Precipitation nowcasting is of great importance for disaster prevention and mitigation.However,precipitation is a complex spatio-temporal phenomenon influenced by various underlying physical factors.Even slight change...Precipitation nowcasting is of great importance for disaster prevention and mitigation.However,precipitation is a complex spatio-temporal phenomenon influenced by various underlying physical factors.Even slight changes in the initial precipitation field can have a significant impact on the future precipitation patterns,making the nowcasting of short-term high-resolution precipitation a major challenge.Traditional deep learning methods often have difficulty capturing the long-term spatial dependence of precipitation and are usually at a low resolution.To address these issues,based upon the Simpler yet Better Video Prediction(SimVP)framework,we proposed a deep generative neural network that incorporates the Simple Parameter-Free Attention Module(SimAM)and Generative Adversarial Networks(GANs)for short-term high-resolution precipitation event forecasting.Through an adversarial training strategy,critical precipitation features were extracted from complex radar echo images.During the adversarial learning process,the dynamic competition between the generator and the discriminator could continuously enhance the model in prediction accuracy and resolution for short-term precipitation.Experimental results demonstrate that the proposed method could effectively forecast short-term precipitation events on various scales and showed the best overall performance among existing methods.展开更多
The application of image super-resolution(SR)has brought significant assistance in the medical field,aiding doctors to make more precise diagnoses.However,solely relying on a convolutional neural network(CNN)for image...The application of image super-resolution(SR)has brought significant assistance in the medical field,aiding doctors to make more precise diagnoses.However,solely relying on a convolutional neural network(CNN)for image SR may lead to issues such as blurry details and excessive smoothness.To address the limitations,we proposed an algorithm based on the generative adversarial network(GAN)framework.In the generator network,three different sizes of convolutions connected by a residual dense structure were used to extract detailed features,and an attention mechanism combined with dual channel and spatial information was applied to concentrate the computing power on crucial areas.In the discriminator network,using InstanceNorm to normalize tensors sped up the training process while retaining feature information.The experimental results demonstrate that our algorithm achieves higher peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM)compared to other methods,resulting in an improved visual quality.展开更多
Facial recognition systems have become increasingly significant in public security efforts. They play a crucial role in apprehending criminals and locating missing children and elderly individuals. Nevertheless, in pr...Facial recognition systems have become increasingly significant in public security efforts. They play a crucial role in apprehending criminals and locating missing children and elderly individuals. Nevertheless, in practical applications, around 30% to 50% of images are obstructed to varied extents, for as by the presence of masks, glasses, or hats. Repairing the masked facial images will enhance face recognition accuracy by 10% to 20%. Simultaneously, market research indicates a rising demand for efficient facial recognition technology within the security and surveillance sectors, with projections suggesting that the global facial recognition market would exceed US$3 billion by 2025. Therefore, finding a prompt and efficient solution to fix the masked face and enhance its accuracy has become a pressing issue that has to be resolved. Currently, the generative adversarial network has shown excellent performance in the field of image restoration, with high precision and good quality of restoration results, but it consumes a lot of computing resources. Based on this, this paper proposes a model architecture that uses the U-Net network to replace the generator in the generative adversarial network, and replaces all traditional convolutional layers with Depthwise Separable Convolutional (DWSC) to make the entire network lightweight. Ultimately, We utilise the Peak Signal-to-Noise Ratio (PSNR) value to assess the efficacy of the developed model. We select samples with occlusion levels ranging from 10%–15% and 20%–30%, yielding PSNR values of 35.51 and 30.33, respectively. In contrast, the PSNR values of the three predominant algorithms in image restoration—PM, ShiftNet, and PICNet—are all below 30, demonstrating the superiority of the model presented in this paper. However, the model presented in this work possesses certain drawbacks. This work employs solely black rectangles to replicate real-life occlusions. Future study should utilise tangible objects, like as sunglasses and masks, to directly imitate occlusions, so enhancing the accuracy of the restoration effect. The model presented in this study can be further expanded from image restoration to video restoration to investigate the potential for dynamic occlusion repair.展开更多
