Liver tumors are one of the most aggressive malignancies in the human body. Computer-aided technology and liver interventional surgery are effective in the prediction, identification and management of liver neoplasms. One of the important processes is to accurately grasp the morphological structure of the liver and liver blood vessels. However, accurate identification and segmentation of hepatic blood vessels in CT images poses a formidable challenge. Manually locating and segmenting liver vessels in CT images is time-consuming and impractical. There is an imperative clinical requirement for a precise and effective algorithm to segment liver vessels. In response to this demand, the current paper advocates a liver vessel segmentation approach that employs an enhanced 3D fully convolutional neural network V-Net. The network model improves the basic network structure according to the characteristics of liver vessels. First, a pyramidal convolution block is introduced between the encoder and decoder of the network to improve the network localization ability. Then, multi-resolution deep supervision is introduced in the network, resulting in more robust segmentation. Finally, by fusing feature maps of different resolutions, the overall segmentation result is predicted. Evaluation experiments on public datasets demonstrate that our improved scheme can increase the segmentation ability of existing network models for liver vessels. Compared with the existing work, the experimental outcomes demonstrate that the technique presented in this manuscript has attained superior performance on the Dice Coefficient index, which can promote the treatment of liver tumors.

In the research of the high-speed sensorless control strategy of an interior permanent-magnet synchronous motor (IPMSM), considering the harmonic influence of inverter nonlinearity on traditional sliding mode observer method, a full-order sliding mode observer (SMO) method based on synchronous frequency tracking filtering is proposed. This method avoids the phase delay defects caused by the use of filters in traditional second-order SMO. Then, the observed extended electromotive force (EMF) signal is filtered using a synchronous frequency tracking (SFT) function. It tracks the changing stator current and filters out harmonics that are not part of the tracking signal to achieve static tracking of the stator current. Then, the rotor speed can be estimated by a Luenberger-based observer. Experimental results verify the effectiveness and feasibility of the proposed method.

Typically, self-sensing cement composites are prepared by directly mixing cement particles with conductive additives, such as carbon black, carbon nanotubes (CNT), and graphene. This study proposes a novel approach to directly coat cement particles with CNT as the conductive filler (CNT@cement) before contact with water, aiming to develop a high-performance self-sensing cement-based sensor. CNT@cement nano/micro hybrid fabrication relies on a simple and efficient method that enables the uniform absorption of CNT on the surface of cement particles without any surfactants, thereby not diminishing the workability and mechanical properties of cement composites. This cement composite exhibits improved microstructure, bending strength, and electrical conductivity. Remarkably, the formulation with the addition of 0.1 wt% CNT@cement hybrid increases the bending strength by about 23% and improves the electrical conductivity by approximately two orders of magnitude, outperforming previously reported cement composites with similar CNT content. Additionally, outstanding piezoresistivity with a fractional change in resistivity of 1.26 is achieved for our cement-based sensor. These findings represent a major step toward the practical application of CNT in structural health monitoring.