RECOGNITION OF CONTACT STATE BY USING NEURAL NETWORK FOR MICROMACHINED ARRAY TYPE TACTILE SENSOR

2005 ◽  
Vol 02 (03) ◽  
pp. 181-190 ◽  
Author(s):  
SEIJI AOYAGI ◽  
TAKAAKI TANAKA ◽  
KENJI MAKIHIRA
Keyword(s):  
Neural Network ◽  
Rotation Angle ◽  
Simulated Data ◽  
Tactile Sensor ◽  
Training Data ◽  
Sensing Element ◽  
Array Type ◽  
Tactile Pattern ◽  
Hidden Layer ◽  
Contact Position

In this paper, a force sensing element having a pillar and a diaphragm is proposed and thereafter fabricated by micromachining. Piezo resistors are fabricated on a silicon diaphragm for detecting distortions caused by a force input to a pillar on the diaphragm. Since a practical arrayed sensor consisting of many of this element is still under development, the output of an assumed arrayed type tactile sensor is simulated by FEM (finite element method). Using simulated data, the possibility of tactile pattern recognition using a neural network (NN) is investigated. The learning method of NN, the number of units of the input layer and the hidden layer, as well as the number of training data are investigated for realizing high probability of recognition. The 14 subjects having different shape and size are recognized. This recognition succeeded even if the contact position and the rotation angle of these objects are changed.

System Identification for Nonlinear Maneuvering of Ships Using Neural Network

2010 ◽  
Vol 54 (01) ◽  
pp. 1-14
Author(s):  
G. Rajesh ◽  
G. Giri Rajasekhar ◽  
S. K. Bhattacharyya
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Simulated Data ◽  
Network Models ◽  
Training Data ◽  
Neural Network Models ◽  
The Neural Network ◽  
Marquardt Algorithm ◽  
Hydrodynamic Derivatives ◽  
Hidden Layer

This paper deals with the application of nonparametric system identification to the nonlinear maneuvering of ships using neural network method. The maneuvering equations contain linear as well as nonlinear terms, and one does not attempt to determine the parameters (or hydrodynamic derivatives) associated with nonlinear terms, rather all nonlinear terms are clubbed together to form one unknown time function per equation, which are sought to be represented by neural network coefficients. The time series used in training the network are obtained from simulated data of zigzag and spiral maneuvers. The neural network has one middle or hidden layer of neurons and the Levenberg-Marquardt algorithm is used to obtain the network coefficients. Using the best choices for number of hidden layer neurons, length of training data, convergence tolerance, and so forth, the performances of the proposed neural network models have been investigated and conclusions drawn.

Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate

2022 ◽  
pp. 1301-1312
Author(s):  
M. Outanoute ◽  
A. Lachhab ◽  
A. Selmani ◽  
H. Oubehar ◽  
A. Snoussi ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Relative Humidity ◽  
Back Propagation ◽  
Particle Swarm ◽  
Simulated Data ◽  
Swarm Optimization ◽  
Generalization Ability ◽  
Optimal Weights ◽  
Hidden Layer

In this article, the authors develop the Particle Swarm Optimization algorithm (PSO) in order to optimise the BP network in order to elaborate an accurate dynamic model that can describe the behavior of the temperature and the relative humidity under an experimental greenhouse system. The PSO algorithm is applied to the Back-Propagation Neural Network (BP-NN) in the training phase to search optimal weights baded on neural networks. This approach consists of minimising the reel function which is the mean squared difference between the real measured values of the outputs of the model and the values estimated by the elaborated neural network model. In order to select the model which possess higher generalization ability, various models of different complexity are examined by the test-error procedure. The best performance is produced by the usage of one hidden layer with fourteen nodes. A comparison of measured and simulated data regarding the generalization ability of the trained BP-NN model for both temperature and relative humidity under greenhouse have been performed and showed that the elaborated model was able to identify the inside greenhouse temperature and humidity with a good accurately.

