With breakthroughs in data processing and pattern recognition through deep learning technologies,the use of advanced algorithmic models for analyzing and interpreting soil spectral information has provided an efficien...With breakthroughs in data processing and pattern recognition through deep learning technologies,the use of advanced algorithmic models for analyzing and interpreting soil spectral information has provided an efficient and economical method for soil quality assessment.However,traditional single-output networks exhibit limitations in the prediction process,particularly in their inability to fully utilize the correlations among various elements.As a result,single-output networks tend to be optimized for a single task,neglecting the interrelationships among different soil elements,which limits prediction accuracy and model generalizability.To overcome this limitation,in this study,a multi-task learning architecture with a progressive extraction network was implemented for the simultaneous prediction of multiple indicators in soil,including nitrogen(N),organic carbon(OC),calcium carbonate(CaCO 3),cation exchange capacity(CEC),and pH.Furthermore,while incorporating the Pearson correlation coefficient,convolutional neural networks,long short-term memory networks and attention mechanisms were combined to extract local abstract features from the original spectra,thereby further improving the model.This architecture is referred to as the Relevance-sharing Progressive Layered Extraction Network.The model employs an adaptive joint loss optimization method to update the weights of individual task losses in the multi-task learning training process.展开更多
In the management of land resources and the protection of cultivated land,the law enforcement of land satellite images is often used as one of the main means.In recent years,the policies and regulations of the law enf...In the management of land resources and the protection of cultivated land,the law enforcement of land satellite images is often used as one of the main means.In recent years,the policies and regulations of the law enforcement of land satellite images have become more and more strict and been adjusted increasingly frequently,playing a decisive role in preventing excessive non-agricultural and non-food urbanization.In the process of the law enforcement,the extraction of suspected illegal buildings is the most important and time-consuming content.Compared with the traditional deep learning model,fully convolutional networks(FCN)has a great advantage in remote sensing image processing because its input images are not limited by size,and both convolution and deconvolution are independent of the overall size of images.In this paper,an intelligent extraction model of suspected illegal buildings from land satellite images based on deep learning FCN was built.Kaiyuan City,Yunnan Province was taken as an example.The verification results show that the global accuracy of this model was 86.6%in the process of building extraction,and mean intersection over union(mIoU)was 73.6%.This study can provide reference for the extraction of suspected illegal buildings in the law enforcement work of land satellite images,and reduce the tedious manual operation to a certain extent.展开更多
Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit feature...Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method.展开更多
Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the err...Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the error compensation model of fuzzy system,is proposed to solve the prob- lem that the component content in countercurrent rare-earth extraction process is hardly measured on-line.An industry experiment in the extraction Y process by HAB using this hybrid soft-sensor proves its effectiveness.展开更多
The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-hei...The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.展开更多
In this paper, the estimation capacities of the response surface methodology (RSM) and artificial neural network (ANN), in a microwave-assisted extraction method to determine the amount of zinc in fish samples were in...In this paper, the estimation capacities of the response surface methodology (RSM) and artificial neural network (ANN), in a microwave-assisted extraction method to determine the amount of zinc in fish samples were investigated. The experiments were carried out based on a 3-level, 4-variable Box–Behnken design. The amount of zinc was considered as a function of four independent variables, namely irradiation power, irradiation time, nitric acid concentration, and temperature. The RSM results showed the quadratic polynomial model can be used to describe the relationship between the various factors and the response. Using the ANN analysis, the optimal configuration of the ANN model was found to be 4-10-1. After predicting the model using RSM and ANN, two methodologies were then compared for their predictive capabilities. The results showed that the ANN model is much more accurate in prediction as compared to the RSM.展开更多
The equilibrium model for multicomponent rare earth extraction is developed using neural networks, which combined with the material balance model could give online prediction of component content in countercurrent rar...The equilibrium model for multicomponent rare earth extraction is developed using neural networks, which combined with the material balance model could give online prediction of component content in countercurrent rare earth (extraction) production. Simulation experiments with industrial operation data prove the effectiveness of the hybrid soft-(sensor).展开更多
A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems(NIDSs).Consequently,network interruptions and loss of sensitive data have ...A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems(NIDSs).Consequently,network interruptions and loss of sensitive data have occurred,which led to an active research area for improving NIDS technologies.In an analysis of related works,it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction(FR)and Machine Learning(ML)techniques on NIDS datasets.However,these datasets are different in feature sets,attack types,and network design.Therefore,this paper aims to discover whether these techniques can be generalised across various datasets.Six ML models are utilised:a Deep Feed Forward(DFF),Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),Decision Tree(DT),Logistic Regression(LR),and Naive Bayes(NB).The accuracy of three Feature Extraction(FE)algorithms is detected;Principal Component Analysis(PCA),Auto-encoder(AE),and Linear Discriminant Analysis(LDA),are evaluated using three benchmark datasets:UNSW-NB15,ToN-IoT and CSE-CIC-IDS2018.Although PCA and AE algorithms have been widely used,the determination of their optimal number of extracted dimensions has been overlooked.The results indicate that no clear FE method or ML model can achieve the best scores for all datasets.The optimal number of extracted dimensions has been identified for each dataset,and LDA degrades the performance of the ML models on two datasets.The variance is used to analyse the extracted dimensions of LDA and PCA.Finally,this paper concludes that the choice of datasets significantly alters the performance of the applied techniques.We believe that a universal(benchmark)feature set is needed to facilitate further advancement and progress of research in this field.展开更多
