Effective g-factors of carriers in inverted InAs/GaSb bilayers

We perform tilt-field transport experiment on inverted InAs/GaSb which hosts quantum spin Hall insulator. By means of coincidence method, Landau level (LL) spectra of electron and hole carriers are systematically studied at different carrier densities tuned by gate voltages. When Fermi level stays in the conduction band, we observe LL crossing and anti-crossing behaviors at odd and even filling factors respectively, with a corresponding g-factor of 11.5. It remains nearly constant for varying filling factors and electron densities. On the contrary, for GaSb holes only a small Zeeman splitting is observed even at large tilt angles, indicating a g-factor of less than 3.Comment: 16 pages containing 4 figure

A regional solar forecasting approach using generative adversarial networks with solar irradiance maps

The intermittent and stochastic nature of solar resource hinders the integration of solar energy into modern power system. Solar forecasting has become an important tool for better photovoltaic (PV) power integration, effective market design, and reliable grid operation. Nevertheless, most existing solar forecasting methods are dedicated to improving forecasting accuracy at site-level (e.g. for individual PV power plants) regardless of the impacts caused by the accumulated penetration of distributed PV systems. To tackle with this issue, this article proposes a novel generative approach for regional solar forecasting considering an entire geographical region of a flexible spatial scale. Specifically, we create solar irradiance maps (SIMs) for solar forecasting for the first time by using spatial Kriging interpolation with satellite-derived solar irradiance data. The sequential SIMs provide a comprehensive view of how solar intensity varies over time and are further used as the inputs for a multi-scale generative adversarial network (GAN) to predict the next-step SIMs. The generated SIM frames can be further transformed into PV power output through a irradiance-to-power model. A case study is conducted in a 24 × 24 km area of Brisbane to validate the proposed method by predicting of both solar irradiance and the output of behind-the-meter (BTM) PV systems at unobserved locations. The approach demonstrates comparable accuracy in terms of solar irradiance forecasting and better predictions in PV power generation compared to the conventional forecasting models with a highest average forecasting skill of 10.93±2.35% for all BTM PV systems. Thus, it can be potentially used to assist solar energy assessment and power system control in a highly-penetrated region

The correlation analysis of gear tooth broken-pitting compound fault and single fault based on Laplacian Eigenmaps

Gear break and pitting are two common faults in transmission system, when these two faults coexist and form a compound fault, the damage speed and frequency of gear transmission system will be greatly increased. Taking the gear fault-pitting compound fault as the object, the dynamic model of gear single fault and compound fault is established, and the vibration characteristics of gear single fault, pitting single fault and broken tooth-pitting compound fault signal are analyzed. The characteristic manifolds of the intrinsic dimension space in the case of gear single failure and compound fault are extracted by using the Laplacian Eigenmaps algorithm, the evolution trend of single fault and compound fault in the overlapping region of the feature space, the degree of correlation and the curvature of the fault circle core are analyzed and obtained. The study found that with the deepening of the fault severity, the overlapping area of fault circle between compound fault and single fault become smaller gradually, that is, the degree of correlation become weakened, tooth broken single fault and compound fault can be identified in mid-late stage of fault, while the pitting single fault and compound fault are in the late stage. The experimental results of gearbox compound fault correlation show that the conclusion of the simulation analysis is correct and effective, which provides a new idea for the diagnosis of mechanical complex faults

Pyrene-Imidazole Based Aggregation Modifier Leads to Enhancement in Efficiency and Environmental Stability for Ternary Organic Solar Cells

A novel pyrene-imidazole derivative (PyPI), which can form effcient π-π stacking in solid film, has been utilized in organic solar cells (OSCs). The stacking of small a molecule PyPI can facilitate a charge transfer and suppress fullerene aggregation. As a result, PTB7-Th: PyPI: PC71BM based ternary OSC exhibits a high power conversion efficiency (PCE) of 10.36%, which presents a 15.88% increase from the binary device (8.94%). Concurrently, the ternary OSC shows a much better thermal and light illumination stability. Under continuous 60°C annealing for 3 h, in atmosphere, the device still remains at 94.13% efficiency more than the pristine state, while the control device remains at 52.47% PCE. Constant illumination under Air Mass (AM) 1.5G irradiation (100 mW cm−2) in atmosphere, the PCE of OSC remains at 72.50%. The high conversion efficiency and excellent environmental stability of the PyPI based ternary OSC, has narrowed the gap between laboratory investigation and industrial production

Observation of a Helical Luttinger-Liquid in InAs/GaSb Quantum Spin Hall Edges

We report on the observation of a helical Luttinger-liquid in the edge of InAs/GaSb quantum spin Hall insulator, which shows characteristic suppression of conductance at low temperature and low bias voltage. Moreover, the conductance shows power-law behavior as a function of temperature and bias voltage. The results underscore the strong electron-electron interaction effect in transport of InAs/GaSb edge states. Because of the fact that the Fermi velocity of the edge modes is controlled by gates, the Luttinger parameter can be fine tuned. Realization of a tunable Luttinger-liquid offers a one-dimensional model system for future studies of predicted correlation effects.Comment: 23 pages, 9 figure

