Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to ...Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.展开更多
Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,signifi...Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,significant variability in shape and location,and low contrast with surrounding tissues,achieving high segmentation accuracy remains challenging.To improve segmentation precision,we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas fromCT images.Firstly,an EfficientNetV2 encoder is employed to extract complex and multi-level features,enhancing the model’s ability to capture the pancreas’s intricate morphology.Then,a residual multi-branch dilated attention(RMDA)module is designed to suppress irrelevant background noise and highlight useful pancreatic features.And re-parameterization Visual Geometry Group(RepVGG)blocks with amulti-branch structure are introduced in the decoder to effectively integrate deep features and low-level details,improving segmentation accuracy.Furthermore,we apply re-parameterization to the model,reducing computations and parameters while accelerating inference and reducing memory usage.Our approach achieves average dice similarity coefficient(DSC)of 85.59%,intersection over union(IoU)of 75.03%,precision of 85.09%,and recall of 86.57%on the NIH pancreas dataset.Compared with other methods,our model has fewer parameters and faster inference speed,demonstrating its enormous potential in practical applications of pancreatic segmentation.展开更多
Based on the finite element-discrete element numerical method,a numerical model of fracture propagation in deflagration fracturing was established by considering the impact of stress wave,quasi-static pressure of expl...Based on the finite element-discrete element numerical method,a numerical model of fracture propagation in deflagration fracturing was established by considering the impact of stress wave,quasi-static pressure of explosive gas,and reflection of stress wave.The model was validated against the results of physical experiments.Taking the shale reservoirs of Silurian Longmaxi Formation in Luzhou area of the Sichuan Basin as an example,the effects of in-situ stress difference,natural fracture parameters,branch wellbore spacing,delay detonation time,and angle between branch wellbore and main wellbore on fracture propagation were identified.The results show that the fracture propagation morphology in deflagration fracturing is less affected by the in-situ stress difference when it is 5-15 MPa,and the tendency of fracture intersection between branch wellbores is significantly weakened when the in-situ stress difference reaches 20 MPa.The increase of natural fracture length promotes the fracture propagation along the natural fracture direction,while the increase of volumetric natural fracture density and angle limits the fracture propagation area and reduces the probability of fracture intersection between branch wells.The larger the branch wellbore spacing,the less probability of the fracture intersection between branch wells,allowing for the fracture propagation in multiple directions.Increasing the delay detonation time decreases the fracture spacing between branch wellbores.When the angle between the branch wellbore and the main wellbore is 45°and 90°,there is a tendency of fracture intersection between branch wellbores.展开更多
To address the challenges of fault line identification and low detection accuracy of wave head in Fault Location(FL)research of distribution networks with complex topologies,this paper proposes an FL method of Multi-B...To address the challenges of fault line identification and low detection accuracy of wave head in Fault Location(FL)research of distribution networks with complex topologies,this paper proposes an FL method of Multi-Branch distribution line based on Maximal Overlap Discrete Wavelet Transform(MODWT)combined with the improved Teager Energy Operator(TEO).Firstly,the current and voltage Traveling Wave(TW)signals at the head of each line are extracted,and the fault-induced components are obtained to determine the fault line by analyzing the polarity of the mutation amount of fault voltage and current TWs.Subsequently,the fault discrimination mark is calculated based on the fault-induced line-mode current and the zero-mode voltage,with the fault type determined by comparing each mark’s value against the fault discrimination table,transforming the FL problem in complex topology into a single-line FL problem.Finally,the fault voltage TW is extracted fromthe fault line,and the wave head detection method based on MODWT combined with improved TEO is used to precisely identify the arrival instants of both the first TW wave head and its first reflection at each line terminal,and then the FL result is calculated by applying the double-ended TW ranging formula that removes the influence of wave velocity.Simulation results demonstrate that the proposed method accurately identifies the fault line and types of faults occurring and maintains the ranging accuracy within 0.5%under various fault scenarios.展开更多