Depressions in landscapes function as buffers for water and sediment. A landscape with depressions has less runoff, less erosion and more sedimentation than that without depressions. Sinks in digital elevation models ...Depressions in landscapes function as buffers for water and sediment. A landscape with depressions has less runoff, less erosion and more sedimentation than that without depressions. Sinks in digital elevation models （DEMs） can be considered the real features that represent depressions in actual landscapes or spurious features that result from errors in DEM creation. In many hydrological and erosion models, all sinks are considered as spurious features and, as a result, these models do not deal with the sinks that represent real depressions. Consequently, the surface runoff and erosion are overestimated due to removing the depressions. Aiming at this problem, this paper presents a new method, which deal with the sinks that represent real depressions. The drainage network is extracted without changing the original DEM. The method includes four steps： detecting pits, detecting depressions, merging depressions, and extracting drainage network. Because the elevations of grid cells are not changed, the method can also avoid producing new fiat areas, which are always produced by the conventional filling methods. The proposed method was applied to the Xihanshui River basin, the upper reach of the Jialingjiang River basin, China, to automatically extract the drainage network based on DEM. The extracted drainage network agrees well with the reality and can be used for further hydrologic analysis and erosion estimation.展开更多
In metal cutting industry it is a common practice to search for optimal combination of cutting parameters in order to maximize the tool life for a fixed minimum value of material removal rate(MRR).After the advent of ...In metal cutting industry it is a common practice to search for optimal combination of cutting parameters in order to maximize the tool life for a fixed minimum value of material removal rate(MRR).After the advent of high-speed milling(HSM)pro cess,lots of experimental and theoretical researches have been done for this purpose which mainly emphasized on the optimization of the cutting parameters.It is highly beneficial to convert raw data into a comprehensive knowledge-based expert system using fuzzy logic as the reasoning mechanism.In this paper an attempt has been presented for the extraction of the rules from fuzzy neural network(FNN)so as to have the most effective knowledge-base for given set of data.Experiments were conducted to determine the best values of cutting speeds that can maximize tool life for different combinations of input parameters.A fuzzy neural network was constructed based on the fuzzification of input parameters and the cutting speed.After training process,raw rule sets were extracted and a rule pruning approach was proposed to obtain concise linguistic rules.The estimation process with fuzzy inference showed that the optimized combination of fuzzy rules provided the estimation error of only 6.34 m/min as compared to 314 m/min of that of randomized combination of rule s.展开更多
Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compres...Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compression as a practical method. The long encoding time results from the need to perform a large number of domain-range matches, the total encoding time is the product of the number of matches and the time to perform each match. In order to improve encoding speed, a hybrid method combining features extraction and self-organization network has been provided, which is based on the feature extraction approach the comparison pixels by pixels between the feature of range blocks and domains blocks. The efficiency of the new method was been proved by examples.展开更多
Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural...Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.展开更多
The anti-hair loss mechanism of Aquilaria sinensis leaf extract(ASE)has been studied by using metabolomics and network pharmacology.Metabolomics was utilized to comprehensively identify the active constituents of ASE,...The anti-hair loss mechanism of Aquilaria sinensis leaf extract(ASE)has been studied by using metabolomics and network pharmacology.Metabolomics was utilized to comprehensively identify the active constituents of ASE,and the network pharmacology was used to elucidate their anti-hair loss mechanism,which was verified by molecular docking technology.572 active compounds were identified from the ASE by metabolomics methods,where there are 1447 corresponding targets and 492 targets related to hair loss,totaling 88 targets.20 core active substances were identified by constructing a network between common targets and active substances,which include vanillic acid,chorionic acid,caffeic acid and apigenin.The five key targets of TNF,TP53,IL6,PPARG,and EGFR were screened out by the PPI network analysis on 88 common targets.The GO and KEGG pathway enrichment analysis showed that the inflammation,hormone balance,cell growth,proliferation,apoptosis,and oxidative stress are involved.Molecular docking studies have confirmed the high binding affinity between core active compounds and key targets.The drug similarity assessment on these core compounds suggested that they have the potential to be used as potential hair loss treatment drugs.This study elucidates the complex molecular mechanism of ASE in treating hair loss,and provides a reference for the future applications in hair care products.展开更多