scholarly journals Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

2020 ◽  
Vol 10 (5) ◽  
pp. 1657 ◽  
Author(s):  
Jieun Baek ◽  
Yosoon Choi
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Deep Neural Network ◽  
Training Data ◽  
Open Pit ◽  
Percentage Error ◽  
Open Pit Mines ◽  
Determination Coefficient ◽  
Packet Data ◽  
Hidden Layer

This paper proposes a deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines. The proposed method utilizes two DNN models that are designed to predict ore production during the morning and afternoon haulage sessions, respectively. The configuration of the input nodes of the DNN models is based on truck-haulage conditions and corresponding operation times. To verify the efficacy of the proposed method, training data for the DNN models were generated by processing packet data collected over the two-month period December 2018 to January 2019. Subsequently, following training under different hidden-layer conditions, it was observed that the prediction accuracy of morning ore production was highest when the number of hidden layers and number of corresponding nodes were four and 50, respectively. The corresponding values of the determination coefficient and mean absolute percentage error (MAPE) were 0.99% and 4.78%, respectively. Further, the prediction accuracy of afternoon ore production was highest when the number of hidden layers was four and the corresponding number of nodes was 50. This yielded determination coefficient and MAPE values of 0.99% and 5.26%, respectively.

scholarly journals Application of Neural Network Artificial for Monitoring Aroma of Coffe Blending Process

2018 ◽  
Vol 2 (3) ◽  
pp. 147
Author(s):  
Susanti Roza ◽  
Zas Ressy Aidha ◽  
Milda Yuliza ◽  
Suryadi ◽  
Surfa Yondri
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Neural Cells ◽  
Arabica Coffee ◽  
Labview Software ◽  
Coffee Powder ◽  
Artificial Neural Network Ann ◽  
Average Success Rate ◽  
Hidden Layer ◽  
The One

This study aims to identify the type of coffee powder aroma from the coffee beans blending using backpropagation artificial neural network (ANN). Backpropagation is a controlled training implementing a weight adjustment pattern to achieve a minimum error value between the the predicted and the actual output. Within this study, the coffee aroma testing utilized electronic tasting sensor system consisted of 4 sensors namely TGS 2611, TGS 2620, TGS 2610 and TGS 2602. The coffee aroma monitoring and data collection in this system applied LabVIEW software as a virtual instrumentation. The testing result of this ANN was able to distinguish the coffee variety of Robusta, Arabica coffee powder and the one without any coffee aroma. The backpropagation architecture was formed by 3 layers consisting of 1 input layer with 4 input nerve cells, 1 hidden layer with 8 neural cells, and 2 output layers by applying the backpropagation training algorithm. The training data was taken from 70 data samples of each circumstance of coffee with 5 testing times. The results of the training and testing showed that the established backpropagation was capable to identify and differenciate the coffee powder in accordance with the given input with different average success rate;  91.96% for Robusta coffee, 100 % for Arabica coffee, and no 84.24% for without coffee aroma.

scholarly journals Identifikasi Mutu Buah Pepaya California (Carica Papaya L.) Menggunakan Metode Jaringan Syaraf Tiruan

2021 ◽  
Vol 10 (1) ◽  
pp. 113-119
Author(s):  
Muhammad Ezar Al Rivan ◽  
Gabriela Repca Sung
Keyword(s):  
Neural Network ◽  
Carica Papaya ◽  
Training Data ◽  
Backpropagation Neural Network ◽  
Neural Network Method ◽  
Network Method ◽  
Hidden Layer ◽  
The Tropics

Papaya is one of the fruits that grows in the tropics area, one of the kinds that people’s love the most is papaya California. The quality identification of papaya California fruit can be measured using color, defect, and size. Color, defect and size extracted from image of papaya. The dataset that used in this research are 150 images papaya California. The dataset consist of 3 quality there are good, fair and low.  Identification of papaya using the backpropagation neural network method with 17 training function in each training data with 3 different neurons in the hidden layer. The best result of the test is using training function trainrp with 10 neurons is 81,33% for accuracy, 73,37% for precision, and 72% for recall, with 20 neurons is 82,67% for accuracy, 75,24% for precision, and 74% for recall, and with 25 neurons is 80,89% for accuracy, 74,42% for precision, and 71,33% for recall.

