Biomechanics of erect posture maintaining (review of equilibrium control models)

2015 ◽  
Vol 15 (4) ◽  
pp. 93-98 ◽  
Author(s):  
K Ryabina ◽  
A Isaev
Keyword(s):  
Erect Posture ◽  
Control Models ◽  
Equilibrium Control

Applicability Verification of Tokamak Plasma Equilibrium Control Models

1983 ◽  
pp. 1233-1238
Author(s):  
K. Yoshioka ◽  
T. Kobayashi ◽  
M. Abe ◽  
S. Uchikawa ◽  
K. Takemaru
Keyword(s):  
Tokamak Plasma ◽  
Plasma Equilibrium ◽  
Control Models ◽  
Equilibrium Control

Comparison of the efficiency of the indexes of Pont and Korkhause with Kernott technique in patients with narrowing of maxilla

2020 ◽  
Vol 20 (3) ◽  
pp. 199-203
Author(s):  
O. I. Admakin ◽  
I. A. Solop ◽  
A. D. Oksentyuk
Keyword(s):  
Dental Treatment ◽  
Standard Analysis ◽  
Clinical Hospital ◽  
Control Models ◽  
Diagnostic Models ◽  
Left And Right ◽  
The Asymmetry ◽  
The Right ◽  
The University ◽  
Medical University

Relevance. The narrowing of the maxilla is one of the most common pathologies in orthodontics. Recent studies show that the narrowing is always asymmetric which is connected to the rotation of the maxilla. To choose the treatment correctly one need a calculation that reveals the asymmetry, which is impossible with using standard indexes.Purpose – to compare efficiency of indexes of Pont and Korkhause with the Kernott's method in patients with narrowing of the maxilla.Materials and methods. The study involved 35 children aged from 8 to 12 years old undergoing dental treatment in the University Children's Clinical Hospital of the First Moscow State Medical University with no comorbidities. For every patient a gypsum model was prepared and after that to carry out the biometrical calculation. In this study two indexes were used: Pont's index and Korkhause's; using this standard analysis the narrowing of the maxilla was revealed. After using Pont's Index and Korkhaus analysis all the models were calculated by the method of Kernott with Kernott's dynamic pentagon.Results. As a result of the analysis of the control diagnostic models a narrowing of the maxilla in 69% of cases (n = 24) was revealed in all cases, the deviation of the size of the dentition was asymmetric. Thus, 65% of the surveyed models showed a narrowing on the right. This narrowing was of a different severity and averaged 15 control models.Conclusions. This shows that for the biometrics of diagnostic models it is necessary to use methods that allow to estimate the width of the dentition rows on the left and on the right separately. To correct the asymmetric narrowing of the dentition, it is preferable to use non-classical expanding devices that act equally on the left and right sides separetly.

ALGORITHM OF CONSTRUCTING CONTROL MODELS OF COMPLEX SYSTEMS IN THE LANGUAGE OF FUNCTIONING TABLES

2020 ◽  
Vol 9 (12) ◽  
pp. 10397-10417
Author(s):  
A. Kabulov ◽  
I. Normatov ◽  
A. Karimov ◽  
E. Navruzov
Keyword(s):  
Complex Systems ◽  
Control Models

scholarly journals Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks

2021 ◽  
Author(s):  
Haitham Baomar ◽  
Peter J. Bentley
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Training Data ◽  
Aviation Industry ◽  
Control Models ◽  
Novel Approach ◽  
Network Modules ◽  
Artificial Neural

AbstractWe describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond.

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

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