The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats(APT).Extracting attack behaviors,i.e.,Tactics,Techniques,Procedures(TTP)from Cy...The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats(APT).Extracting attack behaviors,i.e.,Tactics,Techniques,Procedures(TTP)from Cyber Threat Intelligence(CTI)can facilitate APT actors’profiling for an immediate response.However,it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature.Based on the Adversarial Tactics,Techniques and Common Knowledge(ATT&CK)of threat behavior description,this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network(HTN)and Graph Convolutional Network(GCN)to solve this issue.It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence.With the help of the Bidirectional EncoderRepresentation fromTransformers(BERT)pretraining model to analyze the contextual semantics of cyber threat intelligence,the task of threat behavior identification is transformed into a text classification task,which automatically extracts attack behavior in CTI,then identifies the malware and advanced threat actors.The experimental results show that F1 achieve 94.86%and 92.15%for the multi-label classification tasks of tactics and techniques.Extend the experiment to verify the method’s effectiveness in identifying the malware and threat actors in APT attacks.The F1 for malware and advanced threat actors identification task reached 98.45%and 99.48%,which are better than the benchmark model in the experiment and achieve state of the art.The model can effectivelymodel threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text.展开更多
According to the characteristics of the road features,an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images.Firstly,as the features of the road target are r...According to the characteristics of the road features,an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images.Firstly,as the features of the road target are rich in local details and simple in semantic features,an Encoder-Decoder network with shallow layers and high resolution is designed to improve the ability to represent detail information.Secondly,as the road area is a small proportion in remote sensing images,the cross-entropy loss function is improved,which solves the imbalance between positive and negative samples in the training process.Experiments on large road extraction datasets show that the proposed method gets the recall rate 83.9%,precision 82.5%and F1-score 82.9%,which can extract the road targets in remote sensing images completely and accurately.The Encoder-Decoder network designed in this paper performs well in the road extraction task and needs less artificial participation,so it has a good application prospect.展开更多
Objective In tongue diagnosis,the location,color,and distribution of spots can be used to speculate on the viscera and severity of the heat evil.This work focuses on the image analysis method of artificial intelligenc...Objective In tongue diagnosis,the location,color,and distribution of spots can be used to speculate on the viscera and severity of the heat evil.This work focuses on the image analysis method of artificial intelligence(AI)to study the spotted tongue recognition of traditional Chinese medicine(TCM).Methods A model of spotted tongue recognition and extraction is designed,which is based on the principle of image deep learning and instance segmentation.This model includes multiscale feature map generation,region proposal searching,and target region recognition.Firstly,deep convolution network is used to build multiscale low-and high-abstraction feature maps after which,target candidate box generation algorithm and selection strategy are used to select high-quality target candidate regions.Finally,classification network is used for classifying target regions and calculating target region pixels.As a result,the region segmentation of spotted tongue is obtained.Under non-standard illumination conditions,various tongue images were taken by mobile phones,and experiments were conducted.Results The spotted tongue recognition achieved an area under curve(AUC)of 92.40%,an accuracy of 84.30%with a sensitivity of 88.20%,a specificity of 94.19%,a recall of 88.20%,a regional pixel accuracy index pixel accuracy(PA)of 73.00%,a mean pixel accuracy(m PA)of73.00%,an intersection over union(Io U)of 60.00%,and a mean intersection over union(mIo U)of 56.00%.Conclusion The results of the study verify that the model is suitable for the application of the TCM tongue diagnosis system.Spotted tongue recognition via multiscale convolutional neural network(CNN)would help to improve spot classification and the accurate extraction of pixels of spot area as well as provide a practical method for intelligent tongue diagnosis of TCM.展开更多
Relation extraction is an important task in NLP community.However,some models often fail in capturing Long-distance dependence on semantics,and the interaction between semantics of two entities is ignored.In this pape...Relation extraction is an important task in NLP community.However,some models often fail in capturing Long-distance dependence on semantics,and the interaction between semantics of two entities is ignored.In this paper,we propose a novel neural network model for semantic relation classification called joint self-attention bi-LSTM(SA-Bi-LSTM)to model the internal structure of the sentence to obtain the importance of each word of the sentence without relying on additional information,and capture Long-distance dependence on semantics.We conduct experiments using the SemEval-2010 Task 8 dataset.Extensive experiments and the results demonstrated that the proposed method is effective against relation classification,which can obtain state-ofthe-art classification accuracy just with minimal feature engineering.展开更多
[Objectives]Using Calamine Zinc Oxide Phytocomplex Cream as an example,this study employed network pharmacology to investigate the therapeutic potential and mechanism of action of the combination of calamine,zinc oxid...[Objectives]Using Calamine Zinc Oxide Phytocomplex Cream as an example,this study employed network pharmacology to investigate the therapeutic potential and mechanism of action of the combination of calamine,zinc oxide,and plant extracts in eczema intervention.[Methods]Active constituents of Calamine Zinc Oxide Phytocomplex Cream were identified through screening using the HIT2.0,HERB,and TCMSP databases.Corresponding targets of the active constituents were predicted using NetInfer.The collected targets were intersected with eczema and atopic dermatitis(AD)-related targets obtained from the GeneCards database to identify the effective therapeutic targets of Calamine Zinc Oxide Phytocomplex Cream.The network diagram of effective active constituents versus therapeutic targets for Calamine Zinc Oxide Phytocomplex Cream was constructed and subjected to topological analysis using Cytoscape software.The Protein-Protein Interaction(PPI)network was established and analyzed using the String database,Cytoscape software,and the cytoHubba plugin to identify key hub genes.Gene Ontology(GO)enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were performed on the therapeutic targets using the DAVID database.[Results]Screening identified 57 active constituents in Calamine Zinc Oxide Phytocomplex Cream,corresponding to 601 potential targets.Subsequent analysis revealed 72 core therapeutic targets of Calamine Zinc Oxide Phytocomplex Cream specifically relevant to eczema and AD.Analysis of the network diagram suggested that Calamine Zinc Oxide Phytocomplex Cream may exert anti-inflammatory and immunomodulatory effects through active constituents such as quercetin,luteolin,and apigenin,while concurrently repairing skin barrier function by acting on targets including AKT1,NF-κB,and STAT3.Furthermore,the inclusion of mineral-based medicines provides additional functions such as itch relief and reinforcement of the skin barrier.[Conclusions]Calamine Zinc Oxide Phytocomplex Cream combines organic and inorganic constituents,synergistically alleviating the adverse symptoms of eczema and AD through multiple pathways.展开更多
