By using the equations describing typhoons in the atmosphere, the steady three-dimensional stream fieldand the corresponding pressure and temperature fields are obtained. The three-dimensional velocity fields construc...By using the equations describing typhoons in the atmosphere, the steady three-dimensional stream fieldand the corresponding pressure and temperature fields are obtained. The three-dimensional velocity fields construct anonlinear autonomuos system in the physical space. It is shown that the center of typhoon is a local minimum pressurewith positive vertical vorticity and horizontal convergence in lower levels and a local maximum pressure with negativevertical vorticity and horizontal divergence in the upper levels. Because there exits two saddle-focus points in the autnomous system, there exist the spiral patterns, in which the winds blow spirally in and out of the center in the lowerand upper levels in the Northern Hemisphere and cause the ascending motion near the center and dascending motionnear the edge, respectively . All these are in fair conformity with the observations. It implies that the rotation of earthand the viscosity of air play an important role in the spiral structure of typhoons.展开更多
According to the theory of Matsuoka neural oscillators and with the con- sideration of the fact that the human upper arm mainly consists of six muscles, a new kind of central pattern generator （CPG） neural network c...According to the theory of Matsuoka neural oscillators and with the con- sideration of the fact that the human upper arm mainly consists of six muscles, a new kind of central pattern generator （CPG） neural network consisting of six neurons is pro- posed to regulate the contraction of the upper arm muscles. To verify effectiveness of the proposed CPG network, an arm motion control model based on the CPG is established. By adjusting the CPG parameters, we obtain the neural responses of the network, the angles of joint and hand of the model with MATLAB. The simulation results agree with the results of crank rotation experiments designed by Ohta et al., showing that the arm motion control model based on a CPG network is reasonable and effective.展开更多
Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd st...Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes,which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades.Trajectory clustering has become one of the most popular methods in VMS.However,complex data,such as a large number of samples and parameters,makes it difficult for trajectory clustering to work well with accurate motion segmentation results.This study introduces a spatial-angular stacked sparse autoencoder model(SA-SSAE)with l2-regularization and softmax,a powerful deep learning method for visual motion segmentation to cluster similar motion patterns that belong to the same cluster.The proposed model can extract meaningful high-level features using only spatial-angular features obtained from refined tracklets(a.k.a‘trajectories’).We adopt l2-regularization and sparsity regularization,which can learn sparse representations of features,to guarantee the sparsity of the autoencoders.We employ the softmax layer to map the data points into accurate cluster representations.One of the best advantages of the SA-SSAE framework is it can manage VMS even when individuals move around randomly.This framework helps cluster the motion patterns effectively with higher accuracy.We put forward a new dataset with itsmanual ground truth,including 21 crowd videos.Experiments conducted on two crowd benchmarks demonstrate that the proposed model can more accurately group trajectories than the traditional clustering approaches used in previous studies.The proposed SA-SSAE framework achieved a 0.11 improvement in accuracy and a 0.13 improvement in the F-measure compared with the best current method using the CUHK dataset.展开更多
Dominant statistical patterns of winter Arctic surface wind （WASW） variability and their impacts on Arctic sea ice motion are investigated using the complex vector empirical orthogonal function （CVEOF） method. The...Dominant statistical patterns of winter Arctic surface wind （WASW） variability and their impacts on Arctic sea ice motion are investigated using the complex vector empirical orthogonal function （CVEOF） method. The results indicate that the leading CVEOF of Arctic surface wind variability, which accounts for 33% of the covariance, is characterized by two different and alternating spatial patterns （WASWP1 and WASWP2）. Both WASWP1 and WASWP2 show strong interannual and decadal variations, superposed on their declining trends over past decades. Atmospheric circulation anomalies associated with WASWPI and WASWP2 exhibit, respectively, equivalent barotropic and some baroclinic characteristics, differing from the Arctic dipole anomaly and the seesaw structure anomaly between the Barents Sea and the Beaufort Sea. On decadal time scales, the decline trend of WASWP2 can be attributed to persistent warming of sea surface temperature in the Greenland--Barents--Kara seas from autunm to winter, reflecting the effect of the Arctic warming. The second CVEOF, which accounts for 18% of the covariance, also contains two different spatial patterns （WASWP3 and WASWP4）. Their time evolutions are significantly