By defining fuzzy valued simple functions and giving L1(μ) approximations of fuzzy valued integrably bounded functions by such simple functions, the paper analyses by L1(μ)-norm the approximation capability of four-...By defining fuzzy valued simple functions and giving L1(μ) approximations of fuzzy valued integrably bounded functions by such simple functions, the paper analyses by L1(μ)-norm the approximation capability of four-layer feedforward regular fuzzy neural networks to the fuzzy valued integrably bounded function F : Rn → FcO(R). That is, if the transfer functionσ: R→R is non-polynomial and integrable function on each finite interval, F may be innorm approximated by fuzzy valued functions defined as to anydegree of accuracy. Finally some real examples demonstrate the conclusions.展开更多
The main task of this work is to design a control system for a small tail-sitter Unmanned Aerial Vehicle(UAV)during the transition process.Although reasonable control performance can be obtained through a well-tuned s...The main task of this work is to design a control system for a small tail-sitter Unmanned Aerial Vehicle(UAV)during the transition process.Although reasonable control performance can be obtained through a well-tuned single PID or cascade PID control architecture under nominal conditions,large or fast time-varying disturbances and a wide range of changes in the equilibrium point bring nonlinear characteristics to the transition control during the transition process,which leads to control precision degradation.Meanwhile,the PID controller’s tuning method relies on engineering experiences to a certain extent and the controller parameters need to be retuned under different working conditions,which limits the rapid deployment and preliminary validation.Based on the above issues,a novel control architecture of L1 neural network adaptive control associated with PID control is proposed to improve the compensation ability during the transition process and guarantee the security transition.The L1 neural network adaptive control is revised to solve the multi-input and multi-output problem of the tail-sitter UAV system in this study.Finally,the transition characteristics of the time setting difference between the desired transition speed and the desired transition pitch angle are analyzed.展开更多
Background:1,2,3,4,6-penta-O-galloyl-beta-D-glucose(PGG)is a natural polyphenolic compound derived from multiple medicinal plants with favorable anticancer activity.Methods:In this study,the mechanisms of PGG against ...Background:1,2,3,4,6-penta-O-galloyl-beta-D-glucose(PGG)is a natural polyphenolic compound derived from multiple medicinal plants with favorable anticancer activity.Methods:In this study,the mechanisms of PGG against gastric cancer were explored through network pharmacology and molecular docking.First,the targets of PGG were searched in the Herbal Ingredients’Targets(HIT),Similarity Ensemble Approach(SEA),and Super-PRED databases.The potential targets related to gastric cancer were predicted from the Human Gene Database(GeneCards)and DisGeNET databases.The intersecting targets of PGG and gastric cancer were obtained by Venn diagram and then subjected to protein-protein interaction analysis to screen hub targets.Functional and pathway enrichment of hub targets were analyzed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases.The differential expression and survival analysis of hub targets in gastric cancer were performed based on The Cancer Genome Atlas database.Finally,the affinity of PGG with hub targets was visualized by molecular docking.Results:Three hub targets were screened,including mitogen-activated protein kinase 14(MAPK14),BCL2 like 1(BCL2L1),and vascular endothelial growth factor A(VEGFA).MAPK14 had a higher expression,while BCL2L1 and VEGFA had lower expression in gastric cancer than in normal conditions.Enrichment analysis indicated enrichment of these hub targets in MAPK,neurotrophin,programmed death-ligand 1(PD-L1)checkpoint,phosphatidylinositol 3-kinases/protein kinase B(PI3K-Akt),Ras,and hypoxia-inducible factor-1(HIF-1)signaling pathways.Conclusion:Therefore,network pharmacology and molecular docking analyses revealed that PGG exerts a therapeutic efficacy on gastric cancer by multiple targets(MAPK14,BCL2L1,and VEGFA)and pathways(MAPK,PD-L1 checkpoint,PI3K-Akt,Ras,and HIF-1 pathways).展开更多
