Research on Internet Monitoring System Based on Multi-Layer Text Information Filtering Method through Artificial Neural Networks

2012 ◽  
Vol 532-533 ◽  
pp. 1036-1040
Author(s):  
Jian Qing Liu ◽  
Mei Luo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monitoring System ◽  
Information Filtering ◽  
Monitor System ◽  
Filtering Method ◽  
Media Attention ◽  
Text Information ◽  
Novel Method ◽  
Artificial Neural

Internet monitoring has recently been the focus of media attention and public debate. This paper proposed a novel method of multi-layer smart monitor system to filter unhealthy information using artificial neural networks (ANN). This method classified the text into multilayer and uses RPROP algorithm to implement the text classifier. Finally, the test was deployed and the feasibility of this algorithm was proven.

scholarly journals A Novel Method for Inference of Chemical Compounds of Cycle Index Two with Desired Properties Based on Artificial Neural Networks and Integer Programming

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 124 ◽  
Author(s):  
Jianshen Zhu ◽  
Chenxi Wang ◽  
Aleksandar Shurbevski ◽  
Hiroshi Nagamochi ◽  
Tatsuya Akutsu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Compounds ◽  
Mixed Integer ◽  
Training Phase ◽  
Cycle Index ◽  
Inference Problem ◽  
Inverse Prediction ◽  
Novel Method ◽  
Artificial Neural

Inference of chemical compounds with desired properties is important for drug design, chemo-informatics, and bioinformatics, to which various algorithmic and machine learning techniques have been applied. Recently, a novel method has been proposed for this inference problem using both artificial neural networks (ANN) and mixed integer linear programming (MILP). This method consists of the training phase and the inverse prediction phase. In the training phase, an ANN is trained so that the output of the ANN takes a value nearly equal to a given chemical property for each sample. In the inverse prediction phase, a chemical structure is inferred using MILP and enumeration so that the structure can have a desired output value for the trained ANN. However, the framework has been applied only to the case of acyclic and monocyclic chemical compounds so far. In this paper, we significantly extend the framework and present a new method for the inference problem for rank-2 chemical compounds (chemical graphs with cycle index 2). The results of computational experiments using such chemical properties as octanol/water partition coefficient, melting point, and boiling point suggest that the proposed method is much more useful than the previous method.

scholarly journals Structural Vibration Tests: Use of Artificial Neural Networks for Live Prediction of Structural Stress

2020 ◽  
Vol 10 (23) ◽  
pp. 8542
Author(s):  
Laura Wilmes ◽  
Raymond Olympio ◽  
Kristin M. de Payrebrune ◽  
Markus Schatz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Structure ◽  
Structural Vibration ◽  
Future Application ◽  
Different Types ◽  
Novel Method ◽  
Artificial Neural ◽  
Input Load ◽  
Vibration Tests

One of the ongoing tasks in space structure testing is the vibration test, in which a given structure is mounted onto a shaker and excited by a certain input load on a given frequency range, in order to reproduce the rigor of launch. These vibration tests need to be conducted in order to ensure that the devised structure meets the expected loads of its future application. However, the structure must not be overtested to avoid any risk of damage. For this, the system’s response to the testing loads, i.e., stresses and forces in the structure, must be monitored and predicted live during the test. In order to solve the issues associated with existing methods of live monitoring of the structure’s response, this paper investigated the use of artificial neural networks (ANNs) to predict the system’s responses during the test. Hence, a framework was developed with different use cases to compare various kinds of artificial neural networks and eventually identify the most promising one. Thus, the conducted research accounts for a novel method for live prediction of stresses, allowing failure to be evaluated for different types of material via yield criteria.

scholarly journals Artificial Neural Networks to Predict the Power Output of a PV Panel

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Valerio Lo Brano ◽  
Giuseppina Ciulla ◽  
Mariavittoria Di Falco
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monitoring System ◽  
Power Output ◽  
Department Of Energy ◽  
Electricity Production ◽  
Energy Output ◽  
Weather Data ◽  
Output Data ◽  
Artificial Neural

The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs) for the power energy output forecasting of photovoltaic (PV) modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy) has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP), a recursive neural network (RNN), and a gamma memory (GM) trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed) along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.

scholarly journals A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks

2011 ◽  
Vol 222 (15) ◽  
pp. 2606-2614 ◽  
Author(s):  
Michael J. Watts ◽  
Yuxiao Li ◽  
Bayden D. Russell ◽  
Camille Mellin ◽  
Sean D. Connell ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Novel Method ◽  
Artificial Neural ◽  
Rocky Habitats

scholarly journals AUTOMATIC SHIP DETECTION IN SINGLE-POL SAR IMAGES USING TEXTURE FEATURES IN ARTIFICIAL NEURAL NETWORKS

2015 ◽  
Vol XL-1-W5 ◽  
pp. 395-399 ◽  
Author(s):  
E. Khesali ◽  
H. Enayati ◽  
M. Modiri ◽  
M. Mohseni Aref
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Texture Features ◽  
Speckle Noise ◽  
Sar Images ◽  
Time Saving ◽  
Ship Detection ◽  
Operational Solution ◽  
Novel Method ◽  
Artificial Neural

This paper presents a novel method for detecting ships from high-resolution synthetic aperture radar (SAR) images. This method categorizes ship targets from single-pol SAR images using texture features in artificial neural networks. As such, the method tries to overcome the lack of an operational solution that is able to reliably detect ships with one SAR channel. The method has the following three main stages: 1) feature extraction; 2) feature selection; and 3) ship detection. The first part extracts different texture features from SAR image. These textures include occurrence and co occurrence measures with different window sizes. Then, best features are selected. Finally, the artificial neural network is used to extract ship pixels from sea ones. In post processing stage some morphological filters are used to improve the result. The effectiveness of the proposed method is verified using Sentinel-1 data in VV polarization. Experimental results indicate that the proposed algorithm can be implemented with time-saving, high precision ship extraction, feature analysis, and detection. The results also show that using texture features the algorithm properly discriminates speckle noise from ships.

Indirect aircraft structural monitoring using artificial neural networks

2008 ◽  
Vol 112 (1131) ◽  
pp. 251-265 ◽  
Author(s):  
S. C. Reed
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monitoring System ◽  
Cost Effective ◽  
Military Aircraft ◽  
Royal Air Force ◽  
Parametric Data ◽  
Flight Parameters ◽  
Artificial Neural

Abstract From necessity, military aircraft often operate in a highly fatigue damaging environment and history has shown in lost lives and aircraft the consequences of failure to appreciate fully the usage environment. The need for robust and cost effective structural usage monitoring of military aircraft to ensure operations are conducted within acceptable levels of risk is paramount. Furthermore, increased economic pressures require ever-inventive methods to be employed to maximise the lives of military fleets; structural usage monitoring will be a key asset in this drive. A highly cost effective indirect structural health and usage neural network (SHAUNN) monitoring system is proposed. A SHAUNN uses regression relationships determined by artificial neural networks to predict stresses, strains, loads, or fatigue damage from flight parameters. Within this paper the development of a SHAUNN monitoring system is described. Flight parametric data, captured during Operational Loads Measurement of the Royal Air Force Dominie TMk1 aircraft have been used to predict stresses at the key structural location in the wing, using mapping relationships determined by artificial neural networks. A framework for the development of the SHAUNN monitoring system is discussed and the basic architecture of the multilayer perceptron artificial neural network is described. It is concluded that this technology could provide the basis for an accurate, cost-effective structural usage monitoring system and further work to investigate the prediction of ground – based stresses in the wing is recommended.

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