In this paper we present a novel approach to modeling AlGaN/GaN high electron mobility transistor （HEMT） with an artificial neural network （ANN）. The AlGaN/GaN HEMT device structure and its fabrication process are...In this paper we present a novel approach to modeling AlGaN/GaN high electron mobility transistor （HEMT） with an artificial neural network （ANN）. The AlGaN/GaN HEMT device structure and its fabrication process are described. The circuit-based Neuro-space mapping （neuro-SM） technique is studied in detail. The EEHEMT model is implemented according to the measurement results of the designed device, which serves as a coarse model. An ANN is proposed to model AIGaN/CaN HEMT based on the coarse model. Its optimization is performed. The simulation results from the model are compared with the measurement results. It is shown that the simulation results obtained from the ANN model of A1GaN/GaN HEMT are more accurate than those obtained from the EEHEMT model.展开更多
This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems.Combining Strengths,Weaknesses,Opportunities,Th...This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems.Combining Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data reconstruction.The model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT Analysis.The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data.Reconstructed data is used to retain more semantic information to generate features.The model was applied to species in Southern California,USA,citing SWOT analysis data to train the model.Experiments show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development environments.The model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data domain.This study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.展开更多
Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in ...Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in bank credit default datasets limits the predictive performance of traditional machine learning and deep learning models.To address this issue,this study employs the conditional variational autoencoder-Wasserstein generative adversarial network with gradient penalty(CVAE-WGAN-gp)model for oversampling,generating samples similar to the original default customer data to enhance model prediction performance.To evaluate the quality of the data generated by the CVAE-WGAN-gp model,we selected several bank loan datasets for experimentation.The experimental results demonstrate that using the CVAE-WGAN-gp model for oversampling can significantly improve the predictive performance in credit risk assessment problems.展开更多
Multifocal metalenses are of great concern in optical communications,optical imaging and micro-optics systems,but their design is extremely challenging.In recent years,deep learning methods have provided novel solutio...Multifocal metalenses are of great concern in optical communications,optical imaging and micro-optics systems,but their design is extremely challenging.In recent years,deep learning methods have provided novel solutions to the design of optical planar devices.Here,an approach is proposed to explore the use of generative adversarial networks(GANs)to realize the design of metalenses with different focusing positions at dual wavelengths.This approach includes a forward network and an inverse network,where the former predicts the optical response of meta-atoms and the latter generates structures that meet specific requirements.Compared to the traditional search method,the inverse network demonstrates higher precision and efficiency in designing a dual-wavelength bifocal metalens.The results will provide insights and methodologies for the design of tunable wavelength metalenses,while also highlighting the potential of deep learning in optical device design.展开更多
To enhance the denoising performance of event-based sensors,we introduce a clustering-based temporal deep neural network denoising method(CBTDNN).Firstly,to cluster the sensor output data and obtain the respective clu...To enhance the denoising performance of event-based sensors,we introduce a clustering-based temporal deep neural network denoising method(CBTDNN).Firstly,to cluster the sensor output data and obtain the respective cluster centers,a combination of density-based spatial clustering of applications with noise(DBSCAN)and Kmeans++is utilized.Subsequently,long short-term memory(LSTM)is employed to fit and yield optimized cluster centers with temporal information.Lastly,based on the new cluster centers and denoising ratio,a radius threshold is set,and noise points beyond this threshold are removed.The comprehensive denoising metrics F1_score of CBTDNN have achieved 0.8931,0.7735,and 0.9215 on the traffic sequences dataset,pedestrian detection dataset,and turntable dataset,respectively.And these metrics demonstrate improvements of 49.90%,33.07%,19.31%,and 22.97%compared to four contrastive algorithms,namely nearest neighbor(NNb),nearest neighbor with polarity(NNp),Autoencoder,and multilayer perceptron denoising filter(MLPF).These results demonstrate that the proposed method enhances the denoising performance of event-based sensors.展开更多
