scholarly journals PORE PERCENTAGE ESTIMATION OF PIEZOELECTRIC CERAMICS USING CCANN AND IMAGE MADE WITH SEM

2021 ◽  
pp. 286-293
Author(s):  
Данила Владимирович Мамаев ◽  
Сергей Алексеевич Меркурьев ◽  
Ольга Витальевна Малышкина
Keyword(s):  
Neural Network ◽  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Artificial Neural Network ◽  
Final Stage ◽  
Sodium Niobate ◽  
Potassium Sodium Niobate ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Scanning Electron

Авторами получены образцы пьезоэлектрической керамики ниобата калия натрия с концентрацией пор 10,25 и 40 объемных %. Разработана капсульная свёрточная искусственная нейронная сеть для определения процентного содержания пор по изображению. С помощью растрового электронного микроскопа получено обучающее множество примеров (фотографии поверхности и сколов подготовленных образцов). Разработка и апробация капсульной свёрточной искусственной нейронной сети осуществлена в несколько этапов. На первом проведено ее обучение с помощью метода обратного распространения ошибки. На втором - тестирование на проверочном множестве. На заключительном этапе проведено сравнение полученных результатов с результатами метода сравнения плотности материала. Показано, что данный метод можно использовать для решения задачи определения процентного содержания пор в KNN, поскольку полученные результаты сопоставимы с результатами, полученными другим методом. Установлено, что в образцах, в которых не были специально добавлены поры, также присутствуют поры (порядка 5 %). The authors synthesized samples of piezoelectric potassium sodium niobate ceramics of 10,25 and 40 pore percentage by volume. Capsule convolutional artificial neural network has been developed for estimation of the pore percentage in images. Using the scanning electron microscopy, f learning massive of examples was formed (photographs of surface and edges of so-synthesized samples). Development and approbation of the capsule convolutional artificial neural network was completed in a few stages. During the first stage, the network was trained using a backpropagation method. Secondly, it was tested by a testing set. At the final stage we made a comparison of the acquired results with the results of the density comparing method. The article shows that this method can be used the pore percentage estimation in sodium niobate ceramics because the acquired results are comparable with the results of other methods. It was found that the samples where the pores were not made also have some pore percentage (about 5 %).

IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK IN PREDICTING BIRTH RATE IN BATAM CITY USING BACKPROPAGATION METHOD

JURTEKSI ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 85-94
Author(s):  
Muhammad Jufri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Family Planning ◽  
Population Growth ◽  
Birth Rate ◽  
The Past ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
The Government ◽  
The City

Abstract: The population growth in Indonesia is increasing rapidly every year, so to help the government control the population growth through family planning programs, especially in the city of Batam. This study explains and describes one of the Artificial Terms Network methods, namely Backpropagation, where this method can predict what will happen in the future using data and information in the past. This study aims to predict the birth rate in the city of Batam to help the government with the family planning program. The data used is the annual data on the number of births in the city of Batam in 2016-2020 at The Civil Registry Office. To facilitate the analysis of research data, the data were tested using Matlab R2015b. In this study, the training process was carried out using 3 network architectures, namely 4-10-1, 5-18-1, and 4-43-1. Of these 3 architectures, the best is the 4-43-1 architecture with an accuracy rate of 91% and an MSE value of 0.0012205. The Backpropagation method can predict the amount of population growth in the city of Batam based on existing data in the past.           Keywords: artificial neural network; backpropagation; prediction   Abstrak: Pertumbuhan jumlah penduduk diindonesia yang setiap tahun meningkat dengan pesat, maka untuk membantu pemerintah mengendalikan jumlah pertumbuhan penduduk melalui program keluarga berencana khususnya dikota Batam. Penelitian ini  menjelaskan dan memaparkan tentang salah satu metode Jaringan Syarat Tiruan yaitu Backpropagation, dimana metode ini dapat memprediksi apa yang akan terjadi masa yang akan datang dengan menggunakan data dan informasi dimasa lalu. Penelitian ini bertujuan untuk memprediksi tingkat kelahiran di kota Batam sehingga membatu pemerintah untuk perencanaan keluarga berencana. Data yang digunakan yaitu data tahunan jumlah kelahiran di kota Batam pada tahun 2016-2020 pada Dinas Kependudukan dan Catatan Sipil. Untuk mempermudah analisis data penelitian maka, data diuji menggunakan Matlab R2015b. Pada penelitian ini dilakukan proses pelatihan menggunakan  3 arsitektur jaringan yaitu 4-10-1, 5-18-1, dan 4-43-1. Dari ke-3 arsitektur ini yang terbaik adalah arsitektur 4-43-1 dengan tingkat akurasi sebesar 91% dan nilai MSE 0,0012205. Metode backpropagation mampu memprediksi jumlah pertumbuhan penduduk di kota Batam berdasarkan data yang ada dimasa lalu. Kata kunci: backpropagation; jaringan syaraf tiruan; prediksi 

scholarly journals Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik Terhadap Covid-19

