A Hierarchical Algorithm for In-City Parking Allocation Based on Open Street Map

2022 ◽  
Author(s):  
Mohammad A.R. Abdeen ◽  
Ibrahim A. Nemer ◽  
Tarek R. Sheltami ◽  
Mohamed H. Ahmed ◽  
Mustafa Elnainay
Keyword(s):  
Hierarchical Algorithm

A hierarchical algorithm for fuzzy template matching in emotional facial images

2013 ◽  
Vol 24 (2) ◽  
pp. 201-214 ◽  
Author(s):  
Anisha Halder ◽  
Rajshree Mandal ◽  
Amit Konar
Keyword(s):  
Template Matching ◽  
Hierarchical Algorithm ◽  
Facial Images

scholarly journals A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lu Yu ◽  
Jianling Qu ◽  
Feng Gao ◽  
Yanping Tian
Keyword(s):  
Fault Diagnosis ◽  
Short Term Memory ◽  
Operating Conditions ◽  
Support Vector ◽  
Rolling Bearings ◽  
Hierarchical Algorithm ◽  
Bearing Fault Diagnosis ◽  
Rotary Machinery ◽  
Proposed Model ◽  
Series Of Experiments

Faced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems. Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machinery fields. However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies. Aiming to overcome shortcomings of shallow structures, a novel hierarchical algorithm based on stacked LSTM (long short-term memory) is proposed in this text. Without any preprocessing operation or manual feature extraction, the proposed method constructs a framework of end-to-end fault diagnosis system for rolling bearings. Beneficial from the memorize-forget mechanism of LSTM, features inherent in raw temporal signals are extracted hierarchically and automatically by stacking LSTM. A series of experiments demonstrate that the proposed model can not only achieve up to 99% accuracy but also outperform some state-of-the-art intelligent fault diagnosis methods.

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