Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining s...Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining samples do not correspond one-to-one correctly.Mismatched datasets caused by missing samples make the industrial data unavailable for further machine learning.In order to align the mismatched samples,this article presents a cooperative iteration matching method(CIMM)based on the modified dynamic time warping(DTW).The proposed method regards the sequentially accumulated industrial data as the time series.Mismatched samples are aligned by the DTW.In addition,dynamic constraints are applied to the warping distance of the DTW process to make the alignment more efficient.Then a series of models are trained with the cumulated samples iteratively.Several groups of numerical experiments on different missing patterns and missing locations are designed and analyzed to prove the effectiveness and the applicability of the proposed method.展开更多
Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used pho...Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used photovoltaic forecasting methods,which struggle to handle issues such as non-u-niform lengths of time series data for power generation and meteorological conditions,overlapping photovoltaic characteristics,and nonlinear correlations,an improved method that utilizes spectral clustering and dynamic time warping(DTW)for selecting similar days is proposed to optimize the dataset along the temporal dimension.Furthermore,XGBoost is employed for recursive feature selec-tion.On this basis,to address the issue that single forecasting models excel at capturing different data characteristics and tend to exhibit significant prediction errors under adverse meteorological con-ditions,an improved forecasting model based on Stacking and weighted fusion is proposed to reduce the independent bias and variance of individual models and enhance the predictive accuracy.Final-ly,experimental validation is carried out using real data from a photovoltaic power station in the Xi-aoshan District of Hangzhou,China,demonstrating that the proposed method can still achieve accu-rate and robust forecasting results even under conditions of significant meteorological fluctuations.展开更多
Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design.However,the lack of extensive data and high experimental c...Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design.However,the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties.Moreover,accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods.This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach.A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds.Prior selection techniques,including prior elicitation and prior predictive checking,are also applied during the building procedure to provide the model with more information from previous research findings.The framework is assessed using datasets of varying sizes for 20 pure component properties.For 18 out of the 20 pure component properties,the new models are found to give improved accuracy and predictive power in comparison with other published models,with and without machine learning.展开更多
Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thic...Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness,an intelligent automatic correlation method of oil-bearing strata based on pattern constraints is formed.We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata,constraining the correlation process by stratigraphic sedimentary patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate the correlation of marker layers and the interfaces of each stratum.The application in Shishen 100 block in the Shinan Oilfield of the Bohai Bay Basin shows that the coincidence rate of the marker layers identified by this method is over 95.00%,and the average coincidence rate of identified oil-bearing strata reaches 90.02% compared to artificial correlation results,which is about 17 percentage points higher than that of the existing automatic correlation methods.The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.展开更多
Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports analytics.However,identifying dynamic and complex movements in sports like badminton re...Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports analytics.However,identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns.Deep learning techniques like convolutional neural networks(CNNs),long short-term memory(LSTM),and graph convolutional networks(GCNs)improve recognition in large datasets,while the traditional machine learning methods like SVM(support vector machines),RF(random forest),and LR(logistic regression),combined with handcrafted features and ensemble approaches,perform well but struggle with the complexity of fast-paced sports like badminton.We proposed an ensemble learning model combining support vector machines(SVM),logistic regression(LR),random forest(RF),and adaptive boosting(AdaBoost)for badminton action recognition.The data in this study consist of video recordings of badminton stroke techniques,which have been extracted into spatiotemporal data.The three-dimensional distance between each skeleton point and the right hip represents the spatial features.The temporal features are the results of Fast Dynamic Time Warping(FDTW)calculations applied to 15 frames of each video sequence.The weighted ensemble model employs soft voting classifiers from SVM,LR,RF,and AdaBoost to enhance the accuracy of badminton action recognition.The E2 ensemble model,which combines SVM,LR,and AdaBoost,achieves the highest accuracy of 95.38%.展开更多
