Surface/underwater target classification is a key topic in marine information research.However,the complex underwater environment,coupled with the diversity of target types and their variable characteristics,presents ...Surface/underwater target classification is a key topic in marine information research.However,the complex underwater environment,coupled with the diversity of target types and their variable characteristics,presents significant challenges for classifier design.For shallow-water waveguides with a negative thermocline,a residual neural network(ResNet)model based on the sound field elevation structure is constructed.This model demonstrates robust classification performance even when facing low signal-to-noise ratios and environmental mismatches.Meanwhile,to address the reduced generalization ability caused by limited labeled acoustic data,an improved ResNet model based on unsupervised domain adaptation(“proposed UDA-ResNet”)is further constructed.This model incorporates data on simulated elevation structures of the sound field to augment the training process.Adversarial training is employed to extract domain-invariant features from simulated and trial data.These strategies help reduce the negative impact caused by domain differences.Experimental results demonstrate that the proposed method shows strong surface/underwater target classification ability under limited sample sizes,thus confirming its feasibility and effectiveness.展开更多
The automated interpretation of rock structure can improve the efficiency,accuracy,and consistency of the geological risk assessment of tunnel face.Because of the high uncertainties in the geological images as a resul...The automated interpretation of rock structure can improve the efficiency,accuracy,and consistency of the geological risk assessment of tunnel face.Because of the high uncertainties in the geological images as a result of different regional rock types,as well as in-situ conditions(e.g.,temperature,humidity,and construction procedure),previous automated methods have limited performance in classification of rock structure of tunnel face during construction.This paper presents a framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks(CNN),namely Inception-ResNet-V2(IRV2).A prototype recognition system is implemented to classify 5 types of rock structures including mosaic,granular,layered,block,and fragmentation structures.The proposed IRV2 network is trained by over 35,000 out of 42,400 images extracted from over 150 sections of tunnel faces and tested by the remaining 7400 images.Furthermore,different hyperparameters of the CNN model are introduced to optimize the most efficient algorithm parameter.Among all the discussed models,i.e.,ResNet-50,ResNet-101,and Inception-v4,Inception-ResNet-V2 exhibits the best performance in terms of various indicators,such as precision,recall,F-score,and testing time per image.Meanwhile,the model trained by a large database can obtain the object features more comprehensively,leading to higher accuracy.Compared with the original image classification method,the sub-image method is closer to the reality considering both the accuracy and the perspective of error divergence.The experimental results reveal that the proposed method is optimal and efficient for automated classification of rock structure using the geological images of the tunnel face.展开更多
Light and electron microscopic studies were carried out in order to characterize haemocytes in the bivalve mollusc Meretrix meretrix. According to nucleus and cytoplasm characters, four types of haemocytes were recogn...Light and electron microscopic studies were carried out in order to characterize haemocytes in the bivalve mollusc Meretrix meretrix. According to nucleus and cytoplasm characters, four types of haemocytes were recognized： agranular haemocytes, lymphoid haemocyte, large granular and small granular haemocytes. Agranular hamocyte is the main cell type, accounting for 75%. It is agranular with rich organelles in cytoplasm, including mitochondria, golgi body and endoplasmic reticulum. Glycogen deposits were usually found in this cell type. The number of lymphoid haemocyte accounts for 1% - 2%. This cell type is agranular and shows a high ratio of nucleus to cytoplasm. A few organelles were found. High electrondense granules with diameters of 0.2 - 0.5μm and rich organelles were found in small granular haemocyte. The proportion of this cell type is about 15%. Rich granules of high electron-dense with diameters of 0.8- 2.4μm were found in large granular haemocyte. The proportion of this cell type is about 10%, and the quantity of organelles is the least.展开更多
Ice-induced structural vibration generally decreases with an increase in structural width at the waterline. Definitions of wide/narrow ice-resistant conical structures, according to ice-induced vibration, are directly...Ice-induced structural vibration generally decreases with an increase in structural width at the waterline. Definitions of wide/narrow ice-resistant conical structures, according to ice-induced vibration, are directly related to structure width, sea ice parameters, and clearing modes of broken ice. This paper proposes three clearing modes for broken ice acting on conical structures: complete clearing, temporary ice pile up, and ice pile up. In this paper, sea ice clearing modes and the formation requirements of dynamic ice force are analyzed to explore criteria determining wide/narrow ice-resistant conical structures. According to the direct measurement data of typical prototype structures, quantitative criteria of the ratio of a cone width at waterline(D) to sea ice thickness(h) is proposed. If the ratio is less than 30(narrow conical structure), broken ice is completely cleared and a dynamic ice force is produced; however, if the ratio is larger than 50(wide conical structure), the front stacking of broken ice or dynamic ice force will not occur.展开更多
