The counterflow burner is a combustion device used for research on combustion.By utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitate...The counterflow burner is a combustion device used for research on combustion.By utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitates the optimization of the combustion process and enhances combustion efficiency.Among existing deep convolutional models,InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt.It has garnered significant attention for its computational efficiency,remarkable model accuracy,and exceptional feature extraction capabilities.However,since this model still has limitations in the combustion state recognition task,we propose a Triple-Scale Multi-Stage InceptionNeXt(TSMS-InceptionNeXt)combustion state recognitionmethod based on feature extraction optimization.First,to address the InceptionNeXt model’s limited ability to capture dynamic features in flame images,we introduce Triplet Attention,which applies attention to the width,height,and Red Green Blue(RGB)dimensions of the flame images to enhance its ability to model dynamic features.Secondly,to address the issue of key information loss in the Inception deep convolution layers,we propose a Similarity-based Feature Concentration(SimC)mechanism to enhance the model’s capability to concentrate on critical features.Next,to address the insufficient receptive field of the model,we propose a Multi-Scale Dilated Channel Parallel Integration(MDCPI)mechanism to enhance the model’s ability to extract multi-scale contextual information.Finally,to address the issue of the model’s Multi-Layer Perceptron Head(MlpHead)neglecting channel interactions,we propose a Channel Shuffle-Guided Channel-Spatial Attention(ShuffleCS)mechanism,which integrates information from different channels to further enhance the representational power of the input features.To validate the effectiveness of the method,experiments are conducted on the counterflow burner flame visible light image dataset.The experimental results show that the TSMS-InceptionNeXt model achieved an accuracy of 85.71%on the dataset,improving by 2.38%over the baseline model and outperforming the baseline model’s performance.It achieved accuracy improvements of 10.47%,4.76%,11.19%,and 9.28%compared to the Reparameterized Visual Geometry Group(RepVGG),Squeeze-erunhanced Axial Transoformer(SeaFormer),Simplified Graph Transformers(SGFormer),and VanillaNet models,respectively,effectively enhancing the recognition performance for combustion states in counterflow burners.展开更多
To improve the accuracy in recognizing defects on wood surfaces,a method fusing near infrared spectroscopy(NIR)and machine vision was examined.Larix gmelinii was selected as the raw material,and the experiments focuse...To improve the accuracy in recognizing defects on wood surfaces,a method fusing near infrared spectroscopy(NIR)and machine vision was examined.Larix gmelinii was selected as the raw material,and the experiments focused on the ability of the model to sort defects into four types:live knots,dead knots,pinholes,and cracks.Sample images were taken using an industrial camera,and a morphological algorithm was applied to locate the position of the defects.A portable near infrared spectrometer(900–1800 nm)collected the spectra of these positions.In addition,principal component analysis was utilized on these variables from spectral information and principal component vectors were extracted as the inputs of the model.The results show that a back propagation neural network model exhibited better discrimination accuracy of 92.7%for the training set and 92.0%for the test set.The research reveals that the NIR fusing machine vision is a feasible tool for detecting defects on board surfaces.展开更多
We used principa/component analysis （PCA） and compressed sensing to detect wood defects from wood plate images. PCA makes it possible to reduce data redundancy and feature dimensions and compressed sensing, used as ...We used principa/component analysis （PCA） and compressed sensing to detect wood defects from wood plate images. PCA makes it possible to reduce data redundancy and feature dimensions and compressed sensing, used as a elas- sifter, improves identification accuracy. We extracted 25 features, including geometry and regional features, gray-scale texture features, and invariant moment features, from wood board images and then integrated them using PCA, and se- lected eight principal components to express defects. After the fusion process, we used the features to construct a data dic- tionary, and realized the classification of defects by computing the optimal solution of the data dictionary in l1 norm using the least square method. We tested 50 Xylosma samples of live knots, dead knots, and cracks. The average detection time with PCA feature fusion and without were 0.2015 and 0.7125 ms, respectively. The original detection accuracy by SOM neural network was 87 %, but after compressed sensing, it was 92 %.展开更多