Aiming at the problem of mobile data traffic surge in 5G networks,this paper proposes an effective solution combining massive multiple-input multiple-output techniques with Ultra-Dense Network(UDN)and focuses on solvi...Aiming at the problem of mobile data traffic surge in 5G networks,this paper proposes an effective solution combining massive multiple-input multiple-output techniques with Ultra-Dense Network(UDN)and focuses on solving the resulting challenge of increased energy consumption.A base station control algorithm based on Multi-Agent Proximity Policy Optimization(MAPPO)is designed.In the constructed 5G UDN model,each base station is considered as an agent,and the MAPPO algorithm enables inter-base station collaboration and interference management to optimize the network performance.To reduce the extra power consumption due to frequent sleep mode switching of base stations,a sleep mode switching decision algorithm is proposed.The algorithm reduces unnecessary power consumption by evaluating the network state similarity and intelligently adjusting the agent’s action strategy.Simulation results show that the proposed algorithm reduces the power consumption by 24.61% compared to the no-sleep strategy and further reduces the power consumption by 5.36% compared to the traditional MAPPO algorithm under the premise of guaranteeing the quality of service of users.展开更多
Stimulated emission and lasing of GaN-based laser diodes(LDs)were reported at 1995[1]and 1996[2],right after the breakthrough of p-type doping[3−5],material quality[6]and the invention of high-brightness GaN-based LED...Stimulated emission and lasing of GaN-based laser diodes(LDs)were reported at 1995[1]and 1996[2],right after the breakthrough of p-type doping[3−5],material quality[6]and the invention of high-brightness GaN-based LEDs[7,8].However,it took much longer time for GaN-based LDs to achieve high power,high wall plug efficiency,and long lifetime.Until 2019,Nichia reported blue LDs with these performances[9],which open wide applications with GaN-based blue LDs.展开更多
Photonic crystal surface emitting lasers(PCSELs)utilize the Bragg diffraction of two-dimensional photonic crystals to achieve a single-mode output with a high power and a small divergence angle,and has recently attrac...Photonic crystal surface emitting lasers(PCSELs)utilize the Bragg diffraction of two-dimensional photonic crystals to achieve a single-mode output with a high power and a small divergence angle,and has recently attracted much attention^([1−3]).In 2023,Kyoto University reported GaAs-based 945 nm PCSELs with a continuous-wave(CW)single-mode output power of exceeding 50 W,and a narrow beam divergence angle of 0.05°,demonstrating a brightness of 1 GW·cm^(−2)·sr^(−1),which rivals those of the existing bulky lasers^([4]).展开更多
Dear Editor,This letter studies the stabilization control issue of cyber-physical systems with time-varying delays and aperiodic denial-of-service(DoS)attacks.To address the calculation overload issue caused by networ...Dear Editor,This letter studies the stabilization control issue of cyber-physical systems with time-varying delays and aperiodic denial-of-service(DoS)attacks.To address the calculation overload issue caused by networked predictive control(NPC)approach,an event-based NPC method is proposed.Within the proposed method,the negative effects of time-varying delays and DoS attacks on system performance are compensated.Then,sufficient and necessary conditions are derived to ensure the stability of the closed-loop system.In the end,simulation results are provided to demonstrate the validity of presented method.展开更多
Objective:To explore and verify the mechanism of Gan Dou Ling in improving liver fibrosis in Wilson disease(WD)by network pharmacology and copper loaded mice experiments.Methods:The main chemical components and corres...Objective:To explore and verify the mechanism of Gan Dou Ling in improving liver fibrosis in Wilson disease(WD)by network pharmacology and copper loaded mice experiments.Methods:The main chemical components and corresponding gene targets of each drug in Gan Dou Ling were obtained by using TCMSP database.The database of gene mutation and disease related gene was searched through the GeneCards database,DrugBank database,PharmGKB database and the DisGeNET database.After the intersection of drug and disease target genes.The STRING website was used to analyze the protein-protein interaction degree of target genes,and import the data to Cytoscape software 38.2 to analyze protein interaction network.The GO databases and KEGG databases were obtained in R language for enrichment analysis.On this basis,Masson staining were used to observe the degree of liver fibrosis in copper loaded mouse model,and the results of network pharmacological analysis were verified by Western Blot(WB).Results:A total of 108 drug disease intersection genes were analyzed by network pharmacology.Through PPI network analysis,JUN was found to be the key genes.The enrichment analysis of KEGG pathway showed that MAPK signal pathway was the important potential target pathways.Animal experiments showed that Gan Dou Ling could reduce liver fibrosis and inhibit the phosphorylation of P38,JNK and C-JUN in copper loaded mice.Conclusion:Gan Dou Ling may achieve the effect of treating WD liver fibrosis by inhibiting P38/JNK signaling pathway.展开更多