The complex network of fractures formed by randomly distributed natural fractures in hot-dry rocks(HDRs)complicates the heat transfer regularity of injected fluid.On the basis of the fracture network,exploring the cha...The complex network of fractures formed by randomly distributed natural fractures in hot-dry rocks(HDRs)complicates the heat transfer regularity of injected fluid.On the basis of the fracture network,exploring the characteristics of the fluid flow and heat transfer as influenced by different parameters helps enable efficient resource extraction and effectively promotes the construction of diversified energy utilization structures.Accordingly,accounting for the effect of the thermal shock on the evolution of the permeability of the rock matrix,a thermo-hydromechanical(THM)coupling model is developed to analyze the influences of fracture network characteristics on the heat extraction performance of HDRs.In addition,a large-scale injection and production physical simulation experiment is performed using a newly developed,in-house,large-scale true triaxial experimental system.The corresponding numerical model is established and validated.The good agreement between the numerical and experimental results verifies the reliability and accuracy of the proposed THM model.Subsequently,a two-dimensional model is established under complex fracture network conditions,taking,as a research object,the natural fracture characteristics of HDR in the Qinghai Gonghe Basin in combination with the regional geological information.The effects of different parameters,including the production well location,rock matrix permeability,injection rate,initial fracture width,and number of fractures,on the production temperature and heat extraction performance are systematically analyzed.The results indicate that an increase in the number of fractures,the distance between the injection well and the production well,or the width of the initial fractures leads to an improved heat extraction performance.The number of fractures increased from 11 horizontal fractures and 22 high-angle fractures to 35 horizontal fractures and 70 high-angle fractures,with a 20%increase in heat extraction rate.While the influence of the rock matrix permeability is not highly significant,it cannot be ignored.It is crucial to select an injection rate that is neither too low nor too high,taking into consideration economic factors.展开更多
Objective Forsythia suspensa has long been utilized in traditional Chinese medicine(TCM)for the treatment of IgA nephropathy(IgAN),the most prevalent form of primary glomerular disease.However,the precise mechanisms r...Objective Forsythia suspensa has long been utilized in traditional Chinese medicine(TCM)for the treatment of IgA nephropathy(IgAN),the most prevalent form of primary glomerular disease.However,the precise mechanisms remain inadequately understood.This study seeks to elucidate the underlying mechanisms of Forsythia suspensa extract(FSE)in the treatment of IgAN by employing an integrated approach that combines network pharmacology with in vivo experimental validation.Methods The chemical components of FSE were identified using high-performance liquid chromatography-mass spectrometry(HPLC–MS/MS).Additional chemical components and targets were determined through the Traditional Chinese Medicine Systems Pharmacology database.Potential therapeutic targets for IgAN were sourced from GeneCards and the Comparative Toxicogenomics Database.Subsequently,the enrichment analyses were conducted to evaluate the biological functions and pathways associated with the core targets.Finally,a mousemodel of IgAN was developed to validate the findings of the network pharmacology analysis.Results Through network analysis and HPLC–MS/MS,31 chemical components of FSE were identified.A total of 99 common targets were discovered between FSE and IgAN.The enrichment analyses suggested that FSE may mitigate IgAN primarily by inhibiting the TLR and NF-κB signaling pathways.In vivo experiments demonstrated that FSE reduced inflammation and preserved renal function in mice with IgAN through the Tolllike receptor 9(TLR9)/NF-κB pathway.Conclusion The integration of network pharmacology and animal experiments suggests that FSE alleviates renal inflammation and damage in IgAN through the TLR9/NF-κB signaling pathway.展开更多
文摘With breakthroughs in data processing and pattern recognition through deep learning technologies,the use of advanced algorithmic models for analyzing and interpreting soil spectral information has provided an efficient and economical method for soil quality assessment.However,traditional single-output networks exhibit limitations in the prediction process,particularly in their inability to fully utilize the correlations among various elements.As a result,single-output networks tend to be optimized for a single task,neglecting the interrelationships among different soil elements,which limits prediction accuracy and model generalizability.To overcome this limitation,in this study,a multi-task learning architecture with a progressive extraction network was implemented for the simultaneous prediction of multiple indicators in soil,including nitrogen(N),organic carbon(OC),calcium carbonate(CaCO 3),cation exchange capacity(CEC),and pH.Furthermore,while incorporating the Pearson correlation coefficient,convolutional neural networks,long short-term memory networks and attention mechanisms were combined to extract local abstract features from the original spectra,thereby further improving the model.This architecture is referred to as the Relevance-sharing Progressive Layered Extraction Network.The model employs an adaptive joint loss optimization method to update the weights of individual task losses in the multi-task learning training process.
