In this paper,the adaptive lifting scheme (ALS) and local gradient maps (LGM) are proposed to isolate the transient feature components from the gearbox vibration signals. Based on entropy minimization rule,the ALS is ...In this paper,the adaptive lifting scheme (ALS) and local gradient maps (LGM) are proposed to isolate the transient feature components from the gearbox vibration signals. Based on entropy minimization rule,the ALS is employed to change properties of an initial wavelet and design adaptive wavelet. Then LGM is applied to characterize the transient feature components in detail signal of decomposition results using ALS. In the present studies, the orthogonal Daubechies 4 (Db 4) wavelet is used as the initial wavelet. The proposed method is applied to both simulated signals and vibration signals acquired from a gearbox for periodic impulses detection. The two conventional methods (cepstrum analysis and Hilbert envelope analysis) and the orthogonal Db4 wavelet are also used to analyze the same signals for comparison. The results demonstrate that the proposed method is more effective in extracting transient components from noisy signals.展开更多
The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper ...The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper proposes an automated methodology for mapping burn scars using pairs of Sentinel-2 imagery, exploiting the state-of-the-art eXtreme Gradient Boosting (XGB) machine learning framework. A large database of 64 reference wildfire perimeters in Greece from 2016 to 2019 is used to train the classifier. An empirical methodology for appropriately sampling the training patterns from this database is formulated, which guarantees the effectiveness of the approach and its computational efficiency. A difference (pre-fire minus post-fire) spectral index is used for this purpose, upon which we appropriately identify the clear and fuzzy value ranges. To reduce the data volume, a super-pixel segmentation of the images is also employed, implemented via the QuickShift algorithm. The cross-validation results showcase the effectiveness of the proposed algorithm, with the average commission and omission errors being 9% and 2%, respectively, and the average Matthews correlation coefficient (MCC) equal to 0.93.展开更多
This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting de...This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.展开更多
By combining of the second gradient operator, the second class of integral theorems, the Gaussian-curvature-based integral theorems and the Gaussian （or spherical） mapping, a series of invariants or geometric conser...By combining of the second gradient operator, the second class of integral theorems, the Gaussian-curvature-based integral theorems and the Gaussian （or spherical） mapping, a series of invariants or geometric conservation quantities under Gaussian （or spherical） mapping are revealed. From these mapping invariants important transformations between original curved surface and the spherical surface are derived. The potential applications of these invariants and transformations to geometry are discussed展开更多
The research was elaborated in Kafr El-Dawar area (Egypt northern region) to study the availability of the soil plant nutrients. The research introduced three parameters to comprehensively and carefully describe the a...The research was elaborated in Kafr El-Dawar area (Egypt northern region) to study the availability of the soil plant nutrients. The research introduced three parameters to comprehensively and carefully describe the availability of the soil plant nutrients: potentiality, gradient and anisotropy. Potentiality defines the categories of soil ability to supply plant nutrients;meanwhile gradient expresses the increasing rate of the availability of the soil plant nutrients. The gradient anisotropy refers to the directions or orientation of the increasing rate of the availability of the soil plant nutrients. The introduced parameters enabled to spatially study the availability of the soil plant nutrients. Analytical data, of soil available phosphorus (P), indicated that P ranged from 0.2 ppm to 11.4 ppm to locate all studied soil samples into the low class of the soil nutritional P ability. This was not the case of available potassium (K), where the soil samples were distributed into three available K soil categories: medium, high, and very high. GIS map of soil P nutritional potentiality for plant (potato), displayed the soil studied area in one category, as low P soil nutritional potentiality to coincide with the analytical data classification. Contrary, the K map classified the soil studied area into three categories of soil K nutritional potentiality: medium, high and excessive. This obviously referred that the individual determination of soil K nutritional potentiality is misleading for interpretation of soil tests because it does care of the spatial distribution of soil available K. Nearly, all soil samples had high available micronutrients that they were located in the high category in both classification of analytical data and GIS maps. GIS gradient maps of the soil available plant nutrients referred that the soil plant nutrients, exception of K, had two gradients: non increasing-slight increasing and build up. Gradient of soil available potassium was classified into four classes: non increasing-slight increasing, build up, moderately increasing and hike. Regardless potassium case, the non increasing-slight increasing gradient class dominated the others. GIS maps of anisotropy soil availability of macronutrients (P and K) generally showed that their gradients mainly increased in two directions: north and south. The incasing directions of soil availability of micronutrients coincided with that of the macronutrients.展开更多
DQN等深度强化学习方法的学习过程与工作机制不透明,无法感知其决策依据与决策可靠性,使模型做出的决策饱受质疑,极大限制了深度强化学习的应用场景。为了解释智能体的决策机理,提出一种基于梯度的显著性图生成算法(saliency map genera...DQN等深度强化学习方法的学习过程与工作机制不透明,无法感知其决策依据与决策可靠性,使模型做出的决策饱受质疑,极大限制了深度强化学习的应用场景。为了解释智能体的决策机理,提出一种基于梯度的显著性图生成算法(saliency map generation algorithm based on gradient,SMGG)。使用高层卷积层生成的特征图梯度信息计算不同特征图的重要性,在模型的结构和内部参数已知的情况下,从模型最后一层入手,通过对特征图梯度的计算,生成不同特征图相对于显著性图的权重;对特征重要性进行正向和负向分类,利用有正向影响的权值将特征图中捕获的特征进行加权,构成当前决策的正向解释;利用对其他类别有负向影响的权值将特征图中捕获的特征进行加权,构成当前决策的反向解释。二者共同生成决策的显著性图,得出智能体决策行为的依据,实验证明了该方法的有效性。展开更多
基金Higher School Specialized Research Fund for the Doctoral Program Funding Issue(No.2011021120032)Fundamental Research Funds for the Central Universities(No.2012jdhz23)
文摘In this paper,the adaptive lifting scheme (ALS) and local gradient maps (LGM) are proposed to isolate the transient feature components from the gearbox vibration signals. Based on entropy minimization rule,the ALS is employed to change properties of an initial wavelet and design adaptive wavelet. Then LGM is applied to characterize the transient feature components in detail signal of decomposition results using ALS. In the present studies, the orthogonal Daubechies 4 (Db 4) wavelet is used as the initial wavelet. The proposed method is applied to both simulated signals and vibration signals acquired from a gearbox for periodic impulses detection. The two conventional methods (cepstrum analysis and Hilbert envelope analysis) and the orthogonal Db4 wavelet are also used to analyze the same signals for comparison. The results demonstrate that the proposed method is more effective in extracting transient components from noisy signals.