Carbazole-core multi-branched chromophores 9-ethyl- 3, 6-bis （ 2- { 4- [ 5- （4-tert-butyl-phenyl） - [ 1, 3, 4 ] oxadiazol-2-yl ] - phenyl }-vinyl） -carbazole（3） and 9-ethyl-3-（ 2- {4-[ 5-（4-tert-butyl- phenyl...Carbazole-core multi-branched chromophores 9-ethyl- 3, 6-bis （ 2- { 4- [ 5- （4-tert-butyl-phenyl） - [ 1, 3, 4 ] oxadiazol-2-yl ] - phenyl }-vinyl） -carbazole（3） and 9-ethyl-3-（ 2- {4-[ 5-（4-tert-butyl- phenyl） -[ 1, 3, 4 ] oxadiazol-2-yl ] -phenyl }-vinyl ） -carbazole （ 2 ） are synthesized through Wittig reaction and characterized by nuclear magnetic resonance（NMR）and infrared（IR）. The two- photon absorption properties of chromophores are investigated. These chromophores exhibit large two-photon absorption crosssections and strong blue two-photon excited fluorescence. The cooperative enhancement of two-photon absorption（TPA） in the multi-branched structures is observed. This enhancement is partly attributed to the electronic coupling between the branches. The electronic push-pull structures in the arm and their cooperative effects help the extended charge transfer for TPA.展开更多
In order to address the challenges posed by complex background interference,high miss-detection rates of micro-scale defects,and limited model deployment efficiency in photovoltaic(PV)module defect detection,this pape...In order to address the challenges posed by complex background interference,high miss-detection rates of micro-scale defects,and limited model deployment efficiency in photovoltaic(PV)module defect detection,this paper proposes an efficient detection framework based on an improved YOLOv11 architecture.First,a Re-parameterized Convolution(RepConv)module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency.Second,a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism(MSFF-CBAM)is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial attention.This mechanism effectively strengthens the specificity and robustness of feature representations.Third,a lightweight Dynamic Sampling Module(DySample)is employed to replace conventional upsampling operations,thereby improving the localization accuracy of small-scale defect targets.Experimental evaluations conducted on the PVEL-AD dataset demonstrate that the proposed RMDYOLOv11 model surpasses the baseline YOLOv11 in terms of mean Average Precision(mAP)@0.5,Precision,and Recall,achieving respective improvements of 4.70%,1.51%,and 5.50%.The model also exhibits notable advantages in inference speed and model compactness.Further validation on the ELPV dataset confirms the model’s generalization capability,showing respective performance gains of 1.99%,2.28%,and 1.45%across the same metrics.Overall,the enhanced model significantly improves the accuracy of micro-defect identification on PV module surfaces,effectively reducing both false negatives and false positives.This advancement provides a robust and reliable technical foundation for automated PV module defect detection.展开更多
基金supported by the Natural Science Foundation of the Anhui Higher Education Institutions of China(Grant Nos.2023AH040149 and 2024AH051915)the Anhui Provincial Natural Science Foundation(Grant No.2208085MF168)+1 种基金the Science and Technology Innovation Tackle Plan Project of Maanshan(Grant No.2024RGZN001)the Scientific Research Fund Project of Anhui Medical University(Grant No.2023xkj122).
文摘Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.
基金supported by the Science and Technology Innovation Programof Hunan Province(Grant No.2022RC1021)the Hunan Provincial Natural Science Foundation Project(Grant No.2023JJ60124)+1 种基金the Changsha Natural Science Foundation Project(Grant No.kq2202265)the key project of the Hunan Provincial of Education(Grant No.22A0255).
文摘Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,significant variability in shape and location,and low contrast with surrounding tissues,achieving high segmentation accuracy remains challenging.To improve segmentation precision,we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas fromCT images.Firstly,an EfficientNetV2 encoder is employed to extract complex and multi-level features,enhancing the model’s ability to capture the pancreas’s intricate morphology.Then,a residual multi-branch dilated attention(RMDA)module is designed to suppress irrelevant background noise and highlight useful pancreatic features.And re-parameterization Visual Geometry Group(RepVGG)blocks with amulti-branch structure are introduced in the decoder to effectively integrate deep features and low-level details,improving segmentation accuracy.Furthermore,we apply re-parameterization to the model,reducing computations and parameters while accelerating inference and reducing memory usage.Our approach achieves average dice similarity coefficient(DSC)of 85.59%,intersection over union(IoU)of 75.03%,precision of 85.09%,and recall of 86.57%on the NIH pancreas dataset.Compared with other methods,our model has fewer parameters and faster inference speed,demonstrating its enormous potential in practical applications of pancreatic segmentation.
基金Supported by the National Natural Science Foundation of China(52374004)National Key R&D Program of China(2023YFF0614102,2023YFE0110900).
