Experimental Investigation and Neural Network Modeling for Force System of Retraction T-Spring for Orthodontic Treatment

2010 ◽  
Vol 4 (2) ◽  
Author(s):  
Bahaa I. Kazem ◽  
Nidahal Hussain Ghaib ◽  
Noor M. Hasan Grama
Keyword(s):  
Neural Network ◽  
Cross Section ◽  
Network Model ◽  
Tooth Movement ◽  
Neural Network Modeling ◽  
Prediction Ability ◽  
Force System ◽  
The Third ◽  
Spring Force ◽  
The Moment

In this work three different cross section groups of stainless steel T-Spring, for tooth retraction, have been tested; each spring is activated for 1 mm, 2 mm, and 3 mm, and the resultant force system is evaluated by using a testing apparatus. The results showed that when the cross section and activation distances are increased, the horizontal force and moment increased, while for the moment-to-force ratio, the lowest mean value was at the first activation distance of the first group, and the highest mean values were at the third activation distance of the third group. All three groups at all activation distance are insufficient to produce bodily tooth movement. T-springs of the (0.016×0.022 in.) cross section and with frequent activation provide the best in force system production. An artificial neural network model was trained for simulation of the correlation between input parameters: spring cross section and activation distance, and the outputs spring force system. The network model has prediction ability with low mean error of force prediction (5.707%), and for the moment is (4.048%), and it can successfully reflect the results that were obtained experimentally with less costs and efforts.

Neural network modeling of the wear process of a carbide cutting tool

2021 ◽  
Author(s):  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Cutting Tool ◽  
Tool Material ◽  
Neural Network Modeling ◽  
Wear Process ◽  
Carbide Cutting Tool ◽  
Model Dynamics ◽  
Processing Modes

A neural network model of the wear process of a carbide cutting tool is proposed. This model is considered influence of the cutting dynamics on the tool. The dependence of the wear rate on the processing modes and properties of the processed and tool material is shown. Keywords cutting tool; neural network model; dynamics of the cutting process; wear

SEARCHING BY METOD OF NEURAL NETWORK MODELING OF CONDENSED BENZIMIDAZOLE DERIVATIVES WITH MULTI-TARGET RAGE-INHIBITORY ACTIVITY

2020 ◽  
pp. 248-249
Author(s):  
O. Zhukovskaya ◽  
A. Spasov ◽  
A. Morkovnik ◽  
A. Kochetkov
Keyword(s):  
Neural Network ◽  
Virtual Screening ◽  
Network Model ◽  
Neural Network Model ◽  
Inhibitory Activity ◽  
Network Modeling ◽  
Inhibitory Effect ◽  
Benzimidazole Derivatives ◽  
Neural Network Modeling

Using a multitarget neural network model of RAGE-inhibitory activity, a consensus virtual screening of a library of new condensed benzimidazole derivatives was performed. Compounds with a essential RAGE-inhibitory effect have been found.

scholarly journals Application of Neural Network Model Based on Multispecies Evolutionary Genetic Algorithm to Planning and Design of Diverse Plant Landscape

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuqiang Wu ◽  
Weiwei Guo ◽  
Dinghai Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Plant Species ◽  
Network Model ◽  
Neural Network Model ◽  
Landscape Planning ◽  
Neural Network Modeling ◽  
Planning And Design ◽  
Evolutionary Genetic ◽  
Diverse Plant

In order to explore the feasibility of applying neural network model to landscape planning, based on the multispecies evolutionary genetic algorithm, a neural network model is proposed in this paper for the system design of diverse plant landscape planning. From the perspective of plant species diversity, this paper discusses landscape planning based on a neural network model. This landscape plan involves more than 180 plant species, mainly shrubs, fungi, and so on. The application of multispecies evolutionary genetic algorithm to landscape planning and design and the application of gene level coding and multispecies parallel evolution strategy to the evolutionary design of neural network have guiding significance for plant landscape planning and design. Compared with the traditional neural network modeling method and genetic algorithm, the proposed method has the advantages of wide network structure search space and simple algorithm calculation and design, independent of specific application background, and has strong application and promotion value. This method makes the model performance evaluation index more comprehensive and accurate and the model solution more reasonable. At the same time, combined with the specific status and corresponding changes of various plants in each season, this paper designs a targeted plan to rationally plan the specific spatial layout of the plant landscape and the combination of different types of plant landscapes, so as to effectively improve the quality of the landscape.

