scholarly journals Development of intelligent system for automated traffic control

2020 ◽  
Vol 217 ◽  
pp. 03009
Author(s):  
Yelena Revyakina ◽  
Larissa Cherckesova ◽  
Olga Safaryan ◽  
Vitaliy Porksheyan ◽  
Tatyana Nikishina ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control System ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Intelligent System ◽  
Traffic Lights ◽  
Traffic Jams ◽  
Artificial Neural

This article is devoted to the issue of regulating traffic congestion in major cities of the world using artificial neural networks. Research is aimed at developing import – substituting automated intelligent system that uses artificial neural network to make decisions to optimize traffic congestion by changing the duration of light phases of traffic lights. Multilayer perceptron with sigmoidal activation function is used as neural network. The article describes developing stages of intelligent automated traffic control system that using artificial neural networks allows making informed decisions based on extensive analysis of available information, as well as constantly adapt it to incoming external influences that lead to non – equilibrium state. Practical application of the proposed system is expressed in unloading road sections adjacent to highway; reducing the number of traffic jams in the lanes or reducing the length of the car queue; automating traffic control and reducing the number of emergency cases that require inspection personnel to leave for manual control. System allow improving overall traffic situation by avoiding cascading traffic jams on adjacent sections; prevention of accidents and conflicts between motorists and pedestrians; improving the reliability of adjustment and reducing cost of maintenance infrastructure.

Neural Controlling of Remotely Operated Underwater Vehicle

2011 ◽  
Vol 180 ◽  
pp. 168-174 ◽  
Author(s):  
Andrzej Żak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Control System ◽  
Underwater Vehicle ◽  
Mathematical Basis ◽  
Dynamical Object ◽  
Artificial Neural

The main aim of paper is to introduce the results of research concentrated on controlling remotely operated underwater vehicle using artificial neural networks. Firstly the mathematical basis of neural network using to control dynamical object were introduced. Next the proposed control system which is using technology of artificial neural network was presented. At the end the example results of research on stabilizing movements’ parameters of underwater vehicle using ROV simulator were presented. The paper is finished by summary which include conclusions derive from results of research.

Aeronautical Study for Flight Time Estimation Applying Artificial Neural Network and Genetic Algorithm

2014 ◽  
Vol 919-921 ◽  
pp. 1063-1074
Author(s):  
Yung Ching Lin ◽  
Lee Kuo Lin ◽  
Shao Hong Tsai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Flight Time ◽  
Air Traffic ◽  
Artificial Neural

Since the adoption of open-air policy, people make more frequent use of air travel to do various business or tourism activities. The volume of air traffic has greatly increased, along with the occurrences of traffic jam in the air. Delays of landings or take-offs and the congestions in the approach air space have become commonplace, exacerbating the already heavy workload of air-traffic controllers and the inadequacies of ATC system. Therefore, a study of flight time in ATC operation to help alleviate airspace congestions has become more and more urgent and important. Taking international airway A1 as an example, this study makes use of the known entry time, flight altitude, speed, penetrating and descending as the input of artificial neural networks; the time between departure and transfer point as the output of Artificial Neural Networks, to establish artificial neural network. Applying artificial neural networks and genetic algorithm to the study to simulate the result of actual flight, one can precisely estimate the flight time, thereby making it an efficient air-traffic-control instrument. It can help controllers handle different time segments of air traffic, thus upgrading the quality of air traffic control service.

scholarly journals The concept of a software and analytical complex of the educational process based on ontology and artificial neural networks

2021 ◽  
Vol 11 (3) ◽  
pp. 339-350
Author(s):  
V.V. Antonov ◽  
◽  
G.G. Kulikov ◽  
L.A. Kromina ◽  
L.E. Rodionova ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Professional Education ◽  
Intelligent System ◽  
Educational Process ◽  
Training Dataset ◽  
Network Node ◽  
Ontological Approach ◽  
Artificial Neural

Effective management of the learning process of additional professional education programs at the university is condi-tioned by providing unique needs of students as requested by employers in the real sector of the economy in accord-ance with the selected competencies and areas of training. At the same time, when solving a number of tasks, the algo-rithm of which is unknown, there are more and more actively developed and implemented systems using artificial neu-ral networks, which allow classifying and analyzing data for making managerial decisions. Based on such widespread use of artificial neural networks, there is an increasing need for systematization of data to improve the performance of software analytical complex processing, storage, search and analysis of data, for the implementation of training pro-grams at all stages of the life cycle, taking into account uncertainty. The developed software-analytical complex is pre-sented on the example of a model of an intelligent system used to control and analyze the acquired competencies of students, built on the basis of an ontological approach, a model of continuous quality improvement, which makes it possible to determine the interaction of business processes, their sequence and performance benchmarks. To imple-ment this theory, a neural network node scheme of a software analytic complex capable of data-driven learning has been developed. The presented scheme of a neural network node assumes the use of a supervised learning algorithm when a training dataset arrives at the input.