GANSim is a generative adversarial networks(GANs)-based geomodelling framework with direct conditioning capabilities.To extend GANSim for geomodelling of multi-scenario and non-stationary reservoirs,and to address its...GANSim is a generative adversarial networks(GANs)-based geomodelling framework with direct conditioning capabilities.To extend GANSim for geomodelling of multi-scenario and non-stationary reservoirs,and to address its tendency to overlook single-pixel well facies conditioning data that can cause local facies disconnections around wells,an enhanced GANSim framework is proposed.The effectiveness of the enhanced GANSim is validated using a 3D multi-scenario,non-stationary turbidite fan reservoir.For reservoirs that may involve multiple geological scenarios,two GANSim geomodelling workflows are proposed:(1)training a comprehensive GANSim model that covers all possible geological scenarios;and(2)first performing geological scenario falsification and then training GANSim models only for the unfalsified scenarios.On this basis,a local discriminator architecture is designed to improve facies continuity around wells.The modelling results show that both workflows can generate non-stationary facies models that conform to expected geological patterns and honor conditioning data,and the facies discontinuity issue around wells is effectively resolved.Compared with multipoint geostatistical methods(SNESIM),GANSim exhibits superior capability in reproducing geological patterns and modelling efficiency.Although GANSim requires a long training time,once training is completed,it can be applied to geomodelling reservoirs of arbitrary scale with similar geological structures,achieving modelling speeds approximately 1000 times faster than SNESIM.展开更多
基金Supported by the National Natural Science Foundation of China(No.42306214)the Postdoctoral Innovative Talents Support Program of Shandong Province(No.SDBX2022026)+1 种基金the China Postdoctoral Science Foundation(No.2023M733533)the Special Research Assistant Project of the Chinese Academy of Sciences in 2022。
文摘Precipitation nowcasting is of great importance for disaster prevention and mitigation.However,precipitation is a complex spatio-temporal phenomenon influenced by various underlying physical factors.Even slight changes in the initial precipitation field can have a significant impact on the future precipitation patterns,making the nowcasting of short-term high-resolution precipitation a major challenge.Traditional deep learning methods often have difficulty capturing the long-term spatial dependence of precipitation and are usually at a low resolution.To address these issues,based upon the Simpler yet Better Video Prediction(SimVP)framework,we proposed a deep generative neural network that incorporates the Simple Parameter-Free Attention Module(SimAM)and Generative Adversarial Networks(GANs)for short-term high-resolution precipitation event forecasting.Through an adversarial training strategy,critical precipitation features were extracted from complex radar echo images.During the adversarial learning process,the dynamic competition between the generator and the discriminator could continuously enhance the model in prediction accuracy and resolution for short-term precipitation.Experimental results demonstrate that the proposed method could effectively forecast short-term precipitation events on various scales and showed the best overall performance among existing methods.
文摘The application of image super-resolution(SR)has brought significant assistance in the medical field,aiding doctors to make more precise diagnoses.However,solely relying on a convolutional neural network(CNN)for image SR may lead to issues such as blurry details and excessive smoothness.To address the limitations,we proposed an algorithm based on the generative adversarial network(GAN)framework.In the generator network,three different sizes of convolutions connected by a residual dense structure were used to extract detailed features,and an attention mechanism combined with dual channel and spatial information was applied to concentrate the computing power on crucial areas.In the discriminator network,using InstanceNorm to normalize tensors sped up the training process while retaining feature information.The experimental results demonstrate that our algorithm achieves higher peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM)compared to other methods,resulting in an improved visual quality.
