Remote error estimation of smart meter based on clustering and adaptive gradient descent method

2021 ◽  
pp. 1-11
Author(s):  
Liang Chen ◽  
Youpeng Huang ◽  
Tao Lu ◽  
Sanlei Dang ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Error Estimation ◽  
Gradient Descent ◽  
High Reliability ◽  
Estimation Method ◽  
Descent Method ◽  
Operating Conditions ◽  
Gradient Descent Method ◽  
Smart Meter ◽  
Smart Meters ◽  
Widespread Application

At present, the main way for electric power companies to check the accuracy of electric meters is that professionals regularly bring standard electric meters to the site for verification. With the widespread application of smart meters and the development of data processing technology, remote error estimation based on the operating data of smart meters becomes possible. In this paper, an error estimation method of smart meter based on clustering and adaptive gradient descent method is proposed. Firstly, the fuzzy c-means clustering method is used to preprocess the data to classify the operating conditions of each measurement, and then the adaptive gradient descent method is used to establish the error estimation model. The simulation results show that this method has high error estimation accuracy. This method has a small amount of calculation and high reliability and is suitable for large-scale power grids.

scholarly journals A New In-Flight Alignment Method with an Application to the Low-Cost SINS/GPS Integrated Navigation System

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 512 ◽  
Author(s):  
Zhenglong Lu ◽  
Jie Li ◽  
Xi Zhang ◽  
Kaiqiang Feng ◽  
Xiaokai Wei ◽  
...  
Keyword(s):  
Navigation System ◽  
Gradient Descent ◽  
Low Cost ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Integrated Navigation ◽  
Integration Interval ◽  
Integrated Navigation System

The optimization-based alignment (OBA) methods, which are implemented by the optimal attitude estimation using vector observations—also called double-vectors—have proven to be effective at solving the in-flight alignment (IFA) problem. However, the traditional OBA methods are not applicable for the low-cost strap-down inertial navigation system (SINS) since the error of double-vectors will be accumulated over time due to the substantial drift of micro-electronic- mechanical system (MEMS) gyroscope. Moreover, the existing optimal estimation method is subject to a large computation burden, which results in a low alignment speed. To address these issues, in this article we propose a new fast IFA method based on modified double-vectors construction and the gradient descent method. To be specific, the modified construction method is implemented by reducing the integration interval and identifying the gyroscope bias during the construction procedure, which improves the accuracy of double-vectors and IFA; the gradient descent scheme is adopted to estimate the optimal attitude of alignment without complex matrix operation, which results in the improvement of alignment speed. The effect of different sizes of mini-batch on the performance of the gradient descent method is also discussed. Extensive simulations and vehicle experiments demonstrate that the proposed method has better accuracy and faster alignment speed than the related traditional methods for the low-cost SINS/global positioning system (GPS) integrated navigation system

scholarly journals Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent

2020 ◽  
Vol 34 (04) ◽  
pp. 6909-6916 ◽  
Author(s):  
Pu Zhao ◽  
Pin-yu Chen ◽  
Siyue Wang ◽  
Xue Lin
Keyword(s):  
Gradient Descent ◽  
State Of The Art ◽  
High Reliability ◽  
High Sensitivity ◽  
Black Box ◽  
Descent Method ◽  
Learning Performance ◽  
Gradient Descent Method ◽  
Natural Gradient ◽  
Zeroth Order

Despite the great achievements of the modern deep neural networks (DNNs), the vulnerability/robustness of state-of-the-art DNNs raises security concerns in many application domains requiring high reliability. Various adversarial attacks are proposed to sabotage the learning performance of DNN models. Among those, the black-box adversarial attack methods have received special attentions owing to their practicality and simplicity. Black-box attacks usually prefer less queries in order to maintain stealthy and low costs. However, most of the current black-box attack methods adopt the first-order gradient descent method, which may come with certain deficiencies such as relatively slow convergence and high sensitivity to hyper-parameter settings. In this paper, we propose a zeroth-order natural gradient descent (ZO-NGD) method to design the adversarial attacks, which incorporates the zeroth-order gradient estimation technique catering to the black-box attack scenario and the second-order natural gradient descent to achieve higher query efficiency. The empirical evaluations on image classification datasets demonstrate that ZO-NGD can obtain significantly lower model query complexities compared with state-of-the-art attack methods.

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