This paper presents the structure and contents of a standardized layered classification system of digital geomorphology for China.This digital classification method combines landforms characteristics of morphology wit...This paper presents the structure and contents of a standardized layered classification system of digital geomorphology for China.This digital classification method combines landforms characteristics of morphology with genesis.A total of 15 categories of exogenic and endogenic forces are divided into two broad categories：morpho-genetic and morpho-structural landforms.Polygon patches are used to manage the morpho-genetic types,and solitary points,lines and polygons are used to manage the morpho-structural types.The classification method of digital morpho-genetic types can be divided into seven layers,i.e.basic morphology and altitude,genesis,sub-genesis,morphology,micro-morphology,slope and aspect,material and lithology.The method proposes combinations of matrix forms based on layered indicators.The attributes of every landform types are obtained from all or some of the seven layers.For the 15 forces categories,some classification indicators and calculation methods are presented for the basic morphology,the morphologic and sub-morphologic landforms of the morpho-genetic types.The solitary polygon,linear and point types of morpho-structural landforms are presented respectively.The layered classification method can meet the demands of scale-span geomorphologic mapping for the national primary scales from 1：500,000 to 1：1,000,000.The layers serve as classification indicators,and therefore can be added and reduced according to mapping demands,providing flexible expandability.展开更多
In accordance with the confusion on classification of source rocks, the authors raised a source rock classification for its enriched and dispersed organic matter types based on both Alpern’s idea and maceral genesis/...In accordance with the confusion on classification of source rocks, the authors raised a source rock classification for its enriched and dispersed organic matter types based on both Alpern’s idea and maceral genesis/composition. The determined rock type is roughly similar to palynofacies of Combaz , whereas it is "rock maceral facies (for coal viz. coal facies)" in strictly speaking. Therefore, it is necessary to use the organic ingredients classification proposed by the authors so that it can be used for both maceral analysis and environment research . This source rock classification not only shows sedimentology and diagenetic changes but also acquires organic matter type even if hydrocarbon potential derived from maceral’s geochemical parameters. So, it is considered as genetic classification. The "rock maceral facies" may be transformed to sedimentary organic facies , which is used as quantitative evaluation means if research being perfect.Now, there are many models in terms of structure either for coal or for kerogen. In our opinion, whatever coal or kerogen ought be polymer, then we follow Combaz’s thought and study structure of amorphous kerogens which are accordance with genetic mechanism showing biochemical and geochemical process perfectly. Here, we use the time of flight secondary ion mass spectrometry (TOFSIMS) to expand Combaz’s models from three to five. They are also models for coal.展开更多
Structure-based protein classification can be based on the similarities in primary, second or tertiary structures of proteins. A method using virtual-bond-angles series that transformed the protein space configuration...Structure-based protein classification can be based on the similarities in primary, second or tertiary structures of proteins. A method using virtual-bond-angles series that transformed the protein space configuration into a sequence was used for the classification of three-dimensional structures oi proteins. By transforming the main chains formed by C^a atoms of proteins into sequences, the series of virtual-bond-angles corresponding to the tertiary structure of the proteins were constructed. Then a distance-based hierarchical clustering method similar to Ward method was introduced to classify these virtual-bond-angles series of proteins. 200 files of protein structures were selected from Brookheaven protein data bank, and 11 clusters were classified.展开更多
In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in thi...In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.展开更多
Regional agriculture is the basis of regional sustainable development, so sustainable regional agricultural development is essential to the sustainable development of the whole society and becomes the focus of global ...Regional agriculture is the basis of regional sustainable development, so sustainable regional agricultural development is essential to the sustainable development of the whole society and becomes the focus of global research. The classification of regional agricultural structure is the basic work of regional agriculture study. This paper constructs index system （27 indices） of regional agricultural structure types with the three big indices： natural resources, developmental level of the agro-economy, and agro-ecological conditions. This paper also endows weight to every sub-classification index by means of AHP and obtains the comprehensive evaluation value of the three types of indices with arithmetic average weight approach. The regional agricultural structure can be classified into eight types in accordance with this evaluation results. The empirical study of China shows that the 31 provinces （municipalities and autonomous regions） are of different agriculture structural types. Finally, countermeasures of sustainsable agricultural development are put forward for the different agriculture structure features.展开更多
