In this paper,we present a numerical simulation method of electromagnetic(EM)fields induced by a moving ship(EMFMS),which consist of both the shaft-rate EM field and the static EM field.The shaft-rate EM fields in the...In this paper,we present a numerical simulation method of electromagnetic(EM)fields induced by a moving ship(EMFMS),which consist of both the shaft-rate EM field and the static EM field.The shaft-rate EM fields in the frequency domain are first obtained by solving the partial differential equations together with suitable boundary conditions,and then they are transformed into the time domain by using the inverse Fourier transform.Finally,the static fields are added to obtain the EM fields of a moving ship.The effects of the source current intensity and the source position on the EM fields of a moving ship are discussed in detail.A field example of EM response of a moving ship is presented and its characteristics are analyzed.展开更多
This paper presents an approach for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed method estimates a gaussian mixture model with...This paper presents an approach for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed method estimates a gaussian mixture model with three kernels as cerebrospinal fluid (CSF), normal tissue and Multiple Sclerosis lesions. To estimate this model, an automatic Entropy based EM algorithm is used to find the best estimated Model. Then, Markov random field (MRF) model and EM algorithm are utilized to obtain and upgrade the class conditional probability density function and the apriori probability of each class. After estimation of Model parameters and apriori probability, brain tissues are classified using bayesian classification. To evaluate the result of the proposed method, similarity criteria of different slices related to 20 MS patients are calculated and compared with other methods which include manual segmentation. Also, volume of segmented lesions are computed and compared with gold standard using correlation coefficient. The proposed method has better performance in comparison with previous works which are reported here.展开更多
高斯混合模型(Gaussian mixture model,GMM)可以描述遥感数据的概率密度函数,通过估计各高斯分布的参数,计算后验概率,实现信息提取。为了提高利用GMM进行遥感信息提取的准确度,首先在GMM中使用马尔科夫随机场(Markov random field,MRF...高斯混合模型(Gaussian mixture model,GMM)可以描述遥感数据的概率密度函数,通过估计各高斯分布的参数,计算后验概率,实现信息提取。为了提高利用GMM进行遥感信息提取的准确度,首先在GMM中使用马尔科夫随机场(Markov random field,MRF)计算各像元邻域内各类地物的先验概率,代替各类地物的混合概率,使其反映出各类地物的空间相关性;然后在参数估计过程中利用模拟退火(simulated annealing,SA)思想获得全局最优的参数估计值;最后利用该参数估计值求出每个像元对于各类地物的后验概率,获得各类地物的空间分布。通过对遥感实验场的图像数据进行信息提取,发现所述新方法取得了更好的效果,证明了上述改进的有效性。展开更多
基金This study is supported by the Fundamental Research Funds for the Central Universities(No.201861020)the Wenhai Program of Qingdao National Laboratory for Marine Science and Technology(QNLM)(No.2017WH ZZB0201).
文摘In this paper,we present a numerical simulation method of electromagnetic(EM)fields induced by a moving ship(EMFMS),which consist of both the shaft-rate EM field and the static EM field.The shaft-rate EM fields in the frequency domain are first obtained by solving the partial differential equations together with suitable boundary conditions,and then they are transformed into the time domain by using the inverse Fourier transform.Finally,the static fields are added to obtain the EM fields of a moving ship.The effects of the source current intensity and the source position on the EM fields of a moving ship are discussed in detail.A field example of EM response of a moving ship is presented and its characteristics are analyzed.
文摘This paper presents an approach for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed method estimates a gaussian mixture model with three kernels as cerebrospinal fluid (CSF), normal tissue and Multiple Sclerosis lesions. To estimate this model, an automatic Entropy based EM algorithm is used to find the best estimated Model. Then, Markov random field (MRF) model and EM algorithm are utilized to obtain and upgrade the class conditional probability density function and the apriori probability of each class. After estimation of Model parameters and apriori probability, brain tissues are classified using bayesian classification. To evaluate the result of the proposed method, similarity criteria of different slices related to 20 MS patients are calculated and compared with other methods which include manual segmentation. Also, volume of segmented lesions are computed and compared with gold standard using correlation coefficient. The proposed method has better performance in comparison with previous works which are reported here.