Layout Estimation for Layered Ink of 3D Printer to Reproduce the Desired Line Spread Function of Skin using Simulated Data

2021 ◽  
Author(s):  
Kazuki Nagasawa ◽  
Kensuke Fukumoto ◽  
Wataru Arai ◽  
Kunio Hakkaku ◽  
Satoshi Kaneko ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Three Dimensional ◽  
Simulated Data ◽  
Training Data ◽  
Target Color ◽  
3D Printer ◽  
Line Spread Function ◽  
Spread Function ◽  
Line Spread

In this article, the authors propose a method to estimate the ink layer layout for a three-dimensional (3D) printer. This enables 3D printed skin to be produced with the desired translucency, which they represent as line spread function (LSF). A deep neural network in an encoder–decoder model is used for the estimation. It was previously reported that machine learning is an effective way to formulate the complex relationship between optical properties such as LSF and the ink layer layout in a 3D printer. However, although 3D printers are more widespread, the printing process is still time-consuming. Hence, it may be difficult to collect enough data to train a neural network sufficiently. Therefore, in this research, they prepare the training data, which is the correspondence between an LSF and the ink layer layout in a 3D printer, via computer simulation. They use a method to simulate the subsurface scattering of light for multilayered media. The deep neural network was trained with the simulated data and evaluated using a CG skin object. The result shows that their proposed method can estimate an appropriate ink layer layout that closely reproduces the target color and translucency.

scholarly journals PEMODELAN WAVELET NEURAL NETWORK UNTUK PREDIKSI NILAI TUKAR RUPIAH TERHADAP DOLAR AS

2020 ◽  
Vol 9 (2) ◽  
pp. 217-226
Author(s):  
Tri Yani Elisabeth Nababan ◽  
Budi Warsito ◽  
Agus Rusgiyono
Keyword(s):  
Neural Network ◽  
Exchange Rate ◽  
Wavelet Transforms ◽  
Activation Function ◽  
Wavelet Neural Network ◽  
Training Data ◽  
The Difference ◽  
Combination Of Methods ◽  
Hidden Layer ◽  
The U.S

Each country has its own currency that is used as a tool of exchange rate valid in the transaction process. In the process of transaction between countries often experience problems in terms of payment because of the difference in the value of money prevailing in each country. The price movement of the exchange rate or the value of foreign currencies that fluctuate from time to time it encouraged predictions of the value of the rupiah exchange rate against the U.S. dollar. Wavelet Neural Network (WNN) is a combination of methods between wavelet transforms and Neural networks. WNN modeling begins with wavelet decomposition resulting in wavelet coefficients and scale coefficients. Selection of inputs is based on PACF plots and divides into training data and testing data. To determine the final output by calculating the value of MAPE in data testing. The best architecture on WNN model for prediction of the value of the rupiah exchange rate against the U.S. dollar is a model with sigmoid logistic activation function, 2 neurons in the input layer, 10 neurons in the hidden layer, and 1 neuron in the output layer. The MAPE value is obtained at 0.2221%.  

scholarly journals KLASIFIKASI CITRA DIGITAL BUMBU DAN REMPAH DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)

2020 ◽  
Vol 9 (3) ◽  
pp. 273-282
Author(s):  
Isna Wulandari ◽  
Hasbi Yasin ◽  
Tatik Widiharih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Activation Function ◽  
Training Data ◽  
Young Generation ◽  
Testing Data ◽  
Filter Size ◽  
Hidden Layer ◽  
Herbs And Spices

The recognition of herbs and spices among young generation is still low. Based on research in SMK 9 Bandung, showed that there are 47% of students that did not recognize herbs and spices. The method that can be used to overcome this problem is automatic digital sorting of herbs and spices using Convolutional Neural Network (CNN) algorithm. In this study, there are 300 images of herbs and spices that will be classified into 3 categories. It’s ginseng, ginger and galangal. Data in each category is divided into two, training data and testing data with a ratio of 80%: 20%. CNN model used in classification of digital images of herbs and spices is a model with 2 convolutional layers, where the first convolutional layer has 10 filters and the second convolutional layer has 20 filters. Each filter has a kernel matrix with a size of 3x3. The filter size at the pooling layer is 3x3 and the number of neurons in the hidden layer is 10. The activation function at the convolutional layer and hidden layer is tanh, and the activation function at the output layer is softmax. In this model, the accuracy of training data is 0.9875 and the loss value is 0.0769. The accuracy of testing data is 0.85 and the loss value is 0.4773. Meanwhile, testing new data with 3 images for each category produces an accuracy of 88.89%. Keywords: image classification, herbs and spices, CNN. 