In industrial processes,there exist faults that have complex effect on process variables.Complex and simple faults are defined according to their effect dimensions.The conventional approaches based on structured resid...In industrial processes,there exist faults that have complex effect on process variables.Complex and simple faults are defined according to their effect dimensions.The conventional approaches based on structured residuals cannot isolate complex faults.This paper presents a multi-level strategy for complex fault isolation.An extraction procedure is employed to reduce the complex faults to simple ones and assign them to several levels.On each level,faults are isolated by their different responses in the structured residuals.Each residual is obtained insensitive to one fault but more sensitive to others.The faults on different levels are verified to have different residual responses and will not be confused.An entire incidence matrix containing residual response characteristics of all faults is obtained,based on which faults can be isolated.The proposed method is applied in the Tennessee Eastman process example,and the effectiveness and advantage are demonstrated.展开更多
The scale and characteristics of rock mass are important indexes of the rock mass structural plane classification. This paper firstly analyzes the spatial distribution characteristics, the structural plane types （ori...The scale and characteristics of rock mass are important indexes of the rock mass structural plane classification. This paper firstly analyzes the spatial distribution characteristics, the structural plane types （original structural plane, tectonic structural plane and hypergenic structural plane） and the associated features of the Emeishan basalts and then studies the classification schemes of the built hydropower structure planes of different rock areas （the east district, the central district and the west district） in the Emeishan basalt distribution area, Southwest China. Based on the analysis and comparison of the scale and the engineering geological characteristics of the typical structure planes in the basalt hydroelectric Stations, the types of structural planes are used in the first order classification. The secondary order classification is made by considering the impact factors of rock mass quality, e.g., the state of the structural planes, infilling, joint opening, extending length, the grade of weathering and strength. The engineering geological classification for Emeishan basalt is proposed. Because there are no evidences of a large structure presenting in study area, the first-order （Ⅰ） controlling structural planes do not appear in the classification, there only appear Ⅱ, Ⅲ, Ⅳ and Ⅴ grade structural planes influencing the rock-mass quality. According to the different rock-block types in bedding fault zone, the second-grade （Ⅱ） structural planes consisted of bedding fault zone is further classified into Ⅱ1, Ⅱ2 and Ⅱ3. The third-grade （Ⅲ） structural planes constructed by intraformational faulted zones are not subdivided. According to the different characteristics of intrusion, alteration and weathering unloading structural planes, the Ⅳ grade structure plane is divided into Ⅳ1, Ⅳ2 and Ⅳ3. According to the development characteristics of joints and fractures, the V grade structure plane is divided into fracture Ⅴ1 and columnar joint Ⅴ2. In all, the structural planes are classified into four groups with nine subsets. The research proposes the engineering geological classification of the structural plane for the hydropower project in the Emishan basalts, and the result of the study has a potential application in similar regions.展开更多
The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmenta...The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.展开更多
Improving wound healing efficiency is a key issue for high performance dressings.The surface topology of fibers in wound dressings plays an important role in regulating cell behaviors during the regeneration.Herein,a ...Improving wound healing efficiency is a key issue for high performance dressings.The surface topology of fibers in wound dressings plays an important role in regulating cell behaviors during the regeneration.Herein,a polycaprolactone(PCL)scaffold with a shish-kebab structure was prepared by electrospinning and solution-induced crystallization.L929 cells were used to investigate the behavior of fibroblasts on the multi-level microfiber.The results showed that the shish-kebab fiber-based scaffold enhanced the cell proliferation when compared with the normal fiber and the fiber with a porous structure.Protein absorption,cell adhesive force,and cell modulus also increased by the shish-kebab fiber.The shish-kebab fiber-based scaffold with improved fibroblast-regulation ability can be applied in rapid wound healing.展开更多
