Underground geotechnical engineering encounters persistent challenges in ensuring the stability and safety of surrounding rock structures, particularly within rocky tunnels. Rock reinforcement techniques, including th...Underground geotechnical engineering encounters persistent challenges in ensuring the stability and safety of surrounding rock structures, particularly within rocky tunnels. Rock reinforcement techniques, including the use of steel mesh, are critical to achieving this goal. However, there exists a knowledge gap regarding the comprehensive understanding of the mechanical behavior and failure mechanisms exhibited by steel mesh under diverse loading conditions. This study thoroughly explored the steel mesh's performance throughout the entire loading-failure process, innovating with detailed analysis and modeling techniques. By integrating advanced numerical modeling with laboratory experiments, the study examines the influence of varying reinforcement levels and geometric parameters on the steel mesh strength and deformation characteristics. Sensitivity analysis, employing gray correlation theory, identifies the key factors affecting the mesh performance, while a BP (Backpropagation) neural network model predicts maximum vertical deformation with high accuracy. The findings underscore the critical role of steel diameter and mesh spacing in optimizing peak load capacity, displacement, and energy absorption, offering practical guidelines for design improvements. The use of a Bayesian Regularization (BR) algorithm further enhances the predictive accuracy compared to traditional methods. This research provides new insights into optimizing steel mesh design for underground applications, offering an innovative approach to enhancing structural safety in geotechnical projects.展开更多
An improved self-organizing feature map (SOFM) neural network is presented to generate rectangular and hexagonal lattic with normal vector attached to each vertex. After the neural network was trained, the whole scatt...An improved self-organizing feature map (SOFM) neural network is presented to generate rectangular and hexagonal lattic with normal vector attached to each vertex. After the neural network was trained, the whole scattered data were divided into sub-regions where classified core were represented by the weight vectors of neurons at the output layer of neural network. The weight vectors of the neurons were used to approximate the dense 3-D scattered points, so the dense scattered points could be reduced to a reasonable scale, while the topological feature of the whole scattered points were remained.展开更多
The complex geometric features of subsurface fractures at different scales makes mesh generation challenging and/or expensive.In this paper,we make use of neural style transfer(NST),a machine learning technique,to gen...The complex geometric features of subsurface fractures at different scales makes mesh generation challenging and/or expensive.In this paper,we make use of neural style transfer(NST),a machine learning technique,to generate mesh from rock fracture images.In this new approach,we use digital rock fractures at multiple scales that represent’content’and define uniformly shaped and sized triangles to represent’style’.The 19-layer convolutional neural network(CNN)learns the content from the rock image,including lower-level features(such as edges and corners)and higher-level features(such as rock,fractures,or other mineral fillings),and learns the style from the triangular grids.By optimizing the cost function to achieve approximation to represent both the content and the style,numerical meshes can be generated and optimized.We utilize the NST to generate meshes for rough fractures with asperities formed in rock,a network of fractures embedded in rock,and a sand aggregate with multiple grains.Based on the examples,we show that this new NST technique can make mesh generation and optimization much more efficient by achieving a good balance between the density of the mesh and the presentation of the geometric features.Finally,we discuss future applications of this approach and perspectives of applying machine learning to bridge the gaps between numerical modeling and experiments.展开更多
Retinal prosthesis offers a potential treatment for individuals suffering from photoreceptor degeneration diseases.Establishing biological retinal models and simulating how the biological retina convert incoming light...Retinal prosthesis offers a potential treatment for individuals suffering from photoreceptor degeneration diseases.Establishing biological retinal models and simulating how the biological retina convert incoming light signal into spike trains that can be properly decoded by the brain is a key issue.Some retinal models have been presented,ranking from structural models inspired by the layered architecture to functional models originated from a set of specific physiological phenomena.However,Most of these focus on stimulus image compression,edge detection and reconstruction,but do not generate spike trains corresponding to visual image.In this study,based on stateof-the-art retinal physiological mechanism,including effective visual information extraction,static nonlinear rectification of biological systems and neurons Poisson coding,a cascade model of the retina including the out plexiform layer for information processing and the inner plexiform layer for information encoding was brought forward,which integrates both anatomic connections and functional computations of retina.Using MATLAB software,spike trains corresponding to stimulus image were numerically computed by four steps：linear spatiotemporal filtering,static nonlinear rectification,radial sampling and then Poisson spike generation.The simulated results suggested that such a cascade model could recreate visual information processing and encoding functionalities of the retina,which is helpful in developing artificial retina for the retinally blind.展开更多
Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on p...Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.展开更多
Background: It is unknown whether stapling the mesh affects recurrence rate, incidence of neuralgia, and port-site hernia. We chose to fix it to the exterior reducing port size, cost and pain, at the same comparing th...Background: It is unknown whether stapling the mesh affects recurrence rate, incidence of neuralgia, and port-site hernia. We chose to fix it to the exterior reducing port size, cost and pain, at the same comparing this with traditional mesh stapling. Methods: We conducted a prospective trial for laparoscopic TAPP inguinal hernia repair on 120 patients in which we fixed the mesh to the anterior abdominal wall using either two prolene threads that passed to the exterior and tied in place or traditional mesh stapling. Results: The operative time is ranged from 35 to 70 minutes for external fixation, 30 to 60 minutes for mesh stapling, and 4 to 51 months for follow-up, and no recurrence occurred in both groups during the procedure. Two cases with post TAPP pain in mesh stapling patients are discussed with reduction of the cost and port size in external fixation patients. Conclusion: It is not necessary to secure the mesh during laparoscopic TAPP inguinal hernia repair from the interior and it is fixed only to the exterior allowing a reduction in size of the ports and considerable reduction in cost with elimination of TAPP associated post operative pain.展开更多
The scientific researches in the field of rehabilitation engineering are increasingly providing mechanisms in order to help people with disability to perform simple tasks of day-to-day. Several studies have been carri...The scientific researches in the field of rehabilitation engineering are increasingly providing mechanisms in order to help people with disability to perform simple tasks of day-to-day. Several studies have been carried out highlighting the advantages of using muscle signal in order to control rehabilitation devices, such as experimental prostheses. This paper presents a study investigating the use of forearm surface electromyography (sEMG) signals for classification of several movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an artificial neural network to process signal features to recognize performed movements. The average accuracy reached for the classification of six different movements was 68% - 88%.展开更多
基金funded by the National Natural Science Foundation of China(Grant No.52178396).
文摘Underground geotechnical engineering encounters persistent challenges in ensuring the stability and safety of surrounding rock structures, particularly within rocky tunnels. Rock reinforcement techniques, including the use of steel mesh, are critical to achieving this goal. However, there exists a knowledge gap regarding the comprehensive understanding of the mechanical behavior and failure mechanisms exhibited by steel mesh under diverse loading conditions. This study thoroughly explored the steel mesh's performance throughout the entire loading-failure process, innovating with detailed analysis and modeling techniques. By integrating advanced numerical modeling with laboratory experiments, the study examines the influence of varying reinforcement levels and geometric parameters on the steel mesh strength and deformation characteristics. Sensitivity analysis, employing gray correlation theory, identifies the key factors affecting the mesh performance, while a BP (Backpropagation) neural network model predicts maximum vertical deformation with high accuracy. The findings underscore the critical role of steel diameter and mesh spacing in optimizing peak load capacity, displacement, and energy absorption, offering practical guidelines for design improvements. The use of a Bayesian Regularization (BR) algorithm further enhances the predictive accuracy compared to traditional methods. This research provides new insights into optimizing steel mesh design for underground applications, offering an innovative approach to enhancing structural safety in geotechnical projects.
基金Supported by Science Foundation of Zhejiang (No. 599008) ZUCC Science Research Foundation
文摘An improved self-organizing feature map (SOFM) neural network is presented to generate rectangular and hexagonal lattic with normal vector attached to each vertex. After the neural network was trained, the whole scattered data were divided into sub-regions where classified core were represented by the weight vectors of neurons at the output layer of neural network. The weight vectors of the neurons were used to approximate the dense 3-D scattered points, so the dense scattered points could be reduced to a reasonable scale, while the topological feature of the whole scattered points were remained.