Offshore carbon capture, utilization, and storage(OCCUS) is regarded as a crucial technology for mitigating greenhouse gas emissions.Quantitative monitoring maps of sealed carbon dioxide are necessary in a comprehensi...Offshore carbon capture, utilization, and storage(OCCUS) is regarded as a crucial technology for mitigating greenhouse gas emissions.Quantitative monitoring maps of sealed carbon dioxide are necessary in a comprehensive OCCUS project. A potential high-resolution method for the aforementioned purpose lies in the full-waveform inversion(FWI) of time-lapse seismic data. However, practical applications of FWI are severely restricted by the well-known cycle-skipping problem. A new time-lapse FWI method using cross-correlation-based dynamic time warping(CDTW) is proposed to detect changes in the subsurface property due to carbon dioxide(CO_(2)) injection and address the aforementioned issue. The proposed method, namely CDTW, which combines the advantages of cross-correlation and dynamic time warping, is employed in the automatic estimation of the discrepancy between the seismic signals simulated using the baseline/initial model and those acquired. The proposed FWI method can then back-project the estimated discrepancy to the subsurface space domain, thereby facilitating retrieval of the induced subsurface property change by taking the difference between the inverted baseline and monitor models. Numerical results on pairs of signals prove that CDTW can obtain reliable shifts under amplitude modulation and noise contamination conditions. The performance of CDTW substantially outperforms that of the conventional dynamic time warping method. The proposed time-lapse fullwaveform inversion(FWI) method is applied to the Frio-2 CO_(2) storage model. The baseline and monitor models are inverted from the corresponding time-lapse seismic data. The changes in velocity due to CO_(2) injection are reconstructed by the difference between the baseline and the monitor models.展开更多
In this paper,we study a kind of curvature flow in warped product spaces.We obtain convergence results under barrier conditions and restrictions on prescribed function.We also obtain the asymptotic behavior of a kind ...In this paper,we study a kind of curvature flow in warped product spaces.We obtain convergence results under barrier conditions and restrictions on prescribed function.We also obtain the asymptotic behavior of a kind of inverse curvature flow in Schwarzschild manifold.展开更多
We consider the Hyperverse as a collection of multiverses in a (4 + 1)-dimensional spacetime with gravitational constant G. Multiverses in our model are bouquets of thin shells (with synchronized intrinsic times). If ...We consider the Hyperverse as a collection of multiverses in a (4 + 1)-dimensional spacetime with gravitational constant G. Multiverses in our model are bouquets of thin shells (with synchronized intrinsic times). If gis the gravitational constant of a shell Sand εits thickness, then G~εg. The physical universe is supposed to be one of those thin shells inside the local bouquet called Local Multiverse. Other remarkable objects of the Hyperverse are supposed to be black holes, black lenses, black rings and (generalized) Black Saturns. In addition, Schwarzschild-de Sitter multiversal nurseries can be hidden inside those Black Saturns, leading to their Bousso-Hawking nucleation. It also suggests that black holes in our physical universe might harbor embedded (2 + 1)-dimensional multiverses. This is compatible with outstanding ideas and results of Bekenstein, Hawking-Vaz and Corda about “black holes as atoms” and the condensation of matter on “apparent horizons”. It allows us to formulate conjecture 12.1 about the origin of the Local Multiverse. As an alternative model, we examine spacetime warping of our universe by external universes. It gives data for the accelerated expansion and the cosmological constant Λ, which are in agreement with observation, thus opening a possibility for verification of the multiverse model.展开更多
In this paper, we deal with isommetric immersions of globally null warped product manifolds into Lorentzian manifolds with constant curvature c in codimension k≥3. Under the assumptions that the globally null warped ...In this paper, we deal with isommetric immersions of globally null warped product manifolds into Lorentzian manifolds with constant curvature c in codimension k≥3. Under the assumptions that the globally null warped product manifold has no points with the same constant sectional curvature c as the Lorentzian ambient, we show that such isometric immersion splits into warped product of isometric immersions.展开更多
Crealet is the world leader in sophisticated warp feeding technology and tailored solutions.Controlled warp tension stands also for controlled fabric quality.The Swiss firm presents its latest developments for technic...Crealet is the world leader in sophisticated warp feeding technology and tailored solutions.Controlled warp tension stands also for controlled fabric quality.The Swiss firm presents its latest developments for technical textile applications at Techtextil 2024 in Frankfurt,Germany.展开更多
