An Innovative Air Purification Method and Neural Network Algorithm Applied to Urban Streets

In the present work, multiphysics modeling was used to investigate the feasibility of a photocatalysis-based outdoor air purifying solution that could be used in high polluted streets, especially street canyons. The article focuses on the use of a semi-active photocatalysis in the surfaces of the street as a solution to remove anthropogenic pollutants from the air. The solution is based on lamellae arranged horizontally on the wall of the street, coated with a photocatalyst (TiO2), lightened with UV light, with a dimension of 8 cm × 48 cm × 1 m. Fans were used in the system to create airflow. A high purification percentage was obtained. An artificial neural network (ANN) was used to predict the optimal purification method based on previous simulations, to design purification strategies considering the energy cost. The ANN was used to forecast the amount of purified with a feed-forward neural network and a backpropagation algorithm to train the model.

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Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

Algorithms, Methods, and Applications in Mobile Computing and Communications - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-5693-0.ch007 ◽

2019 ◽

pp. 158-177

Author(s):

Tamer Emara

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power Saving ◽

Ieee 802.16 ◽

Feed Forward Neural Network ◽

Artificial Neural ◽

Adaptive Power ◽

Artificial Neural Network Ann ◽

Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

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ANN Modeling of Motional Resistance for Micro Disk Resonator

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch013 ◽

2020 ◽

pp. 199-216

Author(s):

Manjula A. Sutagundar ◽

Basavaprabhu G. Sheeparamatti ◽

Dakshayani S. Jangamshetti

Keyword(s):

Neural Network ◽

Resonance Frequency ◽

Simulation Software ◽

Equivalent Model ◽

Ann Model ◽

Feed Forward Neural Network ◽

Disk Resonator ◽

Motional Resistance ◽

Artificial Neural Network Ann ◽

Hidden Layer

This article describes how modeling is an integral part of design and development of any system that provides the theoretical characterization of the system and helps in understanding the relations between various parameters of the system, before the system is developed. The capability of an Artificial Neural Network (ANN) to model the complex relations between a set of inputs and outputs is exploited to model the motional resistance and resonance frequency for a contour mode disk resonator. The solution was to develop a multilayer feed forward neural network. The data set required to train the ANN is obtained by developing an electrical equivalent model and through the MEMS simulation software Coventorware. The network is trained using a Levenberg Marquardt algorithm. The number of hidden layers and the number of neurons in each hidden layer is optimized using a genetic algorithm. The ANN model developed an efficient model of the motional resistance and resonance frequency of the disk resonator. The ANN output is compared with the output of an electrical equivalent model and a reported fabricated structure.

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Application of artificial neural network for modeling of phenol mineralization by photo-Fenton process using a multi-lamp reactor

Water Science & Technology ◽

10.2166/wst.2013.731 ◽

2013 ◽

Vol 69 (4) ◽

pp. 768-774 ◽

Cited By ~ 6

Author(s):

André L. N. Mota ◽

Osvaldo Chiavone-Filho ◽

Syllos S. da Silva ◽

Edson L. Foletto ◽

José E. F. Moraes ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Uv Light ◽

Fenton Process ◽

Ann Model ◽

Ferrous Ions ◽

Phenol Mineralization ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer

An artificial neural network (ANN) was implemented for modeling phenol mineralization in aqueous solution using the photo-Fenton process. The experiments were conducted in a photochemical multi-lamp reactor equipped with twelve fluorescent black light lamps (40 W each) irradiating UV light. A three-layer neural network was optimized in order to model the behavior of the process. The concentrations of ferrous ions and hydrogen peroxide, and the reaction time were introduced as inputs of the network and the efficiency of phenol mineralization was expressed in terms of dissolved organic carbon (DOC) as an output. Both concentrations of Fe2+ and H2O2 were shown to be significant parameters on the phenol mineralization process. The ANN model provided the best result through the application of six neurons in the hidden layer, resulting in a high determination coefficient. The ANN model was shown to be efficient in the simulation of phenol mineralization through the photo-Fenton process using a multi-lamp reactor.

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A Review of Neural Network Application for Fault Diagnosis of NPP

Volume 1: Plant Operations, Maintenance, Engineering, Modifications and Life Cycle; Component Reliability and Materials Issues; Next Generation Systems ◽

10.1115/icone17-75255 ◽

2009 ◽

Author(s):

He Wang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Nuclear Power ◽

Layer Structure ◽

Engineering Application ◽

Model Performance ◽

Training Sample ◽

Feed Forward Neural Network ◽

Artificial Neural Network Ann ◽

Hidden Layer

Artificial Neural Network (ANN) with its self-learning capabilities, nonlinear mapping ability and generalization ability, has been widely applied for fault diagnosis of complex system like Nuclear Power Plant (NPP). In this paper, an overview of the application of supervised multi-layer feed-forward neural network for fault diagnosis of NPP is presented, including the following aspects: the acquisition of the training sample data, the determination of appropriate input and output data, the choice of hidden layer structure and the evaluation of network model performance. Finally, a number of key issues about the engineering application of neural network fault diagnosis in practice were discussed.