Integrated with sensors,processors,and radio frequency(RF)communication modules,intelligent bearing could achieve the autonomous perception and autonomous decision-making,guarantying the safety and reliability during ...Integrated with sensors,processors,and radio frequency(RF)communication modules,intelligent bearing could achieve the autonomous perception and autonomous decision-making,guarantying the safety and reliability during their use.However,because of the resource limitations of the end device,processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network(CNN),which involves a great amount of multiplicative operations.To minimize the computation cost of the conventional CNN,based on the idea of AdderNet,a 1-D adder neural network with a wide first-layer kernel(WAddNN)suitable for bearing fault diagnosis is proposed in this paper.The proposed method uses the l1-norm distance between filters and input features as the output response,thus making the whole network almost free of multiplicative operations.The whole model takes the original signal as the input,uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise,and then uses two layers of small kernels for nonlinear mapping.Through experimental comparison with CNN models of the same structure,WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost.The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources.展开更多
This paper focuses on the 2-median location improvement problem on tree networks and the problem is to modify the weights of edges at the minimum cost such that the overall sum of the weighted distance of the vertices...This paper focuses on the 2-median location improvement problem on tree networks and the problem is to modify the weights of edges at the minimum cost such that the overall sum of the weighted distance of the vertices to the respective closest one of two prescribed vertices in the modified network is upper bounded by a given value.l1 norm and l∞norm are used to measure the total modification cost. These two problems have a strong practical application background and important theoretical research value. It is shown that such problems can be transformed into a series of sum-type and bottleneck-type continuous knapsack problems respectively.Based on the property of the optimal solution two O n2 algorithms for solving the two problems are proposed where n is the number of vertices on the tree.展开更多
The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications...The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications is style transfer.Style transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image.CYCLE-GAN is a classic GAN model,which has a wide range of scenarios in style transfer.Considering its unsupervised learning characteristics,the mapping is easy to be learned between an input image and an output image.However,it is difficult for CYCLE-GAN to converge and generate high-quality images.In order to solve this problem,spectral normalization is introduced into each convolutional kernel of the discriminator.Every convolutional kernel reaches Lipschitz stability constraint with adding spectral normalization and the value of the convolutional kernel is limited to[0,1],which promotes the training process of the proposed model.Besides,we use pretrained model(VGG16)to control the loss of image content in the position of l1 regularization.To avoid overfitting,l1 regularization term and l2 regularization term are both used in the object loss function.In terms of Frechet Inception Distance(FID)score evaluation,our proposed model achieves outstanding performance and preserves more discriminative features.Experimental results show that the proposed model converges faster and achieves better FID scores than the state of the art.展开更多
For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be colle...For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.展开更多
OBJECTIVE To investigate the pharmacological effect and mechanism of Sanguisorba officinalis L.(SOL)in non-small cell lung cancer(NSCLC)in vitro and in vivo based on network pharmacology.METHODS Network pharmacology w...OBJECTIVE To investigate the pharmacological effect and mechanism of Sanguisorba officinalis L.(SOL)in non-small cell lung cancer(NSCLC)in vitro and in vivo based on network pharmacology.METHODS Network pharmacology was used to analyze the improving effect of SOL on NSCLC and possible targets.Cell counting kit 8(CCK-8)and 5-ethynyl-2′-deoxyuridine(EdU)staining,Western blotting,flow cytometry of AnnexinⅤ/PI,Hoechst 33342/PI staining detection and immunofluorescence were utilized in vitro.H&E staining,immunohistochemistry staining and Western blotting were performed in vivo.RESULTS Based on network prediction,we analyzed the 208 common targets of SOL and NSCLC.36 core targets in 208 common targets were obtained through cytoscape analysis.And the top 10 core targets included Akt,mTOR,EGFR,etc..KEGG analysis showed that PI3K-Akt