基金supported by Laboratory Directed Research and Development(LDRD)funding from Berkeley Laboratoryby the US Department of Energy(DOE),including the Office of Basic Energy Sciences,Chemical Sciences,Geosciences,and Biosciences Division and the Office of Nuclear Energy,Spent Fuel and Waste Disposition Campaign,both under Contract No.DEAC02-05CH11231 with Berkeley Laboratory。
文摘The complex geometric features of subsurface fractures at different scales makes mesh generation challenging and/or expensive.In this paper,we make use of neural style transfer(NST),a machine learning technique,to generate mesh from rock fracture images.In this new approach,we use digital rock fractures at multiple scales that represent’content’and define uniformly shaped and sized triangles to represent’style’.The 19-layer convolutional neural network(CNN)learns the content from the rock image,including lower-level features(such as edges and corners)and higher-level features(such as rock,fractures,or other mineral fillings),and learns the style from the triangular grids.By optimizing the cost function to achieve approximation to represent both the content and the style,numerical meshes can be generated and optimized.We utilize the NST to generate meshes for rough fractures with asperities formed in rock,a network of fractures embedded in rock,and a sand aggregate with multiple grains.Based on the examples,we show that this new NST technique can make mesh generation and optimization much more efficient by achieving a good balance between the density of the mesh and the presentation of the geometric features.Finally,we discuss future applications of this approach and perspectives of applying machine learning to bridge the gaps between numerical modeling and experiments.
基金supported by the National Natural Science Foundation of China,No.30870649the National Program on Key Basic Research Project of China (973 Program),No.2005CB724302
文摘Retinal prosthesis offers a potential treatment for individuals suffering from photoreceptor degeneration diseases.Establishing biological retinal models and simulating how the biological retina convert incoming light signal into spike trains that can be properly decoded by the brain is a key issue.Some retinal models have been presented,ranking from structural models inspired by the layered architecture to functional models originated from a set of specific physiological phenomena.However,Most of these focus on stimulus image compression,edge detection and reconstruction,but do not generate spike trains corresponding to visual image.In this study,based on stateof-the-art retinal physiological mechanism,including effective visual information extraction,static nonlinear rectification of biological systems and neurons Poisson coding,a cascade model of the retina including the out plexiform layer for information processing and the inner plexiform layer for information encoding was brought forward,which integrates both anatomic connections and functional computations of retina.Using MATLAB software,spike trains corresponding to stimulus image were numerically computed by four steps：linear spatiotemporal filtering,static nonlinear rectification,radial sampling and then Poisson spike generation.The simulated results suggested that such a cascade model could recreate visual information processing and encoding functionalities of the retina,which is helpful in developing artificial retina for the retinally blind.
基金supported by the Capital’s Funds for Health Improvement and Research,No.2022-2-2072(to YG).
文摘Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.
文摘Background: It is unknown whether stapling the mesh affects recurrence rate, incidence of neuralgia, and port-site hernia. We chose to fix it to the exterior reducing port size, cost and pain, at the same comparing this with traditional mesh stapling. Methods: We conducted a prospective trial for laparoscopic TAPP inguinal hernia repair on 120 patients in which we fixed the mesh to the anterior abdominal wall using either two prolene threads that passed to the exterior and tied in place or traditional mesh stapling. Results: The operative time is ranged from 35 to 70 minutes for external fixation, 30 to 60 minutes for mesh stapling, and 4 to 51 months for follow-up, and no recurrence occurred in both groups during the procedure. Two cases with post TAPP pain in mesh stapling patients are discussed with reduction of the cost and port size in external fixation patients. Conclusion: It is not necessary to secure the mesh during laparoscopic TAPP inguinal hernia repair from the interior and it is fixed only to the exterior allowing a reduction in size of the ports and considerable reduction in cost with elimination of TAPP associated post operative pain.
文摘The scientific researches in the field of rehabilitation engineering are increasingly providing mechanisms in order to help people with disability to perform simple tasks of day-to-day. Several studies have been carried out highlighting the advantages of using muscle signal in order to control rehabilitation devices, such as experimental prostheses. This paper presents a study investigating the use of forearm surface electromyography (sEMG) signals for classification of several movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an artificial neural network to process signal features to recognize performed movements. The average accuracy reached for the classification of six different movements was 68% - 88%.
