An integrated approach for process monitoring using wavelet analysis and competitive neural network

2007 ◽  
Vol 45 (1) ◽  
pp. 227-244 ◽  
Author(s):  
Chih-Hsuan Wang ◽  
Way Kuo ◽  
Hairong Qi
Keyword(s):  
Neural Network ◽  
Wavelet Analysis ◽  
Process Monitoring ◽  
Integrated Approach ◽  
Competitive Neural Network

scholarly journals Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System

2018 ◽  
Vol 28 (09) ◽  
pp. 1850007
Author(s):  
Francisco Zamora-Martinez ◽  
Maria Jose Castro-Bleda
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Language Processing ◽  
Traditional Approach ◽  
Computational Cost ◽  
Integrated Approach ◽  
Language Models ◽  
Translation System ◽  
Neural Net ◽  
Network Language

Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.

An Integrated Approach for Traffic Scene Understanding from Monocular Cameras

2021 ◽  
Author(s):  
Malte Oeljeklaus
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Scene Perception ◽  
Integrated Approach ◽  
Image Features ◽  
Road Users ◽  
Environment Model ◽  
Computational Performance ◽  
Spatial Reconstruction ◽  
The Individual

This thesis investigates methods for traffic scene perception with monocular cameras for a basic environment model in the context of automated vehicles. The developed approach is designed with special attention to the computational limitations present in practical systems. For this purpose, three different scene representations are investigated. These consist of the prevalent road topology as the global scene context, the drivable road area and the detection and spatial reconstruction of other road users. An approach is developed that allows for the simultaneous perception of all environment representations based on a multi-task convolutional neural network. The obtained results demonstrate the efficiency of the multi-task approach. In particular, the effects of shareable image features for the perception of the individual scene representations were found to improve the computational performance. Contents Nomenclature VII 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Outline and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Related Work and Fundamental Background 8 2.1 Advances in CNN...

K-Means and Competitive Neural Network Based Clustering of Climate Data

2021 ◽  
Author(s):  
Sandeep Kumar Sunori ◽  
Sudhanshu Maurya ◽  
Amit Mittal ◽  
Kiran Patni ◽  
Shweta Arora ◽  
...  
Keyword(s):  
Neural Network ◽  
Climate Data ◽  
Competitive Neural Network

An Optimizing Method of Competitive Neural Network

2011 ◽  
Vol 467-469 ◽  
pp. 894-899
Author(s):  
Hong Men ◽  
Hai Yan Liu ◽  
Lei Wang ◽  
Yun Peng Pan
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Clustering Analysis ◽  
Learning Rate ◽  
Optimum Number ◽  
Number Of Clusters ◽  
Competitive Neural Network ◽  
Output Neurons ◽  
Simulation Results ◽  
Strong Ability

This paper presents an optimizing method of competitive neural network(CNN):During clustering analysis fixed on the optimum number of output neurons according to the change of DB value，and then adjusted connected weight including increasing ,dividing , delete. Each neuron had the different variety trend of learning rate according with the change of the probability of neurons. The optimizing method made classification more accurate. Simulation results showed that optimized network structure had a strong ability to adjust the number of clusters dynamically and good results of classification.

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