Brain tumor classification is crucial for personalized treatment planning.Although deep learning-based Artificial Intelligence(AI)models can automatically analyze tumor images,fine details of small tumor regions may b...Brain tumor classification is crucial for personalized treatment planning.Although deep learning-based Artificial Intelligence(AI)models can automatically analyze tumor images,fine details of small tumor regions may be overlooked during global feature extraction.Therefore,we propose a brain tumor Magnetic Resonance Imaging(MRI)classification model based on a global-local parallel dual-branch structure.The global branch employs ResNet50 with a Multi-Head Self-Attention(MHSA)to capture global contextual information from whole brain images,while the local branch utilizes VGG16 to extract fine-grained features from segmented brain tumor regions.The features from both branches are processed through designed attention-enhanced feature fusion module to filter and integrate important features.Additionally,to address sample imbalance in the dataset,we introduce a category attention block to improve the recognition of minority classes.Experimental results indicate that our method achieved a classification accuracy of 98.04%and a micro-average Area Under the Curve(AUC)of 0.989 in the classification of three types of brain tumors,surpassing several existing pre-trained Convolutional Neural Network(CNN)models.Additionally,feature interpretability analysis validated the effectiveness of the proposed model.This suggests that the method holds significant potential for brain tumor image classification.展开更多
Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from l...Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from lung cancer case texts,which involves two types of text structuring tasks:attribute discrimination and attribute extraction.This article proposes a joint model,Multi-BGLC,around these two types of tasks,using bidirectional encoder representations from transformers(BERT)as the encoder and fine-tuning the decoder composed of graph convolutional neural network(GCNN)+long short-term memory(LSTM)+conditional random field(CRF)based on cancer case data.The GCNN is used for attribute discrimination,whereas the LSTM and CRF are used for attribute extraction.The experiment verified the effectiveness and accuracy of the model compared with other baseline models.展开更多
Under saturation dosage of all kinds of SP, the free water amount was examined by centrifuge. The distribution of solution and flocculation microstructures in fresh cement paste was observed in three- dimensional spac...Under saturation dosage of all kinds of SP, the free water amount was examined by centrifuge. The distribution of solution and flocculation microstructures in fresh cement paste was observed in three- dimensional space by confocal laser scanning microscope（CLSM）. Results indicate that SP can increase the free water amount by destroying the flocculated cement particle structure and different free water amount is released by different kinds of SP. The changes of the size of flocculation structures and the dispersion of solution were obviously detected with confocal laser scanning microscope： the size of flocculation structures was smaller and more dispersed in fresh cement paste with polycarboxylate superplasticizer, but the size of flocculation structures was bigger and cannot be dispersed uniformly in fresh cement paste with others SP. The multi- level flocculation structures theoretical model of fresh cement paste was then set up. The theory indicates that different kinds of SP with different dispersion strength will open the flocculation structures at different levels, which in turn present different water reducing rate.展开更多
Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remain...Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remains a significant challenge.While advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather conditions.These challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in real-time.To address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational overhead.Specifically,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student model.This framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous systems.Our Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational demands.The proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per class.The results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning models.This work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,offering a scalable solution for weather classification in autonomous driving systems.展开更多
The performance of scene classification of satellite images strongly relies on the discriminative power of the low-level and mid-level feature representation. This paper presents a novel approach, named multi-level ma...The performance of scene classification of satellite images strongly relies on the discriminative power of the low-level and mid-level feature representation. This paper presents a novel approach, named multi-level max-margin analysis （M 3 DA） for semantic classification for high-resolution satellite images. In our M 3 DA model, the maximum entropy discrimination latent Dirichlet allocation （MedLDA） model is applied to learn the topic-level features first, and then based on a bag-of-words repre- sentation of low-level local image features, the large margin nearest neighbor （LMNN） classifier is used to optimize a multiple soft label composed of word-level features （generated by SVM classifier） and topic-level features. The categorization performances on 21-class land-use dataset have demonstrated that the proposed model in multi-level max-margin scheme can distinguish different categories of land-use scenes reasonably.展开更多
Tropoje-Has ophiolitic massif of eastern Mirdita(Albania)ophiolitic belt,is a major source for metallurgical chromite ore in Albania.Massif consists of a thick mantle section of SSZ type,8-10 km thick and
基金supported by the National Natural Science Foundation of China(Grant Nos.62471024 and 62301183)the Open Research Fund of Hanjiang Laboratory(KF2024001).
