scholarly journals An application of Kalman Filtering and Artificial Neural Network with K-NN Position Detection Technique

2017 ◽  
Vol 09 (08) ◽  
pp. 239-249
Author(s):  
Hakan Koyuncu ◽  
Baki Koyuncu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kalman Filtering ◽  
Detection Technique ◽  
Position Detection ◽  
Artificial Neural

A sensor-less stroke detection technique for linear refrigeration compressors using artificial neural network

2020 ◽  
Vol 114 ◽  
pp. 62-70 ◽  
Author(s):  
Hanying Jiang ◽  
Kun Liang ◽  
Zhaohua Li ◽  
Zhennan Zhu ◽  
Xiaoqin Zhi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Detection Technique ◽  
Artificial Neural

scholarly journals A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4238 ◽  
Author(s):  
S. Ananda Kumar ◽  
M. S. P. Subathra ◽  
Nallapaneni Manoj Kumar ◽  
Maria Malvoni ◽  
N. J. Sairamya ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Detection Technique ◽  
Second Step ◽  
Islanding Detection ◽  
Distributed Power ◽  
Distributed Power Generation ◽  
Artificial Neural

Finding an appropriate technique to detect an islanding issue is one of the major challenges associated with the design of a resilient grid-linked photovoltaic-based distributed power generation (PV-DPG) system. In general, the technique used for islanding detection must be able to sense the disruptions from the electric grid and quickly disconnect PV-DPG from the grid. The quick disconnection of PV-DPG mostly avoids power quality problems, damage to power assets, voltage stability issues, and frequency instability. In this paper, a new islanding detection technique that is based on tunable Q-factor wavelet transform (TQWT) and an artificial neural network (ANN) is proposed for PV-DPG. The proposed approach consists of two steps: in the first step, the vital detection parameters are computed by performing simulations considering all possible switching transients, islanding events, and faults from the grid side. Then, the decomposition of obtained signals is done using TQWT on different levels. Using the obtained coefficients, at each level, features such as range, minimum, mean, standard deviation, maximum, energy, and log energy entropy are computed. The optimal feature set was selected as the input for the second step. The classification of the non-islanding and islanding states for PV-DPG is made using the ANN classifier in the second step, which achieved an accuracy of 98%. The results representing the efficiency of the proposed approach in noisy and non-noisy environments are also explained. Overall, it is understood that the proposed islanding detection technique would provide suitable insights to detect an islanding issue.

scholarly journals Secured node detection technique based on artificial neural network for wireless sensor network

2021 ◽  
Vol 11 (1) ◽  
pp. 536
Author(s):  
Bassam Hasan ◽  
Sameer Alani ◽  
Mohammed Ayad Saad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Detection Technique ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Artificial Neural

The wireless sensor network is becoming the most popular network in the last recent years as it can measure the environmental conditions and send them to process purposes. Many vital challenges face the deployment of WSNs such as energy consumption and security issues. Various attacks could be subjects against WSNs and cause damage either in the stability of communication or in the destruction of the sensitive data. Thus, the demands of intrusion detection-based energy-efficient techniques rise dramatically as the network deployment becomes vast and complicated. Qualnet simulation is used to measure the performance of the networks. This paper aims to optimize the energy-based intrusion detection technique using the artificial neural network by using MATLAB Simulink. The results show how the optimized method based on the biological nervous systems improves intrusion detection in WSN. In addition to that, the unsecured nodes are affected the network performance negatively and trouble its behavior. The regress analysis for both methods detects the variations when all nodes are secured and when some are unsecured. Thus, Node detection based on packet delivery ratio and energy consumption could efficiently be implemented in an artificial neural network.

Water level forecasting using a hybrid algorithm of artificial neural networks and local Kalman filtering

2017 ◽  
Vol 233 (1) ◽  
pp. 174-185 ◽  
Author(s):  
Cheng Zhong ◽  
Zhonglian Jiang ◽  
Xiumin Chu ◽  
Tao Guo ◽  
Quan Wen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Kalman Filtering ◽  
Hybrid Algorithm ◽  
Yangtze River ◽  
Stationary Time Series ◽  
The Yangtze River ◽  
Daily Water ◽  
Artificial Neural

The dynamic processes in the tidal reaches of the Yangtze River lead to the complexity of short-term water level forecasting. Historical data of daily water level are obtained for the lower reaches (Anqing–Wuhu–Nanjing) of the Yangtze River. Stationary time series of water level is derived by making the first-order difference with the raw datasets. An artificial neural network–Kalman hybrid model is proposed for water level forecasting, in which the Kalman filtering is introduced for partial data reconstruction. The model is calibrated with the hydrologic daily water level data of years 2014–2016 for MaAnshan station. Comparing with the traditional artificial neural network model, daily water level predictions are improved by the hybrid algorithm. Discrepancies appear under the circumstance of sharp variations of water level observations. Moreover, the implementation strategy of Kalman filtering algorithm is explored, which indicates the superiority of local Kalman filtering.

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