With the rapid deployment of Distributed Energy Resource (DER), advanced inverter functions such as voltage support have been developed. However, with conflict control targets, the voltage support control is concerned to degrade the effectiveness of the islanding detection, which is yet fully investigated. Given the wide application of Slip-Mode Frequency Shift (SMS), this paper takes it as an example of islanding detection method. Firstly, the mechanism of islanded operation in a RLC island system is analyzed, and the relationship between basic electrical quantities is explored. Then the small signal model of such islanding system with the voltage support and SMS control is constructed, and its eigenvalues and sensitivity to parameter change are analyzed to investigate the influence of voltage support on the effectiveness of SMS. Finally, HIL experiments are conducted to verify the above analysis. The results show that during islanded operation, the voltage support control enhances the islanding detection and helps reduce the islanding detection time. The key affecting parameters are identified as the droop gain of voltage support control, open-loop response time and SMS coefficient.

MOTIVATION: Reconstructing and analyzing all blood vessels throughout the brain is significant for understanding brain function, revealing the mechanisms of brain disease, and mapping the whole-brain vascular atlas. Vessel segmentation is a fundamental step in reconstruction and analysis. The whole-brain optical microscopic imaging method enables the acquisition of whole-brain vessel images at the capillary resolution. Due to the massive amount of data and the complex vascular features generated by high-resolution whole-brain imaging, achieving rapid and accurate segmentation of whole-brain vasculature becomes a challenge. RESULTS: We introduce HP-VSP, a high-performance vessel segmentation pipeline based on deep learning. The pipeline consists of three processes: data blocking, block prediction, and block fusion. We used parallel computing to parallelize this pipeline to improve the efficiency of whole-brain vessel segmentation. We also designed a lightweight deep neural network based on multi-resolution vessel feature extraction to segment vessels at different scales throughout the brain accurately. We validated our approach on whole-brain vascular data from three transgenic mice collected by HD-fMOST. The results show that our proposed segmentation network achieves the state-of-the-art level under various evaluation metrics. In contrast, the parameters of the network are only 1% of those of similar networks. The established segmentation pipeline could be used on various computing platforms and complete the whole-brain vessel segmentation in 3 h. We also demonstrated that our pipeline could be applied to the vascular analysis. AVAILABILITY AND IMPLEMENTATION: The dataset is available at http://atlas.brainsmatics.org/a/li2301. The source code is freely available at https://github.com/visionlyx/HP-VSP.

The authors present an approach to fuzzy logic and reasoning using the resolution principle based on a novel operator, the fuzzy operator. After presenting the fuzzy resolution principle, a general methodology for reasoning under uncertainty, based on resolution in this fuzzy logic, is discussed. The calculus presented is referred to as fuzzy operator logic or FOL. Some simple examples illustrating the reasoning process using FOL are included.>