We proposed a detection method for wood defects based on linear discriminant analysis (LDA) and the use of compressed sensor images. Wood surface images were captured, using a camera Oscar F810C IRF camera, and then t...We proposed a detection method for wood defects based on linear discriminant analysis (LDA) and the use of compressed sensor images. Wood surface images were captured, using a camera Oscar F810C IRF camera, and then the image segmentation was performed, and the defect features were extracted from wood board images. To reduce the processing time, LDA algorithm was used to integrate these features and reduce their dimensions. Features after fusion were used to construct a data dictionary and a compressed sensor was designed to recognize the wood defects types. Of the three major defect types, 50 images live knots, dead knots, and cracks were used to test the effects of this method. The average time for feature fusion and classification was 0.446 ms with the classification accuracy of 94%.展开更多
Region-Growing Algorithms （RGAs） are used to grade the quality of manufactured wood flooring. Traditional RGAs are hampered by prob- lems of long segmentation time and low inspection accuracy caused by neighborhood ...Region-Growing Algorithms （RGAs） are used to grade the quality of manufactured wood flooring. Traditional RGAs are hampered by prob- lems of long segmentation time and low inspection accuracy caused by neighborhood search. We used morphological reconstruction with the R com- ponent to construct a novel flaw segmentation method. We initially designed two template images for low and high thresholds, and these were used for seed optimization and inflation growth, respectively. Then the extraction of the flaw skeleton from the low threshold image was realized by applying the erosion termination rules. The seeds in the flaw skeleton were optimized by the pruning method. The geodesic inflection was applied by the high threshold template to realize rapid growth of the flaw area in the floor plate, and region filling and pruning operations were applied for margin optimization. Experi- ments were conducted on 512×512, 256×256 and 128×128 pixel sizes, re- spectively. The 256×256 pixel size proved superior in time-consumption at 0.06 s with accuracy of 100%. But with the region-growing method the same process took 0.22 s with accuracy of 70%. Compared with RGA, our pro- posed method can realize more accurate segmentation, and the speed and accuracy of segmentation can satisfy the requirements for on-line grading of wood flooring.展开更多
Objective:To analyze the effectiveness of Biling Weitong Granules(BLWTG)combined with trimethoprim and vonoprazan in treating reflux esophagitis.Methods:Sixty patients with reflux esophagitis admitted to our hospital ...Objective:To analyze the effectiveness of Biling Weitong Granules(BLWTG)combined with trimethoprim and vonoprazan in treating reflux esophagitis.Methods:Sixty patients with reflux esophagitis admitted to our hospital from March 2020 to March 2023 were selected as study subjects and randomly divided into a control group and an experimental group,with 30 cases in each group.The control group received only the combination treatment of trimethoprim and vonoprazan,while the experimental group was treated with BLWTG based on the control group.The acid reflux and heartburn symptom scores,quality-of-life scores,clinical efficacy,Chinese medicine symptom incidences,and the occurrence of adverse reactions before and after treatment in the two groups were compared.Results:After treatment,the acid reflux and heartburn symptom scores of patients in the experimental group were lower than those of the treatment control group,and the quality-of-life scores were higher than those of the treatment control group(P<0.05).The total clinical efficacy of the experimental group was 96.66%,which was significantly higher than that of the control group(73.33%,P<0.05).After treatment,the incidence of Chinese medicine symptoms,such as nausea and vomiting,abdominal distension and abdominal pain,and loss of appetite of the patients in the experimental group were significantly lower than those of the control group(P<0.05).During the treatment period,there was no significant difference in the incidence of adverse reactions between the two groups,which indicated that the safety of the two treatments was comparable(P>0.05).Conclusion:BLWTG combined with trimethoprim and vonoprazan was safe and reliable in treating reflux esophagitis,effectively relieving the symptoms and improving its clinical efficacy.This treatment is worthy of popularization.展开更多
Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal pr...Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal production.Aiming at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks,an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5(YOLOv5)is proposed.Firstly,we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network(Real-ESRGAN)by changing the U-shaped network(U-Net)to Attention U-Net,to preprocess the images;secondly,we propose a new Focus module that introduces the Marr operator,which can provide more boundary information for the network;again,because Complete Intersection over Union(CIOU)cannot accommodate target borders that are increasing and decreasing,replace CIOU with Extended Intersection over Union(EIOU),while the loss function is changed to Focal and Efficient IOU(Focal-EIOU)due to the different difficulty of sample detection.On the homemade dataset,the precision of our method is 94%,the recall is 70.8%,and the map@.5 is 83.6%,which is an improvement of 1.3%in precision,9.7%in recall,and 7%in map@.5 over the original network.The algorithm can meet the needs of electrolysis tank pole plate abnormal temperature detection,which can lay a technical foundation for improving production efficiency and reducing production waste.展开更多
Satellite remote sensing technology[1] is widely used in all walks of life ,which plays an increasingly remarkable results in natural disasters(sudden and major).With more and more launch and application of high resol...Satellite remote sensing technology[1] is widely used in all walks of life ,which plays an increasingly remarkable results in natural disasters(sudden and major).With more and more launch and application of high resolution satellite, the texture information in remote sensing imagery becomes much more abundant. In the age of big data, for the infrared remote sensor has short life, which annoys many people. In the packaging process, due to a difference in thermal expansion coefficients [2] between the flip-chip bonded MEMS device and the substrate, cooling after bonding can cause the MEMS to buckle. Combine TAIZ theory with the flexure design in CAD to solve the problem. It can be obtained that increasing fold length can reduce warpage. By solving the deformation problem of MEMS devices can facilitate the development of flip chip technology, and make for the further application of the TRIZ theory in the study of remote sensing equipment.展开更多
The application of big data mining can create over a trillion dollars value. Patents contain a great deal of new technologies and new methods which have unique value in the product innovation. In order to improve the ...The application of big data mining can create over a trillion dollars value. Patents contain a great deal of new technologies and new methods which have unique value in the product innovation. In order to improve the effectiveness of big data mining and aid the innovation of products of forestry machinery, the algorithm for closed weighted pattern mining is applied to acquire the function knowledge in the patents of forestry machinery. Compared with the other algorithms for mining patterns, the algorithm is more suitable for the characteristics of patent data. It not only takes into account the importance of different items to reduce the search space effectively, but also avoids achieving excessive uninteresting patterns below the premise that assures quality. The extensive performance study shows that the patterns which are mined by the closed weighted pattern algorithm are more representative and the acquired knowledge has more realistic application significance.展开更多
Pulmonary hypertension(PH)is an insidious pulmonary vasculopathy with high mortality and morbidity and its underlying pathogenesis is still poorly delineated.The hyperproliferation and apoptosis resistance of pulmonar...Pulmonary hypertension(PH)is an insidious pulmonary vasculopathy with high mortality and morbidity and its underlying pathogenesis is still poorly delineated.The hyperproliferation and apoptosis resistance of pulmonary artery smooth muscle cells(PASMCs)contributes to pulmonary vascular remodeling in pulmonary hypertension,which is closely linked to the downregulation of forkhead box transcriptional factor O1(FoxO1)and apoptotic protein caspase 3(Cas-3).Here,PA-targeted co-delivery of a FoxO1 stimulus(paclitaxel,PTX)and Cas-3 was exploited to alleviate monocrotaline-induced pulmonary hypertension.The co-delivery system is prepared by loading the active protein on paclitaxel-crystal nanoparticles,followed by a glucuronic acid coating to target the glucose transporter-1 on the PASMCs.The co-loaded system(170 nm)circulates in the blood over time,accumulates in the lung,effectively targets the PAs,and profoundly regresses the remodeling of pulmonary arteries and improves hemodynamics,leading to a decrease in pulmonary arterial pressure and Fulton's index.Our mechanistic studies suggest that the targeted co-delivery system alleviates experimental pulmonary hypertension primarily via the regression of PASMC proliferation by inhibiting cell cycle progression and promoting apoptosis.Taken together,this targeted co-delivery approach offers a promising avenue to target PAs and cure the intractable vasculopathy in pulmonary hypertension.展开更多
