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.

A simulation-based method using artificial neural networks for solving the inverse kinematic problem of articulated robots

2015 ◽  
Vol 231 (3) ◽  
pp. 470-479 ◽  
Author(s):  
Mustafa Soylak ◽  
Tuğrul Oktay ◽  
İlke Turkmen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Inverse Kinematic ◽  
Plasma Cutting ◽  
Three Dimensional Model ◽  
Inverse Kinematic Problem ◽  
Data Set ◽  
Artificial Neural

In our article, inverse kinematic problem of a plasma cutting robot with three degree of freedom is solved using artificial neural networks. Artificial neural network was trained using joint angle values according to cartesian coordinates ( x, y, z) of end point of a robotic arm. The Levenberg–Marquardt training algorithm was applied to educate artificial neural network. To validate the designed neural network, it was tested using a new test data set which is not applied in training. A simulation was performed on a three-dimensional model of MSC.ADAMS software using angle values obtained from artificial neural network test. It was revealed from this simulation that trajectory of plasma cutting torch obtained using artificial neural network agreed well with desired trajectory.

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.

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.

scholarly journals FEATURES OF DIAGNOSTIC ARTIFICIAL NEURAL NETWORKS FOR HYBRID EXPERT SYSTEMS

2019 ◽  
Vol 26 ◽  
pp. 36-46
Author(s):  
S. KONOVALOV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Expert Systems ◽  
Technical System ◽  
Bulletin Board ◽  
Confidence Factor ◽  
Complex Technical System ◽  
Artificial Neural

In the proposed article, various methods of constructing an artificial neural network as one of the components of a hybrid expert system for diagnosis were investigated. A review of foreign literature in recent years was conducted, where hybrid expert systems were considered as an integral part of complex technical systems in the field of security. The advantages and disadvantages of artificial neural networks are listed, and the main problems in creating hybrid expert systems for diagnostics are indicated, proving the relevance of further development of artificial neural networks for hybrid expert systems. The approaches to the analysis of natural language sentences, which are used for the work of hybrid expert systems with artificial neural networks, are considered. A bulletin board is shown, its structure and principle of operation are described. The structure of the bulletin board is divided into levels and sublevels. At sublevels, a confidence factor is applied. The dependence of the values of the confidence factor on the fulfillment of a particular condition is shown. The links between the levels and sublevels of the bulletin board are also described. As an artificial neural network architecture, the «key-threshold» model is used, the rule of neuron operation is shown. In addition, an artificial neural network has the property of training, based on the application of the penalty property, which is able to calculate depending on the accident situation. The behavior of a complex technical system, as well as its faulty states, are modeled using a model that describes the structure and behavior of a given system. To optimize the data of a complex technical system, an evolutionary algorithm is used to minimize the objective function. Solutions to the optimization problem consist of Pareto solution vectors. Optimization and training tasks are solved by using the Hopfield network. In general, a hybrid expert system is described using semantic networks, which consist of vertices and edges. The reference model of a complex technical system is stored in the knowledge base and updated during the acquisition of new knowledge. In an emergency, or about its premise, with the help of neural networks, a search is made for the cause and the control action necessary to eliminate the accident. The considered approaches, interacting with each other, can improve the operation of diagnostic artificial neural networks in the case of emergency management, showing more accurate data in a short time. In addition, the use of such a network for analyzing the state of health, as well as forecasting based on diagnostic data using the example of a complex technical system, is presented.

scholarly journals An artificial neural network identifies glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

2019 ◽  
Author(s):  
René Janßen ◽  
Jakob Zabel ◽  
Uwe von Lukas ◽  
Matthias Labrenz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Environmental Monitoring ◽  
Microbial Communities ◽  
List Type ◽  
Neural Net ◽  
Next Generation ◽  
Artificial Neural