signaling pathway was the most likely pathway.Furthermore,the mechanism study found that SOL could activate the PI3K/Akt/mTOR signaling pathway in vitro and in vivo.The anti-proliferative effect of SOL in A549 and H1299 cells was measured and validated by CCK-8 and EdU assay.Immunohistochemical results of Ki67 showed that SOL effectively inhibited tumor growth in vivo.SOL also significantly inhibited the migration and invasion of A549 and H1299 cells.SOL significantly increased the percentage of cells with PI signal in A549 and H1299,and the process of cell death of A549 cells indicated that SOL induced apoptosis.The PARP-1 and caspase-3 in A549 and H1299 were found to be activated in a dose manner.The results in vivo were consistent with those in vitro.CONCLUSION SOL-induced,caspase-3-mediated apoptosis via the induction of the PI3K/Akt/mTOR signaling pathway in NSCLC,which further clarified the mechanism of SOL in the inhibition of NSCLC,and thereby provided a possibility for SOL to serve as a novel therapeutic agent for NSCLC in the future.展开更多
The issues of event-triggered exponential L1 filtering are studied for a class of networked linear switched systems.An event-triggered mechanism is proposed to enhance resource utilization in transmission,and save the...The issues of event-triggered exponential L1 filtering are studied for a class of networked linear switched systems.An event-triggered mechanism is proposed to enhance resource utilization in transmission,and save the communication cost of systems as well.Then,the filtering error system is reconstructed as a switched delay system with bounded disturbance through the input delay system approach.By resorting to the Lyapunov-Krasovskii functional approach and the average dwell time(ADT)technique,some interesting results are derived to guarantee the exponential stability with a prescribed L1 disturbance rejection level.Further,an event-triggered exponential L1 filter is designed via solving a set of feasible linear matrix inequalities(LMIs).Finally,the efficiency of the proposed results is verified through a numerical example and a PWM-driven boost converter circuit system.展开更多
基金Supported by the National Natural Science Foundation of China(No:69872039)
文摘By defining fuzzy valued simple functions and giving L1(μ) approximations of fuzzy valued integrably bounded functions by such simple functions, the paper analyses by L1(μ)-norm the approximation capability of four-layer feedforward regular fuzzy neural networks to the fuzzy valued integrably bounded function F : Rn → FcO(R). That is, if the transfer functionσ: R→R is non-polynomial and integrable function on each finite interval, F may be innorm approximated by fuzzy valued functions defined as to anydegree of accuracy. Finally some real examples demonstrate the conclusions.
基金supported by the Natural Science Basic Research Plan in Shaanxi Province,China(No.2021JQ-214)the Fundamental Research Funds for the Central Universities,China(No.300102251101).
文摘The main task of this work is to design a control system for a small tail-sitter Unmanned Aerial Vehicle(UAV)during the transition process.Although reasonable control performance can be obtained through a well-tuned single PID or cascade PID control architecture under nominal conditions,large or fast time-varying disturbances and a wide range of changes in the equilibrium point bring nonlinear characteristics to the transition control during the transition process,which leads to control precision degradation.Meanwhile,the PID controller’s tuning method relies on engineering experiences to a certain extent and the controller parameters need to be retuned under different working conditions,which limits the rapid deployment and preliminary validation.Based on the above issues,a novel control architecture of L1 neural network adaptive control associated with PID control is proposed to improve the compensation ability during the transition process and guarantee the security transition.The L1 neural network adaptive control is revised to solve the multi-input and multi-output problem of the tail-sitter UAV system in this study.Finally,the transition characteristics of the time setting difference between the desired transition speed and the desired transition pitch angle are analyzed.
基金supported by the Natural Science Foundation of Gansu Province[Grant Numbers 22JR5RA930,22JR5RA894]the Talent Project of Lanzhou Science and Technology Bureau[Grant Number 2022-3-44]+1 种基金the projects managed by the Administration of Traditional Chinese Medicine[Grant Number GZKG-2022-54]Intra Hospital Fund of the First Hospital of Lanzhou University[Grant Number ldyyyn2021101].