correlated with the North Atlantic Oscillation （NAO） index and the central Arctic Pattern, respectively, measured by the leading EOF of winter sea level pressure （SLP） north of 70~N. Thus, winter anomalous surface wind pattern associated with the NAO is not the most important surface wind pattern. WASWP3 and WASWP4 primarily reflect natural variability of winter surface wind and neither exhibits an apparent trend that differs from WASWP1 or WASWP2. These dominant surface wind patterns strongly influence Arctic sea ice motion and sea ice exchange between the western and eastern Arctic. Furthermore, the Fram Strait sea ice volume flux is only significantly correlated with WASWP3. The results demonstrate that surface and geostrophic winds are not interchangeable in terms of describing wind field variability over the Arctic Ocean. The results have important implications for understanding and investigating Arctic sea ice variations： Dominant patterns of Arctic surface wind variability, rather than simply whether there are the Arctic dipole anomaly and the Arctic Oscillation （or NAO）, effectively affect the spatial distribution of Arctic sea ice anomalies.展开更多
MSU Stego Video is a public video steganographic tool, which has strong robustness and is regarded as a real video steganographic tool. In order to increase the detection rate, this paper proposes a new steganoalysis ...MSU Stego Video is a public video steganographic tool, which has strong robustness and is regarded as a real video steganographic tool. In order to increase the detection rate, this paper proposes a new steganoalysis method against MSU, which uses the chessboard character of MSU embedded video, proposes a down-sample block-based collusion method to estimate the original frame and checks the chessboard mode of the different frame between tested frame and estimated frame to detect MSU steganographic evidences. To reduce the error introduced by severe movement of the video content, a method that abandons severe motion blocks from detecting is proposed. The experiment results show that the false negative rate of the proposed algorithm is lower than 5%, and the false positive rate is lower than 2%. Our algorithm has significantly better performance than existing algorithms. Especially to the video that has fast motion, the algorithm has more remarkable performance.展开更多
Based on the dynamic monitoring data of crustal deformation, the parameter evolution for the dynamics pattern and fractal dimension of crustal deformation field and the integral activity level of many faults etc. befo...Based on the dynamic monitoring data of crustal deformation, the parameter evolution for the dynamics pattern and fractal dimension of crustal deformation field and the integral activity level of many faults etc. before and after the Tangshan (1976) and Lijiang (1996) strong earthquakes and others are studied by using the method of pattern dynamics. It is exposed that two time space characters, the ordered dimension drop of the deformation field and the accelerated motion of multi fault before an earthquake, are probably caused by the deformation localization and fault softening after the seismogenic process enters the nonlinear stage. They could be an important seismic precursor if they occurred repeatedly before strong earthquakes.展开更多
文摘By using the equations describing typhoons in the atmosphere, the steady three-dimensional stream fieldand the corresponding pressure and temperature fields are obtained. The three-dimensional velocity fields construct anonlinear autonomuos system in the physical space. It is shown that the center of typhoon is a local minimum pressurewith positive vertical vorticity and horizontal convergence in lower levels and a local maximum pressure with negativevertical vorticity and horizontal divergence in the upper levels. Because there exits two saddle-focus points in the autnomous system, there exist the spiral patterns, in which the winds blow spirally in and out of the center in the lowerand upper levels in the Northern Hemisphere and cause the ascending motion near the center and dascending motionnear the edge, respectively . All these are in fair conformity with the observations. It implies that the rotation of earthand the viscosity of air play an important role in the spiral structure of typhoons.
基金supported by the National Natural Science Foundation of China(Nos.11232005 and11472104)
文摘According to the theory of Matsuoka neural oscillators and with the con- sideration of the fact that the human upper arm mainly consists of six muscles, a new kind of central pattern generator （CPG） neural network consisting of six neurons is pro- posed to regulate the contraction of the upper arm muscles. To verify effectiveness of the proposed CPG network, an arm motion control model based on the CPG is established. By adjusting the CPG parameters, we obtain the neural responses of the network, the angles of joint and hand of the model with MATLAB. The simulation results agree with the results of crank rotation experiments designed by Ohta et al., showing that the arm motion control model based on a CPG network is reasonable and effective.
基金This research work is supported by the Deputyship of Research&Innovation,Ministry of Education in Saudi Arabia(Grant Number 758).
