Research on the Cross-Platform Co-Simulation Strategy of Power Systems Based on the Model-Segmentation Algorithm

In recent years, cross-platform co-simulation has become an important development direction of the real-time simulation of power systems. Model segmentation is at the core of the realization of cross-platform joint simulation and parallel real-time simulation of these systems. In essence, it is based on the deep application of a system-decoupling algorithm. In order to solve problems that a single interface cannot, it considers the data interaction of large- and small-step systems at the same time This paper proposes an improved joint-simulation strategy based on the model-segmentation method for the cross-platform joint-simulation technology of a large-scale, flexible direct-power grid sent by the wind farms of RT-lab and Hypersim. Firstly, by studying several common interface algorithms in the current project, the adaptability of different interface algorithms is analyzed. Secondly, the problem of high-frequency oscillation caused by the inductance-decoupling algorithm is improved, and an improved segmentation-model algorithm is proposed. Finally, according to the adaptability, each interface algorithm is applied to the wind-power-based, flexible direct-transmission, dual-platform simulation model that was built for this study. The simulation results verify the feasibility of the improved interface in system decoupling and platform interfacing, and indicate the significantly improved accuracy and stability of the system.

Developmental neuroanatomy of the Rosy Bitterling Rhodeus ocellatus (Teleostei: Cypriniformes)—A microCT study

ABSTRACTBitterlings are a group of teleost fish (Cyprinifromes: Acheilanathidae) notable for their brood parasitic lifestyle. Bitterling embryos develop as parasites inside the gill chamber of their freshwater mussel hosts. However, little is known about brain development in this species. Here, we have imaged the development of the brain of the Rosy Bitterling (Rhodeus ocellatus) at four embryonic stages (165, 185, 210, 235 hours post-fertilization) using micro-computed tomography (microCT) with special emphasis on developmental regionalization and brain ventricular organization. We provide a detailed neuroanatomical account of the development of the brain divisions with reference to The Atlas of Early zebrafish Brain Development and the updated prosomeric model. Segmentation and three-dimensional visualization of the ventricular system were performed in order to clarify changes in the longitudinal brain axis as a result of cephalic flexure during development. During early embryonic and larval development, we find that histological differentiation, tissue boundaries, periventricular proliferation zones, and ventricular spaces are all recognizable using microCT. Importantly, our approach is validated by the fact that the profile of CT values displayed here in the bitterling brain are consistent with genoarchitecture identified in previous studies. We also find developmental heterochrony of the inferior lobe in the Rosy Bitterling compared to the zebrafish. Our study provides a foundation for future studies of the brain development in the Rosy Bitterling, a valuable model species for studying the evolutionary adaptations associated with brood parasitism.

LIVECell—A large-scale dataset for label-free live cell segmentation

Light microscopy combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena. Accurate segmentation of individual cells in images enables exploration of complex biological questions, but can require sophisticated imaging processing pipelines in cases of low contrast and high object density. Deep learning-based methods are considered state-of-the-art for image segmentation but typically require vast amounts of annotated data, for which there is no suitable resource available in the field of label-free cellular imaging. Here, we present LIVECell, a large, high-quality, manually annotated and expert-validated dataset of phase-contrast images, consisting of over 1.6 million cells from a diverse set of cell morphologies and culture densities. To further demonstrate its use, we train convolutional neural network-based models using LIVECell and evaluate model segmentation accuracy with a proposed a suite of benchmarks.

An index based road feature extraction from LANDSAT-8 OLI images

Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (top-hat or bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (SSF), is proposed. Then, an iterative adaptive threshold (IAT) based online search with variational min-max and Markov random fields (MRF) model are used on the SSF image to segment the roads and non-roads. The roads are extracting by using the rules based on the connected component analysis. IAT and MRF model segmentation methods prove the proposed index (RI) able to extract road features productively. The proposed methodology is a combination of saturation based adaptive thresholding and morphology (SATM), and saturation based MRF (SMRF), applied to OLI images of several urban cities of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper.

Direct 3D model extraction method for color volume images

BACKGROUND: There is a great demand for the extraction of organ models from three-dimensional (3D) medical images in clinical medicine diagnosis and treatment. OBJECTIVE: We aimed to aid doctors in seeing the real shape of human organs more clearly and vividly. METHODS: The method uses the minimum eigenvectors of Laplacian matrix to automatically calculate a group of basic matting components that can properly define the volume image. These matting components can then be used to build foreground images with the help of a few user marks. RESULTS: We propose a direct 3D model segmentation method for volume images. This is a process of extracting foreground objects from volume images and estimating the opacity of the voxels covered by the objects. CONCLUSIONS: The results of segmentation experiments on different parts of human body prove the applicability of this method.