.The relevant simulation results prove that the proposed multiple-model GLMB-TBD(MM-GLMB-TBD)algorithm based on K-distributed clutter model can improve the detecting and tracking performance in both estimation error and robustness compared with state-of-the-art algorithms for sea clutter background.Additionally,the simulations show that the proposed MM-GLMB-TBD algorithm can accurately output the multitarget trajectories with considerably less computational complexity compared with the adapted dynamic programming based TBD(DP-TBD)algorithm.Meanwhile,the simulation results also indicate that the proposed MM-GLMB-TBD filter slightly outperforms the JMS particle filter based TBD(JMSMeMBer-TBD)filter in estimation error with the basically same computational cost.Finally,the impact of the mismatches on the clutter model and clutter parameter is investigated for the performance of the MM-GLMB-TBD filter.展开更多
文摘Chaoshan drawnwork handkerchief design exhibits self-similarity and fractal characteristics due to their grid-based structure,overall symmetry,and the way local motifs reflect the whole pattern.To explore the potential of fractals in traditional textile design,a fractal-based generative framework was proposed for efficiently creating drawnwork patterns suitable for practical handicraft production.The research was initiated with an analysis of the structural composition of center,skeleton,and filler motifs extracted from a pattern sample library.Based on this hierarchical classification,the box-counting method was employed to calculate their respective fractal dimensions.Building on fractal art theory,generative algorithms,and studies on the application of Ultra Fractal,a Chaoshan drawnwork fractal design model was established.Using this model,51 drawnwork fractal patterns and 153 handkerchief patterns were generated.These patterns were subsequently applied in real-world production to validate the feasibility and value of fractal techniques in textile design.
基金Project(2020YFC2008605)supported by the National Key Research and Development Project of ChinaProject(52072412)supported by the National Natural Science Foundation of ChinaProject(2021JJ30359)supported by the Natural Science Foundation of Hunan Province,China。
文摘Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.
基金This research was funded by Natural Science Foundation of Jiangsu Province,grant number BK20180579.
文摘The method of describing deformation camouflage spots based on feature space has some shortcomings,such as inaccurate description and difficult reproduction.Depending on the strong fitting ability of the generative adversarial network model,the distribution of deformation camouflage spot pattern can be directly fitted,thus simplifying the process of spot extraction and reproduction.The requirements of background spot extraction are analyzed theoretically.The calculation formula of limiting the range of image spot pixels is given and two kinds of spot data sets,forestland and snowfield,are established.Spot feature is decomposed into shape,size and color features,and a GAN(Generative Adversarial Network)framework is established.The effects of different loss functions on network training results are analyzed in the experiment.In the meantime,when the input dimension of generator network is 128,the balance between sample diversity and quality can be achieved.The effects of sample generation are investigated in two aspects.Subjectively,the probability of the generated spots being distinguished in the background is counted,and the results are all less than 20% and mostly close to zero.Objectively,the features of the spot shape are calculated and the independent sample T-test is applied to verify that the features are from the same distribution,and all the P-Values are much higher than 0.05.Both subjective and objective methods prove that the spots generated by this method are similar to the background spots.The proposed method can directly generate the desired camouflage pattern spots,which provides a new technical method for the deformation camouflage pattern design and camouflage effect evaluation.
基金supported by the Engineering and Physical Sciences Research Council [grant number: EP/N509644/1]。
文摘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.
基金the National Key Research and Development Program of China(No.2016YFC0802904)National Natural Science Foundation of China(No.61671470)Natural Science Foundation of Jiangsu Province(BK20161470).
文摘Ground military target recognition plays a crucial role in unmanned equipment and grasping the battlefield dynamics for military applications, but is disturbed by low-resolution and noisyrepresentation. In this paper, a recognition method, involving a novel visual attention mechanismbased Gabor region proposal sub-network(Gabor RPN) and improved refinement generative adversarial sub-network(GAN), is proposed. Novel central-peripheral rivalry 3D color Gabor filters are proposed to simulate retinal structures and taken as feature extraction convolutional kernels in low-level layer to improve the recognition accuracy and framework training efficiency in Gabor RPN. Improved refinement GAN is used to solve the problem of blurry target classification, involving a generator to directly generate large high-resolution images from small blurry ones and a discriminator to distinguish not only real images vs. fake images but also the class of targets. A special recognition dataset for ground military target, named Ground Military Target Dataset(GMTD), is constructed. Experiments performed on the GMTD dataset effectively demonstrate that our method can achieve better energy-saving and recognition results when low-resolution and noisy-representation targets are involved, thus ensuring this algorithm a good engineering application prospect.
文摘The influence of irradiation by different doses(32)and(60)on microsporogenesis process andmale gametophyte development have beenstudied on different water supplementconditions.We have studied the pollination,fertilization,microsporogenesis and earlyembryogeny processes in Mo and FoMo.It
基金the National Natural Science Foundation of China(61573285).