Three aluminium channel sections of US standard extruded dimension are mounted as cantilevers with x-axis symmetry. The flexural bending and shear that arise with applied axial torsion are each considered theoreticall...Three aluminium channel sections of US standard extruded dimension are mounted as cantilevers with x-axis symmetry. The flexural bending and shear that arise with applied axial torsion are each considered theoretically and numerically in terms of two longitudinal axes of loading not coincident with the shear centre. In particular, the warping displacements, stiffness and stress distributions are calculated for torsion applied to longitudinal axes passing through the section’s centroid and its web centre. The stress conversions derived from each action are superimposed to reveal a net sectional stress distribution. Therein, the influence of the axis position upon the net axial and shear stress distributions is established compared to previous results for each beam when loading is referred to a flexural axis through the shear centre. Within the net stress analysis is, it is shown how the constraint to free warping presented by the end fixing modifies the axial stress. The latter can be identified with the action of a ‘bimoment’ upon each thin-walled section.展开更多
Aspects of the general Vlasov theory are examined separately as applied to a thin-walled channel section cantilever beam under free-end end loading. In particular, the flexural bending and shear that arise under trans...Aspects of the general Vlasov theory are examined separately as applied to a thin-walled channel section cantilever beam under free-end end loading. In particular, the flexural bending and shear that arise under transverse shear and axial torsional loading are each considered theoretically. These analyses involve the location of the shear centre at which transverse shear forces when applied do not produce torsion. This centre, when taken to be coincident with the centre of twist implies an equivalent reciprocal behaviour. That is, an axial torsion applied concentric with the shear centre will twist but not bend the beam. The respective bending and shear stress conversions are derived for each action applied to three aluminium alloy extruded channel sections mounted as cantilevers with a horizontal principal axis of symmetry. Bending and shear are considered more generally for other thin-walled sections when the transverse loading axes at the shear centre are not parallel to the section = s centroidal axes of principal second moments of area. The fixing at one end of the cantilever modifies the St Venant free angular twist and the free warping displacement. It is shown from the Wagner-Kappus torsion theory how the end constrained warping generates an axial stress distribution that varies with the length and across the cross-section for an axial torsion applied to the shear centre. It should be mentioned here for wider applications and validation of the Vlasov theory that attendant papers are to consider in detail bending and torsional loadings applied to other axes through each of the centroid and the web centre. Therein, both bending and twisting arise from transverse shear and axial torsion applied to each position being displaced from the shear centre. Here, the influence of the axis position upon the net axial and shear stress distributions is to be established. That is, the net axial stress from axial torsional loading is identified with the sum of axial stress due to bending and axial stress arising from constrained warping displacements at the fixing. The net shear stress distribution overlays the distributions from axial torsion and that from flexural shear under transverse loading. Both arise when transverse forces are displaced from the shear centre.展开更多
基金the Key National Natural Science Foundation of China(No.U1864211)the National Natural Science Foundation of China(No.11772191)the Natural Science Foundation of Shanghai(No.21ZR1431500)。
文摘Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining samples do not correspond one-to-one correctly.Mismatched datasets caused by missing samples make the industrial data unavailable for further machine learning.In order to align the mismatched samples,this article presents a cooperative iteration matching method(CIMM)based on the modified dynamic time warping(DTW).The proposed method regards the sequentially accumulated industrial data as the time series.Mismatched samples are aligned by the DTW.In addition,dynamic constraints are applied to the warping distance of the DTW process to make the alignment more efficient.Then a series of models are trained with the cumulated samples iteratively.Several groups of numerical experiments on different missing patterns and missing locations are designed and analyzed to prove the effectiveness and the applicability of the proposed method.
基金Supported by the National Natural Science Foundation of China(No.52005442)the Technology Project of Zhejiang Huayun Information Technology Co.,Ltd.(No.HYJT/JS-2020-004).
文摘Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used photovoltaic forecasting methods,which struggle to handle issues such as non-u-niform lengths of time series data for power generation and meteorological conditions,overlapping photovoltaic characteristics,and nonlinear correlations,an improved method that utilizes spectral clustering and dynamic time warping(DTW)for selecting similar days is proposed to optimize the dataset along the temporal dimension.Furthermore,XGBoost is employed for recursive feature selec-tion.On this basis,to address the issue that single forecasting models excel at capturing different data characteristics and tend to exhibit significant prediction errors under adverse meteorological con-ditions,an improved forecasting model based on Stacking and weighted fusion is proposed to reduce the independent bias and variance of individual models and enhance the predictive accuracy.Final-ly,experimental validation is carried out using real data from a photovoltaic power station in the Xi-aoshan District of Hangzhou,China,demonstrating that the proposed method can still achieve accu-rate and robust forecasting results even under conditions of significant meteorological fluctuations.