文摘In the management of land resources and the protection of cultivated land,the law enforcement of land satellite images is often used as one of the main means.In recent years,the policies and regulations of the law enforcement of land satellite images have become more and more strict and been adjusted increasingly frequently,playing a decisive role in preventing excessive non-agricultural and non-food urbanization.In the process of the law enforcement,the extraction of suspected illegal buildings is the most important and time-consuming content.Compared with the traditional deep learning model,fully convolutional networks(FCN)has a great advantage in remote sensing image processing because its input images are not limited by size,and both convolution and deconvolution are independent of the overall size of images.In this paper,an intelligent extraction model of suspected illegal buildings from land satellite images based on deep learning FCN was built.Kaiyuan City,Yunnan Province was taken as an example.The verification results show that the global accuracy of this model was 86.6%in the process of building extraction,and mean intersection over union(mIoU)was 73.6%.This study can provide reference for the extraction of suspected illegal buildings in the law enforcement work of land satellite images,and reduce the tedious manual operation to a certain extent.
基金the National Natural Science Fundation of China (60372001 90407007)the Ph. D. Programs Foundation of Ministry of Education of China (20030614006).
文摘Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method.
基金Supported by National Natural Science Foundation of P.R.China(50474020,60534010,60504006)
文摘Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the error compensation model of fuzzy system,is proposed to solve the prob- lem that the component content in countercurrent rare-earth extraction process is hardly measured on-line.An industry experiment in the extraction Y process by HAB using this hybrid soft-sensor proves its effectiveness.
基金supported by the Fundamental Research Funds for the Central Universities of China(Grant No.2013SCU11006)the Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying,Mapping and Geoinformation of China(Grant NO.DM2014SC02)the Key Laboratory of Geospecial Information Technology,Ministry of Land and Resources of China(Grant NO.KLGSIT201504)
文摘The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.
文摘In this paper, the estimation capacities of the response surface methodology (RSM) and artificial neural network (ANN), in a microwave-assisted extraction method to determine the amount of zinc in fish samples were investigated. The experiments were carried out based on a 3-level, 4-variable Box–Behnken design. The amount of zinc was considered as a function of four independent variables, namely irradiation power, irradiation time, nitric acid concentration, and temperature. The RSM results showed the quadratic polynomial model can be used to describe the relationship between the various factors and the response. Using the ANN analysis, the optimal configuration of the ANN model was found to be 4-10-1. After predicting the model using RSM and ANN, two methodologies were then compared for their predictive capabilities. The results showed that the ANN model is much more accurate in prediction as compared to the RSM.
基金ProjectsupportedbytheNationalTenthFive Year PlanofKeyTechnology (2 0 0 2BA3 15A)
文摘The equilibrium model for multicomponent rare earth extraction is developed using neural networks, which combined with the material balance model could give online prediction of component content in countercurrent rare earth (extraction) production. Simulation experiments with industrial operation data prove the effectiveness of the hybrid soft-(sensor).
文摘A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems(NIDSs).Consequently,network interruptions and loss of sensitive data have occurred,which led to an active research area for improving NIDS technologies.In an analysis of related works,it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction(FR)and Machine Learning(ML)techniques on NIDS datasets.However,these datasets are different in feature sets,attack types,and network design.Therefore,this paper aims to discover whether these techniques can be generalised across various datasets.Six ML models are utilised:a Deep Feed Forward(DFF),Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),Decision Tree(DT),Logistic Regression(LR),and Naive Bayes(NB).The accuracy of three Feature Extraction(FE)algorithms is detected;Principal Component Analysis(PCA),Auto-encoder(AE),and Linear Discriminant Analysis(LDA),are evaluated using three benchmark datasets:UNSW-NB15,ToN-IoT and CSE-CIC-IDS2018.Although PCA and AE algorithms have been widely used,the determination of their optimal number of extracted dimensions has been overlooked.The results indicate that no clear FE method or ML model can achieve the best scores for all datasets.The optimal number of extracted dimensions has been identified for each dataset,and LDA degrades the performance of the ML models on two datasets.The variance is used to analyse the extracted dimensions of LDA and PCA.Finally,this paper concludes that the choice of datasets significantly alters the performance of the applied techniques.We believe that a universal(benchmark)feature set is needed to facilitate further advancement and progress of research in this field.
