scholarly journals Development and experimental study of analyzer to enhance maritime safety

2021 ◽  
Vol 4 (3(112)) ◽  
pp. 27-35
Author(s):  
Pavlo Nosov ◽  
Serhii Zinchenko ◽  
Viktor Plokhikh ◽  
Ihor Popovych ◽  
Yurii Prokopchuk ◽  
...  
Keyword(s):  
Neural Networks ◽  
Maritime Transport ◽  
Test Error ◽  
Navigation Data ◽  
Critical Situation ◽  
The Neural Network ◽  
Simulation Results ◽  
Transport Management ◽  
Critical Situations

On the basis of empirical experimental data, relationships were identified indicating the influence of navigators' response to such vessel control indicators as maneuverability and safety. This formed a hypothesis about a non-random connection between the navigator's actions, response and parameters of maritime transport management. Within the framework of this hypothesis, logical-formal approaches were proposed that allow using server data of both maritime simulators and operating vessels in order to timely identify the occurrence of a critical situation with possible catastrophic consequences. A method for processing navigation data based on the analysis of temporal zones is proposed, which made it possible to prevent manifestations of reduced efficiency of maritime transport management by 22.5 %. Based on cluster analysis and automated neural networks, it was possible to identify temporary vessel control fragments and classify them by the level of danger. At the same time, the neural network test error was only 3.1 %, and the learning error was 3.8 %, which ensures the high quality of simulation results. The proposed approaches were tested using the Navi Trainer 5000 navigation simulator (Wärtsilä Corporation, Finland). The simulation of the system for identifying critical situations in maritime transport management made it possible to reduce the probability of catastrophic situations by 13.5 %. The use of automated artificial neural networks allowed defining critical situations in real time from the database of maritime transport management on the captain's bridge for an individual navigator.

scholarly journals Using high-performance deep learning platform to accelerate object detection

2019 ◽  
pp. 354-360 ◽  
Author(s):  
S O Stepanenko ◽  
P Y Yakimov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Processing Speed ◽  
High Performance ◽  
Object Classification ◽  
High Accuracy ◽  
Learning Platform ◽  
The Neural Network

Object classification with use of neural networks is extremely current today. YOLO is one of the most often used frameworks for object classification. It produces high accuracy but the processing speed is not high enough especially in conditions of limited performance of a computer. This article researches use of a framework called NVIDIA TensorRT to optimize YOLO with the aim of increasing the image processing speed. Saving efficiency and quality of the neural network work TensorRT allows us to increase the processing speed using an optimization of the architecture and an optimization of calculations on a GPU.

scholarly journals Multi-expert annotation of Crohn’s disease images of the small bowel for automatic detection using a convolutional recurrent attention neural network

2021 ◽  
Vol 09 (07) ◽  
pp. E1136-E1144
Author(s):  
Astrid de Maissin ◽  
Remi Vallée ◽  
Mathurin Flamant ◽  
Marie Fondain-Bossiere ◽  
Catherine Le Berre ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Diagnostic Tools ◽  
The Neural Network ◽  
Before And After ◽  
Expert Annotation ◽  
The Impact

Abstract Background and study aims Computer-aided diagnostic tools using deep neural networks are efficient for detection of lesions in endoscopy but require a huge number of images. The impact of the quality of annotation has not been tested yet. Here we describe a multi-expert annotated dataset of images extracted from capsules from Crohn’s disease patients and the impact of the quality of annotations on the accuracy of a recurrent attention neural network. Methods Images of capsule were annotated by a reader first and then reviewed by three experts in inflammatory bowel disease. Concordance analysis between experts was evaluated by Fleiss’ kappa and all the discordant images were, again, read by all the endoscopists to obtain a consensus annotation. A recurrent attention neural network developed for the study was tested before and after the consensus annotation. Available neural networks (ResNet and VGGNet) were also tested under the same conditions. Results The final dataset included 3498 images with 2124 non-pathological (60.7 %), 1360 pathological (38.9 %), and 14 (0.4 %) inconclusive. Agreement of the experts was good for distinguishing pathological and non-pathological images with a kappa of 0.79 (P < 0.0001). The accuracy of our classifier and the available neural networks increased after the consensus annotation with a precision of 93.7 %, sensitivity of 93 %, and specificity of 95 %. Conclusions The accuracy of the neural network increased with improved annotations, suggesting that the number of images needed for the development of these systems could be diminished using a well-designed dataset.

scholarly journals Developing the Automatic Control System Based on Neural Controller