Lithium-sulfur batteries have been regarded as one of most promising next-generation energy storage devices because of their high energy density and low cost.However,polysulfide shuttling and slow kinetics hinder the ...Lithium-sulfur batteries have been regarded as one of most promising next-generation energy storage devices because of their high energy density and low cost.However,polysulfide shuttling and slow kinetics hinder the practical application.We fabricated hierarchically heterostructured CoSe_(2)/MoS_(2) nanoarrays on carbon clothes as the sulfur cathode host.The resulting heterostructures facilitate electron conduction and improve electrolyte wetting.More importantly,the composite heterostructures couple the strong poly-sulfide adsorption of CoSe_(2) and high catalytic activity of MoS_(2) to synergistically accelerate polysulfide conversion,demonstrating higher catalytic activity than their individual components.展开更多
Purpose-The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage,lack of effective features,and small sample sizes,resulting in low fault identifica...Purpose-The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage,lack of effective features,and small sample sizes,resulting in low fault identification accuracy and slow efficiency.The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.Design/methodology/approach-First,to address the impact of background noise on the accuracy of anomaly signals,the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD)method is used to eliminate strong noise in pipeline signals.Secondly,to address the strong data dependency and loss of local features in the Swin Transformer network,a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed.This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities.Thirdly,to address the sparsity and imbalance of anomaly samples,the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.Findings-In the pipeline anomaly audio and environmental datasets such as ESC-50,the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods.Additionally,the model achieved 98.7%accuracy on the preprocessed anomaly audio dataset and 99.0%on the ESC-50 dataset.Originality/value-This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model,addressing noise interference and low accuracy issues in pipeline anomaly detection,and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.展开更多
Purpose-The abnormal behaviors of staff at petroleum stations pose significant safety hazards.Addressing the challenges of high parameter counts,lengthy training periods and low recognition rates in existing 3D ResNet...Purpose-The abnormal behaviors of staff at petroleum stations pose significant safety hazards.Addressing the challenges of high parameter counts,lengthy training periods and low recognition rates in existing 3D ResNet behavior recognition models,this paper proposes GTB-ResNet,a network designed to detect abnormal behaviors in petroleum station staff.Design/methodology/approach-Firstly,to mitigate the issues of excessive parameters and computational complexity in 3D ResNet,a lightweight residual convolution module called the Ghost residual module(GhostNet)is introduced in the feature extraction network.Ghost convolution replaces standard convolution,reducing model parameters while preserving multi-scale feature extraction capabilities.Secondly,to enhance the model’s focus on salient features amidst wide surveillance ranges and small target objects,the triplet attention mechanism module is integrated to facilitate spatial and channel information interaction.Lastly,to address the challenge of short time-series features leading to misjudgments in similar actions,a bidirectional gated recurrent network is added to the feature extraction backbone network.This ensures the extraction of key long time-series features,thereby improving feature extraction accuracy.Findings-The experimental setup encompasses four behavior types:illegal phone answering,smoking,falling(abnormal)and touching the face(normal),comprising a total of 892 videos.Experimental results showcase GTB-ResNet achieving a recognition accuracy of 96.7%with a model parameter count of 4.46 M and a computational complexity of 3.898 G.This represents a 4.4%improvement over 3D ResNet,with reductions of 90.4%in parameters and 61.5%in computational complexity.Originality/value-Specifically designed for edge devices in oil stations,the 3D ResNet network is tailored for real-time action prediction.To address the challenges posed by the large number of parameters in 3D ResNet networks and the difficulties in deployment on edge devices,a lightweight residual module based on ghost convolution is developed.Additionally,to tackle the issue of low detection accuracy of behaviors amidst the noisy environment of petroleum stations,a triple attention mechanism is introduced during feature extraction to enhance focus on salient features.Moreover,to overcome the potential for misjudgments arising from the similarity of actions,a Bi-GRU model is introduced to enhance the extraction of key long-term features.展开更多