2021 ◽  
Vol 5 (2) ◽  
pp. 109-118
Author(s):  
Euis Saraswati ◽  
Yuyun Umaidah ◽  
Apriade Voutama
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Artificial Neural Network ◽  
Text Mining ◽  
Ann Model ◽  
Rapid Spread ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
The Government ◽  
Artificial Neural Network Ann

Coronavirus disease (Covid-19) or commonly called coronavirus. This virus spreads very quickly and even almost infects the whole world, including Indonesia. A large number of cases and the rapid spread of this virus make people worry and even fear the increasing spread of the Covid-19 virus. Information about this virus has also been spread on various social media, one of which is Twitter. Various public opinions regarding the Covid-19 virus are also widely expressed on Twitter. Opinions on a tweet contain positive or negative sentiments. Sentiments of sentiment contained in a tweet can be used as material for consideration and evaluation for the government in dealing with the Covid-19 virus. Based on these problems, a sentiment analysis classification is needed to find out public opinion on the Covid-19 virus. This research uses Artificial Neural Network (ANN) algorithm with the Backpropagation method. The results of this test get 88.62% accuracy, 91.5% precision, and 95.73% recall. The results obtained show that the ANN model is quite good for classifying text mining.

scholarly journals Implementation of Autonomous Control System of The Chain Wheel Robot Using the Backpropagation Artificial Neural Network (ANN) Methods

2020 ◽  
Vol 5 (8) ◽  
pp. 1230-1235
Author(s):  
Nur Rachman Supadmana Muda ◽  
Nugraha Gumilar ◽  
R.Djoko Andreas. Navalino ◽  
Tirton. N ◽  
M.Iman Hidayat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Robot Control ◽  
Back Propagation ◽  
Robot System ◽  
Output Data ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this research is to implement the Artificial Neural Network (ANN) method in combat robots so it can be directed to shoot targets well. The robot control system uses remote control and autonomous. In the autonomous robot system, ANN back propagation method is applied, where the weight value variable depends on ultrasonic sensor, GPS and camera. The microcontroller system will process automatically depending on the sensor input. Output data is used to direct the robot to the target, tracking and shooting. Robot is used chain wheel systems and weapons that used pistol types. The riffle is mounted on the robot can be moved mechanically azimuth and the elevation towards the target then triggered mechanically by the riffle through the activation of data relays from the microcontroller. Thus, the backpropagation method can be applied to robots so it can be functioned autonomously.

scholarly journals Artificial neural network for solving multi-parameter optimization problems

2021 ◽  
Vol 2092 (1) ◽  
pp. 012013
Author(s):  
Krivorotko Olga ◽  
Liu Shuang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problems ◽  
Three Dimensional ◽  
Test Functions ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Quality Of Approximation ◽  
And Function

Abstract An artificial neural network (ANN) is a mathematical or computational model that simulates the structure and function of biological neural networks used to evaluate or approximate functions at given points. After developing the training algorithm, the resulting model will be used to solve image recognition problems, control problems, optimization, etc. In the process of ANN training, the algorithm of backpropagation is used in the case of convex optimization functions. The article is analyzed test functions for experiments and also study the effect of the number of ANN layers on the quality of approximation in cases one-, two- and three-dimensional. The backpropagation method is improved during the experiments with the help of adaptive gradient, as a result of which more accurate approximations of the functions are obtained. This article also presents the numerical results of test functions.

scholarly journals Estimation of Flow Discharge Model at Temef Watershed - East Nusa Tenggara Using TRMM Satellite Data

2021 ◽  
Vol 004 (02) ◽  
pp. 115-126
Author(s):  
Aprianto Nomleni ◽  
Ery Suhartanto ◽  
Donny Harisuseno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tropical Rainfall Measuring Mission ◽  
Polynomial Equation ◽  
Observation Data ◽  
Artificial Neural Network Analysis ◽  
Backpropagation Method ◽  
Discharge Model ◽  
Artificial Neural ◽  
The Relationship

Data collection based on satellite TRMM (Tropical Rainfall Measuring Mission) presents one of the good alternatives in estimating rainfall. TRMM technology can minimize manual rainfall recording errors and improve rainfall accuracy for hydrological analysis. The analysis method used in this research is divided into 3 (three) stages, namely Hydrology analysis, Statistical Analysis and Artificial Neural Network Analysis. From the results of TRMM JAXA analysis in the Temef Watershed Area of East Nusa Tenggara Province obtained TRMM JAXA satellite rainfall relationship to observation data shows rainfall patterns between the two data are interconnected but for cases with very high observation rainfall, TRMM rainfall data tends to be low. From statistical method analysis, the relationship between observation rainfall and TRMM JAXA rainfall obtained results with a "Very Strong" interpretation indicated by the results of 9 years calibration and 1 year validation where the selected equation is a polynomial equation (y=-0,0123x2 + 1,5553x + 20,222). Rain data correction results simulated with Debit data to see the relationship between rain and discharge that occurred, this analysis using Artificial Neural Network with Backpropagation method, the results showed a "Strong" interpretation where statistically the value of Nash-Sutcliffe Efficiency (NSE) 0.920, the coefficient value of correlation of field discharge and TRMM rainfall is 0,877 % and the relative error occurred is 2,62%