基金supported by the Double First-Class Innovation Research Project for People’s Public Security University of China(No.2023SYL07).
文摘Facial recognition systems have become increasingly significant in public security efforts. They play a crucial role in apprehending criminals and locating missing children and elderly individuals. Nevertheless, in practical applications, around 30% to 50% of images are obstructed to varied extents, for as by the presence of masks, glasses, or hats. Repairing the masked facial images will enhance face recognition accuracy by 10% to 20%. Simultaneously, market research indicates a rising demand for efficient facial recognition technology within the security and surveillance sectors, with projections suggesting that the global facial recognition market would exceed US$3 billion by 2025. Therefore, finding a prompt and efficient solution to fix the masked face and enhance its accuracy has become a pressing issue that has to be resolved. Currently, the generative adversarial network has shown excellent performance in the field of image restoration, with high precision and good quality of restoration results, but it consumes a lot of computing resources. Based on this, this paper proposes a model architecture that uses the U-Net network to replace the generator in the generative adversarial network, and replaces all traditional convolutional layers with Depthwise Separable Convolutional (DWSC) to make the entire network lightweight. Ultimately, We utilise the Peak Signal-to-Noise Ratio (PSNR) value to assess the efficacy of the developed model. We select samples with occlusion levels ranging from 10%–15% and 20%–30%, yielding PSNR values of 35.51 and 30.33, respectively. In contrast, the PSNR values of the three predominant algorithms in image restoration—PM, ShiftNet, and PICNet—are all below 30, demonstrating the superiority of the model presented in this paper. However, the model presented in this work possesses certain drawbacks. This work employs solely black rectangles to replicate real-life occlusions. Future study should utilise tangible objects, like as sunglasses and masks, to directly imitate occlusions, so enhancing the accuracy of the restoration effect. The model presented in this study can be further expanded from image restoration to video restoration to investigate the potential for dynamic occlusion repair.
基金supported by the National Natural Science Foundation of China (Grant No. 60776052)
文摘In this paper we present a novel approach to modeling AlGaN/GaN high electron mobility transistor （HEMT） with an artificial neural network （ANN）. The AlGaN/GaN HEMT device structure and its fabrication process are described. The circuit-based Neuro-space mapping （neuro-SM） technique is studied in detail. The EEHEMT model is implemented according to the measurement results of the designed device, which serves as a coarse model. An ANN is proposed to model AIGaN/CaN HEMT based on the coarse model. Its optimization is performed. The simulation results from the model are compared with the measurement results. It is shown that the simulation results obtained from the ANN model of A1GaN/GaN HEMT are more accurate than those obtained from the EEHEMT model.
基金supported by the Fundamental Research Funds for the Liaoning Universities(LJ212410146025).
文摘This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems.Combining Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data reconstruction.The model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT Analysis.The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data.Reconstructed data is used to retain more semantic information to generate features.The model was applied to species in Southern California,USA,citing SWOT analysis data to train the model.Experiments show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development environments.The model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data domain.This study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.
基金supported by National Key R&D Program of China(2022YFA1008000)the National Natural Science Foundation of China(12571297,12101585)+1 种基金the CAS Talent Introduction Program(Category B)the Young Elite Scientist Sponsorship Program by CAST(YESS20220125).
文摘Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in bank credit default datasets limits the predictive performance of traditional machine learning and deep learning models.To address this issue,this study employs the conditional variational autoencoder-Wasserstein generative adversarial network with gradient penalty(CVAE-WGAN-gp)model for oversampling,generating samples similar to the original default customer data to enhance model prediction performance.To evaluate the quality of the data generated by the CVAE-WGAN-gp model,we selected several bank loan datasets for experimentation.The experimental results demonstrate that using the CVAE-WGAN-gp model for oversampling can significantly improve the predictive performance in credit risk assessment problems.