文摘Background:1,2,3,4,6-penta-O-galloyl-beta-D-glucose(PGG)is a natural polyphenolic compound derived from multiple medicinal plants with favorable anticancer activity.Methods:In this study,the mechanisms of PGG against gastric cancer were explored through network pharmacology and molecular docking.First,the targets of PGG were searched in the Herbal Ingredients’Targets(HIT),Similarity Ensemble Approach(SEA),and Super-PRED databases.The potential targets related to gastric cancer were predicted from the Human Gene Database(GeneCards)and DisGeNET databases.The intersecting targets of PGG and gastric cancer were obtained by Venn diagram and then subjected to protein-protein interaction analysis to screen hub targets.Functional and pathway enrichment of hub targets were analyzed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases.The differential expression and survival analysis of hub targets in gastric cancer were performed based on The Cancer Genome Atlas database.Finally,the affinity of PGG with hub targets was visualized by molecular docking.Results:Three hub targets were screened,including mitogen-activated protein kinase 14(MAPK14),BCL2 like 1(BCL2L1),and vascular endothelial growth factor A(VEGFA).MAPK14 had a higher expression,while BCL2L1 and VEGFA had lower expression in gastric cancer than in normal conditions.Enrichment analysis indicated enrichment of these hub targets in MAPK,neurotrophin,programmed death-ligand 1(PD-L1)checkpoint,phosphatidylinositol 3-kinases/protein kinase B(PI3K-Akt),Ras,and hypoxia-inducible factor-1(HIF-1)signaling pathways.Conclusion:Therefore,network pharmacology and molecular docking analyses revealed that PGG exerts a therapeutic efficacy on gastric cancer by multiple targets(MAPK14,BCL2L1,and VEGFA)and pathways(MAPK,PD-L1 checkpoint,PI3K-Akt,Ras,and HIF-1 pathways).
基金support provided by the China National Key Research and Development Program of China under Grant 2019YFB2004300the National Natural Science Foundation of China under Grant 51975065 and 51805051.
文摘Integrated with sensors,processors,and radio frequency(RF)communication modules,intelligent bearing could achieve the autonomous perception and autonomous decision-making,guarantying the safety and reliability during their use.However,because of the resource limitations of the end device,processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network(CNN),which involves a great amount of multiplicative operations.To minimize the computation cost of the conventional CNN,based on the idea of AdderNet,a 1-D adder neural network with a wide first-layer kernel(WAddNN)suitable for bearing fault diagnosis is proposed in this paper.The proposed method uses the l1-norm distance between filters and input features as the output response,thus making the whole network almost free of multiplicative operations.The whole model takes the original signal as the input,uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise,and then uses two layers of small kernels for nonlinear mapping.Through experimental comparison with CNN models of the same structure,WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost.The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources.
基金The National Natural Science Foundation of China(No.10801031)
文摘This paper focuses on the 2-median location improvement problem on tree networks and the problem is to modify the weights of edges at the minimum cost such that the overall sum of the weighted distance of the vertices to the respective closest one of two prescribed vertices in the modified network is upper bounded by a given value.l1 norm and l∞norm are used to measure the total modification cost. These two problems have a strong practical application background and important theoretical research value. It is shown that such problems can be transformed into a series of sum-type and bottleneck-type continuous knapsack problems respectively.Based on the property of the optimal solution two O n2 algorithms for solving the two problems are proposed where n is the number of vertices on the tree.
基金This work is supported by the National Natural Science Foundation of China(No.61702226)the 111 Project(B12018)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK20170200)the Fundamental Research Funds for the Central Universities(No.JUSRP11854).
