Dialogue Control of Collaborative Robots Based on Artifi cial Neural Networks

2021 ◽  
Vol 22 (11) ◽  
pp. 567-576
Author(s):  
A. S. Yuschenko ◽  
Yin Shuai
Keyword(s):  
Neural Networks ◽  
Robot Control ◽  
Current Situation ◽  
Service Robot ◽  
Autonomous Behavior ◽  
Situation Recognition ◽  
Wide Range ◽  
Manipulation Robots ◽  
Special Knowledge ◽  
Autonomous Movement

Collaborative robotics progress is based on the possibility to apply robots to the wide range activity of peoples. Now the user can control the robot without any special knowledge in robotics and safe. The price of such possibilities is complication of control system of robot which now has to aquire an opportunity of autonomous behavior under human’s control, using the necessary sensors and elements of artificial intelligence. In our research we suppose the collaborative robot as mobile robotic device possible to fulfil some work under the human’s speech demands not only in the same space with the human. We also suppose the necessity of bilateral dialogue human-robot to make it clear the task, the current situation, the state as robot as human. The complex task of control, or may be the collaboration of human with his artificial partner need new means of control, situation recognition, speech dialogue management. As a mean to solve the whole complex of problems we propose the combination of different artificial neural networks. Such as convolution networks for image recognition, deep networks for speech recognition, LSTM networks for autonomous movement of robot control in current situation. Investigations in the field of mobile and manipulation robots including the human-robot control have been proceeded for some years in the department "Robotic systems and mechatronics" BMSTU celebrating now it 70th years Jubilee. The reader may find some of the works in the bibliography. In result of all these investigations we obtain the service robot model which may find a wide application.

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

Forward Kinematic Modeling of Conical-Shaped Continuum Manipulators

Robotica ◽  
2021 ◽  
pp. 1-19
Author(s):  
A. H. Bouyom Boutchouang ◽  
Achille Melingui ◽  
J. J. B. Mvogo Ahanda ◽  
Othman Lakhal ◽  
Frederic Biya Motto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Data Collection ◽  
Robot Control ◽  
Deep Neural Networks ◽  
Forward Kinematics ◽  
Kinematic Modeling ◽  
Experimental Platform ◽  
Continuum Manipulators ◽  
Forward Kinematic

SUMMARY Forward kinematics is essential in robot control. Its resolution remains a challenge for continuum manipulators because of their inherent flexibility. Learning-based approaches allow obtaining accurate models. However, they suffer from the explosion of the learning database that wears down the manipulator during data collection. This paper proposes an approach that combines the model and learning-based approaches. The learning database is derived from analytical equations to prevent the robot from operating for long periods. The database obtained is handled using Deep Neural Networks (DNNs). The Compact Bionic Handling robot serves as an experimental platform. The comparison with existing approaches gives satisfaction.

Adaptive robot control using neural networks

1994 ◽  
Vol 41 (2) ◽  
pp. 173-181 ◽  
Author(s):  
M. Saad ◽  
P. Bigras ◽  
L.-A. Dessaint ◽  
K. Al-Haddad
Keyword(s):  
Neural Networks ◽  
Robot Control ◽  
Adaptive Robot Control

scholarly journals Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 854
Author(s):  
Nevena Rankovic ◽  
Dragica Rankovic ◽  
Mirjana Ivanovic ◽  
Ljubomir Lazic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Time Estimation ◽  
Effort Estimation ◽  
Activation Functions ◽  
Estimation Model ◽  
Wide Range ◽  
Software Product ◽  
Artificial Neural

Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

Space Modeling of Problem Solving Strategies of “Prisoner's Dilemma”

2021 ◽  
pp. 99-116
Author(s):  
D.J. Balanev ◽  
Keyword(s):  
Neural Networks ◽  
Social Identity ◽  
Identity Formation ◽  
Social Factors ◽  
Prisoner's Dilemma ◽  
Prisoner’S Dilemma ◽  
Real People ◽  
Space Modeling ◽  
Wide Range ◽  
Before And After

An iterated version of the game "Prisoner's Dilemma" is used as a model of cooperation largely due to the wide range of strategies that the subjects can use. The problem of the effec-tiveness of strategies for solving the Iterated Prisoner's Dilemma (IPD) is most often considered from the point of view of information models, where strategies do not take into account the relationship that arise when real people play. Some of these strategies are obvious, others depend upon social context. In our paper, we use one of the promising directions in the development of studying IPD strategies – the use of artificial neural networks. We use neural networks as a modeling tool and as a part of game environment. The main goal of our work is to build an information model that predicts the behavior of an individual person as well as group of people in the situation of solving of social dilemma. It takes into account social relationship, including those caused by experimental influence, gender differences, and individual differences in the strategy for solving cognitive tasks. The model demonstrates the transition of individual actions into socially determined behavior. Evaluation of the effect of socialization associated with the procedure of the game provides additional information about the effectiveness and characteristics of the experimental impact.The paper defines the minimum unit of analysis of the IPD player's strategy in a group, the identity with which can be considered as a variable. It discusses the influence of the experi-mentally formed group identity on the change of preferred strategies in social dilemmas. We use the possibilities of neural networks as means of categorizing the results of the prisoner's iterative dilemma in terms of the strategy applied by the player, as well as social factors. We define the patterns of changes in the IPD player's strategy before and after socialization are determined. The paper discusses the questions of real player's inclination to use IPD solution strategies in their pure form or to use the same strategy before and after experimental inter-ventions related to social identity formation. It is shown that experimentally induced socialization can be considered as a mechanism for increasing the degree of certainty in the choice of strategies when solving IPD task. It is found out that the models based on neural networks turn out to be more efficient after experi-mentally evoked social identity in a group of 6 people; and the models based on neural net-works are least effective in the case of predicting a subject's belonging to a gender group. When solving IPD problems by real people, it turns out to be possible to talk about generalized strategies that take into account not only the evolutionary properties of «pure» strategies, but also reflect various social factors.

