scholarly journals Optimal Vertically Ascending Flight of an Insect-Like Flapping-Wing Micro Air Vehicle

10.29007/wndn ◽  
2020 ◽  
Author(s):  
Anh Tuan Nguyen ◽  
Hoang Quan Dinh ◽  
Van Thang Nguyen ◽  
Thanh Le Vu Dan ◽  
Thanh Dong Pham ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control Strategies ◽  
Flapping Wing ◽  
Micro Air Vehicle ◽  
Training Data ◽  
Lattice Method ◽  
Air Vehicle ◽  
Wing Geometry ◽  
Artificial Neural

This paper explores the optimal flight condition of an insect-like flapping-wing micro air vehicle (FWMAV) while ascending vertically at constant speeds. The FWMAV is assumed to have the same mass properties and wing geometry as those of the hawkmoth Manduca sexta. The optimization is conducted through the combination of an artificial neural network and a genetic algorithm. The training data for the artificial neural network are provided by the unsteady vortex-lattice method written in the programming language FORTRAN using parallel computation techniques. The results show that the FWMAV has to alter its wing kinematics and flapping frequency to sustain vertically ascending flight. Moreover, while ascending, the FWMAV requires more energy than that in hover. The findings from this work are useful for the design of control strategies used for insect-like FWMAVs

scholarly journals Identification Of Number Using Artificial Neural Network Backpropagation

2018 ◽  
Vol 215 ◽  
pp. 01011
Author(s):  
Sitti Amalia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Data ◽  
Simulation Test ◽  
Backpropagation Algorithm ◽  
Network Parameter ◽  
Artificial Intelligent ◽  
Design And Implementation ◽  
Network Training ◽  
Artificial Neural

This research proposed to design and implementation system of voice pattern recognition in the form of numbers with offline pronunciation. Artificial intelligent with backpropagation algorithm used on the simulation test. The test has been done to 100 voice files which got from 10 person voices for 10 different numbers. The words are consisting of number 0 to 9. The trial has been done with artificial neural network parameters such as tolerance value and the sum of a neuron. The best result is shown at tolerance value varied and a sum of the neuron is fixed. The percentage of this network training with optimal architecture and network parameter for each training data and new data are 82,2% and 53,3%. Therefore if tolerance value is fixed and a sum of neuron varied gave 82,2% for training data and 54,4% for new data

scholarly journals Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network

2021 ◽  
Vol 03 (01) ◽  
pp. 45-52
Author(s):  
Hadjira Maouz ◽  
◽  
Asma Adda ◽  
Salah Hanini ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Aging ◽  
Training Data ◽  
Ethylene Propylene Diene Monomer ◽  
Ann Model ◽  
Ethylene Propylene ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer

scholarly journals Experimental implementation of artificial neural network-based active vibration control & chatter suppression

2021 ◽  
Author(s):  
Yong Xia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Control System ◽  
Vibration Control ◽  
Control Strategies ◽  
Active Vibration Control ◽  
Chatter Suppression ◽  
Active Vibration ◽  
Artificial Neural

Vibration control strategies strive to reduce the effect of harmful vibrations such as machining chatter. In general, these strategies are classified as passive or active. While passive vibration control techniques are generally less complex, there is a limit to their effectiveness. Active vibration control strategies, which work by providing an additional energy supply to vibration systems, on the other hand, require more complex algorithms but can be very effective. In this work, a novel artificial neural network-based active vibration control system has been developed. The developed system can detect the sinusoidal vibration component with the highest power and suppress it in one control cycle, and in subsequent cycles, sinusoidal signals with the next highest power will be suppressed. With artificial neural networks trained to cover enough frequency and amplitude ranges, most of the original vibration can be suppressed. The efficiency of the proposed methodology has been verified experimentally in the vibration control of a cantilever beam. Artificial neural networks can be trained automatically for updated time delays in the system when necessary. Experimental results show that the developed active vibration control system is real time, adaptable, robust, effective and easy to be implemented. Finally, an experimental setup of chatter suppression for a lathe has been successfully implemented, and the successful techniques used in the previous artificial neural network-based active vibration control system have been utilized for active chatter suppression in turning.

scholarly journals Pengaruh Latar Belakang Warna pada Objek Gambar terhadap Hasil Ekstraksi Sinyal EEG

2017 ◽  
Vol 7 (2) ◽  
pp. 161 ◽  
Author(s):  
Catur Atmaji ◽  
Zandy Yudha Perwira
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training System ◽  
Training Data ◽  
Spectrum Estimation ◽  
Quality Classification ◽  
Eeg Data ◽  
Two Systems ◽  
Artificial Neural ◽  
Color Background

In this study, observation on the differences in features quality of EEG records as a result of training on subjects has been made. The features of EEG records were extracted using two different methods, the root mean square which is acquired from the range between 0.5 and 5 seconds and the average of power spectrum estimation from the frequency range between 20 and 40Hz. All of the data consists of a 4-channel recording and produce good quality classification on artificial neural network, with each of which generates training data accuracy over 90%. However, different results are occured when the trained system is tested on other test data. The test results show that the two systems which are trained using training data with object with color background produce higher accuracy than the other two systems which are trained using training data with object without background color, 63.98% and 60.22% compared to 59.68% and 56.45% accuracy respectively. From the use of the features on the artificial neural network classification system, it can be concluded that the training system using EEG data records derived from the visualization of object with color background produces better features than the visualization of object without color background.