基金supported by the Project of the National Natural Science Foundation of China (40671025)the Knowledge Innovation Project of the Chinese Academy of Sciences (No. KZCX2-YW-302)
文摘Depressions in landscapes function as buffers for water and sediment. A landscape with depressions has less runoff, less erosion and more sedimentation than that without depressions. Sinks in digital elevation models （DEMs） can be considered the real features that represent depressions in actual landscapes or spurious features that result from errors in DEM creation. In many hydrological and erosion models, all sinks are considered as spurious features and, as a result, these models do not deal with the sinks that represent real depressions. Consequently, the surface runoff and erosion are overestimated due to removing the depressions. Aiming at this problem, this paper presents a new method, which deal with the sinks that represent real depressions. The drainage network is extracted without changing the original DEM. The method includes four steps： detecting pits, detecting depressions, merging depressions, and extracting drainage network. Because the elevations of grid cells are not changed, the method can also avoid producing new fiat areas, which are always produced by the conventional filling methods. The proposed method was applied to the Xihanshui River basin, the upper reach of the Jialingjiang River basin, China, to automatically extract the drainage network based on DEM. The extracted drainage network agrees well with the reality and can be used for further hydrologic analysis and erosion estimation.
基金supported by International Science and Technology Cooperation project(Grant No.2008DFA71750)
文摘In metal cutting industry it is a common practice to search for optimal combination of cutting parameters in order to maximize the tool life for a fixed minimum value of material removal rate(MRR).After the advent of high-speed milling(HSM)pro cess,lots of experimental and theoretical researches have been done for this purpose which mainly emphasized on the optimization of the cutting parameters.It is highly beneficial to convert raw data into a comprehensive knowledge-based expert system using fuzzy logic as the reasoning mechanism.In this paper an attempt has been presented for the extraction of the rules from fuzzy neural network(FNN)so as to have the most effective knowledge-base for given set of data.Experiments were conducted to determine the best values of cutting speeds that can maximize tool life for different combinations of input parameters.A fuzzy neural network was constructed based on the fuzzification of input parameters and the cutting speed.After training process,raw rule sets were extracted and a rule pruning approach was proposed to obtain concise linguistic rules.The estimation process with fuzzy inference showed that the optimized combination of fuzzy rules provided the estimation error of only 6.34 m/min as compared to 314 m/min of that of randomized combination of rule s.
文摘Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compression as a practical method. The long encoding time results from the need to perform a large number of domain-range matches, the total encoding time is the product of the number of matches and the time to perform each match. In order to improve encoding speed, a hybrid method combining features extraction and self-organization network has been provided, which is based on the feature extraction approach the comparison pixels by pixels between the feature of range blocks and domains blocks. The efficiency of the new method was been proved by examples.
基金Project(61201028)supported by the National Natural Science Foundation of ChinaProject(YWF-12-JFGF-060)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(2011ZD51048)supported by Aviation Science Foundation of China
文摘Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.
文摘The anti-hair loss mechanism of Aquilaria sinensis leaf extract(ASE)has been studied by using metabolomics and network pharmacology.Metabolomics was utilized to comprehensively identify the active constituents of ASE,and the network pharmacology was used to elucidate their anti-hair loss mechanism,which was verified by molecular docking technology.572 active compounds were identified from the ASE by metabolomics methods,where there are 1447 corresponding targets and 492 targets related to hair loss,totaling 88 targets.20 core active substances were identified by constructing a network between common targets and active substances,which include vanillic acid,chorionic acid,caffeic acid and apigenin.The five key targets of TNF,TP53,IL6,PPARG,and EGFR were screened out by the PPI network analysis on 88 common targets.The GO and KEGG pathway enrichment analysis showed that the inflammation,hormone balance,cell growth,proliferation,apoptosis,and oxidative stress are involved.Molecular docking studies have confirmed the high binding affinity between core active compounds and key targets.The drug similarity assessment on these core compounds suggested that they have the potential to be used as potential hair loss treatment drugs.This study elucidates the complex molecular mechanism of ASE in treating hair loss,and provides a reference for the future applications in hair care products.
基金supported by China’s National Key R&D Program,No.2019QY1404the National Natural Science Foundation of China,Grant No.U20A20161,U1836103the Basic Strengthening Program Project,No.2019-JCJQ-ZD-113.