文摘The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications is style transfer.Style transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image.CYCLE-GAN is a classic GAN model,which has a wide range of scenarios in style transfer.Considering its unsupervised learning characteristics,the mapping is easy to be learned between an input image and an output image.However,it is difficult for CYCLE-GAN to converge and generate high-quality images.In order to solve this problem,spectral normalization is introduced into each convolutional kernel of the discriminator.Every convolutional kernel reaches Lipschitz stability constraint with adding spectral normalization and the value of the convolutional kernel is limited to[0,1],which promotes the training process of the proposed model.Besides,we use pretrained model(VGG16)to control the loss of image content in the position of l1 regularization.To avoid overfitting,l1 regularization term and l2 regularization term are both used in the object loss function.In terms of Frechet Inception Distance(FID)score evaluation,our proposed model achieves outstanding performance and preserves more discriminative features.Experimental results show that the proposed model converges faster and achieves better FID scores than the state of the art.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61101122)the National High Technology Research and Development Program of China(Grant No.2012AA120802)the National Science and Technology Major Project of the Ministry of Science and Technology of China(Grant No.2012ZX03004-003)
文摘For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.
基金National Natural Science Foundation of China(81774013,81804221,82074129)and National Science and Technology Major Project of China(2018ZX09721004-006-004)。
文摘OBJECTIVE To investigate the pharmacological effect and mechanism of Sanguisorba officinalis L.(SOL)in non-small cell lung cancer(NSCLC)in vitro and in vivo based on network pharmacology.METHODS Network pharmacology was used to analyze the improving effect of SOL on NSCLC and possible targets.Cell counting kit 8(CCK-8)and 5-ethynyl-2′-deoxyuridine(EdU)staining,Western blotting,flow cytometry of AnnexinⅤ/PI,Hoechst 33342/PI staining detection and immunofluorescence were utilized in vitro.H&E staining,immunohistochemistry staining and Western blotting were performed in vivo.RESULTS Based on network prediction,we analyzed the 208 common targets of SOL and NSCLC.36 core targets in 208 common targets were obtained through cytoscape analysis.And the top 10 core targets included Akt,mTOR,EGFR,etc..KEGG analysis showed that PI3K-Akt signaling pathway was the most likely pathway.Furthermore,the mechanism study found that SOL could activate the PI3K/Akt/mTOR signaling pathway in vitro and in vivo.The anti-proliferative effect of SOL in A549 and H1299 cells was measured and validated by CCK-8 and EdU assay.Immunohistochemical results of Ki67 showed that SOL effectively inhibited tumor growth in vivo.SOL also significantly inhibited the migration and invasion of A549 and H1299 cells.SOL significantly increased the percentage of cells with PI signal in A549 and H1299,and the process of cell death of A549 cells indicated that SOL induced apoptosis.The PARP-1 and caspase-3 in A549 and H1299 were found to be activated in a dose manner.The results in vivo were consistent with those in vitro.CONCLUSION SOL-induced,caspase-3-mediated apoptosis via the induction of the PI3K/Akt/mTOR signaling pathway in NSCLC,which further clarified the mechanism of SOL in the inhibition of NSCLC,and thereby provided a possibility for SOL to serve as a novel therapeutic agent for NSCLC in the future.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.6177322561773236,61873331,61803225in part by the Taishan Scholar Project of Shandong Province under Grant No.TSQN20161033。
文摘The issues of event-triggered exponential L1 filtering are studied for a class of networked linear switched systems.An event-triggered mechanism is proposed to enhance resource utilization in transmission,and save the communication cost of systems as well.Then,the filtering error system is reconstructed as a switched delay system with bounded disturbance through the input delay system approach.By resorting to the Lyapunov-Krasovskii functional approach and the average dwell time(ADT)technique,some interesting results are derived to guarantee the exponential stability with a prescribed L1 disturbance rejection level.Further,an event-triggered exponential L1 filter is designed via solving a set of feasible linear matrix inequalities(LMIs).Finally,the efficiency of the proposed results is verified through a numerical example and a PWM-driven boost converter circuit system.
