In recent years, subsynchronous control interaction (SSCI) has frequently taken place in renewable-connected power systems. To counter this issue, utilities have been seeking tools for fast and ...accurate identification of SSCI events. The main challenges of SSCI monitoring are the time-varying nature and uncertain modes of SSCI events. Accordingly, this paper presents a simple but effective method that takes advantage of intrinsic time-scale decomposition (ITD). The main purpose is to improve the accuracy and robustness of ITD by incorporating the least-squares method. Results show that the proposed method strikes a good balance between dynamic performance and estimation accuracy. More importantly, the method does not require any prior information, and its performance is therefore not affected by the frequency constitution of the SSCI. Comprehensive comparative studies are conducted to demonstrate the usefulness of the method through synthetic signals, electromagnetic temporary program (EMTP) simulations, and field-recorded SSCI data. Finally, real-time simulation tests are conducted to show the feasibility of the method for real-time monitoring.
The identification of homogeneous subgroups of patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding ...neuropsychological mechanisms of various mental disorders. The functional connectivity profiles obtained from functional magnetic resonance imaging (fMRI) data have been shown to be unique to each individual, similar to fingerprints; however, their use in characterizing psychiatric disorders in a clinically useful way is still being studied. In this work, we propose a framework that makes use of functional activity maps for subgroup identification using the Gershgorin disc theorem. The proposed pipeline is designed to analyze a large-scale multi-subject fMRI dataset with a fully data-driven method, a new constrained independent component analysis algorithm based on entropy bound minimization (c-EBM), followed by an eigenspectrum analysis approach. A set of resting-state network (RSN) templates is generated from an independent dataset and used as constraints for c-EBM. The constraints present a foundation for subgroup identification by establishing a connection across the subjects and aligning subject-wise separate ICA analyses. The proposed pipeline was applied to a dataset comprising 464 psychiatric patients and discovered meaningful subgroups. Subjects within the identified subgroups share similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas including dorsolateral prefrontal cortex and anterior cingulate cortex. Three sets of cognitive test scores were used to verify the identified subgroups, and most of them showed significant differences across subgroups, which provides further confirmation of the identified subgroups. In summary, this work represents an important step forward in using neuroimaging data to characterize mental disorders.
Purpose
Glutamate weighted Chemical Exchange Saturation Transfer (GluCEST) MRI is a noninvasive technique for mapping parenchymal glutamate in the brain. Because of the sensitivity to field (B0) ...inhomogeneity, the total acquisition time is prolonged due to the repeated image acquisitions at several saturation offset frequencies, which can cause practical issues such as increased sensitivity to patient motions. Because GluCEST signal is derived from the small z‐spectrum difference, it often has a low signal‐to‐noise‐ratio (SNR). We proposed a novel deep learning (DL)‐based algorithm armed with wide activation neural network blocks to address both issues.
Methods
B0 correction based on reduced saturation offset acquisitions was performed for the positive and negative sides of the z‐spectrum separately. For each side, a separate deep residual network was trained to learn the nonlinear mapping from few CEST‐weighted images acquired at different ppm values to the one at 3 ppm (where GluCEST peaks) in the same side of the z‐spectrum.
Results
All DL‐based methods outperformed the “traditional” method visually and quantitatively. The wide activation blocks‐based method showed the highest performance in terms of Structural Similarity Index (SSIM) and peak signal‐to‐noise ratio (PSNR), which were 0.84 and 25dB respectively. SNR increases in regions of interest were over 8dB.
Conclusion
We demonstrated that the new DL‐based method can reduce the entire GluCEST imaging time by ˜50% and yield higher SNR than current state‐of‐the‐art.
Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, ...limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data's true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the "shared" subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs.