Neural network modeling of the implosion process in the tasks of optimization of oil production process

2021 ◽  
Author(s):  
A.R. Mukhutdinov ◽  
Z.R. Vakhidova ◽  
M.G. Efimov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Network Modeling ◽  
Water Hammer ◽  
Design Parameters ◽  
Neural Network Modeling ◽  
Artificial Neural

An increase in the productivity of oil wells is possible with the use of a promising technology based on implosion and a device for its implementation. It is known that the effectiveness of the technology depends on the design parameters of the device. Currently, a promising way to study processes is computer modeling based on modern information technologies. Therefore, solving forecasting problems using modern software based on artificial neural networks (ANNs) is an urgent task of scientific and practical interest. In this regard, the aim of the work is to develop a neural network model and its application to identify the features of the influence of the diameter and length of the implosion chamber of the device on the pressure of a water hammer during implosion. In the software environment, the following have been created and tested: a method for developing a neural network model; a method of conducting a computational experiment with it. The possibility of neural network modeling of the implosion process has been studied. The results of predicting the output parameter, in this case the pressure of the water hammer, on a pre-trained network, with a relative error of 3.5%, using the knowledge base are demonstrated. The results of applying the methodology for solving forecasting problems using software based on artificial neural networks are presented. It was found that the diameter and length of the implosion chamber significantly affect the pressure of the water hammer. The practical significance of the work lies in the ability to determine the required values of the diameter and length of the implosion chamber of the device at a given level of water hammer pressure.

Neural network model of the wells' drilling speed and modes predicting in complex reservoirs

2021 ◽  
pp. 55-76
Author(s):  
Yu. E. Katanov
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Mutual Influence ◽  
Neural Network Modeling ◽  
Drilling Rate ◽  
Well Drilling ◽  
Technological Factors ◽  
Qualitative And Quantitative ◽  
Drilling Method

The article considers the problem connected with the study of well drilling rates in complex reservoirs. Its solution is presented in the form of a neural network model that takes into account the structural, geomechanical and technological features of the «rock mass — well» system.The possibility of predicting the well drilling method with different strength and structural-lithological characteristics of the massif, based on neural network modeling, is presented.The purpose of this study is to obtain mathematical models for analysis of the probabilistic and statistical patterns of well drilling processes in conditions of uncertainty.The scientific novelty of the work performed is the qualitative and quantitative assessment of the mutual influence of geological and technological factors on the well drilling rate; search for optimal well drilling modes in complex reservoirs on the basis of mathematical modeling.

A model for estimating the influence of chlorine scattering at its emergency releases into the atmosphere at a potentially hazardous object in the city of Krasnoyarsk

2021 ◽  
Vol 94 ◽  
pp. 105-116
Author(s):  
A. L. Khrulkevich ◽  
◽  
Y. V. Grebnev ◽  
A. I. Ovsyanik ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Simulation Modeling ◽  
Emergency Situation ◽  
Weather Conditions ◽  
Integral Model ◽  
Neural Network Modeling ◽  
Hazardous Substance ◽  
The City

Introduction. The article considers the risk of occurrence and development of an emergency situation caused by the occurrence of a landscape fire and the transition of a fire to technological buildings with further depressurization of containers containing chlorine. One of the threats to the city of Krasnoyarsk is chemically hazardous facilities that have the task of providing life support to the population and are located in complete isolation. These objects do not have a road connection with the coastline, which makes it practically impossible to use forces and means designed to respond to operational events at these objects in a timely manner. Goals and objectives. The aim of the study was to simulate the conditions of chlorine scattering during its accidental releases into the atmosphere and to identify the dependencies of the scattering parameters on the technological features of the release, weather conditions, as well as the characteristics of the environment where the release occurs. Methods. To simulate an emergency situation at a water treatment plant, the method of simulation modeling using the TOXI+Risk software product was used, and the method of neural network forecasting using the Scikit-Learn library in the Python programming language was used. Results and discussion. The simulation results demonstrated the possibility of using neural network modeling to solve the problem of short-term forecasting of the areas of dispersion of a chemically dangerous substance (chlorine). The analytical method and the neural network method are compared. Proposals have been developed to reduce the potential risk of an emergency. Conclusions. The use of a neural network model makes it possible to increase the speed of calculating the concentrations of AHS at various points in space in comparison with the use of a traditional integral model, as well as to assess the potential danger of scattering AHS in the event of destruction of the tank in the presence of a terrain model. However, the considered neural network model can predict the concentration exclusively in the training ranges of weather conditions. The combination of neural network and integrated models makes it possible to solve the problems of industrial safety under any circumstances. Key words: emergency chemically hazardous substance, emergency, chlorine, risk, threat, simulation modeling, forecasting, neural network model, analytical model.