文摘Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes,which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades.Trajectory clustering has become one of the most popular methods in VMS.However,complex data,such as a large number of samples and parameters,makes it difficult for trajectory clustering to work well with accurate motion segmentation results.This study introduces a spatial-angular stacked sparse autoencoder model(SA-SSAE)with l2-regularization and softmax,a powerful deep learning method for visual motion segmentation to cluster similar motion patterns that belong to the same cluster.The proposed model can extract meaningful high-level features using only spatial-angular features obtained from refined tracklets(a.k.a‘trajectories’).We adopt l2-regularization and sparsity regularization,which can learn sparse representations of features,to guarantee the sparsity of the autoencoders.We employ the softmax layer to map the data points into accurate cluster representations.One of the best advantages of the SA-SSAE framework is it can manage VMS even when individuals move around randomly.This framework helps cluster the motion patterns effectively with higher accuracy.We put forward a new dataset with itsmanual ground truth,including 21 crowd videos.Experiments conducted on two crowd benchmarks demonstrate that the proposed model can more accurately group trajectories than the traditional clustering approaches used in previous studies.The proposed SA-SSAE framework achieved a 0.11 improvement in accuracy and a 0.13 improvement in the F-measure compared with the best current method using the CUHK dataset.
基金supported by the National Key Basic Research Project of China (Grant nos.2013CBA01804,2015CB453200)the National Natural Science Foundation of China (Grant nos.41475080,41221064)the Ocean Public Welfare Scientific Research Project of China (Grant no.201205007)
文摘Dominant statistical patterns of winter Arctic surface wind （WASW） variability and their impacts on Arctic sea ice motion are investigated using the complex vector empirical orthogonal function （CVEOF） method. The results indicate that the leading CVEOF of Arctic surface wind variability, which accounts for 33% of the covariance, is characterized by two different and alternating spatial patterns （WASWP1 and WASWP2）. Both WASWP1 and WASWP2 show strong interannual and decadal variations, superposed on their declining trends over past decades. Atmospheric circulation anomalies associated with WASWPI and WASWP2 exhibit, respectively, equivalent barotropic and some baroclinic characteristics, differing from the Arctic dipole anomaly and the seesaw structure anomaly between the Barents Sea and the Beaufort Sea. On decadal time scales, the decline trend of WASWP2 can be attributed to persistent warming of sea surface temperature in the Greenland--Barents--Kara seas from autunm to winter, reflecting the effect of the Arctic warming. The second CVEOF, which accounts for 18% of the covariance, also contains two different spatial patterns （WASWP3 and WASWP4）. Their time evolutions are significantly correlated with the North Atlantic Oscillation （NAO） index and the central Arctic Pattern, respectively, measured by the leading EOF of winter sea level pressure （SLP） north of 70~N. Thus, winter anomalous surface wind pattern associated with the NAO is not the most important surface wind pattern. WASWP3 and WASWP4 primarily reflect natural variability of winter surface wind and neither exhibits an apparent trend that differs from WASWP1 or WASWP2. These dominant surface wind patterns strongly influence Arctic sea ice motion and sea ice exchange between the western and eastern Arctic. Furthermore, the Fram Strait sea ice volume flux is only significantly correlated with WASWP3. The results demonstrate that surface and geostrophic winds are not interchangeable in terms of describing wind field variability over the Arctic Ocean. The results have important implications for understanding and investigating Arctic sea ice variations： Dominant patterns of Arctic surface wind variability, rather than simply whether there are the Arctic dipole anomaly and the Arctic Oscillation （or NAO）, effectively affect the spatial distribution of Arctic sea ice anomalies.
基金Supported by the National Natural Science Foundation of China(60970114)Doctoral Fund of Ministry of Education of China(20110141130006)
文摘MSU Stego Video is a public video steganographic tool, which has strong robustness and is regarded as a real video steganographic tool. In order to increase the detection rate, this paper proposes a new steganoalysis method against MSU, which uses the chessboard character of MSU embedded video, proposes a down-sample block-based collusion method to estimate the original frame and checks the chessboard mode of the different frame between tested frame and estimated frame to detect MSU steganographic evidences. To reduce the error introduced by severe movement of the video content, a method that abandons severe motion blocks from detecting is proposed. The experiment results show that the false negative rate of the proposed algorithm is lower than 5%, and the false positive rate is lower than 2%. Our algorithm has significantly better performance than existing algorithms. Especially to the video that has fast motion, the algorithm has more remarkable performance.
文摘Based on the dynamic monitoring data of crustal deformation, the parameter evolution for the dynamics pattern and fractal dimension of crustal deformation field and the integral activity level of many faults etc. before and after the Tangshan (1976) and Lijiang (1996) strong earthquakes and others are studied by using the method of pattern dynamics. It is exposed that two time space characters, the ordered dimension drop of the deformation field and the accelerated motion of multi fault before an earthquake, are probably caused by the deformation localization and fault softening after the seismogenic process enters the nonlinear stage. They could be an important seismic precursor if they occurred repeatedly before strong earthquakes.
