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.

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.

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.

Hysteresis Neural Network Modeling and Compensation of Piezoelectric Actuator

2014 ◽  
Vol 635-637 ◽  
pp. 1426-1430
Author(s):  
Fei Qian ◽  
Zhen Wu Guo ◽  
Su An Xu ◽  
Gui Rong Wang ◽  
Yun Tang Li
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Control Method ◽  
Network Modeling ◽  
Piezoelectric Actuators ◽  
Neural Network Modeling ◽  
Hysteretic Model ◽  
Nonlinear Hysteresis ◽  
Hysteresis Characteristics

By studying on the nonlinear hysteresis characteristics of piezoelectric actuators,this paper proposes a neural network modeling method based on polynomial fitting algorithm and a compound control method for compensation of the hysteresis.Simulation shows that the fitting error of neural network model is 1.42%. According to the developed hysteretic model,PID and feed-forward control methods are applied to the system.The result is that the tracking relative error of control system is 1.59%,so the tracking precision of system is improved significantly.This indicates that the neural network model reflects the hysteresis characteristics of piezoelectric actuators accurately,and this control method is an effective compensation control for hysteresis in piezoelectric actuators.

scholarly journals Neural network model as a way of processing complex systems of econometric equations characterizing the interaction of the Russian Arctic

2018 ◽  
Vol 170 ◽  
pp. 01025 ◽  
Author(s):  
Sergey Antipov
Keyword(s):  
Neural Network ◽  
Complex Systems ◽  
Network Model ◽  
Neural Network Model ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Russian Arctic

This article analyses the possibility of applying neural network modeling for the purpose of automation of a large number of calculations of econometric equations coefficients in order to obtain adequate predictive results.

scholarly journals Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L2,1-Norm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoyi Guo ◽  
Wei Zhou ◽  
Qun Lu ◽  
Aiyan Du ◽  
Yinghua Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Dry Weight ◽  
Hemodialysis Patients ◽  
Normal Weight ◽  
The Body ◽  
Functional Link ◽  
Patient Will ◽  
Better Than

Dry weight is the normal weight of hemodialysis patients after hemodialysis. If the amount of water in diabetes is too much (during hemodialysis), the patient will experience hypotension and shock symptoms. Therefore, the correct assessment of the patient’s dry weight is clinically important. These methods all rely on professional instruments and technicians, which are time-consuming and labor-intensive. To avoid this limitation, we hope to use machine learning methods on patients. This study collected demographic and anthropometric data of 476 hemodialysis patients, including age, gender, blood pressure (BP), body mass index (BMI), years of dialysis (YD), and heart rate (HR). We propose a Sparse Laplacian regularized Random Vector Functional Link (SLapRVFL) neural network model on the basis of predecessors. When we evaluate the prediction performance of the model, we fully compare SLapRVFL with the Body Composition Monitor (BCM) instrument and other models. The Root Mean Square Error (RMSE) of SLapRVFL is 1.3136, which is better than other methods. The SLapRVFL neural network model could be a viable alternative of dry weight assessment.

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

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.

Sign in / Sign up

Export Citation Format

Share Document