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Applying the Tilt depth and Tilt Euler techniques of gravity data to decipher the basement depth in Sichuan Basin, China

Chen Qing, Dong Yunpeng, Tan Xianfeng, Wang Jiabei, Huang Xiaoyu, Chen Hao

Acta Geophysica

2021

Vol. 69, no. 6

2173--2186

The tilt angle (i.e., TDR) provides an efficient way to recognize the horizontal locations of multi-source geological bodies at different depths and inclination angles. The tilt-depth method was initially derived by applying magnetic formulas and used to calculate the depth of magnetic sources. Recently researchers have attempted to extend this method to interpret depths in gravity field data. The tilt-depth method of gravity anomalies (i.e., GTilt-depth) could capture the depth of a buried source effectively, which makes it superior at deciphering the basement relief. Meanwhile, Tilt-Euler deconvolution (i.e., Euler deconvolution of TDR) has been utilized for estimating a source’s position from gridded data automatically, which requires no structural index. However, analytical singularities can be produced when performing inversion with the Tilt-Euler deconvolution owning to the derivatives of TDR being incalculable when the horizontal derivative is zero. The improved Tilt-Euler deconvolution provided an efficient way to eliminate analytical singularities and obtain more stable solutions. The results from the theoretical model show that the GTilt-depth method and improved Tilt-Euler deconvolution could be applied to calculate the buried depths more accurately and effectively. Application of these methods shows that they are able to capture more detailed features, and provide more straightforward and accurate results of depth, than traditional methods. Furthermore, the results obtained from the gravity data in Sichuan Basin show that the basement depth ranges from 3 to 11 km, and 3 to 7 km in the central uplift, which contains a local depression with a depth of 8 km. The basement exhibits a general pattern of “shallow in middle and deep in east and west”, which is consistent with the results revealed by gravityseismic jointly interpreted profile. This research provides a better indication of the basement structure when interpreting the regional geology in Sichuan Basin.

Gastric histopathology image segmentation using a hierarchical conditional random field

Sun Changhao, Li Chen, Zhang Jinghua, Rahaman Md Mamunur, Ai Shiliang, Chen Hao, Kulwa Frank, Li Yixin, Li Xiaoyan, Jiang Tao

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 4

1535--1555

For the Convolutional Neural Networks (CNNs) applied in the intelligent diagnosis of gastric cancer, existing methods mostly focus on individual characteristics or network frameworks without a policy to depict the integral information. Mainly, conditional random field (CRF), an efficient and stable algorithm for analyzing images containing complicated contents, can characterize spatial relation in images. In this paper, a novel hierarchical conditional random field (HCRF) based gastric histopathology image segmentation (GHIS) method is proposed, which can automatically localize abnormal (cancer) regions in gastric histopathology images obtained by an optical microscope to assist histopathologists in medical work. This HCRF model is built up with higher order potentials, including pixel-level and patch-level potentials, and graph-based post-processing is applied to further improve its segmentation performance. Especially, a CNN is trained to build up the pixel-level potentials and another three CNNs are fine-tuned to build up the patch-level potentials for sufficient spatial segmentation information. In the experiment, a hematoxylin and eosin (H&E) stained gastric histopathological dataset with 560 abnormal images are divided into training, validation and test sets with a ratio of 1 : 1 :2. Finally, segmentation accuracy, recall and specificity of 78.91%, 65.59%, and 81.33% are achieved on the test set. Our HCRF model demonstrates high segmentation performance and shows its effectiveness and future potential in the GHIS field.

Influence of different supply modes on the performance of linear induction motors

Pałka Ryszard, Woronowicz Konrad, Kotwas Jan, Xing Wang, Chen Hao

Archives of Electrical Engineering

2019

Vol. 68, nr 3

473--483

This paper deals with the modelling of traction linear induction motors (LIMs) for public transportation. The magnetic end effect inherent to these motors causes an asymmetry of their phase impedances. Thus, if the LIM is supplied from the three-phase symmetrical voltage, its phase currents become asymmetric. This effect must be taken into consideration when simulating the LIMs’ performance. Otherwise, when the motor phase currents are assumed to be symmetric in the simulation, the simulation results are in error. This paper investigates the LIM performance, considering the end-effect induced asymmetry of the phase currents, and presents a comparative study of the LIM performance characteristics in both the voltage and the current mode.