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
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
Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper,...Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network（PNN） where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rocktypes. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.展开更多
Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep l...Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based methods.In order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets.Three kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training process.Benefit from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real data.Moreover,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model.The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases.And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.展开更多
The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5 G era,...The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5 G era, service based architecture is introduced into mobile networks. The monolithic network elements(e.g., MME, PGW, etc.) are split into smaller network functions to provide customized services. However, the management and deployment of network functions in service based 5 G core network are still big challenges. In this paper, we propose a novel management architecture for 5 G service based core network based on NFV and SDN. Combined with SDN, NFV and edge computing, the proposed framework can provide distributed and on-demand deployment of network functions, service guaranteed network slicing, flexible orchestration of network functions and optimal workload allocation. Simulations are conducted to show that the proposed framework and algorithm are effective in terms of reducing network operating cost.展开更多
The advent of the Age of Information brings about bright prospects to Network-based Language Learning(NBLL).The thesis adopts the Engagement Theory as guided principles.The purpose is to use the novel NBLL model effec...The advent of the Age of Information brings about bright prospects to Network-based Language Learning(NBLL).The thesis adopts the Engagement Theory as guided principles.The purpose is to use the novel NBLL model effectively with the help of modern technology especially in less-developed areas.This thesis focuses on network-based experimental study.The research shows that the students under NBLL environment have cultivated the capabilities in information collection,computer operation,and information evaluation,as well as the abilities in problem solving,reasoning with criticism,and cooperating with others.展开更多
Sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the conf...Sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the configuration space.However,the quality of the initial solution is not guaranteed,and the convergence speed to the optimal solution is slow.In this paper,we present a novel image-based path planning algorithm to overcome these limitations.Specifically,a generative adversarial network(GAN)is designed to take the environment map(denoted as RGB image)as the input without other preprocessing works.The output is also an RGB image where the promising region(where a feasible path probably exists)is segmented.This promising region is utilized as a heuristic to achieve non-uniform sampling for the path planner.We conduct a number of simulation experiments to validate the effectiveness of the proposed method,and the results demonstrate that our method performs much better in terms of the quality of the initial solution and the convergence speed to the optimal solution.Furthermore,apart from the environments similar to the training set,our method also works well on the environments which are very different from the training set.展开更多
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(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Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network（PNN） where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rocktypes. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.
基金financially supported by the NSFC(Grant No.41974126 and 41674116)the National Key Research and Development Program of China(Grant No.2018YFA0702501)the 13th 5-Year Basic Research Program of China National Petroleum Corporation(CNPC)(2018A-3306)。
文摘Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based methods.In order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets.Three kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training process.Benefit from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real data.Moreover,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model.The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases.And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.
基金supported by China Ministry of Education-CMCC Research Fund Project No.MCM20160104National Science and Technology Major Project No.No.2018ZX03001016+1 种基金Beijing Municipal Science and technology Commission Research Fund Project No.Z171100005217001Fundamental Research Funds for Central Universities NO.2018RC06
文摘The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5 G era, service based architecture is introduced into mobile networks. The monolithic network elements(e.g., MME, PGW, etc.) are split into smaller network functions to provide customized services. However, the management and deployment of network functions in service based 5 G core network are still big challenges. In this paper, we propose a novel management architecture for 5 G service based core network based on NFV and SDN. Combined with SDN, NFV and edge computing, the proposed framework can provide distributed and on-demand deployment of network functions, service guaranteed network slicing, flexible orchestration of network functions and optimal workload allocation. Simulations are conducted to show that the proposed framework and algorithm are effective in terms of reducing network operating cost.
文摘The advent of the Age of Information brings about bright prospects to Network-based Language Learning(NBLL).The thesis adopts the Engagement Theory as guided principles.The purpose is to use the novel NBLL model effectively with the help of modern technology especially in less-developed areas.This thesis focuses on network-based experimental study.The research shows that the students under NBLL environment have cultivated the capabilities in information collection,computer operation,and information evaluation,as well as the abilities in problem solving,reasoning with criticism,and cooperating with others.
基金This work was partially supported by National Key R&D Program of China(2019YFB1312400)Shenzhen Key Laboratory of Robotics Perception and Intelligence(ZDSYS20200810171800001)+1 种基金Hong Kong RGC GRF(14200618)Hong Kong RGC CRF(C4063-18G).
文摘Sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the configuration space.However,the quality of the initial solution is not guaranteed,and the convergence speed to the optimal solution is slow.In this paper,we present a novel image-based path planning algorithm to overcome these limitations.Specifically,a generative adversarial network(GAN)is designed to take the environment map(denoted as RGB image)as the input without other preprocessing works.The output is also an RGB image where the promising region(where a feasible path probably exists)is segmented.This promising region is utilized as a heuristic to achieve non-uniform sampling for the path planner.We conduct a number of simulation experiments to validate the effectiveness of the proposed method,and the results demonstrate that our method performs much better in terms of the quality of the initial solution and the convergence speed to the optimal solution.Furthermore,apart from the environments similar to the training set,our method also works well on the environments which are very different from the training set.