2015 ◽  
Vol 44 (3) ◽  
pp. 262-270 ◽  
Author(s):  
Jun Su ◽  
Markiyan Nakonechnyi ◽  
Orest Ivakhiv ◽  
Anatoliy Sachenko
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Input Circuit ◽  
Nonlinear Functions ◽  
Neural Controller ◽  
Discrete Transformation ◽  
Network Training ◽  
Simulation Results ◽  
The Mathematical Model

Mostly the dynamics of controlled objects is often described by nonlinear equalizations. Last years themethodology of neural networks is engaged into designing the systems controlling such objects, in particular due to theinfluence of nonlinearities can be taken into account by nonlinear functions of the activation. Such methodology brings someintelligence to the designed system.Authors proposed the purposeful procedure of forming the structure of the neural controller according the desired lawof the control using the discrete transformation of the motion equation. Requirements to the mathematical model of thereference and method of network training are determined, and the control quality is estimated at traditional passing thedisagreement error in the controller input and for the proposed new configuration of its input circuit, namely with separatedinputs. Simulation results confirmed providing the better quality of the system control.DOI: http://dx.doi.org/10.5755/j01.itc.44.3.7717

scholarly journals Flattening Layer Pruning in Convolutional Neural Networks

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1147
Author(s):  
Ernest Jeczmionek ◽  
Piotr A. Kowalski
Keyword(s):  
Neural Networks ◽  
Network Topology ◽  
Global Sensitivity Analysis ◽  
Global Sensitivity ◽  
The Neural Network ◽  
Input Variables ◽  
The Impact ◽  
Different Levels ◽  
Pruning Methods

The rapid growth of performance in the field of neural networks has also increased their sizes. Pruning methods are getting more and more attention in order to overcome the problem of non-impactful parameters and overgrowth of neurons. In this article, the application of Global Sensitivity Analysis (GSA) methods demonstrates the impact of input variables on the model’s output variables. GSA gives the ability to mark out the least meaningful arguments and build reduction algorithms on these. Using several popular datasets, the study shows how different levels of pruning correlate to network accuracy and how levels of reduction negligibly impact accuracy. In doing so, pre- and post-reduction sizes of neural networks are compared. This paper shows how Sobol and FAST methods with common norms can largely decrease the size of a network, while keeping accuracy relatively high. On the basis of the obtained results, it is possible to create a thesis about the asymmetry between the elements removed from the network topology and the quality of the neural network.

scholarly journals Influence of the Training Set Value on the Quality of the Neural Network to Identify Selected Moulding Sand Properties

2013 ◽  
Vol 13 (2) ◽  
pp. 49-52
Author(s):  
J. Jakubski ◽  
St. M. Dobosz ◽  
K. Major-Gabryś
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Assessment Method ◽  
Training Set ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Moulding Sand ◽  
Small Change

Abstract Artificial neural networks are one of the modern methods of the production optimisation. An attempt to apply neural networks for controlling the quality of bentonite moulding sands is presented in this paper. This is the assessment method of sands suitability by means of detecting correlations between their individual parameters. This paper presents the next part of the study on usefulness of artificial neural networks to support rebonding of green moulding sand, using chosen properties of moulding sands, which can be determined fast. The effect of changes in the training set quantity on the quality of the network is presented in this article. It has been shown that a small change in the data set would change the quality of the network, and may also make it necessary to change the type of network in order to obtain good results.

Power System Fault Diagnosis Based on Wavelet Transform and Neural Networks

2014 ◽  
Vol 705 ◽  
pp. 255-258 ◽  
Author(s):  
Jun Liu ◽  
Lin Li ◽  
Qing Tao Long
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Power System ◽  
Electric Power ◽  
Electric Power System ◽  
Singularity Detection ◽  
Fault Recognition ◽  
The Neural Network ◽  
Simulation Results

Using the principle of wavelet transform in the aspect of signal singularity detection analyzes and detects the electric power system fault signal. Then we extract signal feature near the fault moment and sent the feature vectors into the neural network. The simulation results fully prove the effectiveness and superiority of combining wavelet transform and neural network in electric power system fault recognition.

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.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

SIMULATION OF DIRECT SPREAD SPECTRUM COMMUNICATION SYSTEM ON THE HARDWARE MODULE EMONA DATEX. EVALUATION OF THE EFFECT OF NOISE ON THE QUALITY OF DEMODULATION

2019 ◽  
Author(s):  
Maksim Zhmakin ◽  
Irina Chadyuk ◽  
Aleksey Nadymov
Keyword(s):  
Spread Spectrum ◽  
Communication System ◽  
Spread Spectrum Communication ◽  
Direct Spread ◽  
Simulation Results

A variant of implementation of a communication system with direct spread spectrum is presented in this article, simulation results are also presented, the main parameters of the system are taken, and conclusions are drawn.

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

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