基金support from the National Natural Science Foundation of China(22150410338 and 61973268)is gratefully acknowledged.
文摘Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design.However,the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties.Moreover,accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods.This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach.A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds.Prior selection techniques,including prior elicitation and prior predictive checking,are also applied during the building procedure to provide the model with more information from previous research findings.The framework is assessed using datasets of varying sizes for 20 pure component properties.For 18 out of the 20 pure component properties,the new models are found to give improved accuracy and predictive power in comparison with other published models,with and without machine learning.
基金Supported by the National Natural Science Foundation of China(42272110)CNPC-China University of Petroleum(Beijing)Strategic Cooperation Project(ZLZX2020-02).
文摘Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness,an intelligent automatic correlation method of oil-bearing strata based on pattern constraints is formed.We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata,constraining the correlation process by stratigraphic sedimentary patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate the correlation of marker layers and the interfaces of each stratum.The application in Shishen 100 block in the Shinan Oilfield of the Bohai Bay Basin shows that the coincidence rate of the marker layers identified by this method is over 95.00%,and the average coincidence rate of identified oil-bearing strata reaches 90.02% compared to artificial correlation results,which is about 17 percentage points higher than that of the existing automatic correlation methods.The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.
基金supported by the Center for Higher Education Funding(BPPT)and the Indonesia Endowment Fund for Education(LPDP),as acknowledged in decree number 02092/J5.2.3/BPI.06/9/2022。
文摘Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports analytics.However,identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns.Deep learning techniques like convolutional neural networks(CNNs),long short-term memory(LSTM),and graph convolutional networks(GCNs)improve recognition in large datasets,while the traditional machine learning methods like SVM(support vector machines),RF(random forest),and LR(logistic regression),combined with handcrafted features and ensemble approaches,perform well but struggle with the complexity of fast-paced sports like badminton.We proposed an ensemble learning model combining support vector machines(SVM),logistic regression(LR),random forest(RF),and adaptive boosting(AdaBoost)for badminton action recognition.The data in this study consist of video recordings of badminton stroke techniques,which have been extracted into spatiotemporal data.The three-dimensional distance between each skeleton point and the right hip represents the spatial features.The temporal features are the results of Fast Dynamic Time Warping(FDTW)calculations applied to 15 frames of each video sequence.The weighted ensemble model employs soft voting classifiers from SVM,LR,RF,and AdaBoost to enhance the accuracy of badminton action recognition.The E2 ensemble model,which combines SVM,LR,and AdaBoost,achieves the highest accuracy of 95.38%.
文摘Offshore carbon capture, utilization, and storage(OCCUS) is regarded as a crucial technology for mitigating greenhouse gas emissions.Quantitative monitoring maps of sealed carbon dioxide are necessary in a comprehensive OCCUS project. A potential high-resolution method for the aforementioned purpose lies in the full-waveform inversion(FWI) of time-lapse seismic data. However, practical applications of FWI are severely restricted by the well-known cycle-skipping problem. A new time-lapse FWI method using cross-correlation-based dynamic time warping(CDTW) is proposed to detect changes in the subsurface property due to carbon dioxide(CO_(2)) injection and address the aforementioned issue. The proposed method, namely CDTW, which combines the advantages of cross-correlation and dynamic time warping, is employed in the automatic estimation of the discrepancy between the seismic signals simulated using the baseline/initial model and those acquired. The proposed FWI method can then back-project the estimated discrepancy to the subsurface space domain, thereby facilitating retrieval of the induced subsurface property change by taking the difference between the inverted baseline and monitor models. Numerical results on pairs of signals prove that CDTW can obtain reliable shifts under amplitude modulation and noise contamination conditions. The performance of CDTW substantially outperforms that of the conventional dynamic time warping method. The proposed time-lapse fullwaveform inversion(FWI) method is applied to the Frio-2 CO_(2) storage model. The baseline and monitor models are inverted from the corresponding time-lapse seismic data. The changes in velocity due to CO_(2) injection are reconstructed by the difference between the baseline and the monitor models.
基金Supported by the National Natural Science Foundation of China(12031017 and 11971424).
文摘In this paper,we study a kind of curvature flow in warped product spaces.We obtain convergence results under barrier conditions and restrictions on prescribed function.We also obtain the asymptotic behavior of a kind of inverse curvature flow in Schwarzschild manifold.
