Adaptive Electricity Scheduling in Microgrids

Microgrid (MG) is a promising component for future smart grid (SG) deployment. The balance of supply and demand of electric energy is one of the most important requirements of MG management. In this paper, we present a novel framework for smart energy management based on the concept of quality-of-service in electricity (QoSE). Specifically, the resident electricity demand is classified into basic usage and quality usage. The basic usage is always guaranteed by the MG, while the quality usage is controlled based on the MG state. The microgrid control center (MGCC) aims to minimize the MG operation cost and maintain the outage probability of quality usage, i.e., QoSE, below a target value, by scheduling electricity among renewable energy resources, energy storage systems, and macrogrid. The problem is formulated as a constrained stochastic programming problem. The Lyapunov optimization technique is then applied to derive an adaptive electricity scheduling algorithm by introducing the QoSE virtual queues and energy storage virtual queues. The proposed algorithm is an online algorithm since it does not require any statistics and future knowledge of the electricity supply, demand and price processes. We derive several "hard" performance bounds for the proposed algorithm, and evaluate its performance with trace-driven simulations. The simulation results demonstrate the efficacy of the proposed electricity scheduling algorithm.Comment: 12 pages, extended technical repor

Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets

Learning against label noise is a vital topic to guarantee a reliable performance for deep neural networks. Recent research usually refers to dynamic noise modeling with model output probabilities and loss values, and then separates clean and noisy samples. These methods have gained notable success. However, unlike cherry-picked data, existing approaches often cannot perform well when facing imbalanced datasets, a common scenario in the real world. We thoroughly investigate this phenomenon and point out two major issues that hinder the performance, i.e., \emph{inter-class loss distribution discrepancy} and \emph{misleading predictions due to uncertainty}. The first issue is that existing methods often perform class-agnostic noise modeling. However, loss distributions show a significant discrepancy among classes under class imbalance, and class-agnostic noise modeling can easily get confused with noisy samples and samples in minority classes. The second issue refers to that models may output misleading predictions due to epistemic uncertainty and aleatoric uncertainty, thus existing methods that rely solely on the output probabilities may fail to distinguish confident samples. Inspired by our observations, we propose an Uncertainty-aware Label Correction framework~(ULC) to handle label noise on imbalanced datasets. First, we perform epistemic uncertainty-aware class-specific noise modeling to identify trustworthy clean samples and refine/discard highly confident true/corrupted labels. Then, we introduce aleatoric uncertainty in the subsequent learning process to prevent noise accumulation in the label noise modeling process. We conduct experiments on several synthetic and real-world datasets. The results demonstrate the effectiveness of the proposed method, especially on imbalanced datasets

Measurement Uncertainty of Antenna Efficiency Measured Using the Two-Antenna Method in a Reverberation Chamber

With decades of development, the reverberation chamber (RC) has been proven to be a popular facility to determine antenna efficiency. One-, two- and three-antenna methods have been proposed to measure antenna efficiency without the need of a reference antenna. Due to the stochastic nature of RC-based measurements, the statistical analysis of the uncertainty is indispensable. Recently, the statistical uncertainty models for the one- and three-antenna methods were derived, however, the statistical model for the two-antenna method is still unknown to date. In this paper, the statistical uncertainty model of the two-antenna method is proposed. The approximated relative uncertainty is also given. The derived statistical uncertainty is verified by both simulations and measurements. It is experimentally verified that the statistical model can cope with hybrid stirring and assess the measurement uncertainty with and without frequency stirring in an efficient and convenient way

WAP four-disulfide core domain protein 2 promotes metastasis of human ovarian cancer by regulation of metastasis-associated genes.

BACKGROUND: WAP four-disulfide core domain protein 2 (WFDC2) shows a tumor-restricted upregulated pattern of expression in ovarian cancer. METHODS: In this study, we evaluated the role of WFCD2 in tumor mobility, invasion and metastasis of ovarian cancer in clinical tissue and in ovarian cancer cells, both in vitro and in vivo. RESULTS: Our results revealed WFCD2 was overexpressed in ovarian tissues, and the expression level of WFCD2 was associated with metastasis and lymph node metastasis. Higher expression of WFCD2 was also observed in aggressive HO8910-PM cells than in HO8910 cells, and WFCD2 knockdown halted cell migration, invasion, tumorigenicity and metastasis in ovarian cancer cells, both in vitro and in vivo. Knockdown of WFDC2 induced the down-regulation of ICAM-1, CD44, and MMP2. CONCLUSION: In summary, our work demonstrates that WFCD2 promotes metastasis in ovarian cancer. These findings suggest that WFCD2 plays a critical role in promoting metastasis and may constitute a potential therapeutic target of ovarian cancer

A Correntropy-Based Proportionate Affine Projection Algorithm for Estimating Sparse Channels with Impulsive Noise

A novel robust proportionate affine projection (AP) algorithm is devised for estimating sparse channels, which often occur in network echo and wireless communication channels. The newly proposed algorithm is realized by using the maximum correntropy criterion (MCC) and the data reusing scheme used in AP to overcome the identification performance degradation of the traditional PAP algorithm in impulsive noise environments. The proposed algorithm is referred to as the proportionate affine projection maximum correntropy criterion (PAPMCC) algorithm, which is derived in the context of channel estimation framework. Many simulation results were obtained to verify that the PAPMCC algorithm is superior to early reported AP algorithms with different input signals under impulsive noise environments

Confidence-based iterative efficient large-scale stereo matching

In this study, we integrate confidence into efficient large-scale stereo (ELAS) matching to produce a more accurate approach to binocular stereo for high-resolution image matching. Elas ensures good performance in the presence of poorly textured and slanted surfaces, but one of its deficiencies is its unsatisfactory ability to capture disparity discontinuities. Our formulation explicitly models the effects of confidence as a likelihood term in a principled manner using the Bayes rule. Because it is an iterative method, we associate each point with a variable confidence value and update this value based on a given confidence updating rule. Meanwhile, complementary support points are selected from stable points whose confidence value exceeds a predefined threshold, which differs from ELAS, whose support points are matched in advance and kept unchanged in the subsequent process. Confidence also plays a vital role in avoiding expensive computation, and the adjustment of support points makes disparity estimation more flexible. Quantitative evaluation demonstrates the effectiveness and efficiency of the proposed formulation in improving the accuracy of disparity estimation