基金National Natural Science Foundation of China(No.61975029)。
文摘Multifocal metalenses are of great concern in optical communications,optical imaging and micro-optics systems,but their design is extremely challenging.In recent years,deep learning methods have provided novel solutions to the design of optical planar devices.Here,an approach is proposed to explore the use of generative adversarial networks(GANs)to realize the design of metalenses with different focusing positions at dual wavelengths.This approach includes a forward network and an inverse network,where the former predicts the optical response of meta-atoms and the latter generates structures that meet specific requirements.Compared to the traditional search method,the inverse network demonstrates higher precision and efficiency in designing a dual-wavelength bifocal metalens.The results will provide insights and methodologies for the design of tunable wavelength metalenses,while also highlighting the potential of deep learning in optical device design.
基金supported by the National Natural Science Foundation of China(No.62134004).
文摘To enhance the denoising performance of event-based sensors,we introduce a clustering-based temporal deep neural network denoising method(CBTDNN).Firstly,to cluster the sensor output data and obtain the respective cluster centers,a combination of density-based spatial clustering of applications with noise(DBSCAN)and Kmeans++is utilized.Subsequently,long short-term memory(LSTM)is employed to fit and yield optimized cluster centers with temporal information.Lastly,based on the new cluster centers and denoising ratio,a radius threshold is set,and noise points beyond this threshold are removed.The comprehensive denoising metrics F1_score of CBTDNN have achieved 0.8931,0.7735,and 0.9215 on the traffic sequences dataset,pedestrian detection dataset,and turntable dataset,respectively.And these metrics demonstrate improvements of 49.90%,33.07%,19.31%,and 22.97%compared to four contrastive algorithms,namely nearest neighbor(NNb),nearest neighbor with polarity(NNp),Autoencoder,and multilayer perceptron denoising filter(MLPF).These results demonstrate that the proposed method enhances the denoising performance of event-based sensors.
基金supported by National Natural Science Foundation of China(62271096,U20A20157)Natural Science Foundation of Chongqing,China(CSTB2023NSCQ-LZX0134)+3 种基金University Innovation Research Group of Chongqing(CXQT20017)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202300632)the Chongqing Postdoctoral Special Funding Project(2022CQBSHTB2057).
文摘Aiming at the problem of mobile data traffic surge in 5G networks,this paper proposes an effective solution combining massive multiple-input multiple-output techniques with Ultra-Dense Network(UDN)and focuses on solving the resulting challenge of increased energy consumption.A base station control algorithm based on Multi-Agent Proximity Policy Optimization(MAPPO)is designed.In the constructed 5G UDN model,each base station is considered as an agent,and the MAPPO algorithm enables inter-base station collaboration and interference management to optimize the network performance.To reduce the extra power consumption due to frequent sleep mode switching of base stations,a sleep mode switching decision algorithm is proposed.The algorithm reduces unnecessary power consumption by evaluating the network state similarity and intelligently adjusting the agent’s action strategy.Simulation results show that the proposed algorithm reduces the power consumption by 24.61% compared to the no-sleep strategy and further reduces the power consumption by 5.36% compared to the traditional MAPPO algorithm under the premise of guaranteeing the quality of service of users.
基金supported by the Natural Science Foundation of Jiangsu Province(Grant.BK20232042).
文摘Stimulated emission and lasing of GaN-based laser diodes(LDs)were reported at 1995[1]and 1996[2],right after the breakthrough of p-type doping[3−5],material quality[6]and the invention of high-brightness GaN-based LEDs[7,8].However,it took much longer time for GaN-based LDs to achieve high power,high wall plug efficiency,and long lifetime.Until 2019,Nichia reported blue LDs with these performances[9],which open wide applications with GaN-based blue LDs.
基金funded by National Key R&D Program of China(Grant Nos.2024YFB3612200,2023YFB3609601,2022YFB3604300,2022YFB2802801,2022YFB3604802)Natural Science Foundation of China(Grant Nos.U24A20300,62174174,62274177,62275263,62325406,62374172,62304242,62304240,62404241)+4 种基金Youth Innovation Promotion Association of CAS(Grant Nos.2022323 and 2022324)Key R&D Program of Jiangsu Province(Grant No.BE2023018-2)Basic Research Program of Jiangsu(Grant No.BK20240126)Suzhou Science and Technology Program(Grant Nos.SYC2022089,ZXL2024379,and ZXL2024376)Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2022A1515110482 and 2022A1515110004).
