Deep learning techniques have outstanding performance in feature extraction and modelfitting.In thefield of aero-engine fault diagnosis,the intro-duction of deep learning technology is of great significance.The aero-engi...Deep learning techniques have outstanding performance in feature extraction and modelfitting.In thefield of aero-engine fault diagnosis,the intro-duction of deep learning technology is of great significance.The aero-engine is the heart of the aircraft,and its stable operation is the primary guarantee of the aircraft.In order to ensure the normal operation of the aircraft,it is necessary to study and diagnose the faults of the aero-engine.Among the many engine fail-ures,the one that occurs more frequently and is more hazardous is the wheeze,which often poses a great threat toflight safety.On the basis of analyzing the mechanism of aero-engine surge,an aero-engine surge fault diagnosis method based on deep learning technology is proposed.In this paper,key sensor data are obtained by analyzing different engine sensor data.An aero-engine surge data-set acquisition algorithm(ASDA)is proposed to sample the fault and normal points to generate the training set,validation set and test set.Based on neural net-work models such as one-dimensional convolutional neural network(1D-CNN),convolutional neural network(RNN),and long-short memory neural network(LSTM),different neural network optimization algorithms are selected to achieve fault diagnosis and classification.The experimental results show that the deep learning technique has good effect in aero-engine surge fault diagnosis.The aero-engine surge fault diagnosis network(ASFDN)proposed in this paper achieves better results.Through training,the network achieves more than 99%classification accuracy for the test set.展开更多
Quality control of ginseng currently is mainly based on ginsenoside analysis,but rarely focuses on the volatile organic components.In the current work,an untargeted metabolomics approach,by headspace solid-phase micro...Quality control of ginseng currently is mainly based on ginsenoside analysis,but rarely focuses on the volatile organic components.In the current work,an untargeted metabolomics approach,by headspace solid-phase micro-extraction gas chromatography/mass spectrometry(HS-SPME-GC/MS),was elaborated and further employed to holistically compare the compositional difference of the volatile components simultaneously from 12 Panax herbal medicines,which included P.ginseng(PG),P.quinquefolius(PQ),P.notoginseng(PN),red ginseng(PGR),P.ginseng leaf(PGL),P.quinquefolius leaf(PQL),P.notoginseng leaf(PNL),P.ginseng flower(PGF),P.quinquefolius flower(PQF),P.notoginseng flower(PNF),P.japonicus(PJ),and P.japonicus var.major(PJvm).Chromatographic separation was performed on an HP-5MS elastic quartz capillary column using helium as the carrier gas,enabling good resolution within 1 h.We were able to characterize totally 259 volatile compounds,including 82 terpenes(T),46 alcohols(Alc),29 ketones(K),25 aldehydes(Ald),21 esters(E),and the others.By analyzing 90 batches of ginseng samples based on the untargeted metabolomics workflows,236 differential ions were unveiled,and accordingly 36 differential volatile components were discovered.It is the first report that simultaneously compares the compositional difference of volatile components among 12 Panax herbal medicines,and useful information is provided for the quality control of ginseng aside from the well-known ginsenosides.展开更多
Various ion sources are key components to prepare functional coatings,such as diamond-like carbon(DLC)films.In this article,we present our trying of surface modification on basis of Si-incorporation diamond-like carbo...Various ion sources are key components to prepare functional coatings,such as diamond-like carbon(DLC)films.In this article,we present our trying of surface modification on basis of Si-incorporation diamond-like carbon(Si-DLC)produced by a magnetic field enhanced radio frequency ion source,which is established to get high density plasma with the help of magnetic field.Under proper deposition process,a contact angle of 111°hydrophobic surface was achieved without any surface patterning,where nanostructure SiC grains appeared within the amorphous microstructure.The surface property was influenced by ion flow parameters as well as the resultant surface microstructure.The magnetic field enhanced radio frequency ion source developed in this paper was useful for protective film applications.展开更多
Image conversion refers to converting an image from one style to another and ensuring that the content of the image remains unchanged.Using Generative Adversarial Networks(GAN)for image conversion can achieve good res...Image conversion refers to converting an image from one style to another and ensuring that the content of the image remains unchanged.Using Generative Adversarial Networks(GAN)for image conversion can achieve good results.However,if there are enough samples,any image in the target domain can be mapped to the same set of inputs.On this basis,the Cycle Consistency Generative Adversarial Network(CycleGAN)was developed.This article verifies and discusses the advantages and disadvantages of the CycleGAN model in image style conversion.CycleGAN uses two generator networks and two discriminator networks.The purpose is to learn the mapping relationship and inverse mapping relationship between the source domain and the target domain.It can reduce the mapping and improve the quality of the generated image.Through the idea of loop,the loss of information in image style conversion is reduced.When evaluating the results of the experiment,the degree of retention of the input image content will be judged.Through the experimental results,CycleGAN can understand the artist’s overall artistic style and successfully convert real landscape paintings.The advantage is that most of the content of the original picture can be retained,and only the texture line of the picture is changed to a level similar to the artist’s style.展开更多
基金supported by Scientific Research Starting Project of SWPU[No.0202002131604]Major Science and Technology Project of Sichuan Province[No.8ZDZX0143,2019YFG0424]+2 种基金Ministry of Education Collaborative Education Project of China[No.952]Fundamental Research Project[Nos.549,550]Development of Aero-engine Test and training platform based on Simulation Technology[18ZA0030].
