Physics-guided Residual Learning for Probabilistic Power Flow Analysis

Probabilistic power flow (PPF) analysis is critical to power system operation and planning. PPF aims at obtaining probabilistic descriptions of the state of the system with stochastic power injections (e.g., renewable power generation and load demands). Given power injection samples, numerical methods repeatedly run classic power flow (PF) solvers to find the voltage phasors. However, the computational burden is heavy due to many PF simulations. Recently, many data-driven based PF solvers have been proposed due to the availability of sufficient measurements. This paper proposes a novel neural network (NN) framework which can accurately approximate the non-linear AC-PF equations. The trained NN works as a rapid PF solver, significantly reducing the heavy computational burden in classic PPF analysis. Inspired by residual learning, we develop a fully connected linear layer between the input and output in the multilayer perceptron (MLP). To improve the NN training convergence, we propose three schemes to initialize the NN weights of the shortcut connection layer based on the physical characteristics of AC-PF equations. Specifically, two model-based methods require the knowledge of system topology and line parameters, while the purely data-driven method can work without power grid parameters. Numerical tests on five benchmark systems show that our proposed approaches achieve higher accuracy in estimating voltage phasors than existing methods. In addition, three meticulously designed initialization schemes help the NN training process converge faster, which is appealing under limited training time.Comment: Probabilistic power flow, data-driven, residual learning, neural network, physics-guided initializatio

Existence of APAV(q,k) with q a prime power ≡5(mod8) and k≡1(mod4)

AbstractStinson introduced authentication perpendicular arrays APAλ(t,k,v), as a special kind of perpendicular arrays, to construct authentication and secrecy codes. Ge and Zhu introduced APAV(q,k) to study APA1(2,k,v) for k=5, 7. Chen and Zhu determined the existence of APAV(q,k) with q a prime power ≡3(mod4) and odd k>1. In this article, we show that for any prime power q≡5(mod8) and any k≡1(mod4) there exists an APAV(q,k) whenever q>((E+E2+4F)/2)2, where E=[(7k−23)m+3]25m−3, F=m(2m+1)(k−3)25m and m=(k−1)/4

Optimal load scheduling of household appliances considering consumer preferences : an experimental analysis

Abstract: This paper discusses an experimental study of the home appliances scheduling problem that incorporates realistic aspects. The residential load scheduling problem is solved while considering consumer’s preferences. The objective function minimizes the weighted sum of electricity cost by earning relevant incentives, and the scheduling inconvenience. The objective of this study is five-fold. First, it sought to develop and solve a binary integer linear programming optimization model for the problem. Second, it examined the factors that might affect the obtained schedule of residential loads. Third, it aimed to test the performance of a developed optimization model under different experimental scenarios. Fourth, it proposes a conceptual definition of a new parameter in the problem, the so-called “flexibility ratio”. Finally, it adds a data set for use in the literature on the home appliance scheduling problem, which can be used to test the performance of newly-developed approaches to the solution of this problem. This paper presents the results of experimental analysis using four factors: problem size, flexibility ratio, time slot length and the objective function weighting factor. The experimental results show the main and interaction effects, where these exist, on three performance measures: the electricity cost, inconvenience and the optimization model computation time

Pose-disentangled Contrastive Learning for Self-supervised Facial Representation

Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets heavily. However, analytically, current contrastive-based self-supervised learning still performs unsatisfactorily for learning facial representation. More specifically, existing contrastive learning (CL) tends to learn pose-invariant features that cannot depict the pose details of faces, compromising the learning performance. To conquer the above limitation of CL, we propose a novel Pose-disentangled Contrastive Learning (PCL) method for general self-supervised facial representation. Our PCL first devises a pose-disentangled decoder (PDD) with a delicately designed orthogonalizing regulation, which disentangles the pose-related features from the face-aware features; therefore, pose-related and other pose-unrelated facial information could be performed in individual subnetworks and do not affect each other's training. Furthermore, we introduce a pose-related contrastive learning scheme that learns pose-related information based on data augmentation of the same image, which would deliver more effective face-aware representation for various downstream tasks. We conducted a comprehensive linear evaluation on three challenging downstream facial understanding tasks, i.e., facial expression recognition, face recognition, and AU detection. Experimental results demonstrate that our method outperforms cutting-edge contrastive and other self-supervised learning methods with a great margin

Bayesian Nonlinear Tensor Regression with Functional Fused Elastic Net Prior

Tensor regression methods have been widely used to predict a scalar response from covariates in the form of a multiway array. In many applications, the regions of tensor covariates used for prediction are often spatially connected with unknown shapes and discontinuous jumps on the boundaries. Moreover, the relationship between the response and the tensor covariates can be nonlinear. In this article, we develop a nonlinear Bayesian tensor additive regression model to accommodate such spatial structure. A functional fused elastic net prior is proposed over the additive component functions to comprehensively model the nonlinearity and spatial smoothness, detect the discontinuous jumps, and simultaneously identify the active regions. The great flexibility and interpretability of the proposed method against the alternatives are demonstrated by a simulation study and an analysis on facial feature data

Inhibition of AKT2 Enhances Sensitivity to Gemcitabine via Regulating PUMA and NF-κB Signaling Pathway in Human Pancreatic Ductal Adenocarcinoma

Invasion, metastasis and resistance to conventional chemotherapeutic agents are obstacles to successful treatment of pancreatic cancer, and a better understanding of the molecular basis of this malignancy may lead to improved therapeutics. In the present study, we investigated whether AKT2 silencing sensitized pancreatic cancer L3.6pl, BxPC-3, PANC-1 and MIAPaCa-2 cells to gemcitabine via regulating PUMA (p53-upregulated modulator of apoptosis) and nuclear factor (NF)-κB signaling pathway. MTT, TUNEL, EMSA and NF-κB reporter assays were used to detect tumor cell proliferation, apoptosis and NF-κB activity. Western blotting was used to detect different protein levels. Xenograft of established tumors was used to evaluate primary tumor growth and apoptosis after treatment with gemcitabine alone or in combination with AKT2 siRNA. Gemcitabine activated AKT2 and NF-κB in MIAPaCa-2 and L3.6pl cells in vitro or in vivo, and in PANC-1 cells only in vivo. Gemcitabine only activated NF-κB in BxPC-3 cells in vitro. The presence of PUMA was necessary for gemcitabine-induced apoptosis only in BxPC-3 cells in vitro. AKT2 inhibition sensitized gemcitabine-induced apoptosis via PUMA upregulation in MIAPaCa-2 cells in vitro, and via NF-κB activity inhibition in L3.6pl cells in vitro. In PANC-1 and MIAPaCa-2 cells in vivo, AKT2 inhibition sensitized gemcitabine-induced apoptosis and growth inhibition via both PUMA upregulation and NF-κB inhibition. We suggest that AKT2 inhibition abrogates gemcitabine-induced activation of AKT2 and NF-κB, and promotes gemcitabine-induced PUMA upregulation, resulting in chemosensitization of pancreatic tumors to gemcitabine, which is probably an important strategy for the treatment of pancreatic cancer