scholarly journals Modeling of daily runoff from a small agricultural watershed using artificial neural network with resampling techniques

2014 ◽  
Vol 17 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Gurjeet Singh ◽  
Rabindra K. Panda ◽  
Marc Lamers
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Agricultural Watershed ◽  
Training Data ◽  
Optimum Number ◽  
Training Process ◽  
Data Set ◽  
Reported Study ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The reported study was undertaken in a small agricultural watershed, namely, Kapgari in Eastern India having a drainage area of 973 ha. The watershed was subdivided into three sub-watersheds on the basis of drainage network and land topography. An attempt was made to relate the continuously monitored runoff data from the sub-watersheds and the whole-watershed with the rainfall and temperature data using the artificial neural network (ANN) technique. The reported study also evaluated the bias in the prediction of daily runoff with shorter length of training data set using different resampling techniques with the ANN modeling. A 10-fold cross-validation (CV) technique was used to find the optimum number of hidden neurons in the hidden layer and to avoid neural network over-fitting during the training process for shorter length of data. The results illustrated that the ANN models developed with shorter length of training data set avoid neural network over-fitting during the training process, using a 10-fold CV method. Moreover, the biasness was investigated using the bootstrap resampling technique based ANN (BANN) for short length of training data set. In comparison with the 10-fold CV technique, the BANN is more efficient in solving the problems of the over-fitting and under-fitting during training of models for shorter length of data set.

scholarly journals Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
R. Manjula Devi ◽  
S. Kuppuswami ◽  
R. C. Suganthe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Model ◽  
Training Data ◽  
Experimental Result ◽  
Training Algorithms ◽  
Data Set ◽  
Training Time ◽  
Input Sample ◽  
Artificial Neural

Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do not categorize perfectly in the previous epoch which dynamically reducing the number of input samples exhibited to the network at every single epoch without affecting the network’s accuracy. Thus decreasing the size of the training set can reduce the training time, thereby ameliorating the training speed. This LAST algorithm also determines how many epochs the particular input sample has to skip depending upon the successful classification of that input sample. This LAST algorithm can be incorporated into any supervised training algorithms. Experimental result shows that the training speed attained by LAST algorithm is preferably higher than that of other conventional training algorithms.

scholarly journals A Machine Learning-based Pipeline for the Classification of CTX-M in Metagenomics Samples

Processes ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 235 ◽  
Author(s):  
Diego Ceballos ◽  
Diana López-Álvarez ◽  
Gustavo Isaza ◽  
Reinel Tabares-Soto ◽  
Simón Orozco-Arias ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Bacterial Infections ◽  
Training Data ◽  
Metagenomic Data ◽  
Process Unit ◽  
M Gene ◽  
Central Process ◽  
Artificial Neural

Bacterial infections are a major global concern, since they can lead to public health problems. To address this issue, bioinformatics contributes extensively with the analysis and interpretation of in silico data by enabling to genetically characterize different individuals/strains, such as in bacteria. However, the growing volume of metagenomic data requires new infrastructure, technologies, and methodologies that support the analysis and prediction of this information from a clinical point of view, as intended in this work. On the other hand, distributed computational environments allow the management of these large volumes of data, due to significant advances in processing architectures, such as multicore CPU (Central Process Unit) and GPGPU (General Propose Graphics Process Unit). For this purpose, we developed a bioinformatics workflow based on filtered metagenomic data with Duk tool. Data formatting was done through Emboss software and a prototype of a workflow. A pipeline was also designed and implemented in bash script based on machine learning. Further, Python 3 programming language was used to normalize the training data of the artificial neural network, which was implemented in the TensorFlow framework, and its behavior was visualized in TensorBoard. Finally, the values from the initial bioinformatics process and the data generated during the parameterization and optimization of the Artificial Neural Network are presented and validated based on the most optimal result for the identification of the CTX-M gene group.

scholarly journals PREDIKSI BEBAN ENERGI LISTRIK APJ KOTA SEMARANG MENGGUNAKAN METODE RADIAL BASIS FUNCTION (RBF)

eLEKTRIKA ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 21
Author(s):  
Mukti Dwi Cahyo ◽  
Sri Heranurweni ◽  
Harmini Harmini
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Peak Load ◽  
Electricity Demand ◽  
Training Data ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Electric power is one of the main needs of society today, ranging from household consumers to industry. The demand for electricity increases every year. So as to achieve adjustments between power generation and power demand, the electricity provider (PLN) must know the load needs or electricity demand for some time to come. There are many studies on the prediction of electricity loads in electricity, but they are not specific to each consumer sector. One of the predictions of this electrical load can be done using the Radial Basis Function Artificial Neural Network (ANN) method. This method uses training data learning from 2010 - 2017 as a reference data. Calculations with this method are based on empirical experience of electricity provider planning which is relatively difficult to do, especially in terms of corrections that need to be made to changes in load. This study specifically predicts the electricity load in the Semarang Rayon network service area in 2019-2024. The results of this Artificial Neural Network produce projected electricity demand needs in 2019-2024 with an average annual increase of 1.01% and peak load in 2019-2024. The highest peak load in 2024 and the dominating average is the household sector with an increase of 1% per year. The accuracy results of the Radial Basis Function model reached 95%.

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