This study presents a framework for predicting geological characteristics based on integrating a stacking classification algorithm(SCA) with a grid search(GS) and K-fold cross validation(K-CV). The SCA includes two le...This study presents a framework for predicting geological characteristics based on integrating a stacking classification algorithm(SCA) with a grid search(GS) and K-fold cross validation(K-CV). The SCA includes two learner layers: a primary learner’s layer and meta-classifier layer. The accuracy of the SCA can be improved by using the GS and K-CV. The GS was developed to match the hyper-parameters and optimise complicated problems. The K-CV is commonly applied to changing the validation set in a training set. In general, a GS is usually combined with K-CV to produce a corresponding evaluation index and select the best hyper-parameters. The torque penetration index(TPI) and field penetration index(FPI) are proposed based on shield parameters to express the geological characteristics. The elbow method(EM) and silhouette coefficient(Si) are employed to determine the types of geological characteristics(K) in a Kmeans++ algorithm. A case study on mixed ground in Guangzhou is adopted to validate the applicability of the developed model. The results show that with the developed framework, the four selected parameters, i.e. thrust, advance rate, cutterhead rotation speed and cutterhead torque, can be used to effectively predict the corresponding geological characteristics.展开更多
Gully erosion is one of the important problems creating barrier to agricultural development.The present research used the radial basis function neural network(RBFnn)and its ensemble with random sub-space(RSS)and rotat...Gully erosion is one of the important problems creating barrier to agricultural development.The present research used the radial basis function neural network(RBFnn)and its ensemble with random sub-space(RSS)and rotation forest(RTF)ensemble Meta classifiers for the spatial mapping of gully erosion susceptibility(GES)in Hinglo river basin.120 gullies were marked and grouped into four-fold.A total of 23 factors including topographical,hydrological,lithological,and soil physio-chemical properties were effectively used.GES maps were built by RBFnn,RSS-RBFnn,and RTF-RBFnn models.The very high susceptibility zone of RBFnn,RTF-RBFnn and RSS-RBFnn models covered 6.75%,6.72%and 6.57%in Fold-1,6.21%,6.10%and 6.09%in Fold-2,6.26%,6.13%and 6.05%in Fold-3 and 7%,6.975%and 6.42%in Fold-4 of the basin.Receiver operating characteristics(ROC)curve and statistical techniques such as mean-absolute-error(MAE),root-mean-absolute-error(RMSE)and relative gully density area(R-index)methods were used for evaluating the GES maps.The results of the ROC,MAE,RMSE and R-index methods showed that the models of susceptibility to gully erosion have excellent predictive efficiency.The simulation results based on machine learning are satisfactory and outstanding and could be used to forecast the areas vulnerable to gully erosion.展开更多
A k-fold n-coloring of G is a mapping φ: V (G) → Zk(n) where Zk(n) is the collection of all ksubsets of {1,2,...,n} such that φ(u) ∩φ(v) = φ if uv ∈ E(G).If G has a k-fold n-coloring,i.e.,G is k-fold n-colorabl...A k-fold n-coloring of G is a mapping φ: V (G) → Zk(n) where Zk(n) is the collection of all ksubsets of {1,2,...,n} such that φ(u) ∩φ(v) = φ if uv ∈ E(G).If G has a k-fold n-coloring,i.e.,G is k-fold n-colorable.Let the smallest integer n such that G is k-fold n-colorable be the k-th chromatic number,denoted by χk(G).In this paper,we show that any outerplanar graph is k-fold 2k-colorable or k-fold χk(C*)-colorable,where C* is a shortest odd cycle of G.Moreover,we investigate that every planar graph with odd girth at least 10k-9(k 3) can be k-fold (2k + 1)-colorable.展开更多
Unlike the detection of marked on-street parking spaces,detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking.In urban citi...Unlike the detection of marked on-street parking spaces,detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking.In urban cities with heavy traffic flow,these challenges can result in traffic disruptions,rear-end collisions,sideswipes,and congestion as drivers struggle to make decisions.We propose a real-time detection system for on-street parking spaces using YOLO models and recommend the most suitable space based on KD-tree search.Lightweight versions of YOLOv5,YOLOv7-tiny,and YOLOv8 with different architectures are trained.Among the models,YOLOv5s with SPPF at the backbone achieved an F1-score of 0.89,which was selected for validation using k-fold cross-validation on our dataset.The Low variance and standard deviation recorded across folds indicate the model’s generalizability,reliability,and stability.Inference with KD-tree using predictions from the YOLO models recorded FPS of 37.9 for YOLOv5,67.2 for YOLOv7-tiny,and 67.0 for YOLOv8.The models successfully detect both marked and unmarked empty parking spaces on test data with varying inference speeds and FPS.These models can be efficiently deployed for real-time applications due to their high FPS,inference speed,and lightweight nature.In comparison with other state-of-the-art models,our models outperform them,further demonstrating their effectiveness.展开更多
基金funded by“The Pearl River Talent Recruitment Program”of Guangdong Province in 2019(Grant No.2019CX01G338)the Research Funding of Shantou University for New Faculty Member(Grant No.NTF19024-2019).