scholarly journals DEVELOPMENT OF 5 DOF ROBOTIC ARM PROTOTYPE CONTROL SYSTEM IN BRACHYTHERAPY PREPARATION BASED ON ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 15 (2) ◽  
pp. 42
Author(s):  
Adi Abimanyu, M.Eng ◽  
Misbah Habib Putra ◽  
Muhtadan Muhtadan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Control System ◽  
Inverse Kinematic ◽  
Robotic Arm ◽  
Cancer Tissue ◽  
Artificial Neural ◽  
Radioactive Sources

Brachytherapy is a cancer treatment that uses radioactive sources with temporary or permanent implantation in cancer tissue. The theraphy uses a radioactive Ir-192 source wrapped in a stainless steel capsule with a diameter of 0.5 mm and a length of 4 mm. The Center for Radioisotopes and Radiopharmaceutical Technology applies a remote manipulator to manufacture microcapsules, which affects the accuracy and risks of the radiation received by the operator. Therefore, to solve this problem, it is necessary to design a 5 DoF robotic arm based on artificial neural networks as a radioactive source transfer tool to improve the precision and safety of operators in preparing the radioactive sources. In developing the 5 DoF robotic arm control system, the NImyRIO was employed, which can control the servo motor, relay pump and valve reality, image processing, and inverse kinematic. The inverse kinematic uses the neural network method with a forward kinematic validation. The inverse kinematic test obtains the RMSE value of 2.78932 for x, 5.05205 for y, and 12.641 for z in the inverse kinematic test of artificial neural networks. Therefore, the inverse kinematic accuracy of the artificial neural network needs to be redeveloped.

Construction project control using artificial neural networks

1997 ◽  
Vol 11 (1) ◽  
pp. 45-57 ◽  
Author(s):  
H. Al-Tabtabai ◽  
N. Kartam ◽  
I. Flood ◽  
A.P. Alex
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control System ◽  
Construction Project ◽  
Project Control ◽  
Cost Variance ◽  
The Neural Network ◽  
Network Modules ◽  
Artificial Neural

AbstractArtificial neural networks are finding wide application to a variety of problems in civil engineering. This paper describes how artificial neural networks can be applied in the area of construction project control. A project control system capable of predicting and monitoring project performance (e.g., cost variance and schedule variance) based on observations made from the project environment is described. This project control system has five neural network modules that allow a project manager to automatically generate revised project plans at regular intervals during the progress of the project. These five modules are similar in design and implementation. Therefore, this paper will present the main issues involved in the development of one of these five neural network modules, that is, the module for identifying schedule variance. A description of a graphical user interface integrating the neural network modules developed with project management software, and a discussion on the power and limitations of the overall system conclude the paper.

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network

2015 ◽  
Author(s):  
M. A. Rafe Biswas ◽  
Melvin D. Robinson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Operating Conditions ◽  
Direct Methanol ◽  
Artificial Neural ◽  
Methanol Fuel Cell

A direct methanol fuel cell can convert chemical energy in the form of a liquid fuel into electrical energy to power devices, while simultaneously operating at low temperatures and producing virtually no greenhouse gases. Since the direct methanol fuel cell performance characteristics are inherently nonlinear and complex, it can be postulated that artificial neural networks represent a marked improvement in performance prediction capabilities. Artificial neural networks have long been used as a tool in predictive modeling. In this work, an artificial neural network is employed to predict the performance of a direct methanol fuel cell under various operating conditions. This work on the experimental analysis of a uniquely designed fuel cell and the computational modeling of a unique algorithm has not been found in prior literature outside of the authors and their affiliations. The fuel cell input variables for the performance analysis consist not only of the methanol concentration, fuel cell temperature, and current density, but also the number of cells and anode flow rate. The addition of the two typically unconventional variables allows for a more distinctive model when compared to prior neural network models. The key performance indicator of our neural network model is the cell voltage, which is an average voltage across the stack and ranges from 0 to 0:8V. Experimental studies were carried out using DMFC stacks custom-fabricated, with a membrane electrode assembly consisting of an additional unique liquid barrier layer to minimize water loss through the cathode side to the atmosphere. To determine the best fit of the model to the experimental cell voltage data, the model is trained using two different second order training algorithms: OWO-Newton and Levenberg-Marquardt (LM). The OWO-Newton algorithm has a topology that is slightly different from the topology of the LM algorithm by the employment of bypass weights. It can be concluded that the application of artificial neural networks can rapidly construct a predictive model of the cell voltage for a wide range of operating conditions with an accuracy of 10−3 to 10−4. The results were comparable with existing literature. The added dimensionality of the number of cells provided insight into scalability where the coefficient of the determination of the results for the two multi-cell stacks using LM algorithm were up to 0:9998. The model was also evaluated with empirical data of a single-cell stack.

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