文摘The counterflow burner is a combustion device used for research on combustion.By utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitates the optimization of the combustion process and enhances combustion efficiency.Among existing deep convolutional models,InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt.It has garnered significant attention for its computational efficiency,remarkable model accuracy,and exceptional feature extraction capabilities.However,since this model still has limitations in the combustion state recognition task,we propose a Triple-Scale Multi-Stage InceptionNeXt(TSMS-InceptionNeXt)combustion state recognitionmethod based on feature extraction optimization.First,to address the InceptionNeXt model’s limited ability to capture dynamic features in flame images,we introduce Triplet Attention,which applies attention to the width,height,and Red Green Blue(RGB)dimensions of the flame images to enhance its ability to model dynamic features.Secondly,to address the issue of key information loss in the Inception deep convolution layers,we propose a Similarity-based Feature Concentration(SimC)mechanism to enhance the model’s capability to concentrate on critical features.Next,to address the insufficient receptive field of the model,we propose a Multi-Scale Dilated Channel Parallel Integration(MDCPI)mechanism to enhance the model’s ability to extract multi-scale contextual information.Finally,to address the issue of the model’s Multi-Layer Perceptron Head(MlpHead)neglecting channel interactions,we propose a Channel Shuffle-Guided Channel-Spatial Attention(ShuffleCS)mechanism,which integrates information from different channels to further enhance the representational power of the input features.To validate the effectiveness of the method,experiments are conducted on the counterflow burner flame visible light image dataset.The experimental results show that the TSMS-InceptionNeXt model achieved an accuracy of 85.71%on the dataset,improving by 2.38%over the baseline model and outperforming the baseline model’s performance.It achieved accuracy improvements of 10.47%,4.76%,11.19%,and 9.28%compared to the Reparameterized Visual Geometry Group(RepVGG),Squeeze-erunhanced Axial Transoformer(SeaFormer),Simplified Graph Transformers(SGFormer),and VanillaNet models,respectively,effectively enhancing the recognition performance for combustion states in counterflow burners.
基金supported by the State Administration of Forestry and Grass of the 948 Project of China(Grant No.2015-4-52)the support of the Fundamental Research Funds for the Central Universities(Grant No.2572017DB05)the support of the Natural Science Foundation of Heilongjiang Province(Grant No.C2017005)
文摘To improve the accuracy in recognizing defects on wood surfaces,a method fusing near infrared spectroscopy(NIR)and machine vision was examined.Larix gmelinii was selected as the raw material,and the experiments focused on the ability of the model to sort defects into four types:live knots,dead knots,pinholes,and cracks.Sample images were taken using an industrial camera,and a morphological algorithm was applied to locate the position of the defects.A portable near infrared spectrometer(900–1800 nm)collected the spectra of these positions.In addition,principal component analysis was utilized on these variables from spectral information and principal component vectors were extracted as the inputs of the model.The results show that a back propagation neural network model exhibited better discrimination accuracy of 92.7%for the training set and 92.0%for the test set.The research reveals that the NIR fusing machine vision is a feasible tool for detecting defects on board surfaces.
基金financially supported by the Fund of Forestry 948 Project(2011-4-04)the Fundamental Research Funds for the Central Universities(DL13CB02,DL13BB21)the Natural Science Foundation of Heilongjiang Province(C201415)
文摘We used principa/component analysis （PCA） and compressed sensing to detect wood defects from wood plate images. PCA makes it possible to reduce data redundancy and feature dimensions and compressed sensing, used as a elas- sifter, improves identification accuracy. We extracted 25 features, including geometry and regional features, gray-scale texture features, and invariant moment features, from wood board images and then integrated them using PCA, and se- lected eight principal components to express defects. After the fusion process, we used the features to construct a data dic- tionary, and realized the classification of defects by computing the optimal solution of the data dictionary in l1 norm using the least square method. We tested 50 Xylosma samples of live knots, dead knots, and cracks. The average detection time with PCA feature fusion and without were 0.2015 and 0.7125 ms, respectively. The original detection accuracy by SOM neural network was 87 %, but after compressed sensing, it was 92 %.