文摘The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats(APT).Extracting attack behaviors,i.e.,Tactics,Techniques,Procedures(TTP)from Cyber Threat Intelligence(CTI)can facilitate APT actors’profiling for an immediate response.However,it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature.Based on the Adversarial Tactics,Techniques and Common Knowledge(ATT&CK)of threat behavior description,this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network(HTN)and Graph Convolutional Network(GCN)to solve this issue.It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence.With the help of the Bidirectional EncoderRepresentation fromTransformers(BERT)pretraining model to analyze the contextual semantics of cyber threat intelligence,the task of threat behavior identification is transformed into a text classification task,which automatically extracts attack behavior in CTI,then identifies the malware and advanced threat actors.The experimental results show that F1 achieve 94.86%and 92.15%for the multi-label classification tasks of tactics and techniques.Extend the experiment to verify the method’s effectiveness in identifying the malware and threat actors in APT attacks.The F1 for malware and advanced threat actors identification task reached 98.45%and 99.48%,which are better than the benchmark model in the experiment and achieve state of the art.The model can effectivelymodel threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text.
基金National Natural Science Foundation of China(Nos.61673017,61403398)and Natural Science Foundation of Shaanxi Province(Nos.2017JM6077,2018ZDXM-GY-039)。
文摘According to the characteristics of the road features,an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images.Firstly,as the features of the road target are rich in local details and simple in semantic features,an Encoder-Decoder network with shallow layers and high resolution is designed to improve the ability to represent detail information.Secondly,as the road area is a small proportion in remote sensing images,the cross-entropy loss function is improved,which solves the imbalance between positive and negative samples in the training process.Experiments on large road extraction datasets show that the proposed method gets the recall rate 83.9%,precision 82.5%and F1-score 82.9%,which can extract the road targets in remote sensing images completely and accurately.The Encoder-Decoder network designed in this paper performs well in the road extraction task and needs less artificial participation,so it has a good application prospect.
基金Anhui Province College Natural Science Fund Key Project of China(KJ2020ZD77)the Project of Education Department of Anhui Province(KJ2020A0379)。
文摘Objective In tongue diagnosis,the location,color,and distribution of spots can be used to speculate on the viscera and severity of the heat evil.This work focuses on the image analysis method of artificial intelligence(AI)to study the spotted tongue recognition of traditional Chinese medicine(TCM).Methods A model of spotted tongue recognition and extraction is designed,which is based on the principle of image deep learning and instance segmentation.This model includes multiscale feature map generation,region proposal searching,and target region recognition.Firstly,deep convolution network is used to build multiscale low-and high-abstraction feature maps after which,target candidate box generation algorithm and selection strategy are used to select high-quality target candidate regions.Finally,classification network is used for classifying target regions and calculating target region pixels.As a result,the region segmentation of spotted tongue is obtained.Under non-standard illumination conditions,various tongue images were taken by mobile phones,and experiments were conducted.Results The spotted tongue recognition achieved an area under curve(AUC)of 92.40%,an accuracy of 84.30%with a sensitivity of 88.20%,a specificity of 94.19%,a recall of 88.20%,a regional pixel accuracy index pixel accuracy(PA)of 73.00%,a mean pixel accuracy(m PA)of73.00%,an intersection over union(Io U)of 60.00%,and a mean intersection over union(mIo U)of 56.00%.Conclusion The results of the study verify that the model is suitable for the application of the TCM tongue diagnosis system.Spotted tongue recognition via multiscale convolutional neural network(CNN)would help to improve spot classification and the accurate extraction of pixels of spot area as well as provide a practical method for intelligent tongue diagnosis of TCM.
文摘Relation extraction is an important task in NLP community.However,some models often fail in capturing Long-distance dependence on semantics,and the interaction between semantics of two entities is ignored.In this paper,we propose a novel neural network model for semantic relation classification called joint self-attention bi-LSTM(SA-Bi-LSTM)to model the internal structure of the sentence to obtain the importance of each word of the sentence without relying on additional information,and capture Long-distance dependence on semantics.We conduct experiments using the SemEval-2010 Task 8 dataset.Extensive experiments and the results demonstrated that the proposed method is effective against relation classification,which can obtain state-ofthe-art classification accuracy just with minimal feature engineering.
基金Supported by Putuo District Science and Technology R&D Platform Project,Shanghai(2024QX04).
