The doubly-fed induction generator (DFIG) wind turbine system, which is composed of the wind turbine, generator, rotor-side converter, grid-side converter, and so on, is a typical multi-time scale system. The dynamic processes at different time scales do not exist in isolation. Furthermore, neglecting the coupling of parameters of different time scales to reduce the order of the model will lead to deviation between the simulation results and the actual results, which may not be suitable for power system transient analysis. This paper proposes an electromechanical transient model and an electromagnetic transient model of the DFIG wind turbine system that consider the interaction of multiple time-scale dynamic processes. Firstly, the paper applies the modal analysis method to explain the multi-time scale characteristics of the DFIG wind turbine system. Secondly, the variation in the eigenvalues of the DFIG wind turbine system before and after the order reduction and the coupling between variables and the system, as well as the coupling between variables of different time scales, are analyzed to obtain the preliminary 21-order simplified model. Thirdly, considering the weak coupling characteristics between the mechanical part and the electromagnetic part of the DFIG wind turbine system, the 21-order simplified model is decomposed into a 15-order electromagnetic transient model and a six-order electromechanical transient model on the basis of their time scales. Then, according to the balance between simulation time and simulation accuracy, the 14-order electromagnetic transient model and the 10 or 12-order electromechanical transient model are finally obtained. Finally, the rationality of the simplified models is verified by simulations under two large disturbance conditions, namely wind speed abrupt change and voltage sag. The obtained simplified models have reference significance for improving the simulation speed of a wind power grid-connected system and analyzing the internal mechanism of the DFIG wind turbine system’s stability.

In order to improve the accuracy of the mechanical key component model constructed by 3D laser scanning and reduce the economic loss to the marine port, the factors affecting the accuracy of 3D laser scanning were studied. By studying the error of the instrument, errors related to the target object, errors caused by external environmental conditions, errors caused by human operations, and errors caused by different measurement methods were discussed. In view of the errors caused by different measurement methods and different measurement methods for different parts, the scanning experiment was carried out and the obtained data was analyzed. A method of improving the scanning accuracy was proposed, which made the three-dimensional scanning more accurate. This method was applied to the design of key part structures for marine port machinery. The results show that the research in this paper can help designers accurately and completely obtain 3D point cloud data of mechanical key parts, improve scanning accuracy and reconstruct 3D model. Therefore, according to the application prospect of 3D scanning technology and the status quo in China, it is of great practical significance to study the accuracy of 3D laser scanning technology.

In clinical settings, computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) are commonly employed in brain imaging to assist clinicians in determining the type of stroke in patients. However, these modalities are associated with potential hazards or limitations. In contrast, microwave imaging emerges as a promising technique, offering advantages such as non-ionizing radiation, low cost, lightweight, and portability. The primary challenges faced by microwave tomography include the severe ill-posedness of the electromagnetic inverse scattering problem and the time-consuming nature and unsatisfactory resolution of iterative quantitative algorithms. This paper proposes a learning electric field enhancement imaging method (LEFEIM) to achieve quantitative brain imaging based on a microwave tomography system. LEFEIM comprises two cascaded networks. The first, based on a convolutional neural network, utilizes the electric field from the receiving antenna to predict the electric field distribution within the imaging domain. The second network employs the electric field distribution as input to learn the dielectric constant distribution, thereby realizing quantitative brain imaging. Compared to the Born Iterative Method (BIM), LEFEIM significantly improves imaging time, while enhancing imaging quality and goodness-of-fit to a certain extent. Simultaneously, LEFEIM exhibits anti-noise capabilities.

Large-scale wind-thermal-bundled power that is transmitted by mixed ultra-high voltage direct current (UHVDC)/ultra-high voltage alternating current (UHVAC) systems has become crucial for large-scale wind farms in China. Equations describing the electromagnetic power characteristics under short circuits for UHVAC lines and UHVDC blocks are derived based on an analysis of the external characteristics of a doubly fed wind farm and UHVDC systems. The effect of wind power penetration ratio on rotor angle transient stability is analysed, and the optimal wind power penetration ratio under the constraint of rotor angle transient stability is determined. The effects of system parameters, such as the UHVDC transmission capacity and the reactance of UHVAC lines on the optimal wind power penetration ratio are discussed. The trend of rotor angle stability varies from a monotonic deterioration to concave, and the optimal wind power penetration ratio increases from 0 to 30% under an UHVDC block when the reactance of UHVAC lines increases from 0.005 to 0.02. The optimal wind power penetration ratio under a short circuit increases from 40% to 60% when the reactance of UHVAC lines decreases from 0.02 to 0.006 and decreases from 40% to 30% when the capacity of UHVDC decreases from 3200 MW to 1600 MW. The analysis is verified by simulating an actual system in China’s Northwest Power Grid.

This paper proposes a method that can discover users' frequent access patterns underlying users' browsing Web behaviors. Firstly, the paper introduces the concept of access pattern according to a user's access path, and secondly puts forward a revised algorithm (FAP-Mining) based on the FP-tree algorithm to mine frequent access patterns. The new algorithm first constructs a frequent access pattern tree and then mines users' frequent access patterns on the tree. The algorithm is accurate and scalable for mining frequent access patterns with different lengths.