Permanent Deformation Process of Asphalt Concrete Pavement Based on Neural Network Modeling

2013 ◽  
Vol 341-342 ◽  
pp. 1486-1490
Author(s):  
Fu Cheng Yin ◽  
Guang Chun Zhou
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rbf Neural Network ◽  
Permanent Deformation ◽  
Basic Function ◽  
Neural Network Modeling ◽  
Output Data ◽  
Pavement Layers ◽  
Asphalt Concrete Pavement

This paper numerically simulates the deflection response of layers on the cross section of a medium-strength subgrade (MFC) flexible pavement under repeating load, by a radial basic function (RBF) neural network model. The RBF modeling focuses on the functional relationship between the local points in the top deflection curves of pavement layers. The input and output data of the RBF model utilizes the last deflection profiles on the tops of four layers in the test. The deflection curve of the pavement surface is set as the input data since its developing process can been watched and measured in the test. The deflection curves of the other three layers are as the output data, because their deflection process was invisible in the test. Thus, the deflection process of the pavement layers invisible in the test can be simulated by the trained RBF neural network model, which results in a further analysis based on the obtained simulation data.

Neural Network Modeling of Maximum Insertion Force of Bevel-Tip Surgical Needle

2018 ◽  
Author(s):  
Sai Teja Reddy Gidde ◽  
Tololupe Verissimo ◽  
Nuo Chen ◽  
Parsaoran Hutapea ◽  
Byoung-gook Loh
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Modeling ◽  
The Body ◽  
Neural Network Modeling ◽  
Tissue Deformation ◽  
Input Output ◽  
Insertion Force ◽  
Tissue Phantom

Recently there has been a growing interest to develop innovative surgical needles for percutaneous interventional procedures. Needles are commonly used to reach target locations inside of the body for various medical interventions. The effectiveness of these procedures depends on the accuracy with which the needle tips reach the targets, such as a biopsy procedure to assess cancerous cells and tumors. One of the major issues in needle steering is the force during insertion, also known as the insertion (penetration) force. The insertion force causes tissue damage as well as tissue deformation. It has been well studied that tissue deformation causes the needle to deviate from its target thus causing an ineffective procedure. Simulation of surgical procedures provides an effective method for a robot-assisted surgery for pre- and intra-operative planning. Accurate modeling of the mechanical behavior on the interface of surgical needles and organs, specifically the insertion force, has been well recognized as a major challenge. Overcoming such obstacle by development of robust numerical models will enable realistic force feedback to the user during surgical simulation. This study investigates feasibility of predicting the insertion force of bevel-tip needles based on experimental data using neural network modeling. Simulation of the proposed neural network model is performed using Kera’s Python Deep Learning Library with TensorFlow as a backend. The insertion forces of needles with different bevel-tip angles in gel tissue phantom are measured using a specially designed automated needle insertion test setup. Input-output datasets are generated where the inputs are defined as bevel-tip angles and gel tissue phantom stiffness, and the output is defined as the insertion force. A properly trained neural network then maps the input data to the output data and the input-output dataset is supplied to train a neural network. Its performance is then evaluated using different and unseen input-output dataset. This paper shows that the proposed neural network model accurately predicts the insertion force.

scholarly journals Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Forests ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 16 ◽  
Author(s):  
Haojie Chai ◽  
Xianming Chen ◽  
Yingchun Cai ◽  
Jingyao Zhao
Keyword(s):  
Neural Network ◽  
Moisture Content ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
High Frequency ◽  
Neural Network Modeling ◽  
Drying Process ◽  
Measuring Point ◽  
Bp Neural Network Model

The moisture content (MC) control is vital in the wood drying process. The study was based on BP (Back Propagation) neural network algorithm to predict the change of wood MC during the drying process of a high frequency vacuum. The data of real-time online measurement were used to construct the model, the drying time, position of measuring point, and internal temperature and pressure of wood as inputs of BP neural network model. The model structure was 4-6-1 and the decision coefficient R2 and Mean squared error (Mse) of the training sample were 0.974 and 0.07355, respectively, indicating that the neural network model had superb generalization ability. Compared with the experimental measurements, the predicted values conformed to the variation law and size of experimental values, and the error was about 2% and the MC prediction error of measurement points along thickness direction was within 2%. Hence, the BP neural network model could successfully simulate and predict the change of wood MC during the high frequency drying process.

SYSTEM ORIENTED NEURAL NETWORKS — PROBLEM FORMULATION, METHODOLOGY AND APPLICATION

2006 ◽  
Vol 20 (02) ◽  
pp. 143-158 ◽  
Author(s):  
KANG LI ◽  
JIAN-XUN PENG
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Fitness Function ◽  
A Priori ◽  
Network Models ◽  
Problem Formulation ◽  
Neural Network Modeling ◽  
Neural Network Models

A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful "white box" neural network model with better generalization performance. In this paper, the problem formulation, the neural network configuration, and the associated optimization software are discussed in detail. This methodology is then applied to a practical real-world system to illustrate its effectiveness.

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