基金supported by the State Forestry Administration‘‘948’’projects(2015-4-52)Fundamental Research Funds for the Central Universities(2572016BB05)+1 种基金Natural Science Foundation of Heilongjiang Province(C2015054)Heilongjiang Postdoctoral Research Fund(LBH-Q14014)
文摘We proposed a detection method for wood defects based on linear discriminant analysis (LDA) and the use of compressed sensor images. Wood surface images were captured, using a camera Oscar F810C IRF camera, and then the image segmentation was performed, and the defect features were extracted from wood board images. To reduce the processing time, LDA algorithm was used to integrate these features and reduce their dimensions. Features after fusion were used to construct a data dictionary and a compressed sensor was designed to recognize the wood defects types. Of the three major defect types, 50 images live knots, dead knots, and cracks were used to test the effects of this method. The average time for feature fusion and classification was 0.446 ms with the classification accuracy of 94%.
基金financially supported by the Fundamental Research Funds for the Central Universities(DL12EB04-03),(DL13CB02)the Natural Science Foundation of Heilongjiang Province(LC2011C25)
文摘Region-Growing Algorithms （RGAs） are used to grade the quality of manufactured wood flooring. Traditional RGAs are hampered by prob- lems of long segmentation time and low inspection accuracy caused by neighborhood search. We used morphological reconstruction with the R com- ponent to construct a novel flaw segmentation method. We initially designed two template images for low and high thresholds, and these were used for seed optimization and inflation growth, respectively. Then the extraction of the flaw skeleton from the low threshold image was realized by applying the erosion termination rules. The seeds in the flaw skeleton were optimized by the pruning method. The geodesic inflection was applied by the high threshold template to realize rapid growth of the flaw area in the floor plate, and region filling and pruning operations were applied for margin optimization. Experi- ments were conducted on 512×512, 256×256 and 128×128 pixel sizes, re- spectively. The 256×256 pixel size proved superior in time-consumption at 0.06 s with accuracy of 100%. But with the region-growing method the same process took 0.22 s with accuracy of 70%. Compared with RGA, our pro- posed method can realize more accurate segmentation, and the speed and accuracy of segmentation can satisfy the requirements for on-line grading of wood flooring.
基金This research was funded by the Baoding Science and Technology Plan Project management(2341ZF318)。
文摘Objective:To analyze the effectiveness of Biling Weitong Granules(BLWTG)combined with trimethoprim and vonoprazan in treating reflux esophagitis.Methods:Sixty patients with reflux esophagitis admitted to our hospital from March 2020 to March 2023 were selected as study subjects and randomly divided into a control group and an experimental group,with 30 cases in each group.The control group received only the combination treatment of trimethoprim and vonoprazan,while the experimental group was treated with BLWTG based on the control group.The acid reflux and heartburn symptom scores,quality-of-life scores,clinical efficacy,Chinese medicine symptom incidences,and the occurrence of adverse reactions before and after treatment in the two groups were compared.Results:After treatment,the acid reflux and heartburn symptom scores of patients in the experimental group were lower than those of the treatment control group,and the quality-of-life scores were higher than those of the treatment control group(P<0.05).The total clinical efficacy of the experimental group was 96.66%,which was significantly higher than that of the control group(73.33%,P<0.05).After treatment,the incidence of Chinese medicine symptoms,such as nausea and vomiting,abdominal distension and abdominal pain,and loss of appetite of the patients in the experimental group were significantly lower than those of the control group(P<0.05).During the treatment period,there was no significant difference in the incidence of adverse reactions between the two groups,which indicated that the safety of the two treatments was comparable(P>0.05).Conclusion:BLWTG combined with trimethoprim and vonoprazan was safe and reliable in treating reflux esophagitis,effectively relieving the symptoms and improving its clinical efficacy.This treatment is worthy of popularization.