文摘Surface/underwater target classification is a key topic in marine information research.However,the complex underwater environment,coupled with the diversity of target types and their variable characteristics,presents significant challenges for classifier design.For shallow-water waveguides with a negative thermocline,a residual neural network(ResNet)model based on the sound field elevation structure is constructed.This model demonstrates robust classification performance even when facing low signal-to-noise ratios and environmental mismatches.Meanwhile,to address the reduced generalization ability caused by limited labeled acoustic data,an improved ResNet model based on unsupervised domain adaptation(“proposed UDA-ResNet”)is further constructed.This model incorporates data on simulated elevation structures of the sound field to augment the training process.Adversarial training is employed to extract domain-invariant features from simulated and trial data.These strategies help reduce the negative impact caused by domain differences.Experimental results demonstrate that the proposed method shows strong surface/underwater target classification ability under limited sample sizes,thus confirming its feasibility and effectiveness.
基金supported by the Natural Science Foundation Committee Program of China(Grant Nos.1538009 and 51778474)Science and Technology Project of Yunnan Provincial Transportation Department(Grant No.25 of 2018)+1 种基金the Fundamental Research Funds for the Central Universities in China(Grant No.0200219129)Key innovation team program of innovation talents promotion plan by MOST of China(Grant No.2016RA4059)。
文摘The automated interpretation of rock structure can improve the efficiency,accuracy,and consistency of the geological risk assessment of tunnel face.Because of the high uncertainties in the geological images as a result of different regional rock types,as well as in-situ conditions(e.g.,temperature,humidity,and construction procedure),previous automated methods have limited performance in classification of rock structure of tunnel face during construction.This paper presents a framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks(CNN),namely Inception-ResNet-V2(IRV2).A prototype recognition system is implemented to classify 5 types of rock structures including mosaic,granular,layered,block,and fragmentation structures.The proposed IRV2 network is trained by over 35,000 out of 42,400 images extracted from over 150 sections of tunnel faces and tested by the remaining 7400 images.Furthermore,different hyperparameters of the CNN model are introduced to optimize the most efficient algorithm parameter.Among all the discussed models,i.e.,ResNet-50,ResNet-101,and Inception-v4,Inception-ResNet-V2 exhibits the best performance in terms of various indicators,such as precision,recall,F-score,and testing time per image.Meanwhile,the model trained by a large database can obtain the object features more comprehensively,leading to higher accuracy.Compared with the original image classification method,the sub-image method is closer to the reality considering both the accuracy and the perspective of error divergence.The experimental results reveal that the proposed method is optimal and efficient for automated classification of rock structure using the geological images of the tunnel face.
文摘Light and electron microscopic studies were carried out in order to characterize haemocytes in the bivalve mollusc Meretrix meretrix. According to nucleus and cytoplasm characters, four types of haemocytes were recognized： agranular haemocytes, lymphoid haemocyte, large granular and small granular haemocytes. Agranular hamocyte is the main cell type, accounting for 75%. It is agranular with rich organelles in cytoplasm, including mitochondria, golgi body and endoplasmic reticulum. Glycogen deposits were usually found in this cell type. The number of lymphoid haemocyte accounts for 1% - 2%. This cell type is agranular and shows a high ratio of nucleus to cytoplasm. A few organelles were found. High electrondense granules with diameters of 0.2 - 0.5μm and rich organelles were found in small granular haemocyte. The proportion of this cell type is about 15%. Rich granules of high electron-dense with diameters of 0.8- 2.4μm were found in large granular haemocyte. The proportion of this cell type is about 10%, and the quantity of organelles is the least.
基金Foundation item： Supported by the National Natural Science Foundation of China （Grant No. 41306087）, Public Science and Technology Research Funds Projects of Ocean （Grant No. 201505019）
文摘Ice-induced structural vibration generally decreases with an increase in structural width at the waterline. Definitions of wide/narrow ice-resistant conical structures, according to ice-induced vibration, are directly related to structure width, sea ice parameters, and clearing modes of broken ice. This paper proposes three clearing modes for broken ice acting on conical structures: complete clearing, temporary ice pile up, and ice pile up. In this paper, sea ice clearing modes and the formation requirements of dynamic ice force are analyzed to explore criteria determining wide/narrow ice-resistant conical structures. According to the direct measurement data of typical prototype structures, quantitative criteria of the ratio of a cone width at waterline(D) to sea ice thickness(h) is proposed. If the ratio is less than 30(narrow conical structure), broken ice is completely cleared and a dynamic ice force is produced; however, if the ratio is larger than 50(wide conical structure), the front stacking of broken ice or dynamic ice force will not occur.
