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This article proposes a matrix auto-transformer switched-capacitor dc–dc converter to achieve a high voltage conversion ratio, high efficiency, and high power density for 48-V data-center applications. On the high-voltage side, the proposed converter can fully leverage the benefits of high-performance low voltage stress devices similar to the multilevel modular switched-capacitor converter. Compared with the traditional isolated LLC converter with a matrix transformer, the proposed solution utilized a matrix autotransformer concept with merged primary and secondary side windings, thus leading to reduced transformer winding loss. The resonant inductor could be integrated into the transformer similar to the LLC converter. Because of the matrix autotransformer design, it can achieve a current doubler rectifier on the low voltage side. For less than 8-V low output voltage application, the current doubler rectifier design can fully utilize the best figure-of-merit 25-V device, which is more efficient than the full-bridge rectifier solution using two 25-V devices during the operation. All the devices can achieve zero voltage switching or zero current switching and can be naturally clamped without additional clamping circuits. A 500-W 48-V to 6-V dc–dc converter hardware prototype has been developed with optimized device selection and integrated matrix autotransformer design. Both simulation and experiment results have been provided to validate the features and benefits of the proposed converter. The maximum efficiency of the proposed converter can reach 98.33%. 

Switched Tank Converter(STC) is one kind of Resonant Switch Capacitor(ReSC) that can be considered as a good candidate for data center application with high power efficiency and high power density. On the other hand, LLC converter can also realize very good performance for low voltage application. Although STC can realize relatively higher efficiency than LLC converter in the light load since the core loss is saved, LLC can keep relatively higher efficiency in the heavy load than STC does since the conduction loss of LLC is smaller. The main reason is because transformer’s winding resistance is smaller than semiconductor devices’ resistance, and this is very important for high current application.In order to utilize the benefits of the STC and the LLC converter together, this paper proposes a family of the novel Switch Capacitor based Integrated Matrix Autotransformer LLC Converters (SCIMAC). The proposed converters share the same high voltage side circuit of the STC with low voltage stress devices. Different from the traditional LLC converter with an isolated transformer, the proposed SCIMAC utilizes one autotransformer with only the secondary side windings similar to LLC's secondary side. There are several advantages that can be realized of the SCIMAC: 1). Low figure of merit (FOM) devices can be adopted to realize higher efficiency due to the low voltage stress of the SCIMAC. 2). Higher power efficiency can be realized when compared with STC converter in heavy load because the resistance of the autotransformer’s windings is lower than semiconductor devices’ resistance. 3). The primary side winding loss of the transformer is saved to further increase the efficiency. 4) ZVS turning on can be realized by the magnetizing current of the core. 

Abstract Understanding earthquake foreshocks is essential for deciphering earthquake rupture physics and can aid seismic hazard mitigation. With regional dense seismic arrays, we identify immediate foreshocks of 527 0.9M5.4 events of the 2019 Ridgecrest earthquake sequence, including 48 earthquakes with series of immediate foreshocks. These immediate foreshocks are adjacent to the mainshocks occurring within 100 s of the mainshocks, and their P waves share high resemblances with the mainshock P waves. However, attributes of the immediate‐foreshock P waves, including the amplitudes and preceding times, do not clearly scale with the mainshock magnitudes. Our observations suggest that earthquake rupture may initiate in a universal fashion but evolves stochastically. This indicates that earthquake rupture development is likely controlled by fine‐scale fault heterogeneities in the Ridgecrest fault system, and the final magnitude is the only difference between small and large earthquakes. 

Abstract Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal‐to‐noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake‐like signals originating from nontectonic sources.