文摘Deep learning techniques have outstanding performance in feature extraction and modelfitting.In thefield of aero-engine fault diagnosis,the intro-duction of deep learning technology is of great significance.The aero-engine is the heart of the aircraft,and its stable operation is the primary guarantee of the aircraft.In order to ensure the normal operation of the aircraft,it is necessary to study and diagnose the faults of the aero-engine.Among the many engine fail-ures,the one that occurs more frequently and is more hazardous is the wheeze,which often poses a great threat toflight safety.On the basis of analyzing the mechanism of aero-engine surge,an aero-engine surge fault diagnosis method based on deep learning technology is proposed.In this paper,key sensor data are obtained by analyzing different engine sensor data.An aero-engine surge data-set acquisition algorithm(ASDA)is proposed to sample the fault and normal points to generate the training set,validation set and test set.Based on neural net-work models such as one-dimensional convolutional neural network(1D-CNN),convolutional neural network(RNN),and long-short memory neural network(LSTM),different neural network optimization algorithms are selected to achieve fault diagnosis and classification.The experimental results show that the deep learning technique has good effect in aero-engine surge fault diagnosis.The aero-engine surge fault diagnosis network(ASFDN)proposed in this paper achieves better results.Through training,the network achieves more than 99%classification accuracy for the test set.
基金National Natural Science Foundation of China(Grant No.81872996)Natural Science Foundation of Tianjin of China(Grant No.20JCYBJC00060).
文摘Quality control of ginseng currently is mainly based on ginsenoside analysis,but rarely focuses on the volatile organic components.In the current work,an untargeted metabolomics approach,by headspace solid-phase micro-extraction gas chromatography/mass spectrometry(HS-SPME-GC/MS),was elaborated and further employed to holistically compare the compositional difference of the volatile components simultaneously from 12 Panax herbal medicines,which included P.ginseng(PG),P.quinquefolius(PQ),P.notoginseng(PN),red ginseng(PGR),P.ginseng leaf(PGL),P.quinquefolius leaf(PQL),P.notoginseng leaf(PNL),P.ginseng flower(PGF),P.quinquefolius flower(PQF),P.notoginseng flower(PNF),P.japonicus(PJ),and P.japonicus var.major(PJvm).Chromatographic separation was performed on an HP-5MS elastic quartz capillary column using helium as the carrier gas,enabling good resolution within 1 h.We were able to characterize totally 259 volatile compounds,including 82 terpenes(T),46 alcohols(Alc),29 ketones(K),25 aldehydes(Ald),21 esters(E),and the others.By analyzing 90 batches of ginseng samples based on the untargeted metabolomics workflows,236 differential ions were unveiled,and accordingly 36 differential volatile components were discovered.It is the first report that simultaneously compares the compositional difference of volatile components among 12 Panax herbal medicines,and useful information is provided for the quality control of ginseng aside from the well-known ginsenosides.
文摘Various ion sources are key components to prepare functional coatings,such as diamond-like carbon(DLC)films.In this article,we present our trying of surface modification on basis of Si-incorporation diamond-like carbon(Si-DLC)produced by a magnetic field enhanced radio frequency ion source,which is established to get high density plasma with the help of magnetic field.Under proper deposition process,a contact angle of 111°hydrophobic surface was achieved without any surface patterning,where nanostructure SiC grains appeared within the amorphous microstructure.The surface property was influenced by ion flow parameters as well as the resultant surface microstructure.The magnetic field enhanced radio frequency ion source developed in this paper was useful for protective film applications.
基金supported by Scientific Research Starting Project of SWPU[No.0202002131604]Major Science and Technology Project of Sichuan Province[Nos.8ZDZX0143,2019YFG0424]+1 种基金Ministry of Education Collaborative Education Project of China[No.952]Fundamental Research Project[Nos.549,550].
文摘Image conversion refers to converting an image from one style to another and ensuring that the content of the image remains unchanged.Using Generative Adversarial Networks(GAN)for image conversion can achieve good results.However,if there are enough samples,any image in the target domain can be mapped to the same set of inputs.On this basis,the Cycle Consistency Generative Adversarial Network(CycleGAN)was developed.This article verifies and discusses the advantages and disadvantages of the CycleGAN model in image style conversion.CycleGAN uses two generator networks and two discriminator networks.The purpose is to learn the mapping relationship and inverse mapping relationship between the source domain and the target domain.It can reduce the mapping and improve the quality of the generated image.Through the idea of loop,the loss of information in image style conversion is reduced.When evaluating the results of the experiment,the degree of retention of the input image content will be judged.Through the experimental results,CycleGAN can understand the artist’s overall artistic style and successfully convert real landscape paintings.The advantage is that most of the content of the original picture can be retained,and only the texture line of the picture is changed to a level similar to the artist’s style.