基金Key Project of the National Natural Science Foundation of China, No.40871177 No.40830529 No.40971063
文摘This paper presents the structure and contents of a standardized layered classification system of digital geomorphology for China.This digital classification method combines landforms characteristics of morphology with genesis.A total of 15 categories of exogenic and endogenic forces are divided into two broad categories：morpho-genetic and morpho-structural landforms.Polygon patches are used to manage the morpho-genetic types,and solitary points,lines and polygons are used to manage the morpho-structural types.The classification method of digital morpho-genetic types can be divided into seven layers,i.e.basic morphology and altitude,genesis,sub-genesis,morphology,micro-morphology,slope and aspect,material and lithology.The method proposes combinations of matrix forms based on layered indicators.The attributes of every landform types are obtained from all or some of the seven layers.For the 15 forces categories,some classification indicators and calculation methods are presented for the basic morphology,the morphologic and sub-morphologic landforms of the morpho-genetic types.The solitary polygon,linear and point types of morpho-structural landforms are presented respectively.The layered classification method can meet the demands of scale-span geomorphologic mapping for the national primary scales from 1：500,000 to 1：1,000,000.The layers serve as classification indicators,and therefore can be added and reduced according to mapping demands,providing flexible expandability.
基金National Natural Science Foundation of China(4 9672 13 1)
文摘In accordance with the confusion on classification of source rocks, the authors raised a source rock classification for its enriched and dispersed organic matter types based on both Alpern’s idea and maceral genesis/composition. The determined rock type is roughly similar to palynofacies of Combaz , whereas it is "rock maceral facies (for coal viz. coal facies)" in strictly speaking. Therefore, it is necessary to use the organic ingredients classification proposed by the authors so that it can be used for both maceral analysis and environment research . This source rock classification not only shows sedimentology and diagenetic changes but also acquires organic matter type even if hydrocarbon potential derived from maceral’s geochemical parameters. So, it is considered as genetic classification. The "rock maceral facies" may be transformed to sedimentary organic facies , which is used as quantitative evaluation means if research being perfect.Now, there are many models in terms of structure either for coal or for kerogen. In our opinion, whatever coal or kerogen ought be polymer, then we follow Combaz’s thought and study structure of amorphous kerogens which are accordance with genetic mechanism showing biochemical and geochemical process perfectly. Here, we use the time of flight secondary ion mass spectrometry (TOFSIMS) to expand Combaz’s models from three to five. They are also models for coal.
文摘Structure-based protein classification can be based on the similarities in primary, second or tertiary structures of proteins. A method using virtual-bond-angles series that transformed the protein space configuration into a sequence was used for the classification of three-dimensional structures oi proteins. By transforming the main chains formed by C^a atoms of proteins into sequences, the series of virtual-bond-angles corresponding to the tertiary structure of the proteins were constructed. Then a distance-based hierarchical clustering method similar to Ward method was introduced to classify these virtual-bond-angles series of proteins. 200 files of protein structures were selected from Brookheaven protein data bank, and 11 clusters were classified.
基金sponsored by National Key R&D Program of China(2018YFC1504504)Youth Foundation of Yunnan Earthquake Agency(2021K01)Project of Yunnan Earthquake Agency“Chuan bang dai”(CQ3-2021001).
文摘In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.
基金supported by the Natural Sci-ence Foundation of Jiangsu Province (Grant No. BK2005040)the MOHURD Program of China (Grant No. 06-R5-10).
文摘Regional agriculture is the basis of regional sustainable development, so sustainable regional agricultural development is essential to the sustainable development of the whole society and becomes the focus of global research. The classification of regional agricultural structure is the basic work of regional agriculture study. This paper constructs index system （27 indices） of regional agricultural structure types with the three big indices： natural resources, developmental level of the agro-economy, and agro-ecological conditions. This paper also endows weight to every sub-classification index by means of AHP and obtains the comprehensive evaluation value of the three types of indices with arithmetic average weight approach. The regional agricultural structure can be classified into eight types in accordance with this evaluation results. The empirical study of China shows that the 31 provinces （municipalities and autonomous regions） are of different agriculture structural types. Finally, countermeasures of sustainsable agricultural development are put forward for the different agriculture structure features.