Multiband FMCW Radar LSS-target Detection Dataset (LSS-FMCWR-1.0) and High-resolution Micromotion Feature Extraction Method

Detection of small, slow-moving targets, such as drones using Unmanned Aerial Vehicles (UAVs) poses considerable challenges to radar target detection and recognition technology. There is an urgent need to establish relevant datasets to support the development and application of techniques for detecting small, slow-moving targets. This paper presents a dataset for detecting low-speed and small-size targets using a multiband Frequency Modulated Continuous Wave (FMCW) radar. The dataset utilizes Ku-band and L-band FMCW radar to collect echo data from six UAV types and exhibits diverse temporal and frequency domain resolutions and measurement capabilities by modulating radar cycles and bandwidth, generating an LSS-FMCWR-1.0 dataset (Low Slow Small, LSS). To further enhance the capability for extracting micro-Doppler features from UAVs, this paper proposes a method for UAV micro-Doppler extraction and parameter estimation based on the local maximum synchroextracting transform. Based on the Short Time Fourier Transform (STFT), this method extracts values at the maximum energy point in the time-frequency domain to retain useful signals and refine the time-frequency energy representation. Validation and analysis using the LSS-FMCWR-1.0 dataset demonstrate that this approach reduces entropy on an average by 5.3 dB and decreases estimation errors in rotor blade length by 27.7% compared with traditional time-frequency methods. Moreover, the proposed method provides the foundation for subsequent target recognition efforts because it balances high time-frequency resolution and parameter estimation capabilities

On the use of sky images for intra-hour solar forecasting benchmarking: Comparison of indirect and direct approaches

The transient stability of the grid is challenged by short-term photovoltaic output fluctuations, which are mainly caused by local clouds. To address this issue, intra-hour solar forecasting has been widely adopted. Sky images have been proved as promising sources to produce intra-hour solar forecasts. To incorporate with cloud dynamics, sky images are typically embedded into solar forecasting models either indirectly or directly. While the performance of these methods varies across different forecasting environments, a detailed analysis on indirect and direct approaches have not been investigated yet. In this research, we conduct a comprehensive study on the performance of 7 commonly-used sky image-based solar forecasting approaches, including four indirect and three direct models. A total of 72 forecasting settings are established to evaluate the performance of these models. Three critical parameters are specially considered, namely image resolution, image sequence length, and forecast horizon. Results show that among these forecasting models, the stacking ensemble learning and the convolutional neural network + long short-term memory network model typically show the best forecasting performance for indirect and direct workflows, respectively. Compared with the direct approaches, the indirect approaches advance at detecting ramp events with an average the ramp score of 21.65 W/(m2×min). The direct approaches, on the other hand, outperform the indirect approaches on forecasting accuracy with an average forecast skill of 24.62%. The results of this work can be used as a general guideline for intra-hour solar forecasting benchmark selection

Radar Intelligent Processing Technology and Application for Weak Target

Weak target signal processing is the cornerstone and prerequisite for radar to achieve excellent detection performance. In complex practical applications, due to strong clutter interference, weak target signals, unclear image features, and difficult effective feature extraction, weak target detection and recognition have always been challenging in the field of radar processing. Conventional model-based processing methods do not accurately match the actual working background and target characteristics, leading to weak universality. Recently, deep learning has made significant progress in the field of radar intelligent information processing. By building deep neural networks, deep learning algorithms can automatically learn feature representations from a large amount of radar data, improving the performance of target detection and recognition. This article systematically reviews and summarizes recent research progress in the intelligent processing of weak radar targets in terms of signal processing, image processing, feature extraction, target classification, and target recognition. This article discusses noise and clutter suppression, target signal enhancement, low- and high-resolution radar image and feature processing, feature extraction, and fusion. In response to the limited generalization ability, single feature expression, and insufficient interpretability of existing intelligent processing applications for weak targets, this article underscores future developments from the aspects of small sample object detection (based on transfer learning and reinforcement learning), multidimensional and multifeature fusion, network model interpretability, and joint knowledge- and data-driven processing

Deep Learning-Based Multi-Step Solar Forecasting for PV Ramp-Rate Control Using Sky Images

Solar forecasting is one of the most promising approaches to address the intermit PV power generation by providing predictions before upcoming ramp events. In this paper, a novel multi-step forecasting (MSF) scheme is proposed for PV power ramp-rate control (PRRC). This method utilizes an ensemble of deep ConvNets without additional time-series models and exogenous variables, thus more suitable for industrial applications. The MSF strategy can make multiple predictions in comparison with a single forecasting point produced by a conventional method while maintaining the same high temporal resolution. Besides, stacked sky images that integrate temporal-spatial (ST) information of cloud motions are used to further improve the forecasting performance. The results demonstrate a favorable forecasting accuracy in comparison to the existing forecasting models with the highest skill score of 17.7%. In the PRRC application, the MSF-based PRRC can detect more ramp-rates violations with a higher control rate of 98.9% compared with the conventional forecasting based control. Thus, the PV generation can be effectively smoothed with less energy curtailment on both clear and cloudy days using the proposed approach