文摘[Objectives]Using Calamine Zinc Oxide Phytocomplex Cream as an example,this study employed network pharmacology to investigate the therapeutic potential and mechanism of action of the combination of calamine,zinc oxide,and plant extracts in eczema intervention.[Methods]Active constituents of Calamine Zinc Oxide Phytocomplex Cream were identified through screening using the HIT2.0,HERB,and TCMSP databases.Corresponding targets of the active constituents were predicted using NetInfer.The collected targets were intersected with eczema and atopic dermatitis(AD)-related targets obtained from the GeneCards database to identify the effective therapeutic targets of Calamine Zinc Oxide Phytocomplex Cream.The network diagram of effective active constituents versus therapeutic targets for Calamine Zinc Oxide Phytocomplex Cream was constructed and subjected to topological analysis using Cytoscape software.The Protein-Protein Interaction(PPI)network was established and analyzed using the String database,Cytoscape software,and the cytoHubba plugin to identify key hub genes.Gene Ontology(GO)enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were performed on the therapeutic targets using the DAVID database.[Results]Screening identified 57 active constituents in Calamine Zinc Oxide Phytocomplex Cream,corresponding to 601 potential targets.Subsequent analysis revealed 72 core therapeutic targets of Calamine Zinc Oxide Phytocomplex Cream specifically relevant to eczema and AD.Analysis of the network diagram suggested that Calamine Zinc Oxide Phytocomplex Cream may exert anti-inflammatory and immunomodulatory effects through active constituents such as quercetin,luteolin,and apigenin,while concurrently repairing skin barrier function by acting on targets including AKT1,NF-κB,and STAT3.Furthermore,the inclusion of mineral-based medicines provides additional functions such as itch relief and reinforcement of the skin barrier.[Conclusions]Calamine Zinc Oxide Phytocomplex Cream combines organic and inorganic constituents,synergistically alleviating the adverse symptoms of eczema and AD through multiple pathways.
基金supported by the Major Program of National Natural Science Foundation of China(No.52192622)the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project(No.SKLGP2022Z018).
文摘The complex network of fractures formed by randomly distributed natural fractures in hot-dry rocks(HDRs)complicates the heat transfer regularity of injected fluid.On the basis of the fracture network,exploring the characteristics of the fluid flow and heat transfer as influenced by different parameters helps enable efficient resource extraction and effectively promotes the construction of diversified energy utilization structures.Accordingly,accounting for the effect of the thermal shock on the evolution of the permeability of the rock matrix,a thermo-hydromechanical(THM)coupling model is developed to analyze the influences of fracture network characteristics on the heat extraction performance of HDRs.In addition,a large-scale injection and production physical simulation experiment is performed using a newly developed,in-house,large-scale true triaxial experimental system.The corresponding numerical model is established and validated.The good agreement between the numerical and experimental results verifies the reliability and accuracy of the proposed THM model.Subsequently,a two-dimensional model is established under complex fracture network conditions,taking,as a research object,the natural fracture characteristics of HDR in the Qinghai Gonghe Basin in combination with the regional geological information.The effects of different parameters,including the production well location,rock matrix permeability,injection rate,initial fracture width,and number of fractures,on the production temperature and heat extraction performance are systematically analyzed.The results indicate that an increase in the number of fractures,the distance between the injection well and the production well,or the width of the initial fractures leads to an improved heat extraction performance.The number of fractures increased from 11 horizontal fractures and 22 high-angle fractures to 35 horizontal fractures and 70 high-angle fractures,with a 20%increase in heat extraction rate.While the influence of the rock matrix permeability is not highly significant,it cannot be ignored.It is crucial to select an injection rate that is neither too low nor too high,taking into consideration economic factors.
基金supported by the Natural Science Foundation of China(82560923)Natural Science Foundation of InnerMongolia(2019MS08008)+1 种基金Natural Science Foundation of Inner Mongolia Joint Program(2023LHMS08075)General Project of Inner Mongolia Medical University(YKD2025MS026).
文摘Objective Forsythia suspensa has long been utilized in traditional Chinese medicine(TCM)for the treatment of IgA nephropathy(IgAN),the most prevalent form of primary glomerular disease.However,the precise mechanisms remain inadequately understood.This study seeks to elucidate the underlying mechanisms of Forsythia suspensa extract(FSE)in the treatment of IgAN by employing an integrated approach that combines network pharmacology with in vivo experimental validation.Methods The chemical components of FSE were identified using high-performance liquid chromatography-mass spectrometry(HPLC–MS/MS).Additional chemical components and targets were determined through the Traditional Chinese Medicine Systems Pharmacology database.Potential therapeutic targets for IgAN were sourced from GeneCards and the Comparative Toxicogenomics Database.Subsequently,the enrichment analyses were conducted to evaluate the biological functions and pathways associated with the core targets.Finally,a mousemodel of IgAN was developed to validate the findings of the network pharmacology analysis.Results Through network analysis and HPLC–MS/MS,31 chemical components of FSE were identified.A total of 99 common targets were discovered between FSE and IgAN.The enrichment analyses suggested that FSE may mitigate IgAN primarily by inhibiting the TLR and NF-κB signaling pathways.In vivo experiments demonstrated that FSE reduced inflammation and preserved renal function in mice with IgAN through the Tolllike receptor 9(TLR9)/NF-κB pathway.Conclusion The integration of network pharmacology and animal experiments suggests that FSE alleviates renal inflammation and damage in IgAN through the TLR9/NF-κB signaling pathway.
