The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a c...The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.展开更多
Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the los...Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the loss in the production of cotton.Although several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex background.Due to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease classification.Therefore in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×1000.After that,the extracted features are serially concatenated having a feature vector lengthN×2000.Themost prominent features are selected usingEmperor PenguinOptimizer(EPO)method.The method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle cotton-leaf-infection-II.The EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,respectively.The classification is performed using 5,7,and 10 folds cross-validation.The Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.展开更多
The tight reservoirs of the Fengcheng Formation at the southern margin of the Mahu Sag have strong heterogeneity due to the diversity in their pore types, sizes, and structures. The microscopic characteristics of tigh...The tight reservoirs of the Fengcheng Formation at the southern margin of the Mahu Sag have strong heterogeneity due to the diversity in their pore types, sizes, and structures. The microscopic characteristics of tight reservoirs and the mechanisms that generate them are of significance in identifying the distribution of high-quality reservoirs and in improving the prediction accuracy of sweet spots in tight oil reservoirs. In this paper, high-pressure mercury intrusion (HPMI) and nuclear magnetic resonance (NMR) experiments were carried out on samples from the tight reservoirs in the study area. These experimental results were combined with cluster analysis, fractal theory, and microscopic observations to qualitatively and quantitatively evaluate pore types, sizes, and structures. A classification scheme was established that divides the reservoir into four types, based on the microstructure characteristics of samples, and the genetic mechanisms that aided the development of reservoir microstructure were analyzed. The results show that the lower limit for the tight reservoir in the Fengcheng Formation is Φ of 3.5% and K of 0.03 mD. The pore throat size and distribution span gradually decrease from Type I, through Type II and Type III reservoirs to non-reservoirs, and the pore type also evolves from dominantly intergranular pores to intercrystalline pores. The structural trend shows a decrease in the ball-stick pore-throat system and an increase in the branch-like pore-throat system. The dual effects of sedimentation and diagenesis shape the microscopic characteristics of pores and throats. The sorting, roundness, and particle size of the original sediments determine the original physical properties of the reservoir. The diagenetic environment of ‘two alkalinity stages and one acidity stage’ influenced the evolution of pore type and size. Although the cementation of authigenic minerals in the early alkaline environment adversely affected reservoir properties, it also alleviated the damage of the later compaction to some extent. Dissolution in the mid-term acidic environment greatly improved the physical properties of this tight reservoir, making dissolution pores an important reservoir space. The late alkaline environment occurred after large-scale oil and gas accumulation. During this period, the cementation of authigenic minerals had a limited effect on the reservoir space occupied by crude oil. It had a more significant impact on the sand bodies not filled with oil, making them function as barriers.展开更多
基金support of the National Key R&D Program of China(No.2022YFC2803903)the Key R&D Program of Zhejiang Province(No.2021C03013)the Zhejiang Provincial Natural Science Foundation of China(No.LZ20F020003).
文摘The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.
基金supported by the Technology Development Program of MSS[No.S3033853]by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A4A1031509).
文摘Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the loss in the production of cotton.Although several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex background.Due to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease classification.Therefore in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×1000.After that,the extracted features are serially concatenated having a feature vector lengthN×2000.Themost prominent features are selected usingEmperor PenguinOptimizer(EPO)method.The method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle cotton-leaf-infection-II.The EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,respectively.The classification is performed using 5,7,and 10 folds cross-validation.The Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.
基金supported by a Major Projects grant of the China National Petroleum Corporation(Project No.2021DJ1003).
文摘The tight reservoirs of the Fengcheng Formation at the southern margin of the Mahu Sag have strong heterogeneity due to the diversity in their pore types, sizes, and structures. The microscopic characteristics of tight reservoirs and the mechanisms that generate them are of significance in identifying the distribution of high-quality reservoirs and in improving the prediction accuracy of sweet spots in tight oil reservoirs. In this paper, high-pressure mercury intrusion (HPMI) and nuclear magnetic resonance (NMR) experiments were carried out on samples from the tight reservoirs in the study area. These experimental results were combined with cluster analysis, fractal theory, and microscopic observations to qualitatively and quantitatively evaluate pore types, sizes, and structures. A classification scheme was established that divides the reservoir into four types, based on the microstructure characteristics of samples, and the genetic mechanisms that aided the development of reservoir microstructure were analyzed. The results show that the lower limit for the tight reservoir in the Fengcheng Formation is Φ of 3.5% and K of 0.03 mD. The pore throat size and distribution span gradually decrease from Type I, through Type II and Type III reservoirs to non-reservoirs, and the pore type also evolves from dominantly intergranular pores to intercrystalline pores. The structural trend shows a decrease in the ball-stick pore-throat system and an increase in the branch-like pore-throat system. The dual effects of sedimentation and diagenesis shape the microscopic characteristics of pores and throats. The sorting, roundness, and particle size of the original sediments determine the original physical properties of the reservoir. The diagenetic environment of ‘two alkalinity stages and one acidity stage’ influenced the evolution of pore type and size. Although the cementation of authigenic minerals in the early alkaline environment adversely affected reservoir properties, it also alleviated the damage of the later compaction to some extent. Dissolution in the mid-term acidic environment greatly improved the physical properties of this tight reservoir, making dissolution pores an important reservoir space. The late alkaline environment occurred after large-scale oil and gas accumulation. During this period, the cementation of authigenic minerals had a limited effect on the reservoir space occupied by crude oil. It had a more significant impact on the sand bodies not filled with oil, making them function as barriers.