文摘Owing to the wide range of applications in various fields,generative models have become increasingly popular.However,they do not handle spatio-temporal features well.Inspired by the recent advances in these models,this paper designs a distributed spatio-temporal generative adversarial network(STGAN-D)that,given some initial data and random noise,generates a consecutive sequence of spatio-temporal samples which have a logical relationship.This paper builds a spatio-temporal discriminator to distinguish whether the samples generated by the generator meet the requirements for time and space coherence,and builds a controller for distributed training of the network gradient updated to separate the model training and parameter updating,to improve the network training rate.The model is trained on the skeletal dataset and the traffic dataset.In contrast to traditional generative adversarial networks(GANs),the proposed STGAN-D can generate logically coherent samples with the corresponding spatial and temporal features while avoiding mode collapse.In addition,this paper shows that the proposed model can generate different styles of spatio-temporal samples given different random noise inputs,and the controller can improve the network training rate.This model will extend the potential range of applications of GANs to areas such as traffic information simulation and multiagent adversarial simulation.
文摘Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual perception,significantly increasing the utility of low-resolution images.In this study,an improved image superresolution reconstruction model based on Generative Adversarial Networks(SRGAN)was proposed.This model introduced a channel and spatial attention mechanism(CSAB)in the generator,allowing it to effectively leverage the information from the input image to enhance feature representations and capture important details.The discriminator was designed with an improved PatchGAN architecture,which more accurately captured local details and texture information of the image.With these enhanced generator and discriminator architectures and an optimized loss function design,this method demonstrated superior performance in image quality assessment metrics.Experimental results showed that this model outperforms traditional methods,presenting more detailed and realistic image details in the visual effects.
基金Supported by the Major Science and Technology Project of Jilin Province(20220301010GX)the International Scientific and Technological Cooperation(20240402071GH).
文摘The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade temperature regulation performance.To address these challenges,we propose a composite control scheme combining fuzzy logic and a variable-gain generalized supertwisting algorithm(VG-GSTA).Firstly,a one-dimensional(1D)fuzzy logic controler(FLC)for the pump ensures stable coolant flow,while a two-dimensional(2D)FLC for the fan regulates the stack temperature near the reference value.The VG-GSTA is then introduced to eliminate steady-state errors,offering resistance to disturbances and minimizing control oscillations.The equilibrium optimizer is used to fine-tune VG-GSTA parameters.Co-simulation verifies the effectiveness of our method,demonstrating its advantages in terms of disturbance immunity,overshoot suppression,tracking accuracy and response speed.
基金supported by the National Key Research and Development Program of China(2023YFA1010200,2020YFA0713100)the National Natural Science Foundation of China(12071453)the Innovation Program for Quantum Science and Technology(2021ZD0302902).
文摘Given two graphs G and H,the Ramsey number R(G,H)is the smallest positive integer N such that every 2-coloring of the edges of K_(N)contains either a red G or a blue H.Let K_(N-1)■K_(1,k)be the graph obtained from K_(N-1)by adding anew vertexνconnecting k vertices of K_(N-1).A graph G withχ(G)=k+1 is called edge-critical if G contains an edge e such thatχ(G-e)=k.A considerable amount of research has been conducted by previous scholars on Ramsey numbers ofgraphs.In this study,we show that for an edge-critical graph G with x(G)=k+1,when k≥2,1≥2,and n is sufficiently large,R(G,K_(1)+nK_(t))=knt+1 and r,(G,K_(1)+nK_(t))=(k-1)nt+1.
文摘The Type-2 generalized Feistel structure is widely used in block cipher design.This work conducts a quantum key recovery attack on TWINE-80,a lightweight block cipher based on the improved Type-2 generalized Feistel structure.By constructing a round function,a new 7-round quantum distinguisher for TWINE-80 is identified.Leveraging the reuse characteristics of round keys in the algorithm,three pairs of repeated round keys are discovered during the 5-round transformation process.Using Grover’s algorithm to search for partial round keys,a 17-round quantum key recovery attack on TWINE-80 is successfully implemented,with a time complexity of 296 and requiring 327 qubits.Compared to similar studies,this work reduces the time complexity by 26 and slightly decreases the required quantum resources by 12 qubits.
基金Projects(52008403,52378421)supported by the National Natural Science Foundation of ChinaProject(2022-Key-10)supported by the Science and Technology Research and Development Program Project of China Railway Group LimitedProject(202207)supported by the Hunan Provincial Transportation Science and Technology,China。
文摘The existing analytical models for umbrella arch method(UAM)based on elastic foundation beams often overlook the influence of the surrounding soil beyond the beam edges on the shear stresses acting on the beam.Consequently,such models fail to adequately reflect the continuity characteristics of soil deformation.Leveraging the Pasternak foundation-Euler beam model,this study considers the generalized shear force on the beam to account for the influence of soil outside the beam ends on the shear stress.An analytical model for the deformation and internal forces of finite-length beams subjected to arbitrary loads is derived based on the initial parameter method under various conditions.The mechanical model of the elastic foundation beam for advanced umbrella arch under typical tunnel excavation cycles is established,yielding analytical solutions for the longitudinal response of the umbrella arch.The reliability of the analytical model is verified with the existing test data.The improved model addresses anomalies in existing models,such as abnormal upward deformation in the loosened segment and maximum deflection occurring within the soil mass.Additionally,dimensionless characteristic parameters reflecting the relative stiffness between the umbrella arch structure and the foundation soil are proposed.Results indicate that the magnitude of soil characteristic parameters significantly influences the deformation and internal forces of the umbrella arch.Within common ranges of soil values,the maximum deformation and internal forces of the umbrella arch under semi-logarithmic coordinates exhibit nearly linear decay with decreasing soil characteristic parameters.The impact of tunnel excavation height on the stress of unsupported sections of the umbrella arch is minor,but it is more significant for umbrella arch buried within the soil mass.Conversely,the influence of tunnel excavation advance on the umbrella arch is opposite.
