88 research outputs found

    Enhanced Photodetection in Graphene-Integrated Photonic Crystal Cavity

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    We demonstrate the controlled enhancement of photoresponsivity in a graphene photodetector by coupling to slow light modes in a long photonic crystal linear defect cavity. Near the Brillouin zone (BZ) boundary, spectral coupling of multiple cavity modes results in broad-band photocurrent enhancement from 1530 nm to 1540 nm. Away from the BZ boundary, individual cavity resonances enhance the photocurrent eight-fold in narrow resonant peaks. Optimization of the photocurrent via critical coupling of the incident field with the graphene-cavity system is discussed. The enhanced photocurrent demonstrates the feasibility of a wavelength-scale graphene photodetector for efficient photodetection with high spectral selectivity and broadband response

    “Super-deblended” dust emission in galaxies. I. The GOODS-North catalog and the cosmic star formation rate density out to redshift 6

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    We present a new technique to measure multi-wavelength “super-deblended” photometry from highly confused images, which we apply to Herschel and ground-based far-infrared (FIR) and (sub-)millimeter (mm) data in the northern field of the Great Observatories Origins Deep Survey. There are two key novelties. First, starting with a large database of deep Spitzer 24 μm and VLA 20 cm detections that are used to define prior positions for fitting the FIR/submm data, we perform an active selection of useful priors independently at each frequency band, moving from less to more confused bands. Exploiting knowledge of redshift and all available photometry, we identify hopelessly faint priors that we remove from the fitting pool. This approach significantly reduces blending degeneracies and allows reliable photometry to be obtained for galaxies in FIR+mm bands. Second, we obtain well-behaved, nearly Gaussian flux density uncertainties, individually tailored to all fitted priors for each band. This is done by exploiting extensive simulations that allow us to calibrate the conversion of formal fitting uncertainties to realistic uncertainties, depending on directly measurable quantities. We achieve deeper detection limits with high fidelity measurements and uncertainties at FIR+mm bands. As an illustration of the utility of these measurements, we identify 70 galaxies with z≥slant 3 and reliable FIR+mm detections. We present new constraints on the cosmic star formation rate density at 3< z< 6, finding a significant contribution from z≥slant 3 dusty galaxies that are missed by optical-to-near-infrared color selection. Photometric measurements for 3306 priors, including more than 1000 FIR+mm detections, are released publicly with our catalog

    DCFF-MTAD: A Multivariate Time-Series Anomaly Detection Model Based on Dual-Channel Feature Fusion

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    The detection of anomalies in multivariate time-series data is becoming increasingly important in the automated and continuous monitoring of complex systems and devices due to the rapid increase in data volume and dimension. To address this challenge, we present a multivariate time-series anomaly detection model based on a dual-channel feature extraction module. The module focuses on the spatial and time features of the multivariate data using spatial short-time Fourier transform (STFT) and a graph attention network, respectively. The two features are then fused to significantly improve the model’s anomaly detection performance. In addition, the model incorporates the Huber loss function to enhance its robustness. A comparative study of the proposed model with existing state-of-the-art ones was presented to prove the effectiveness of the proposed model on three public datasets. Furthermore, by using in shield tunneling applications, we verify the effectiveness and practicality of the model

    Research on Short-term Multi-objective Optimization Scheduling oriented Peak Regulation of Power Network

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    With the succession of river basins and inter-basin hydropower stations, the joint optimal operation of cascade hydropower stations in the river basin has large-scale, nonlinear, strong coupling, and multi-target characteristics, and must consider the effects of hydrometeorology, water demand, and power grid security. Focusing on the preparation of short-term power generation plans for cascade hydropower stations on the Qingjiang River, a comprehensive multi-objetive power generation planning model with the largest total power generation and the least load variance on the power grid is established. Based on the constraint processing method of multi-objective optimization scheduling in long-term, the optimal flow distribution technology is adopted to improve the accuracy of power generation planning. The above model is solved by using SMPSO. The results show that the improved algorithm can effectively overcome the shortcomings of slow convergence speed and easy convergence to local optimum. It can improve the power generation efficiency of the whole cascade while responding to the peaking demand of the power grid and provide a new solution to the short-term power generation planning ideas

    Research on Short-term Multi-objective Optimization Scheduling oriented Peak Regulation of Power Network

    No full text
    With the succession of river basins and inter-basin hydropower stations, the joint optimal operation of cascade hydropower stations in the river basin has large-scale, nonlinear, strong coupling, and multi-target characteristics, and must consider the effects of hydrometeorology, water demand, and power grid security. Focusing on the preparation of short-term power generation plans for cascade hydropower stations on the Qingjiang River, a comprehensive multi-objetive power generation planning model with the largest total power generation and the least load variance on the power grid is established. Based on the constraint processing method of multi-objective optimization scheduling in long-term, the optimal flow distribution technology is adopted to improve the accuracy of power generation planning. The above model is solved by using SMPSO. The results show that the improved algorithm can effectively overcome the shortcomings of slow convergence speed and easy convergence to local optimum. It can improve the power generation efficiency of the whole cascade while responding to the peaking demand of the power grid and provide a new solution to the short-term power generation planning ideas
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