Recent years have seen increased research interest in replacing the computationally intensive Magnetic resonance (MR) image reconstruction process with deep neural networks. We claim in this paper ...that the traditional image reconstruction methods and deep learning (DL) are mutually complementary and can be combined to achieve better image reconstruction quality. To test this hypothesis, a hybrid DL image reconstruction method was proposed by combining a state-of-the-art deep learning network, namely a generative adversarial network with cycle loss (CycleGAN), with a traditional data reconstruction algorithm: Projection Onto Convex Set (POCS). The output of the first iteration's training results of the CycleGAN was updated by POCS and used as the extra training data for the second training iteration of the CycleGAN. The method was validated using sub-sampled Magnetic resonance imaging data. Compared with other state-of-the-art, DL-based methods (e.g., U-Net, GAN, and RefineGAN) and a traditional method (compressed sensing), our method showed the best reconstruction results.
This paper is concerned with both voltage and frequency regulation problem for AC microgrid under false data injection (FDI) attacks in the secondary control layer. A distributed convergent observer ...is first designed based on an iterative mean estimation technique, which can accurately estimate the attack signals with theoretical proof of convergence provided. Based on the iterative mean estimation information, a distributed resilient controller is designed to compensate for the effect of cyber attacks in the secondary control layer. It is theoretically shown that the proposed strategy can restore accurate regulation of the voltage and frequency of AC microgrid in the presence of FDI attacks in the secondary control layer. Finally, an AC microgrid system containing four distributed generations and two leader nodes is constructed in MATLAB/Simulink for simulation, and the results validate that the proposed controller is effective for regulating voltage and frequency.
Arterial spin labeling (ASL) perfusion MRI is a noninvasive technique for measuring cerebral blood flow (CBF) in a quantitative manner. A technical challenge in ASL MRI is data processing because of ...the inherently low signal-to-noise-ratio (SNR). Deep learning (DL) is an emerging machine learning technique that can learn a nonlinear transform from acquired data without using any explicit hypothesis. Such a high flexibility may be particularly beneficial for ASL denoising. In this paper, we proposed and validated a DL-based ASL MRI denoising algorithm (DL-ASL).
The DL-ASL network was constructed using convolutional neural networks (CNNs) with dilated convolution and wide activation residual blocks to explicitly take the inter-voxel correlations into account, and preserve spatial resolution of input image during model learning.
DL-ASL substantially improved the quality of ASL CBF in terms of SNR. Based on retrospective analyses, DL-ASL showed a high potential of reducing 75% of the original acquisition time without sacrificing CBF measurement quality.
DL-ASL achieved improved denoising performance for ASL MRI as compared with current routine methods in terms of higher PSNR, SSIM and Radiologic scores. With the help of DL-ASL, much fewer repetitions may be prescribed in ASL MRI, resulting in a great reduction of the total acquisition time.
In this paper we consider the long time behavior for an n-dimensional population model driven by truncated α-stable processes for interacting species. Some sufficient conditions for ergodicity or ...transience for our systems are given. This paper discloses the different jump measures impact on such pure-jumps population dynamics.
The subsynchronous damping controller (SSDC) has been widely recognized for its excellent performance and low cost in subsynchronous oscillation (SSO) mitigation for the doubly fed induction ...generator (DFIG)-based wind power system. However, the existing SSDCs are various and lack a systematic comparison. To fill this gap, the structures and parameter design methods of common SSDCs are sorted and compared in this paper. It is found that the rotor-current-based method performs best in terms of dynamic performance and robustness, as it can mitigate SSO for all working conditions in the test, while the feasibility range of other methods is much smaller. Therefore, the influence of different parameters in a rotor-current-based SSDC on SSO mitigation is further researched, leading to a guideline for parameter selection. More importantly, to address the challenge of time-varying oscillation frequency, an adaptive frequency selection method is proposed based on the eigensystem realization algorithm, which can accurately track the SSO frequency within 5~45 Hz. The results of the root locus analysis and hardware-in-the-loop experiment demonstrate that the improved rotor-current-based SSDC performs better than other existing methods, and it does not affect the normal operation of the DFIG.
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