文摘The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper proposes an automated methodology for mapping burn scars using pairs of Sentinel-2 imagery, exploiting the state-of-the-art eXtreme Gradient Boosting (XGB) machine learning framework. A large database of 64 reference wildfire perimeters in Greece from 2016 to 2019 is used to train the classifier. An empirical methodology for appropriately sampling the training patterns from this database is formulated, which guarantees the effectiveness of the approach and its computational efficiency. A difference (pre-fire minus post-fire) spectral index is used for this purpose, upon which we appropriately identify the clear and fuzzy value ranges. To reduce the data volume, a super-pixel segmentation of the images is also employed, implemented via the QuickShift algorithm. The cross-validation results showcase the effectiveness of the proposed algorithm, with the average commission and omission errors being 9% and 2%, respectively, and the average Matthews correlation coefficient (MCC) equal to 0.93.
基金This work was supported in part by the National Natural Science Foundation of China(61601418,41602362,61871259)in part by the Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring(2020-5)+1 种基金in part by the Qilian Mountain National Park Research Center(Qinghai)(grant number:GKQ2019-01)in part by the Geomatics Technology and Application Key Laboratory of Qinghai Province,Grant No.QHDX-2019-01.
文摘This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.
基金Project supported by the National Natural Science Foundation of China (No.10572076)
文摘By combining of the second gradient operator, the second class of integral theorems, the Gaussian-curvature-based integral theorems and the Gaussian （or spherical） mapping, a series of invariants or geometric conservation quantities under Gaussian （or spherical） mapping are revealed. From these mapping invariants important transformations between original curved surface and the spherical surface are derived. The potential applications of these invariants and transformations to geometry are discussed
文摘The research was elaborated in Kafr El-Dawar area (Egypt northern region) to study the availability of the soil plant nutrients. The research introduced three parameters to comprehensively and carefully describe the availability of the soil plant nutrients: potentiality, gradient and anisotropy. Potentiality defines the categories of soil ability to supply plant nutrients;meanwhile gradient expresses the increasing rate of the availability of the soil plant nutrients. The gradient anisotropy refers to the directions or orientation of the increasing rate of the availability of the soil plant nutrients. The introduced parameters enabled to spatially study the availability of the soil plant nutrients. Analytical data, of soil available phosphorus (P), indicated that P ranged from 0.2 ppm to 11.4 ppm to locate all studied soil samples into the low class of the soil nutritional P ability. This was not the case of available potassium (K), where the soil samples were distributed into three available K soil categories: medium, high, and very high. GIS map of soil P nutritional potentiality for plant (potato), displayed the soil studied area in one category, as low P soil nutritional potentiality to coincide with the analytical data classification. Contrary, the K map classified the soil studied area into three categories of soil K nutritional potentiality: medium, high and excessive. This obviously referred that the individual determination of soil K nutritional potentiality is misleading for interpretation of soil tests because it does care of the spatial distribution of soil available K. Nearly, all soil samples had high available micronutrients that they were located in the high category in both classification of analytical data and GIS maps. GIS gradient maps of the soil available plant nutrients referred that the soil plant nutrients, exception of K, had two gradients: non increasing-slight increasing and build up. Gradient of soil available potassium was classified into four classes: non increasing-slight increasing, build up, moderately increasing and hike. Regardless potassium case, the non increasing-slight increasing gradient class dominated the others. GIS maps of anisotropy soil availability of macronutrients (P and K) generally showed that their gradients mainly increased in two directions: north and south. The incasing directions of soil availability of micronutrients coincided with that of the macronutrients.
文摘DQN等深度强化学习方法的学习过程与工作机制不透明,无法感知其决策依据与决策可靠性,使模型做出的决策饱受质疑,极大限制了深度强化学习的应用场景。为了解释智能体的决策机理,提出一种基于梯度的显著性图生成算法(saliency map generation algorithm based on gradient,SMGG)。使用高层卷积层生成的特征图梯度信息计算不同特征图的重要性,在模型的结构和内部参数已知的情况下,从模型最后一层入手,通过对特征图梯度的计算,生成不同特征图相对于显著性图的权重;对特征重要性进行正向和负向分类,利用有正向影响的权值将特征图中捕获的特征进行加权,构成当前决策的正向解释;利用对其他类别有负向影响的权值将特征图中捕获的特征进行加权,构成当前决策的反向解释。二者共同生成决策的显著性图,得出智能体决策行为的依据,实验证明了该方法的有效性。