基金Supported by the National Natural Science Foundation of China(60574047)the National High Technology Research and Development Program of China(2007AA04Z168,2009AA04Z154)the Research Fund for the Doctoral Program of Higher Education in China(20050335018)
文摘In industrial processes,there exist faults that have complex effect on process variables.Complex and simple faults are defined according to their effect dimensions.The conventional approaches based on structured residuals cannot isolate complex faults.This paper presents a multi-level strategy for complex fault isolation.An extraction procedure is employed to reduce the complex faults to simple ones and assign them to several levels.On each level,faults are isolated by their different responses in the structured residuals.Each residual is obtained insensitive to one fault but more sensitive to others.The faults on different levels are verified to have different residual responses and will not be confused.An entire incidence matrix containing residual response characteristics of all faults is obtained,based on which faults can be isolated.The proposed method is applied in the Tennessee Eastman process example,and the effectiveness and advantage are demonstrated.
基金funded by the National Natural Science Foundation of China (Grant No.41072228)
文摘The scale and characteristics of rock mass are important indexes of the rock mass structural plane classification. This paper firstly analyzes the spatial distribution characteristics, the structural plane types （original structural plane, tectonic structural plane and hypergenic structural plane） and the associated features of the Emeishan basalts and then studies the classification schemes of the built hydropower structure planes of different rock areas （the east district, the central district and the west district） in the Emeishan basalt distribution area, Southwest China. Based on the analysis and comparison of the scale and the engineering geological characteristics of the typical structure planes in the basalt hydroelectric Stations, the types of structural planes are used in the first order classification. The secondary order classification is made by considering the impact factors of rock mass quality, e.g., the state of the structural planes, infilling, joint opening, extending length, the grade of weathering and strength. The engineering geological classification for Emeishan basalt is proposed. Because there are no evidences of a large structure presenting in study area, the first-order （Ⅰ） controlling structural planes do not appear in the classification, there only appear Ⅱ, Ⅲ, Ⅳ and Ⅴ grade structural planes influencing the rock-mass quality. According to the different rock-block types in bedding fault zone, the second-grade （Ⅱ） structural planes consisted of bedding fault zone is further classified into Ⅱ1, Ⅱ2 and Ⅱ3. The third-grade （Ⅲ） structural planes constructed by intraformational faulted zones are not subdivided. According to the different characteristics of intrusion, alteration and weathering unloading structural planes, the Ⅳ grade structure plane is divided into Ⅳ1, Ⅳ2 and Ⅳ3. According to the development characteristics of joints and fractures, the V grade structure plane is divided into fracture Ⅴ1 and columnar joint Ⅴ2. In all, the structural planes are classified into four groups with nine subsets. The research proposes the engineering geological classification of the structural plane for the hydropower project in the Emishan basalts, and the result of the study has a potential application in similar regions.
基金supported by the National Natural Science Foundation of China(Grant Nos.42077232 and 42077235)the Key Research and Development Plan of Jiangsu Province(Grant No.BE2022156).
文摘The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.
基金Fundamental Research Funds for the Central Universities,China(Nos.2232018G-01 and 2232020G-01)Initial Research Funds for Young Teachers of Donghua University,China。
文摘Improving wound healing efficiency is a key issue for high performance dressings.The surface topology of fibers in wound dressings plays an important role in regulating cell behaviors during the regeneration.Herein,a polycaprolactone(PCL)scaffold with a shish-kebab structure was prepared by electrospinning and solution-induced crystallization.L929 cells were used to investigate the behavior of fibroblasts on the multi-level microfiber.The results showed that the shish-kebab fiber-based scaffold enhanced the cell proliferation when compared with the normal fiber and the fiber with a porous structure.Protein absorption,cell adhesive force,and cell modulus also increased by the shish-kebab fiber.The shish-kebab fiber-based scaffold with improved fibroblast-regulation ability can be applied in rapid wound healing.
