IMPLEMENTATION OF ADAPTIVE PRODUCT CUSTOMIZATION PROCESSES IN MASS PRODUCTION

2021 ◽  
pp. 52-58
Author(s):  
A. V. Martynov ◽  
G. S. Nikonova ◽  
А. N. Kondratyev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Incident Wave ◽  
Mass Production ◽  
Mathematical Apparatus ◽  
Adaptive Calibration ◽  
Adaptive Correction ◽  
Speed Up ◽  
Product Customization

The article presents the results of research and implementation of adaptive calibration systems used in setting up mass-produced products. The proposed method allows you to speed up the tuning of the HF transceiver. In the process of tuning, with the help of a neural network, an adaptive correction of the approximating function of the incident wave sensor is made according to the accumulated data, which makes it possible to reduce the time for tuning the transceiver. The introduction of the mathematical apparatus of neural networks can be applied in the process of mass production for other products.

scholarly journals Parallelization of Neural Network Training for NLP with Hogwild!

2017 ◽  
Vol 109 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Valentin Deyringer ◽  
Alexander Fraser ◽  
Helmut Schmid ◽  
Tsuyoshi Okita
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suitable Method ◽  
Neural Network Training ◽  
Training Time ◽  
Pos Tagging ◽  
Network Training ◽  
Speed Up

Abstract Neural Networks are prevalent in todays NLP research. Despite their success for different tasks, training time is relatively long. We use Hogwild! to counteract this phenomenon and show that it is a suitable method to speed up training Neural Networks of different architectures and complexity. For POS tagging and translation we report considerable speedups of training, especially for the latter. We show that Hogwild! can be an important tool for training complex NLP architectures.

Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah

2021 ◽  
Vol 5 (2) ◽  
pp. 312-318
Author(s):  
Rima Dias Ramadhani ◽  
Afandi Nur Aziz Thohari ◽  
Condro Kartiko ◽  
Apri Junaidi ◽  
Tri Ginanjar Laksana ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Computer Science ◽  
No Value ◽  
Convolutional Neural Networks ◽  
Training Process ◽  
Indonesian Government ◽  
Speed Up ◽  
Model Training

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.

Detecting Eyes and Lips Using Neural Networks and SURF Features

2012 ◽  
pp. 338-354 ◽  
Author(s):  
Artem A. Lenskiy ◽  
Jong-Soo Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Success Rate ◽  
Detection Rate ◽  
Facial Feature ◽  
Speed Up ◽  
Surf Features ◽  
Left And Right ◽  
The Right

In this chapter, the authors elaborate on the facial image segmentation and the detection of eyes and lips using two neural networks. The first neural network is applied to segment skin-colors and the second to detect facial features. As for input vectors, for the second network the authors apply speed-up robust features (SURF) that are not subject to scale and brightness variations. The authors carried out the detection of eyes and lips on two well-known facial feature databases, Caltech. and PICS. Caltech gave a success rate of 92.4% and 92.2% for left and right eyes and 85% for lips, whereas the PCIS database gave 96.9% and 95.3% for left and right eyes and 97.3% for lips. Using videos captured in real environment, among all videos, the authors achieved an average detection rate of 94.7% for the right eye and 95.5% for the left eye with a 86.9% rate for the lips

scholarly journals DEVELOPMENT OF A METHODOLOGY FOR FORMALIZING THE INVESTMENT DECISION-MAKING PROCESS BASED ON THE HOPFIELD NEURAL NETWORK

2019 ◽  
pp. 65-72
Author(s):  
Olga RUZAKOVA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Investment Decision ◽  
Methodological Approach ◽  
Hopfield Neural Network ◽  
Hopfield Network ◽  
Decision Making Process ◽  
Mathematical Apparatus ◽  
Investment Process

The article presents a methodological approach to assessing the investment attractiveness of an enterprise based on the Hopfield neural network mathematical apparatus. An extended set of evaluation parameters of the investment process has been compiled. An algorithm for formalizing the decision-making process regarding the investment attractiveness of the enterprise based on the mathematical apparatus of neural networks has been developed. The proposed approach allows taking into account the constantly changing sets of quantitative and qualitative parameters, identifying the appropriate level of investment attractiveness of the enterprise with minimal money and time expenses – one of the standards of the Hopfield network, which is most similar to the one that characterizes the activity of the enterprise. Developed complex formalization of the investment process allows you to make investment decisions in the context of incompleteness and heterogeneity of information, based on the methodological tools of neural networks.

scholarly journals APPLICATION OF NEURAL NETWORK AUTO ENCODERS OF UNET TYPE FOR INVERSE PHOTOLITOGRAPHY TASKS

2021 ◽  
Author(s):  
Vladimir Keremet ◽  
Yakov Karandashev ◽  
Aleksey Kuzovkov ◽  
Georgy Teplov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Modeling Processes ◽  
Network Approach ◽  
Process Technology ◽  
Neural Network Approach ◽  
Simulation Accuracy ◽  
Network Modules ◽  
Speed Up

The paper discusses the issue of the applicability of neural networks to the problems of designing microelectronics. The integration of neural network modules into the elements of specialized EDA systems can significantly speed up the modeling processes at different stages of design. The application of a multilayer convolutional architecture of a neural network of the UNET type to the problem of direct and inverse computational photolithography is considered. Using this neural network approach, we were able to speed up the computation of a photo mask for a 90nm process technology by two orders of magnitude and achieve simulation accuracy that surpasses standard inverse photolithography (ILT) methods.

scholarly journals The application of the artificial neural network in analysing the spread quality

2018 ◽  
Vol 226 ◽  
pp. 04042
Author(s):  
Marko Petkovic ◽  
Marija Blagojevic ◽  
Vladimir Mladenovic
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Food Processing ◽  
New Approach ◽  
Confectionery Products ◽  
Speed Up ◽  
Artificial Neural

In this paper, we introduce a new approach in food processing using an artificial intelligence. The main focus is simulation of production of spreads and chocolate as representative confectionery products. This approach aids to speed up, model, optimize, and predict the parameters of food processing trying to increase quality of final products. An artificial intelligence is used in field of neural networks and methods of decisions.

scholarly journals High Performance Implementation of 3D Convolutional Neural Networks on a GPU

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang Lan ◽  
Zelong Wang ◽  
Mei Wen ◽  
Chunyuan Zhang ◽  
Yijie Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
The Other ◽  
Memory Requirement ◽  
Video Classification ◽  
Filtering Algorithm ◽  
Speed Up ◽  
The Cost

Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Speed up Neural Network Learning by GPGPU

2011 ◽  
Vol 131 (11) ◽  
pp. 1889-1894
Author(s):  
Yuta Tsuchida ◽  
Michifumi Yoshioka
Keyword(s):  
Neural Network ◽  
Neural Network Learning ◽  
Network Learning ◽  
Speed Up

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

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