216 research outputs found
Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach with Safe Gradient Flow
Deep reinforcement learning approaches are becoming appealing for the design
of nonlinear controllers for voltage control problems, but the lack of
stability guarantees hinders their deployment in real-world scenarios. This
paper constructs a decentralized RL-based controller featuring two components:
a transient control policy and a steady-state performance optimizer. The
transient policy is parameterized as a neural network, and the steady-state
optimizer represents the gradient of the long-term operating cost function. The
two parts are synthesized through a safe gradient flow framework, which
prevents the violation of reactive power capacity constraints. We prove that if
the output of the transient controller is bounded and monotonically decreasing
with respect to its input, then the closed-loop system is asymptotically stable
and converges to the optimal steady-state solution. We demonstrate the
effectiveness of our method by conducting experiments with IEEE 13-bus and
123-bus distribution system test feeders.Comment: Published in IEEE Control Systems Letters, vol. 7, pp. 2845-2850,
2023 with CDC presentatio
Dynamical commensuration effect in a two-dimensional Yukawa solid modulated by periodic substrates
Transverse depinning dynamics of a periodic-square-substrate modulated
two-dimensional dusty plasma solid driven by a constant force in the
longitudinal direction are investigated using Langevin dynamical simulations.
During the increase of the commensuration ratio (the number ratio of particles
to substrate potential wells), the nonmonotonic variation trend of the critical
transverse depinning force is observed. It is found that the local maxima and
minima of the critical transverse depinning force just correspond to the
dynamical commensurate and incommensurate conditions, respectively. The
dynamical commensurate effect is also clearly exhibited from the stable
one-dimensional channel particle trajectories and the highly ordered structure,
however, both the particle trajectories and the structure are more disordered
under the incommensurate conditions. The nonmonotonic variation of the critical
transverse depinning force is attributed to the stability of the lattice
structure under various commensuration ratios.Comment: 9 pages, 4 postscript figure
Characteristics, current exploration practices, and prospects of continental shale oil in China
Oil generation in the continental shale has laid the resource foundation for the originality and development of Chinaâs petroleum industry; continental shale oil production is blazing a new trail in this ďŹeld. In this paper, based on the geological conditions of continental shale oil in China, it is found that the main types of shale oil generally have four basic geological characteristics, which are large-scale continuous distribution, the domination of inorganic pores, the enrichment of âsweet areasâ, and initial production that is controlled by relatively high organic maturity and high yield that is governed by relatively high formation pressure. Then, as examples for the geological characteristics and development practice of continental shale oil, four key areas of Longdong, Gulong, Jimsar, and Jiyang are systematically summarized. Finally, the future prospects of continental shale oil in China are put forward. Middle-high maturity shale oil is currently the main force of development, and middle-low maturity shale oil also has a considerable development prospect after technological improvement. Meanwhile, âsweet area/spot sectionsâ assessment and technological innovation are still research areas to be improved.Cited as: Wang, X., Li, J., Jiang, W., Zhang, H., Feng Y., Yang Z. Characteristics, current exploration practices, and prospects of continental shale oil in China. Advances in Geo-Energy Research, 2022, 6(6): 454-459. https://doi.org/10.46690/ager.2022.06.0
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides
Objectives: To develop and validate a deep learning (DL)-based primary tumor
biopsy signature for predicting axillary lymph node (ALN) metastasis
preoperatively in early breast cancer (EBC) patients with clinically negative
ALN.
Methods: A total of 1,058 EBC patients with pathologically confirmed ALN
status were enrolled from May 2010 to August 2020. A DL core-needle biopsy
(DL-CNB) model was built on the attention-based multiple instance-learning
(AMIL) framework to predict ALN status utilizing the DL features, which were
extracted from the cancer areas of digitized whole-slide images (WSIs) of
breast CNB specimens annotated by two pathologists. Accuracy, sensitivity,
specificity, receiver operating characteristic (ROC) curves, and areas under
the ROC curve (AUCs) were analyzed to evaluate our model.
Results: The best-performing DL-CNB model with VGG16_BN as the feature
extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865)
in predicting positive ALN metastasis in the independent test cohort.
Furthermore, our model incorporating the clinical data, which was called
DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially
for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The
interpretation of DL-CNB model showed that the top signatures most predictive
of ALN metastasis were characterized by the nucleus features including density
( = 0.015), circumference ( = 0.009), circularity ( = 0.010), and
orientation ( = 0.012).
Conclusion: Our study provides a novel DL-based biomarker on primary tumor
CNB slides to predict the metastatic status of ALN preoperatively for patients
with EBC. The codes and dataset are available at
https://github.com/bupt-ai-cz/BALNMPComment: Update Table 1 and corresponding description
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