文摘Brain tumor classification is crucial for personalized treatment planning.Although deep learning-based Artificial Intelligence(AI)models can automatically analyze tumor images,fine details of small tumor regions may be overlooked during global feature extraction.Therefore,we propose a brain tumor Magnetic Resonance Imaging(MRI)classification model based on a global-local parallel dual-branch structure.The global branch employs ResNet50 with a Multi-Head Self-Attention(MHSA)to capture global contextual information from whole brain images,while the local branch utilizes VGG16 to extract fine-grained features from segmented brain tumor regions.The features from both branches are processed through designed attention-enhanced feature fusion module to filter and integrate important features.Additionally,to address sample imbalance in the dataset,we introduce a category attention block to improve the recognition of minority classes.Experimental results indicate that our method achieved a classification accuracy of 98.04%and a micro-average Area Under the Curve(AUC)of 0.989 in the classification of three types of brain tumors,surpassing several existing pre-trained Convolutional Neural Network(CNN)models.Additionally,feature interpretability analysis validated the effectiveness of the proposed model.This suggests that the method holds significant potential for brain tumor image classification.
基金the Special Project of the Shanghai Municipal Commission of Economy and Information Technology for Promoting High-Quality Industrial Development(No.2024-GZL-RGZN-02011)the Shanghai City Digital Transformation Project(No.202301002)the Project of Shanghai Shenkang Hospital Development Center(No.SHDC22023214)。
文摘Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from lung cancer case texts,which involves two types of text structuring tasks:attribute discrimination and attribute extraction.This article proposes a joint model,Multi-BGLC,around these two types of tasks,using bidirectional encoder representations from transformers(BERT)as the encoder and fine-tuning the decoder composed of graph convolutional neural network(GCNN)+long short-term memory(LSTM)+conditional random field(CRF)based on cancer case data.The GCNN is used for attribute discrimination,whereas the LSTM and CRF are used for attribute extraction.The experiment verified the effectiveness and accuracy of the model compared with other baseline models.
基金Funded by the National Natural Science Foundation of China(No.50872151)the Outstanding Innovative Talents Program of China University of Mining&Technology(Beijing)(No.00-800015Z637)
文摘Under saturation dosage of all kinds of SP, the free water amount was examined by centrifuge. The distribution of solution and flocculation microstructures in fresh cement paste was observed in three- dimensional space by confocal laser scanning microscope（CLSM）. Results indicate that SP can increase the free water amount by destroying the flocculated cement particle structure and different free water amount is released by different kinds of SP. The changes of the size of flocculation structures and the dispersion of solution were obviously detected with confocal laser scanning microscope： the size of flocculation structures was smaller and more dispersed in fresh cement paste with polycarboxylate superplasticizer, but the size of flocculation structures was bigger and cannot be dispersed uniformly in fresh cement paste with others SP. The multi- level flocculation structures theoretical model of fresh cement paste was then set up. The theory indicates that different kinds of SP with different dispersion strength will open the flocculation structures at different levels, which in turn present different water reducing rate.
文摘Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remains a significant challenge.While advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather conditions.These challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in real-time.To address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational overhead.Specifically,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student model.This framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous systems.Our Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational demands.The proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per class.The results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning models.This work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,offering a scalable solution for weather classification in autonomous driving systems.
基金Supported by the Open Projects Program of National Laboratory of Pattern Recognition and the National Natural Science Foundation of China(91338113)
文摘The performance of scene classification of satellite images strongly relies on the discriminative power of the low-level and mid-level feature representation. This paper presents a novel approach, named multi-level max-margin analysis （M 3 DA） for semantic classification for high-resolution satellite images. In our M 3 DA model, the maximum entropy discrimination latent Dirichlet allocation （MedLDA） model is applied to learn the topic-level features first, and then based on a bag-of-words repre- sentation of low-level local image features, the large margin nearest neighbor （LMNN） classifier is used to optimize a multiple soft label composed of word-level features （generated by SVM classifier） and topic-level features. The categorization performances on 21-class land-use dataset have demonstrated that the proposed model in multi-level max-margin scheme can distinguish different categories of land-use scenes reasonably.
文摘Tropoje-Has ophiolitic massif of eastern Mirdita(Albania)ophiolitic belt,is a major source for metallurgical chromite ore in Albania.Massif consists of a thick mantle section of SSZ type,8-10 km thick and