AbstractArtificial neural networks can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network to support environmental monitoring efforts in case of a contamination event by detecting induced changes towards the microbial communities. The neural net was trained on taxonomic cluster count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140 days to determine the effects of the herbicide glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the clusters primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species in cases of glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data.Highlight bullet pointsAn artificial neural net was able to identify glyphosate-affected microbial community assemblages based on next generation sequencing dataDecision-relevant taxonomic clusters can be identified by a stochastically subsetting approachJust a fraction of present clusters is needed for classificationFiltering of input data improves classification

scholarly journals A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing

2021 ◽  
Author(s):  
Kathakali Sarkar ◽  
Deepro Bonnerjee ◽  
Rajkamal Srivastava ◽  
Sangram Bagh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Basic Concept ◽  
Single Layer ◽  
Artificial Neural ◽  
Engineered Bacteria ◽  
Network Type

Here, we adapted the basic concept of artificial neural networks (ANN) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible...

scholarly journals Experimental implementation of artificial neural network-based active vibration control & chatter suppression

2021 ◽  
Author(s):  
Yong Xia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Control System ◽  
Vibration Control ◽  
Control Strategies ◽  
Active Vibration Control ◽  
Chatter Suppression ◽  
Active Vibration ◽  
Artificial Neural

Vibration control strategies strive to reduce the effect of harmful vibrations such as machining chatter. In general, these strategies are classified as passive or active. While passive vibration control techniques are generally less complex, there is a limit to their effectiveness. Active vibration control strategies, which work by providing an additional energy supply to vibration systems, on the other hand, require more complex algorithms but can be very effective. In this work, a novel artificial neural network-based active vibration control system has been developed. The developed system can detect the sinusoidal vibration component with the highest power and suppress it in one control cycle, and in subsequent cycles, sinusoidal signals with the next highest power will be suppressed. With artificial neural networks trained to cover enough frequency and amplitude ranges, most of the original vibration can be suppressed. The efficiency of the proposed methodology has been verified experimentally in the vibration control of a cantilever beam. Artificial neural networks can be trained automatically for updated time delays in the system when necessary. Experimental results show that the developed active vibration control system is real time, adaptable, robust, effective and easy to be implemented. Finally, an experimental setup of chatter suppression for a lathe has been successfully implemented, and the successful techniques used in the previous artificial neural network-based active vibration control system have been utilized for active chatter suppression in turning.

Forecasting Stock Returns with Artificial Neural Networks

2011 ◽  
pp. 47-79 ◽  
Author(s):  
Suraphan Thawornwong ◽  
David Enke
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Stock Returns ◽  
Future Research ◽  
Network Architectures ◽  
Network Applications ◽  
Artificial Neural ◽  
Neural Network Applications

During the last few years there has been growing literature on applications of artificial neural networks to business and financial domains. In fact, a great deal of attention has been placed in the area of stock return forecasting. This is due to the fact that once artificial neural network applications are successful, monetary rewards will be substantial. Many studies have reported promising results in successfully applying various types of artificial neural network architectures for predicting stock returns. This chapter reviews and discusses various neural network research methodologies used in 45 journal articles that attempted to forecast stock returns. Modeling techniques and suggestions from the literature are also compiled and addressed. The results show that artificial neural networks are an emerging and promising computational technology that will continue to be a challenging tool for future research.

Artificial Neural Network for Solving the Inventory Control Problem in Fuzzy Environments

2021 ◽  
pp. 126-143
Author(s):  
Mostafijur Rahaman ◽  
Sankar Prasad Mondal ◽  
Shariful Alam
Keyword(s):  
Neural Network ◽  
Differential Equation ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Inventory Control ◽  
Solution Procedure ◽  
Control Problems ◽  
Fuzzy Environment ◽  
Artificial Neural

In this chapter, different inventory control problems are formulated in fuzzy environment and solved by artificial neural network. Due to present the non-linearity associated with the differential equation in fuzzy environment, the solution procedure may be very complicated. To avoid the situation, artificial neural networks play an important role. In this chapter, different inventory control problems are formulated in fuzzy environment and solved by artificial neural network. Due to present the non-linearity associated with the differential equation in fuzzy environment, the solution procedure may be very complicated. To avoid the situation, artificial neural networks play an important role.

Artificial Neural Networks

2011 ◽  
pp. 222-243
Author(s):  
Joarder Kamruzzaman ◽  
Ruhul Sarker
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Neural Network Models ◽  
Network Applications ◽  
The Neural Network ◽  
Artificial Neural ◽  
Neural Network Applications

The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.

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