Application of neural networks in membrane separation

2020 ◽  
Vol 36 (2) ◽  
pp. 265-310 ◽  
Author(s):  
Morteza Asghari ◽  
Amir Dashti ◽  
Mashallah Rezakazemi ◽  
Ebrahim Jokar ◽  
Hadi Halakoei
Keyword(s):  
Neural Networks ◽  
Chemical Engineering ◽  
Membrane Separation ◽  
Mechanistic Model ◽  
Computing Time ◽  
Permeate Flux ◽  
Practical Applications ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Physical And Chemical

AbstractArtificial neural networks (ANNs) as a powerful technique for solving complicated problems in membrane separation processes have been employed in a wide range of chemical engineering applications. ANNs can be used in the modeling of different processes more easily than other modeling methods. Besides that, the computing time in the design of a membrane separation plant is shorter compared to many mass transfer models. The membrane separation field requires an alternative model that can work alone or in parallel with theoretical or numerical types, which can be quicker and, many a time, much more reliable. They are helpful in cases when scientists do not thoroughly know the physical and chemical rules that govern systems. In ANN modeling, there is no requirement for a deep knowledge of the processes and mathematical equations that govern them. Neural networks are commonly used for the estimation of membrane performance characteristics such as the permeate flux and rejection over the entire range of the process variables, such as pressure, solute concentration, temperature, superficial flow velocity, etc. This review investigates the important aspects of ANNs such as methods of development and training, and modeling strategies in correlation with different types of applications [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), etc.]. It also deals with particular types of ANNs that have been confirmed to be effective in practical applications and points out the advantages and disadvantages of using them. The combination of ANN with accurate model predictions and a mechanistic model with less accurate predictions that render physical and chemical laws can provide a thorough understanding of a process.

scholarly journals Assessment automation of road traffic safety with account for road conditions of an individual itinerary

2021 ◽  
Vol 2091 (1) ◽  
pp. 012051
Author(s):  
D A Skorobogatchenko ◽  
V V Borovik ◽  
A I Frolovichev
Keyword(s):  
Neural Networks ◽  
Traffic Safety ◽  
Road Traffic ◽  
Traditional Approach ◽  
Fuzzy Neural Networks ◽  
Automated System ◽  
Current Time ◽  
Road Traffic Safety ◽  
Wide Range ◽  
Accident Rates

Abstract The paper substantiates the need to develop an automated system for traffic safety assessment in urban agglomerations, taking into account road conditions. The authors suggest a methodology for assessment of road traffic accidents, which makes it possible to take into account a wide range of factors affecting them. The methodology is based on complementing the traditional approach of final accident rate calculation with algorithms for collecting and analyzing data using Big Data tools, in particular, convolutional neural networks, fuzzy neural networks such as ANFIS, and cluster analysis using the k-means method. All accident rates are grouped according to the principle of homogeneity of acquisition of information for their calculation. Further, one of the data processing tools is applied to each group. As a result, labor intensity is reduced and the effectiveness of the application of the method of final accident rates increases. For practical calculations, the authors have developed a client-server application that uses data on geometric characteristics, current traffic situation, weather and climatic effects at the time of the trip along a specific itinerary. By means of application use, the analysis of traffic safety on a number of routes in Volgograd was carried out and the results are presented in comparison with the calculations made via the traditional method. It is shown that the use of information about the current situation on a specific section of the road network in terms of the current time significantly increases the accuracy of calculations.

scholarly journals Moscow As An International Financial Centre

2012 ◽  
Vol 6 ◽  
Author(s):  
Elizaveta Ogloblina
Keyword(s):  
International Finance ◽  
Russian Federation ◽  
Current Situation ◽  
Financial Situation ◽  
Comparative Analyses ◽  
Financial Centre ◽  
Wide Range ◽  
The Subject ◽  
The Russian Federation ◽  
And Function

The creation, development, and function of the international financial centres (IFC)  is the subject of the international finance investigation. The article deals with the perspectives of the Russian IFC.  The paper embraces a wide range of challenges that are faced nowadays in the Russian Federation as a whole, and particularly Moscow. The article presents the comparative analyses of different rating and indices, which  reflect the current situation in the business and financial situation of the country.

Sign in / Sign up

Export Citation Format

Share Document