文摘Photonic crystal surface emitting lasers(PCSELs)utilize the Bragg diffraction of two-dimensional photonic crystals to achieve a single-mode output with a high power and a small divergence angle,and has recently attracted much attention^([1−3]).In 2023,Kyoto University reported GaAs-based 945 nm PCSELs with a continuous-wave(CW)single-mode output power of exceeding 50 W,and a narrow beam divergence angle of 0.05°,demonstrating a brightness of 1 GW·cm^(−2)·sr^(−1),which rivals those of the existing bulky lasers^([4]).
基金supported by the National Natural Science Foundation of China(61433003,60904003,11602019).
文摘Dear Editor,This letter studies the stabilization control issue of cyber-physical systems with time-varying delays and aperiodic denial-of-service(DoS)attacks.To address the calculation overload issue caused by networked predictive control(NPC)approach,an event-based NPC method is proposed.Within the proposed method,the negative effects of time-varying delays and DoS attacks on system performance are compensated.Then,sufficient and necessary conditions are derived to ensure the stability of the closed-loop system.In the end,simulation results are provided to demonstrate the validity of presented method.
基金supported by the National Natural Science Foundation of China(No.81973825)the Natural Science Research Foundation of Anhui Province Universities(No.KJ2020A0436)。
文摘Objective:To explore and verify the mechanism of Gan Dou Ling in improving liver fibrosis in Wilson disease(WD)by network pharmacology and copper loaded mice experiments.Methods:The main chemical components and corresponding gene targets of each drug in Gan Dou Ling were obtained by using TCMSP database.The database of gene mutation and disease related gene was searched through the GeneCards database,DrugBank database,PharmGKB database and the DisGeNET database.After the intersection of drug and disease target genes.The STRING website was used to analyze the protein-protein interaction degree of target genes,and import the data to Cytoscape software 38.2 to analyze protein interaction network.The GO databases and KEGG databases were obtained in R language for enrichment analysis.On this basis,Masson staining were used to observe the degree of liver fibrosis in copper loaded mouse model,and the results of network pharmacological analysis were verified by Western Blot(WB).Results:A total of 108 drug disease intersection genes were analyzed by network pharmacology.Through PPI network analysis,JUN was found to be the key genes.The enrichment analysis of KEGG pathway showed that MAPK signal pathway was the important potential target pathways.Animal experiments showed that Gan Dou Ling could reduce liver fibrosis and inhibit the phosphorylation of P38,JNK and C-JUN in copper loaded mice.Conclusion:Gan Dou Ling may achieve the effect of treating WD liver fibrosis by inhibiting P38/JNK signaling pathway.
文摘GANSim is a generative adversarial networks(GANs)-based geomodelling framework with direct conditioning capabilities.To extend GANSim for geomodelling of multi-scenario and non-stationary reservoirs,and to address its tendency to overlook single-pixel well facies conditioning data that can cause local facies disconnections around wells,an enhanced GANSim framework is proposed.The effectiveness of the enhanced GANSim is validated using a 3D multi-scenario,non-stationary turbidite fan reservoir.For reservoirs that may involve multiple geological scenarios,two GANSim geomodelling workflows are proposed:(1)training a comprehensive GANSim model that covers all possible geological scenarios;and(2)first performing geological scenario falsification and then training GANSim models only for the unfalsified scenarios.On this basis,a local discriminator architecture is designed to improve facies continuity around wells.The modelling results show that both workflows can generate non-stationary facies models that conform to expected geological patterns and honor conditioning data,and the facies discontinuity issue around wells is effectively resolved.Compared with multipoint geostatistical methods(SNESIM),GANSim exhibits superior capability in reproducing geological patterns and modelling efficiency.Although GANSim requires a long training time,once training is completed,it can be applied to geomodelling reservoirs of arbitrary scale with similar geological structures,achieving modelling speeds approximately 1000 times faster than SNESIM.
