Artificial Neural Networks as an alternative to traditional fall detection methods

2013 ◽  
Author(s):  
Marcela Vallejo ◽  
Claudia V. Isaza ◽  
Jose D. Lopez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fall Detection ◽  
Detection Methods ◽  
Artificial Neural

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

scholarly journals Development of prestress-loss identification method in the cable-anchorage systemusing impedance responses and artificial neural networks

2021 ◽  
Vol 63 (3) ◽  
pp. 33-39
Author(s):  
Tran Huu Tin Luu ◽  
◽  
Duc Duy Ho ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Simulations ◽  
Prestressed Concrete ◽  
Detection Methods ◽  
Prestress Loss ◽  
Prestressing Force ◽  
Anchorage System ◽  
Artificial Neural ◽  
Different Levels

In this paper, a method for identifying the loss of prestressing force (prestress-loss) in the cable-anchorage system of prestressed concrete structures using the impedance responses and artificial neural networks (ANNs) is developed. First, theories of impedance responses and damage detection methods for diagnosing the occurrence and the severity of prestress-loss are presented. In which, the occurrence of prestress-loss is determined by MAPD (Mean Absolute Percentage Deviation) index. Then, the severity of the prestress-loss is determined by ANNs. The feasibility of the developed method is verified by numerical simulations for a real cable-anchorage system with different levels of prestress-loss. The reliability of the numerical simulations for impedance responses is evaluated by comparison to experimental results. Finally, the occurrence and severity of the prestress-loss are exactly identified by the proposed method. The results of this study show that the proposed method is highly effective in determining the prestress-loss in the cable-anchorage system

scholarly journals Artificial neural networks based techniques for anomaly detection in Apache Spark

2019 ◽  
Vol 23 (2) ◽  
pp. 1345-1360 ◽  
Author(s):  
Ahmad Alnafessah ◽  
Giuliano Casale
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Anomaly Detection ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Apache Spark ◽  
Detection Methods ◽  
Support Vector ◽  
Data Systems ◽  
Artificial Neural

Abstract Late detection and manual resolutions of performance anomalies in Cloud Computing and Big Data systems may lead to performance violations and financial penalties. Motivated by this issue, we propose an artificial neural network based methodology for anomaly detection tailored to the Apache Spark in-memory processing platform. Apache Spark is widely adopted by industry because of its speed and generality, however there is still a shortage of comprehensive performance anomaly detection methods applicable to this platform. We propose an artificial neural networks driven methodology to quickly sift through Spark logs data and operating system monitoring metrics to accurately detect and classify anomalous behaviors based on the Spark resilient distributed dataset characteristics. The proposed method is evaluated against three popular machine learning algorithms, decision trees, nearest neighbor, and support vector machine, as well as against four variants that consider different monitoring datasets. The results prove that our proposed method outperforms other methods, typically achieving 98–99% F-scores, and offering much greater accuracy than alternative techniques to detect both the period in which anomalies occurred and their type.

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