文摘This study presents a framework for predicting geological characteristics based on integrating a stacking classification algorithm(SCA) with a grid search(GS) and K-fold cross validation(K-CV). The SCA includes two learner layers: a primary learner’s layer and meta-classifier layer. The accuracy of the SCA can be improved by using the GS and K-CV. The GS was developed to match the hyper-parameters and optimise complicated problems. The K-CV is commonly applied to changing the validation set in a training set. In general, a GS is usually combined with K-CV to produce a corresponding evaluation index and select the best hyper-parameters. The torque penetration index(TPI) and field penetration index(FPI) are proposed based on shield parameters to express the geological characteristics. The elbow method(EM) and silhouette coefficient(Si) are employed to determine the types of geological characteristics(K) in a Kmeans++ algorithm. A case study on mixed ground in Guangzhou is adopted to validate the applicability of the developed model. The results show that with the developed framework, the four selected parameters, i.e. thrust, advance rate, cutterhead rotation speed and cutterhead torque, can be used to effectively predict the corresponding geological characteristics.
文摘Gully erosion is one of the important problems creating barrier to agricultural development.The present research used the radial basis function neural network(RBFnn)and its ensemble with random sub-space(RSS)and rotation forest(RTF)ensemble Meta classifiers for the spatial mapping of gully erosion susceptibility(GES)in Hinglo river basin.120 gullies were marked and grouped into four-fold.A total of 23 factors including topographical,hydrological,lithological,and soil physio-chemical properties were effectively used.GES maps were built by RBFnn,RSS-RBFnn,and RTF-RBFnn models.The very high susceptibility zone of RBFnn,RTF-RBFnn and RSS-RBFnn models covered 6.75%,6.72%and 6.57%in Fold-1,6.21%,6.10%and 6.09%in Fold-2,6.26%,6.13%and 6.05%in Fold-3 and 7%,6.975%and 6.42%in Fold-4 of the basin.Receiver operating characteristics(ROC)curve and statistical techniques such as mean-absolute-error(MAE),root-mean-absolute-error(RMSE)and relative gully density area(R-index)methods were used for evaluating the GES maps.The results of the ROC,MAE,RMSE and R-index methods showed that the models of susceptibility to gully erosion have excellent predictive efficiency.The simulation results based on machine learning are satisfactory and outstanding and could be used to forecast the areas vulnerable to gully erosion.
基金supported by National Natural Science Foundation of China (Grant No.10971198)the Natural Science Foundation of Zhejiang Province,China (Grant No.Y607467)
文摘A k-fold n-coloring of G is a mapping φ: V (G) → Zk(n) where Zk(n) is the collection of all ksubsets of {1,2,...,n} such that φ(u) ∩φ(v) = φ if uv ∈ E(G).If G has a k-fold n-coloring,i.e.,G is k-fold n-colorable.Let the smallest integer n such that G is k-fold n-colorable be the k-th chromatic number,denoted by χk(G).In this paper,we show that any outerplanar graph is k-fold 2k-colorable or k-fold χk(C*)-colorable,where C* is a shortest odd cycle of G.Moreover,we investigate that every planar graph with odd girth at least 10k-9(k 3) can be k-fold (2k + 1)-colorable.
基金supports this paper.Project Nos.NSTC-112-2221-E-324-003 MY3,NSTC-111-2622-E-324-002 and NSTC-112-2221-E-324-011-MY2.
文摘Unlike the detection of marked on-street parking spaces,detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking.In urban cities with heavy traffic flow,these challenges can result in traffic disruptions,rear-end collisions,sideswipes,and congestion as drivers struggle to make decisions.We propose a real-time detection system for on-street parking spaces using YOLO models and recommend the most suitable space based on KD-tree search.Lightweight versions of YOLOv5,YOLOv7-tiny,and YOLOv8 with different architectures are trained.Among the models,YOLOv5s with SPPF at the backbone achieved an F1-score of 0.89,which was selected for validation using k-fold cross-validation on our dataset.The Low variance and standard deviation recorded across folds indicate the model’s generalizability,reliability,and stability.Inference with KD-tree using predictions from the YOLO models recorded FPS of 37.9 for YOLOv5,67.2 for YOLOv7-tiny,and 67.0 for YOLOv8.The models successfully detect both marked and unmarked empty parking spaces on test data with varying inference speeds and FPS.These models can be efficiently deployed for real-time applications due to their high FPS,inference speed,and lightweight nature.In comparison with other state-of-the-art models,our models outperform them,further demonstrating their effectiveness.