文摘Based on the finite element-discrete element numerical method,a numerical model of fracture propagation in deflagration fracturing was established by considering the impact of stress wave,quasi-static pressure of explosive gas,and reflection of stress wave.The model was validated against the results of physical experiments.Taking the shale reservoirs of Silurian Longmaxi Formation in Luzhou area of the Sichuan Basin as an example,the effects of in-situ stress difference,natural fracture parameters,branch wellbore spacing,delay detonation time,and angle between branch wellbore and main wellbore on fracture propagation were identified.The results show that the fracture propagation morphology in deflagration fracturing is less affected by the in-situ stress difference when it is 5-15 MPa,and the tendency of fracture intersection between branch wellbores is significantly weakened when the in-situ stress difference reaches 20 MPa.The increase of natural fracture length promotes the fracture propagation along the natural fracture direction,while the increase of volumetric natural fracture density and angle limits the fracture propagation area and reduces the probability of fracture intersection between branch wells.The larger the branch wellbore spacing,the less probability of the fracture intersection between branch wells,allowing for the fracture propagation in multiple directions.Increasing the delay detonation time decreases the fracture spacing between branch wellbores.When the angle between the branch wellbore and the main wellbore is 45°and 90°,there is a tendency of fracture intersection between branch wellbores.
基金funded by the project of Guizhou Power Grid Co.,Ltd.Guiyang Power Supply Bureau(No.GZKJXM20232317).
文摘To address the challenges of fault line identification and low detection accuracy of wave head in Fault Location(FL)research of distribution networks with complex topologies,this paper proposes an FL method of Multi-Branch distribution line based on Maximal Overlap Discrete Wavelet Transform(MODWT)combined with the improved Teager Energy Operator(TEO).Firstly,the current and voltage Traveling Wave(TW)signals at the head of each line are extracted,and the fault-induced components are obtained to determine the fault line by analyzing the polarity of the mutation amount of fault voltage and current TWs.Subsequently,the fault discrimination mark is calculated based on the fault-induced line-mode current and the zero-mode voltage,with the fault type determined by comparing each mark’s value against the fault discrimination table,transforming the FL problem in complex topology into a single-line FL problem.Finally,the fault voltage TW is extracted fromthe fault line,and the wave head detection method based on MODWT combined with improved TEO is used to precisely identify the arrival instants of both the first TW wave head and its first reflection at each line terminal,and then the FL result is calculated by applying the double-ended TW ranging formula that removes the influence of wave velocity.Simulation results demonstrate that the proposed method accurately identifies the fault line and types of faults occurring and maintains the ranging accuracy within 0.5%under various fault scenarios.
基金The National Natural Science Foundation of China(No.60678042)the Natural Science Foundation of Jiangsu Province(No.BK2006553)the Pre-Research Project of the National Natural Science Foundation supported by Southeast University(No.9207041399)
文摘Carbazole-core multi-branched chromophores 9-ethyl- 3, 6-bis （ 2- { 4- [ 5- （4-tert-butyl-phenyl） - [ 1, 3, 4 ] oxadiazol-2-yl ] - phenyl }-vinyl） -carbazole（3） and 9-ethyl-3-（ 2- {4-[ 5-（4-tert-butyl- phenyl） -[ 1, 3, 4 ] oxadiazol-2-yl ] -phenyl }-vinyl ） -carbazole （ 2 ） are synthesized through Wittig reaction and characterized by nuclear magnetic resonance（NMR）and infrared（IR）. The two- photon absorption properties of chromophores are investigated. These chromophores exhibit large two-photon absorption crosssections and strong blue two-photon excited fluorescence. The cooperative enhancement of two-photon absorption（TPA） in the multi-branched structures is observed. This enhancement is partly attributed to the electronic coupling between the branches. The electronic push-pull structures in the arm and their cooperative effects help the extended charge transfer for TPA.
基金supported by the Gansu Provincial Department of Education Industry Support Plan Project(2025CYZC-018).
文摘In order to address the challenges posed by complex background interference,high miss-detection rates of micro-scale defects,and limited model deployment efficiency in photovoltaic(PV)module defect detection,this paper proposes an efficient detection framework based on an improved YOLOv11 architecture.First,a Re-parameterized Convolution(RepConv)module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency.Second,a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism(MSFF-CBAM)is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial attention.This mechanism effectively strengthens the specificity and robustness of feature representations.Third,a lightweight Dynamic Sampling Module(DySample)is employed to replace conventional upsampling operations,thereby improving the localization accuracy of small-scale defect targets.Experimental evaluations conducted on the PVEL-AD dataset demonstrate that the proposed RMDYOLOv11 model surpasses the baseline YOLOv11 in terms of mean Average Precision(mAP)@0.5,Precision,and Recall,achieving respective improvements of 4.70%,1.51%,and 5.50%.The model also exhibits notable advantages in inference speed and model compactness.Further validation on the ELPV dataset confirms the model’s generalization capability,showing respective performance gains of 1.99%,2.28%,and 1.45%across the same metrics.Overall,the enhanced model significantly improves the accuracy of micro-defect identification on PV module surfaces,effectively reducing both false negatives and false positives.This advancement provides a robust and reliable technical foundation for automated PV module defect detection.