文摘Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal production.Aiming at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks,an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5(YOLOv5)is proposed.Firstly,we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network(Real-ESRGAN)by changing the U-shaped network(U-Net)to Attention U-Net,to preprocess the images;secondly,we propose a new Focus module that introduces the Marr operator,which can provide more boundary information for the network;again,because Complete Intersection over Union(CIOU)cannot accommodate target borders that are increasing and decreasing,replace CIOU with Extended Intersection over Union(EIOU),while the loss function is changed to Focal and Efficient IOU(Focal-EIOU)due to the different difficulty of sample detection.On the homemade dataset,the precision of our method is 94%,the recall is 70.8%,and the map@.5 is 83.6%,which is an improvement of 1.3%in precision,9.7%in recall,and 7%in map@.5 over the original network.The algorithm can meet the needs of electrolysis tank pole plate abnormal temperature detection,which can lay a technical foundation for improving production efficiency and reducing production waste.
基金supported by the Fundamental Research Funds for the Central Universities (DL12EB01-02, DL12CB05) and Heilongjiang Postdoctoral Fund (Grant No. LBH-Z11277) and Natrual Science Foundation for Returness of Heilongjiang Province of China (LC2011C25).
文摘Satellite remote sensing technology[1] is widely used in all walks of life ,which plays an increasingly remarkable results in natural disasters(sudden and major).With more and more launch and application of high resolution satellite, the texture information in remote sensing imagery becomes much more abundant. In the age of big data, for the infrared remote sensor has short life, which annoys many people. In the packaging process, due to a difference in thermal expansion coefficients [2] between the flip-chip bonded MEMS device and the substrate, cooling after bonding can cause the MEMS to buckle. Combine TAIZ theory with the flexure design in CAD to solve the problem. It can be obtained that increasing fold length can reduce warpage. By solving the deformation problem of MEMS devices can facilitate the development of flip chip technology, and make for the further application of the TRIZ theory in the study of remote sensing equipment.
基金Supported by the Fundamental Research Funds for the Central Universities(DL12EB01-02, DL12CB05) and Heilongjiang Postdoctoral Fund(Grant No. LBH-Z11277) and Natrual Science Foundation for Returness of Heilongjiang Province of China(LC2011C25).
文摘The application of big data mining can create over a trillion dollars value. Patents contain a great deal of new technologies and new methods which have unique value in the product innovation. In order to improve the effectiveness of big data mining and aid the innovation of products of forestry machinery, the algorithm for closed weighted pattern mining is applied to acquire the function knowledge in the patents of forestry machinery. Compared with the other algorithms for mining patterns, the algorithm is more suitable for the characteristics of patent data. It not only takes into account the importance of different items to reduce the search space effectively, but also avoids achieving excessive uninteresting patterns below the premise that assures quality. The extensive performance study shows that the patterns which are mined by the closed weighted pattern algorithm are more representative and the acquired knowledge has more realistic application significance.
基金supported by the National Natural Science Foundation of China(81872823,82073782,and 82170063)the Shanghai Science and Technology Committee(19430741500,China)+3 种基金the Key Laboratory of Modern Chinese Medicine Preparation of Ministry of Education of Jiangxi University of Traditional Chinese Medicine(zdsys-202103,China)the Medical Science and Technique Development Foundation of Nanjing Municipal Government(QRX17013,China)the Key Project from Medical Science and Technique Development Foundation of Nanjing Municipal Government(ZKX20017,China)the Science Foundation of Ministry of Health of Jiangsu Province in China(ZDA2020016)。
文摘Pulmonary hypertension(PH)is an insidious pulmonary vasculopathy with high mortality and morbidity and its underlying pathogenesis is still poorly delineated.The hyperproliferation and apoptosis resistance of pulmonary artery smooth muscle cells(PASMCs)contributes to pulmonary vascular remodeling in pulmonary hypertension,which is closely linked to the downregulation of forkhead box transcriptional factor O1(FoxO1)and apoptotic protein caspase 3(Cas-3).Here,PA-targeted co-delivery of a FoxO1 stimulus(paclitaxel,PTX)and Cas-3 was exploited to alleviate monocrotaline-induced pulmonary hypertension.The co-delivery system is prepared by loading the active protein on paclitaxel-crystal nanoparticles,followed by a glucuronic acid coating to target the glucose transporter-1 on the PASMCs.The co-loaded system(170 nm)circulates in the blood over time,accumulates in the lung,effectively targets the PAs,and profoundly regresses the remodeling of pulmonary arteries and improves hemodynamics,leading to a decrease in pulmonary arterial pressure and Fulton's index.Our mechanistic studies suggest that the targeted co-delivery system alleviates experimental pulmonary hypertension primarily via the regression of PASMC proliferation by inhibiting cell cycle progression and promoting apoptosis.Taken together,this targeted co-delivery approach offers a promising avenue to target PAs and cure the intractable vasculopathy in pulmonary hypertension.