Failure pressure prediction of pipeline with single corrosion defect using artificial neural network

2021 ◽  
pp. 166-171
Author(s):  
К. Т. Чин ◽  
Т. Арумугам ◽  
С. Каруппанан ◽  
М. Овинис
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Failure Pressure ◽  
Corrosion Defect ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Описываются разработка и применение искусственной нейронной сети (ИНС) для прогнозирования предельного давления трубопровода с точечным коррозионным дефектом, подверженного воздействию только внутреннего давления. Модель ИНС разработана на основе данных, полученных по результатам множественных полномасштабных испытаний на разрыв труб API 5L (класс от X42 до X100). Качество работы модели ИНС проверено в сравнении с данными для обучения, получен коэффициент детерминации R = 0,99. Модель дополнительно протестирована с учетом данных о предельном давлении корродированных труб API 5L X52 и X80. Установлено, что разработанная модель ИНС позволяет прогнозировать предельное давление с приемлемой погрешностью. С использованием данной модели проведена оценка влияния длины и глубины коррозионных дефектов на предельное давление. Выявлено, что глубина коррозии является более значимым фактором разрушения корродированного трубопровода. This paper describes the development and application of artificial neural network (ANN) to predict the failure pressure of single corrosion affected pipes subjected to internal pressure only. The development of the ANN model is based on the results of sets of full-scale burst test data of pipe grades ranging from API 5L X42 to X100. The ANN model was developed using MATLAB’s Neural Network Toolbox with 1 hidden layer and 30 neurons. Before further deployment, the developed ANN model was compared against the training data and it produced a coefficient of determination ( R ) of 0.99. The developed ANN model was further tested against a set of failure pressure data of API 5L X52 and X80 grade corroded pipes. Results revealed that the developed ANN model is able to predict the failure pressure with good margins of error. Furthermore, the developed ANN model was used to determine the failure trends when corrosion defect length and depth were varied. Results from this failure trend analysis revealed that corrosion defect depth is the most significant parameter when it comes to corroded pipeline failure.

Optimization of the Composite Truncated Cone Structure Layers under Buckling Load

2012 ◽  
Vol 225 ◽  
pp. 144-149
Author(s):  
Hadi Samareh Salavati Pour ◽  
Mojtaba Sadighi ◽  
Abdolvahed Kami
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Back Propagation ◽  
Activation Function ◽  
Buckling Load ◽  
Training Data ◽  
Training Algorithm ◽  
Cone Structure ◽  
Truncated Cone ◽  
Hidden Layer

The orientation of fibers in the layers is an important factor that must be obtained in order to predict how well the finished composite product will perform under real-world working conditions. In this research, a five-layer glass-epoxy composite truncated cone structure under buckling load was considered. The simulation of the structure was done utilizing finite element method and was confirmed comparing with the published experimental results. Then the effect of different orientation of fibers on the buckling load was considered. For this, a computer programing was developed to compute the buckling load for different orientations of fibers in each layer. These orientations were produced randomly with the delicacy of 15 degrees. Finally, neural network and genetic algorithm methods were utilized to obtain the optimum orientations of fibers in each layer using the training data obtained from finite element simulation. There are many parameters such as the number of hidden layers, the number of neurons in each hidden layer, the training algorithm, the activation function and so on which must be specified properly in development of a neural network model. The number of hidden layers and number of neurons in each layer was obtained by try and error method. In this study, multilayer back-propagation (BP) neural network with the Levenberg-Marquardt training algorithm (trainlm) was used. Finally, the results showed that the truncated cone with optimum layers withstand considerably more buckling load.

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