Morphological and experimental studies of the somitomeric organization of the segmental plate in snapping turtle embryos

Development ◽  
1984 ◽  
Vol 84 (1) ◽  
pp. 35-48
Author(s):  
David S. Packard ◽  
Stephen Meier
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Final Stage ◽  
Experimental Studies ◽  
The Other ◽  
Chelydra Serpentina ◽  
Microscopy Imaging ◽  
Snapping Turtle ◽  
Somite Formation ◽  
Scanning Electron

The segmental plate mesoderm of snapping turtle embryos (Chelydra serpentina) was examined with stereoscanning electron microscopy imaging. A metameric pattern was detected along the entire length of the segmental plates. This pattern consisted of a tandem sequence of mesodermal units, called somitomeres. Each somitomere was oval to cubic in shape and the processes of the constituent mesodermal cells tended to be arranged in concentric rings about the centre of the somitomere. Several experiments from a previous study (Packard, 1980b) of snapping turtle segmental plates were repeated, but, instead of culturing the explants and observing the numbers of somites that formed, the explants were fixed immediately for scanning electron microscopy and the number of somitomeres was counted. The segmental plates were found to contain an average of 6·5 ± 0·7 somitomeres, which is almost identical to the average number of somites formed by such segmental plates when cultured (6·6 ± 1·2). Furthermore, the number of somitomeres was identical in right and left explants removed from the same embryo, and the number of somitomeres was consistent regardless of the length of the segmental plate. Both of these observations are identical to those made previously for somite formation in culture. This association between numbers of somitomeres and somites strongly suggests that one gives rise to the other. Finally, it was demonstrated that for each somite formed by a segmental plate in culture, the segmental plate contained one less somitomere. This showed in a direct manner that turtle somitomeres become somites. It was concluded that the segmental plate mesoderm of snapping turtle embryos is already segmented, and that the ‘segmentation’ seen under a dissecting microscope is actually the final stage of somitomere differentiation into an epithelial somite.

scholarly journals APLIKASI PENGENALAN PLAT NOMOR KENDARAAN DI UNIVERSITAS RIAU

2019 ◽  
Vol 9 (3) ◽  
pp. 47-52
Author(s):  
Noprizal ◽  
Feri Candra
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Digital Image Processing ◽  
Digital Image ◽  
Training Data ◽  
License Plate ◽  
Backpropagation Method ◽  
Artificial Neural

Abstract Vehicle license plate recognition application has been found in shopping centers, university, and other agency buildings with various methods of recognition. Some examples of methods used such as digital image processing techniques, neural networks and so forth. This study makes an application for the introduction of license plates, especially for student vehicle license plates in the university area. This application is developed with Digital Image Processing Methods and Artificial Neural Networks. In this study, 900 training data are used, taken from 200 photo vehicle number plates, to train 36 characters that contain 26 alphabets and 10 decimal numbers. The training data is used to test 30 photos of vehicle license plates. Plate photos used as training and testing data are the Indonesian standard with black and white plates. Artificial Neural Network used to recognize vehicle license plate by using the Backpropagation method with parameters Epoch 1000, Hidden layer1 with node 60, Hidden layer2 with node 55, Goal 0.001. The final conclusion of this Study shows that the use of Artificial Neural Network Backpropagation method is very good, with the best testing accuracy obtained, namely 98% and 1.25 error. Keywords : digital image processing, artificial neural networks, vehicle license plate Abstrak Aplikasi pengenalan plat nomor kendaraan sudah banyak ditemukan di pusat perbelanjaan, universitas, dan gedung instansi dengan berbagai metode pengenalan. Beberapa contoh metode yang digunakan seperti teknik pengolahan citra digital, jaringan syaraf tiruan dan lain sebagainya. Disini penulis membuat sebuah aplikasi pengenalan plat nomor kendaraan khususnya untuk plat nomor kendaraan mahasiswa yang ada dilikungan Universitas Riau. Aplikasi ini dikembangkan dengan metode pengolahan citra digital dan jaringan syaraf tiruan. Pada penelitian ini, digunakan 700 data pelatihan yang diambil dari 200 foto plat nomor, untuk melatih 36 karakter. Data pelatihan tersebut kemudian digunakan untuk menguji 30 foto plat nomor kendaraan. Foto plat yang dijadikan untuk data pelatihan dan pengujian yaitu plat standar indonesia yang berwarna hitam dan putih. Jaringan syaraf tiruan yang digunakan untuk melakukan pengenalan yaitu dengan Metode Backpropagation dengan parameter Epoch 1000, Hidden layer1 dengan jumlah node 60, Hidden layer2 dengan jumlah node 55, Goal  0,001. Kesimpulan akhir dari penelitian ini yaitu menunjukan bahwa penggunaan Metode Backpropagation jaringan syaraf tiruan ini sangat bagus, dengan akurasi pengujian terbaik yang didapat yaitu 98% dengan eror 1,25. Kata kunci: pengolahan citra digital, jaringan syaraf tiruan, Backpropagation, plat nomor  

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