基金the National Natural Science Foundation of China(Nos.21776121 and 22075131)The numerical calculations were carried out at the computing facilities in the High-Performance Computing Center(HPCC)of Nanjing University.
文摘Lithium-sulfur batteries have been regarded as one of most promising next-generation energy storage devices because of their high energy density and low cost.However,polysulfide shuttling and slow kinetics hinder the practical application.We fabricated hierarchically heterostructured CoSe_(2)/MoS_(2) nanoarrays on carbon clothes as the sulfur cathode host.The resulting heterostructures facilitate electron conduction and improve electrolyte wetting.More importantly,the composite heterostructures couple the strong poly-sulfide adsorption of CoSe_(2) and high catalytic activity of MoS_(2) to synergistically accelerate polysulfide conversion,demonstrating higher catalytic activity than their individual components.
文摘Purpose-The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage,lack of effective features,and small sample sizes,resulting in low fault identification accuracy and slow efficiency.The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.Design/methodology/approach-First,to address the impact of background noise on the accuracy of anomaly signals,the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD)method is used to eliminate strong noise in pipeline signals.Secondly,to address the strong data dependency and loss of local features in the Swin Transformer network,a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed.This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities.Thirdly,to address the sparsity and imbalance of anomaly samples,the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.Findings-In the pipeline anomaly audio and environmental datasets such as ESC-50,the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods.Additionally,the model achieved 98.7%accuracy on the preprocessed anomaly audio dataset and 99.0%on the ESC-50 dataset.Originality/value-This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model,addressing noise interference and low accuracy issues in pipeline anomaly detection,and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.
文摘Purpose-The abnormal behaviors of staff at petroleum stations pose significant safety hazards.Addressing the challenges of high parameter counts,lengthy training periods and low recognition rates in existing 3D ResNet behavior recognition models,this paper proposes GTB-ResNet,a network designed to detect abnormal behaviors in petroleum station staff.Design/methodology/approach-Firstly,to mitigate the issues of excessive parameters and computational complexity in 3D ResNet,a lightweight residual convolution module called the Ghost residual module(GhostNet)is introduced in the feature extraction network.Ghost convolution replaces standard convolution,reducing model parameters while preserving multi-scale feature extraction capabilities.Secondly,to enhance the model’s focus on salient features amidst wide surveillance ranges and small target objects,the triplet attention mechanism module is integrated to facilitate spatial and channel information interaction.Lastly,to address the challenge of short time-series features leading to misjudgments in similar actions,a bidirectional gated recurrent network is added to the feature extraction backbone network.This ensures the extraction of key long time-series features,thereby improving feature extraction accuracy.Findings-The experimental setup encompasses four behavior types:illegal phone answering,smoking,falling(abnormal)and touching the face(normal),comprising a total of 892 videos.Experimental results showcase GTB-ResNet achieving a recognition accuracy of 96.7%with a model parameter count of 4.46 M and a computational complexity of 3.898 G.This represents a 4.4%improvement over 3D ResNet,with reductions of 90.4%in parameters and 61.5%in computational complexity.Originality/value-Specifically designed for edge devices in oil stations,the 3D ResNet network is tailored for real-time action prediction.To address the challenges posed by the large number of parameters in 3D ResNet networks and the difficulties in deployment on edge devices,a lightweight residual module based on ghost convolution is developed.Additionally,to tackle the issue of low detection accuracy of behaviors amidst the noisy environment of petroleum stations,a triple attention mechanism is introduced during feature extraction to enhance focus on salient features.Moreover,to overcome the potential for misjudgments arising from the similarity of actions,a Bi-GRU model is introduced to enhance the extraction of key long-term features.
