scholarly journals External Breaking Vibration Identification Method of Transmission Line Tower Based on Solar-Powered RFID Sensor and CNN

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 519
Author(s):  
Fangming Deng ◽  
Kaiyun Wen ◽  
Zhongxin Xie ◽  
Huafeng Liu ◽  
Jin Tong
Keyword(s):  
Transmission Line ◽  
Radio Frequency Identification ◽  
Descent Method ◽  
Identification Accuracy ◽  
Batch Size ◽  
Optimum Number ◽  
Gradient Descent Method ◽  
Super Capacitor ◽  
Identification Method ◽  
Solar Powered

This paper proposes an external breaking vibration identification method of transmission line tower based on a radio frequency identification (RFID) sensor and deep learning. The RFID sensor is designed to obtain the vibration signal of the transmission line tower. In order to achieve long-time monitoring and longer working distance, the proposed RFID sensor tag employs a photovoltaic cell combined with a super capacitor as the power management module. convolution neural network (CNN) is adopted to extract the characteristics of vibration signals and relevance vector machine (RVM) is then employed to achieve vibration pattern identification. Furthermore, the Softmax classifier and gradient descent method are used to adjust the weights and thresholds of CNN, so as to obtain a high-precision identification structure. The experiment results show that the minimum sensitivity of the proposed solar-powered RFID sensor tag is −29 dBm and the discharge duration of the super capacitor is 63.35 h when the query frequencies are 5/min. The optimum batch size of CNN is 5, and the optimum number of convolution cores in the first layer and the second layer are 2 and 4, respectively. The maximum number of iterations is 10 times. The vibration identification accuracy of the proposed method is over 99% under three different conditions.

scholarly journals Why Does Large Batch Training Result in Poor Generalization? A Comprehensive Explanation and a Better Strategy from the Viewpoint of Stochastic Optimization

2018 ◽  
Vol 30 (7) ◽  
pp. 2005-2023 ◽  
Author(s):  
Tomoumi Takase ◽  
Satoshi Oyama ◽  
Masahito Kurihara
Keyword(s):  
Gradient Descent ◽  
Optimization Problems ◽  
Descent Method ◽  
Batch Size ◽  
Gradient Descent Method ◽  
Neural Network Training ◽  
Nonconvex Optimization Problems ◽  
Large Batch ◽  
Network Training ◽  
Comprehensive Framework

We present a comprehensive framework of search methods, such as simulated annealing and batch training, for solving nonconvex optimization problems. These methods search a wider range by gradually decreasing the randomness added to the standard gradient descent method. The formulation that we define on the basis of this framework can be directly applied to neural network training. This produces an effective approach that gradually increases batch size during training. We also explain why large batch training degrades generalization performance, which previous studies have not clarified.

scholarly journals 3D-Web-GIS RFID Location Sensing System for Construction Objects

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Chien-Ho Ko
Keyword(s):  
Radio Frequency Identification ◽  
Gradient Descent ◽  
Target Object ◽  
Descent Method ◽  
Web Gis ◽  
Gradient Descent Method ◽  
Construction Site ◽  
Sensing System ◽  
Location Sensing ◽  
3D Web

Construction site managers could benefit from being able to visualize on-site construction objects. Radio frequency identification (RFID) technology has been shown to improve the efficiency of construction object management. The objective of this study is to develop a 3D-Web-GIS RFID location sensing system for construction objects. An RFID 3D location sensing algorithm combining Simulated Annealing (SA) and a gradient descent method is proposed to determine target object location. In the algorithm, SA is used to stabilize the search process and the gradient descent method is used to reduce errors. The locations of the analyzed objects are visualized using the 3D-Web-GIS system. A real construction site is used to validate the applicability of the proposed method, with results indicating that the proposed approach can provide faster, more accurate, and more stable 3D positioning results than other location sensing algorithms. The proposed system allows construction managers to better understand worksite status, thus enhancing managerial efficiency.

scholarly journals Mesoscale Modelling of Concretes Subjected to Triaxial Loadings: Mechanical Properties and Fracture Behaviour

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1099
Author(s):  
Qingqing Chen ◽  
Yuhang Zhang ◽  
Tingting Zhao ◽  
Zhiyong Wang ◽  
Zhihua Wang
Keyword(s):  
Mechanical Properties ◽  
Tensile Stress ◽  
Failure Criterion ◽  
Mesoscale Model ◽  
Fracture Behaviour ◽  
Descent Method ◽  
Triaxial Stress ◽  
Gradient Descent Method ◽  
Stress States ◽  
Shear Failures

The mechanical properties and fracture behaviour of concretes under different triaxial stress states were investigated based on a 3D mesoscale model. The quasistatic triaxial loadings, namely, compression–compression–compression (C–C–C), compression–tension–tension (C–T–T) and compression–compression–tension (C–C–T), were simulated using an implicit solver. The mesoscopic modelling with good robustness gave reliable and detailed damage evolution processes under different triaxial stress states. The lateral tensile stress significantly influenced the multiaxial mechanical behaviour of the concretes, accelerating the concrete failure. With low lateral pressures or tensile stress, axial cleavage was the main failure mode of the specimens. Furthermore, the concretes presented shear failures under medium lateral pressures. The concretes experienced a transition from brittle fracture to plastic failure under high lateral pressures. The Ottosen parameters were modified by the gradient descent method and then the failure criterion of the concretes in the principal stress space was given. The failure criterion could describe the strength characteristics of concrete materials well by being fitted with experimental data under different triaxial stress states.

scholarly journals Unknown SAR Target Identification Method Based on Feature Extraction Network and KLD–RPA Joint Discrimination

2021 ◽  
Vol 13 (15) ◽  
pp. 2901
Author(s):  
Zhiqiang Zeng ◽  
Jinping Sun ◽  
Congan Xu ◽  
Haiyang Wang
Keyword(s):  
Feature Extraction ◽  
Position Angle ◽  
Automatic Target Recognition ◽  
Identification Accuracy ◽  
Training Dataset ◽  
Target Feature ◽  
Practical Applications ◽  
Feature Vectors ◽  
Identification Method ◽  
Leibler Divergence

Recently, deep learning (DL) has been successfully applied in automatic target recognition (ATR) tasks of synthetic aperture radar (SAR) images. However, limited by the lack of SAR image target datasets and the high cost of labeling, these existing DL based approaches can only accurately recognize the target in the training dataset. Therefore, high precision identification of unknown SAR targets in practical applications is one of the important capabilities that the SAR–ATR system should equip. To this end, we propose a novel DL based identification method for unknown SAR targets with joint discrimination. First of all, the feature extraction network (FEN) trained on a limited dataset is used to extract the SAR target features, and then the unknown targets are roughly identified from the known targets by computing the Kullback–Leibler divergence (KLD) of the target feature vectors. For the targets that cannot be distinguished by KLD, their feature vectors perform t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction processing to calculate the relative position angle (RPA). Finally, the known and unknown targets are finely identified based on RPA. Experimental results conducted on the MSTAR dataset demonstrate that the proposed method can achieve higher identification accuracy of unknown SAR targets than existing methods while maintaining high recognition accuracy of known targets.

Sign in / Sign up

Export Citation Format

Share Document