基金supported by the China National Postdoctoral Program for Innovative Talents(BX20200039)the Special Fund Project of“Mount Taishan Scholars”Construction Project in Shandong Province(ts20081130).
文摘A generalized multiple-mode prolate spherical wave functions (PSWFs) multi-carrier with index modulation approach is proposed with the purpose of improving the spectral efficiency of PSWFs multi-carrier systems. The proposed method,based on the optimized multi-index modulation, does not limit the number of signals in the first and second constellations and abandons the concept of limiting the number of signals in different constellations. It successfully increases the spectrum efficiency of the system while expanding the number of modulation symbol combinations and the index dimension of PSWFs signals. The proposed method outperforms the PSWFs multi-carrier index modulation method based on optimized multiple indexes in terms of spectrum efficiency, but at the expense of system computational complexity and bit error performance. For example, with n=10 subcarriers and a bit error rate of 1×10^(-5),spectral efficiency can be raised by roughly 12.4%.
文摘Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.
基金supported in part by the National Natural Science Foundation of China(Grant No.62276274)Shaanxi Natural Science Foundation(Grant No.2023-JC-YB-528)Chinese aeronautical establishment(Grant No.201851U8012)。
文摘The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.
文摘Special Issue Guest Editors·Michael Gofman,Senior Lecturer in Finance at the Hebrew University of Jerusalem·Zhao Jin,Assistant Professor of Finance,CKGSB Special Issue Information Artificial intelligence(AI)is becoming an increasingly important tool for fund managers,CFOs,regulators,traders,investors,and entrepreneurs.The generative AI revolution that started with the ChatGPT,has spurred a gale of creative destruction that poses risks and opportunities to most firms in the world.
文摘In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.
基金supported by the National Natural Science Foundation of China(Grant Nos.11472137 and U2141246)。
文摘In this paper,a dynamic modeling method of motor driven electromechanical system is presented,and the uncertainty quantification of mechanism motion is investigated based on this method.The main contribution is to propose a novel mechanism-motor coupling dynamic modeling method,in which the relationship between mechanism motion and motor rotation is established according to the geometric coordination of the system.The advantages of this include establishing intuitive coupling between the mechanism and motor,facilitating the discussion for the influence of both mechanical and electrical parameters on the mechanism,and enabling dynamic simulation with controller to take the randomness of the electric load into account.Dynamic simulation considering feedback control of ammunition delivery system is carried out,and the feasibility of the model is verified experimentally.Based on probability density evolution theory,we comprehensively discuss the effects of system parameters on mechanism motion from the perspective of uncertainty quantization.Our work can not only provide guidance for engineering design of ammunition delivery mechanism,but also provide theoretical support for modeling and uncertainty quantification research of mechatronics system.
基金supported by the Fund for Foreign Scholars in University Research and Teaching Programs(B18039)Shaanxi Youth Fund(202J-JC-QN-0668).
文摘A generalized labeled multi-Bernoulli(GLMB)filter with motion mode label based on the track-before-detect(TBD)strategy for maneuvering targets in sea clutter with heavy tail,in which the transitions of the mode of target motions are modeled by using jump Markovian system(JMS),is presented in this paper.The close-form solution is derived for sequential Monte Carlo implementation of the GLMB filter based on the TBD model.In update,we derive a tractable GLMB density,which preserves the cardinality distribution and first-order moment of the labeled multi-target distribution of interest as well as minimizes the Kullback-Leibler divergence(KLD),to enable the next recursive cycle.The relevant simulation results prove that the proposed multiple-model GLMB-TBD(MM-GLMB-TBD)algorithm based on K-distributed clutter model can improve the detecting and tracking performance in both estimation error and robustness compared with state-of-the-art algorithms for sea clutter background.Additionally,the simulations show that the proposed MM-GLMB-TBD algorithm can accurately output the multitarget trajectories with considerably less computational complexity compared with the adapted dynamic programming based TBD(DP-TBD)algorithm.Meanwhile,the simulation results also indicate that the proposed MM-GLMB-TBD filter slightly outperforms the JMS particle filter based TBD(JMSMeMBer-TBD)filter in estimation error with the basically same computational cost.Finally,the impact of the mismatches on the clutter model and clutter parameter is investigated for the performance of the MM-GLMB-TBD filter.