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Soft Computing Research For Weather Prediction Using Multilayer Architecture

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9390.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 3779-3783

Keyword(s):

Neural Network ◽

Time Series Data ◽

Weather Prediction ◽

Series Data ◽

Feed Forward Neural Network ◽

Rainfall Prediction ◽

Agriculture Sector ◽

Precise Prediction ◽

Multilayer Architecture ◽

Artificial Neural Network Ann

Rainfall prediction is helpful for the agriculture sector. Early prediction of drought and torrent situations is achieved through time series data. For the precise prediction, Artificial Neural Network(ANN) technique is used. The rainy dataset is tested using Feed Forward Neural Network(FFNN). The performance of this model is evaluated using Mean Square Error(MSE) and Magnitude of Relative Error(MRE). Better performance achieved when compared with other data mining techniques.

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Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

10.4018/978-1-6684-2408-7.ch022 ◽

2022 ◽

pp. 471-489

Author(s):

Tamer Emara

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power Saving ◽

Ieee 802.16 ◽

Feed Forward Neural Network ◽

Artificial Neural ◽

Adaptive Power ◽

Artificial Neural Network Ann ◽

Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

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Development of the Enhanced Piece-Wise Linear Neural Network Algorithm

10.4018/978-1-6684-2408-7.ch013 ◽

2022 ◽

pp. 283-305

Author(s):

Veronica K. Chan ◽

Christine W. Chan

Keyword(s):

Neural Network ◽

Linear Equations ◽

Extraction Algorithm ◽

Ann Models ◽

Neural Network Algorithm ◽

Regression Problems ◽

Linear Neural Network ◽

Artificial Neural Network Ann ◽

Comparison Of The Results ◽

Dual Objectives

This chapter discusses development, application, and enhancement of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The dual objectives of developing the algorithms are (1) to generate good predictive models comparable in performance to the original artificial neural network (ANN) models and (2) to “open up” the black box of a neural network model and provide explicit information in the form of rules that are expressed as linear equations. The enhanced PWL-ANN algorithm improves upon the PWL-ANN algorithm because it can locate more than two breakpoints and better approximate the hidden sigmoid activation functions of the ANN. Comparison of the results produced by the two versions of the PWL-ANN algorithm showed that the enhanced PWL-ANN models provide higher predictive accuracies and improved fidelities compared to the originally trained ANN models than the PWL-ANN models.

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Development of the Enhanced Piece-Wise Linear Neural Network Algorithm

Advances in Computational Intelligence and Robotics - Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence ◽

10.4018/978-1-7998-3038-2.ch006 ◽

2020 ◽

pp. 104-126

Author(s):

Veronica K. Chan ◽

Christine W. Chan

Keyword(s):

Neural Network ◽

Linear Equations ◽

Extraction Algorithm ◽

Ann Models ◽

Neural Network Algorithm ◽

Regression Problems ◽

Linear Neural Network ◽

Artificial Neural Network Ann ◽

Comparison Of The Results ◽

Dual Objectives

This chapter discusses development, application, and enhancement of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The dual objectives of developing the algorithms are (1) to generate good predictive models comparable in performance to the original artificial neural network (ANN) models and (2) to “open up” the black box of a neural network model and provide explicit information in the form of rules that are expressed as linear equations. The enhanced PWL-ANN algorithm improves upon the PWL-ANN algorithm because it can locate more than two breakpoints and better approximate the hidden sigmoid activation functions of the ANN. Comparison of the results produced by the two versions of the PWL-ANN algorithm showed that the enhanced PWL-ANN models provide higher predictive accuracies and improved fidelities compared to the originally trained ANN models than the PWL-ANN models.

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Prediction of Rainfall using Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a2242.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 1374-1377

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Cloud Cover ◽

Feed Forward Neural Network ◽

Proposed Model ◽

Wide Range ◽

New Methodologies ◽

Complex Relationships ◽

Artificial Neural Network Ann

Rainfall is one of the major livelihood of this world. Each and every organism in this universe need some of water to order to survive in its own living conditions. As rainfall is the main source of water and its need to agriculture is inevitable, there arises a necessity to analyze the pattern of the rainfall. The main aim of our paper is to predict the rainfall considering various factors like temperature, pressure, cloud cover, wind speed, pollution and precipitation. There are various ideas and new methodologies proposed in order to predict rainfall. But our proposed concept is based on machine learning because of its wide range of development and preferability nowadays. Among the various technologies built in Machine Learning (ML), Feed Forward Neural Network (FFNN) which is the simplest form of Artificial Neural Network (ANN) is preferred because this model learns the complex relationships among the various input parameters and helps to model them easily. Rainfall in our proposed model is predicted using different parameters influencing the rainfall along with their combinations and patterns. The experimental results depicts that the proposed model based on FFNN indicates suitable accuracy.

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