文摘We consider the Hyperverse as a collection of multiverses in a (4 + 1)-dimensional spacetime with gravitational constant G. Multiverses in our model are bouquets of thin shells (with synchronized intrinsic times). If gis the gravitational constant of a shell Sand εits thickness, then G~εg. The physical universe is supposed to be one of those thin shells inside the local bouquet called Local Multiverse. Other remarkable objects of the Hyperverse are supposed to be black holes, black lenses, black rings and (generalized) Black Saturns. In addition, Schwarzschild-de Sitter multiversal nurseries can be hidden inside those Black Saturns, leading to their Bousso-Hawking nucleation. It also suggests that black holes in our physical universe might harbor embedded (2 + 1)-dimensional multiverses. This is compatible with outstanding ideas and results of Bekenstein, Hawking-Vaz and Corda about “black holes as atoms” and the condensation of matter on “apparent horizons”. It allows us to formulate conjecture 12.1 about the origin of the Local Multiverse. As an alternative model, we examine spacetime warping of our universe by external universes. It gives data for the accelerated expansion and the cosmological constant Λ, which are in agreement with observation, thus opening a possibility for verification of the multiverse model.
文摘In this paper, we deal with isommetric immersions of globally null warped product manifolds into Lorentzian manifolds with constant curvature c in codimension k≥3. Under the assumptions that the globally null warped product manifold has no points with the same constant sectional curvature c as the Lorentzian ambient, we show that such isometric immersion splits into warped product of isometric immersions.
文摘Crealet is the world leader in sophisticated warp feeding technology and tailored solutions.Controlled warp tension stands also for controlled fabric quality.The Swiss firm presents its latest developments for technical textile applications at Techtextil 2024 in Frankfurt,Germany.
文摘Three aluminium channel sections of US standard extruded dimension are mounted as cantilevers with x-axis symmetry. The flexural bending and shear that arise with applied axial torsion are each considered theoretically and numerically in terms of two longitudinal axes of loading not coincident with the shear centre. In particular, the warping displacements, stiffness and stress distributions are calculated for torsion applied to longitudinal axes passing through the section’s centroid and its web centre. The stress conversions derived from each action are superimposed to reveal a net sectional stress distribution. Therein, the influence of the axis position upon the net axial and shear stress distributions is established compared to previous results for each beam when loading is referred to a flexural axis through the shear centre. Within the net stress analysis is, it is shown how the constraint to free warping presented by the end fixing modifies the axial stress. The latter can be identified with the action of a ‘bimoment’ upon each thin-walled section.
文摘Aspects of the general Vlasov theory are examined separately as applied to a thin-walled channel section cantilever beam under free-end end loading. In particular, the flexural bending and shear that arise under transverse shear and axial torsional loading are each considered theoretically. These analyses involve the location of the shear centre at which transverse shear forces when applied do not produce torsion. This centre, when taken to be coincident with the centre of twist implies an equivalent reciprocal behaviour. That is, an axial torsion applied concentric with the shear centre will twist but not bend the beam. The respective bending and shear stress conversions are derived for each action applied to three aluminium alloy extruded channel sections mounted as cantilevers with a horizontal principal axis of symmetry. Bending and shear are considered more generally for other thin-walled sections when the transverse loading axes at the shear centre are not parallel to the section = s centroidal axes of principal second moments of area. The fixing at one end of the cantilever modifies the St Venant free angular twist and the free warping displacement. It is shown from the Wagner-Kappus torsion theory how the end constrained warping generates an axial stress distribution that varies with the length and across the cross-section for an axial torsion applied to the shear centre. It should be mentioned here for wider applications and validation of the Vlasov theory that attendant papers are to consider in detail bending and torsional loadings applied to other axes through each of the centroid and the web centre. Therein, both bending and twisting arise from transverse shear and axial torsion applied to each position being displaced from the shear centre. Here, the influence of the axis position upon the net axial and shear stress distributions is to be established. That is, the net axial stress from axial torsional loading is identified with the sum of axial stress due to bending and axial stress arising from constrained warping displacements at the fixing. The net shear stress distribution overlays the distributions from axial torsion and that from flexural shear under transverse loading. Both arise when transverse forces are displaced from the shear centre.
