Additive Neural Network Based Static and Dynamic Distortion Modeling for Prior-Knowledge-Free Nyquist ADC Characterization

2021 ◽  
Author(s):  
Danfeng Zhai ◽  
Peizhe Li ◽  
Jiushan Zhang ◽  
Chixiao Chen ◽  
Fan Ye ◽  
...  
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Dynamic Distortion

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

scholarly journals Case Study of Deep Learning Model of Temperature-Induced Deflection of a Cable-Stayed Bridge Driven by Data Knowledge

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2293
Author(s):  
Zixiang Yue ◽  
Youliang Ding ◽  
Hanwei Zhao ◽  
Zhiwen Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Prior Knowledge ◽  
Learning Model ◽  
Bridge Management ◽  
Learning Network ◽  
Cable Stayed Bridge ◽  
Main Girder ◽  
Deep Learning Model

A cable-stayed bridge is a typical symmetrical structure, and symmetry affects the deformation characteristics of such bridges. The main girder of a cable-stayed bridge will produce obvious deflection under the inducement of temperature. The regression model of temperature-induced deflection is hoped to provide a comparison value for bridge evaluation. Based on the temperature and deflection data obtained by the health monitoring system of a bridge, establishing the correlation model between temperature and temperature-induced deflection is meaningful. It is difficult to complete a high-quality model only by the girder temperature. The temperature features based on prior knowledge from the mechanical mechanism are used as the input information in this paper. At the same time, to strengthen the nonlinear ability of the model, this paper selects an independent recurrent neural network (IndRNN) for modeling. The deep learning neural network is compared with machine learning neural networks to prove the advancement of deep learning. When only the average temperature of the main girder is input, the calculation accuracy is not high regardless of whether the deep learning network or the machine learning network is used. When the temperature information extracted by the prior knowledge is input, the average error of IndRNN model is only 2.53%, less than those of BPNN model and traditional RNN. Combining knowledge with deep learning is undoubtedly the best modeling scheme. The deep learning model can provide a comparison value of bridge deformation for bridge management.

scholarly journals Study on estimating quantum discord by neural network with prior knowledge

2019 ◽  
Vol 18 (11) ◽  
Author(s):  
Yong-Lei Liu ◽  
An-Min Wang ◽  
Yi Sun ◽  
Peng-Fei Zhang ◽  
Guo-Dong Wang
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Quantum Discord

Weighing Fusion Method for Truck Scales Based on Prior Knowledge and Neural Network Ensembles

2014 ◽  
Vol 63 (2) ◽  
pp. 250-259 ◽  
Author(s):  
Haijun Lin ◽  
Yaping Lin ◽  
Jingrong Yu ◽  
Zhaosheng Teng ◽  
Lucai Wang
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Fusion Method ◽  
Neural Network Ensembles ◽  
Network Ensembles

scholarly journals Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network

2017 ◽  
Author(s):  
Jufeng Yang ◽  
Dongyu She ◽  
Ming Sun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prior Knowledge ◽  
State Of The Art ◽  
Ground Truth ◽  
Majority Voting ◽  
Emotion Classification ◽  
Voting Scheme ◽  
Label Distribution ◽  
Similarity Information

Visual sentiment analysis is attracting more and more attention with the increasing tendency to express emotions through visual contents. Recent algorithms in convolutional neural networks (CNNs) considerably advance the emotion classification, which aims to distinguish differences among emotional categories and assigns a single dominant label to each image. However, the task is inherently ambiguous since an image usually evokes multiple emotions and its annotation varies from person to person. In this work, we address the problem via label distribution learning (LDL) and develop a multi-task deep framework by jointly optimizing both classification and distribution prediction. While the proposed method prefers to the distribution dataset with annotations of different voters, the majority voting scheme is widely adopted as the ground truth in this area, and few dataset has provided multiple affective labels. Hence, we further exploit two weak forms of prior knowledge, which are expressed as similarity information between labels, to generate emotional distribution for each category. The experiments conducted on both distribution datasets, i.e., Emotion6, Flickr_LDL, Twitter_LDL, and the largest single emotion dataset, i.e., Flickr and Instagram, demonstrate the proposed method outperforms the state-of-the-art approaches.

scholarly journals Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Shaobo Wang ◽  
Pan Zhao ◽  
Biao Yu ◽  
Weixin Huang ◽  
Huawei Liang
Keyword(s):  
Neural Network ◽  
Knowledge Base ◽  
Prior Knowledge ◽  
Prediction Models ◽  
Short Term Memory ◽  
A Priori ◽  
Urban Environments ◽  
Intelligent Vehicles ◽  
Trajectory Prediction ◽  
Reasoning System

An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. Vehicle trajectories are not only constrained by a priori knowledge about road structure, traffic signs, and traffic rules but also affected by posterior knowledge about different driving styles of drivers. The existing prediction models cannot fully combine the prior and posterior knowledge in the driving scene and perform well only in a specific traffic scenario. This paper presents a long short-term memory (LSTM) neural network driven by knowledge. First, a driving knowledge base is constructed to describe the prior knowledge about a driving scenario. Then, the prediction reference baseline (PRB) based on driving knowledge base is determined by using the rule-based online reasoning system. Finally, the future trajectory of the target vehicle is predicted by an LSTM neural network based on the prediction reference baseline, while the predicted trajectory considers both posterior and prior knowledge without increasing the computation complexity. The experimental results show that the proposed trajectory prediction model can adapt to different driving scenarios and predict trajectories with high accuracy due to the unique combination of the prior and posterior knowledge in the driving scene.

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