Study purpose. Determination of the peculiarities of the manifestation of strength in competitive exercises of highly qualified armwrestlers with different strength abilities.
Materials and methods. ...The study involved the 4 best armwrestlers in the world weighing from 80 to 100 kg (m = 87.50 ± 2.47 kg) in 2017–2020. Four power test exercises have been identified that ensure the performance of a competitive action in armwrestling: flexion of the fingers, stretch with a hammer, hook and bending the hand. Strength indicators in all test exercises were measured with an FL1K 0.5N, 1000N electric strain gauge dynamometer, Kern & Sohn GmbH (China), fixed on the armwrestling table using an author’s block device.
Results. The results of the correlation analysis of the relationships between the studied indicators confirm the presence of a difference in the direction and strength of the relationships between the strength and time characteristics of the efforts of athletes with different strength and speed-strength abilities. Thus, out of 36 correlation indicators, the data of armwrestlers 1 and 2 have 11 modules with very high connection strength (r = 0.926–0.999), of which 7 modules are with time and 4 modules are with force characteristics of efforts. Athletes 3 and 4 also have 3 modules with very strong connections (r = 0.916–0.948) and 8 modules with strong connections (r = 0.739–0.886), of which 7 modules are with strength indicators and 4 modules are related to time characteristics. But the other correlation indicators have very weak (r < 0.29) and weak (r = 0.3–0.5) levels of relationships.
Conclusions. Analysis of the time and force characteristics of the manifestation of force in the process of achieving boundary resistance allows us to clearly establish the genetically determined speed and strength abilities of the explosive, fast and slow force of armwrestlers.
Purpose: approbation of a complex of speed-strength characteristics for monitoring the dynamics of strength exercises of the world’s leading armwrestlers weighing over 100 kg.
Material and Methods. ...The study involved the 3 best arm wrestlers in the world weighing over 100 kg (116.00 ± 18.03 kg) in 2017–2020. Four power test exercises have been identified that ensure the performance of a competitive action in arm wrestling: flexion of the fingers, stretch with a hammer, hook and bending the hand. These exercises were performed with the left and right hands. Strength indicators in all test exercises were measured with an FL1K 0.5N, 1000N electric strain gauge dynamometer, Kern & Sohn GmbH (China) with an accuracy class of up to 50 g, fixed on a specialized armwrestling table using a specially made an author’s block device. In the course of statistical analysis, the following parameters were determined and calculated: maximum (F) and relative (F1 = ƩF / m) strength, kg; total strength index in four strength exercises (ƩF = F1 + F2 + F3 + F4), kg; time to reach maximum strength (Ʃt = t1 + t2 + t3 + t4), s; speed-strength index (J = ƩF / Ʃt), kg/ms; average strength, index of four exercises ( = ƩF / 4), kg; total strength gradient of four exercises (Ʃt0,5F), ms; speed-strength index in the first 500 ms (J500 = ƩF500 / Ʃt500), kg/ms; time to reach 1 kg force (t1 = Ʃt0.5F / (0.5×F)), ms/kg; Pearson’s correlation analysis; Factor analysis.
Results. As a result of the study, the main data on the speed-strength indicators of armwrestlers were obtained and analyzed. In the process of testing, according to the indicators of time periods and these efforts of dynamic strength, the features of the manifestation of explosive, fast and slow strength of arm wrestlers weighing over 100 kg were established. Determining the relationship between
strength and speed-strength indicators using factor analysis made it possible to establish two factors that determine the overall variance of the sample. The first factor with a contribution of 70.9 % to the total sample formed the temporal characteristics of effort in test exercises, such as the time to reach maximum effort (r = 0.979), speed-strength index (r = 0.986), force gradient. (r = 0.986) and the time to reach a force of 1 kg (r = 0.979). The second factor with a factor loading of 29.1 % was the maximum force (r = 0.960), the average test strength (r = 0.961) and the achieved force in 500 ms (r = 0.716). Thus, the results of the correlation and factorial analyzes of the strength and speed-strength indicators of armwrestlers weighing more than 100 kg indicate the priority of the temporal characteristics of efforts over strength in a competitive exercise.
Conclusions. The study made it possible to test a complex of speed-strength indicators for monitoring the functional state of the world’s leading armwrestlers weighing over 100 kg, an approved system of criteria for time and power characteristics of efforts in competitive exercises allows you to monitor the state of athletes to monitor and predict success in armwrestling. The author’s device used in the study made it possible to automate the process of measurements with high mobility, as well as immediately create a database
on the power and speed-strength capabilities of armwrestlers with high accuracy.
USE OF MACHINE LEARNING IN CYBER SECURITY Ivanichenko, Yevhen; Sablina, Mylana; Kravchuk, Kateryna
Kìberbezpeka. osvìta, nauka, tehnìka,
6/2021, Letnik:
4, Številka:
12
Journal Article
Recenzirano
The urgency of the topic is the integration of machine learning technologies into cybersecurity systems. After getting acquainted with the technical literature, the main technologies of machine ...learning that are implemented in the organization of cybersecurity were formulated. Acquainted with the main type of artificial neural network used in the prevention and detection of cyber threats and found that the main to consider the general application of machine learning technologies are artificial neural networks based on a multilayer perceptron with inverse error propagation. It is proposed to use indicators of compromise cyberattacks as initial information for automatic machine learning systems. Emphasis is placed on the main types of data that can be used by surveillance subsystems for information security and cybersecurity to perform tasks and prevent, classify and predict cybersecurity events. According to the results of the analysis, the main problem areas for their implementation in information security systems are identified. The problem of using machine learning (ML) in cybersecurity is difficult to solve, because advances in this area open up many opportunities, from which it is difficult to choose effective means of implementation and decision-making. In addition, this technology can also be used by hackers to create a cyber attack. The purpose of the study is to implement machine learning in information security and cybersecurity technology, and to depict a model based on self-learning
ВИКОРИСТАННЯ МАШИННОГО НАВЧАННЯ В КІБЕРБЕЗПЕЦІ Yevhen Ivanichenko; Mylana Sablina; Kateryna Kravchuk
Kìberbezpeka. osvìta, nauka, tehnìka,
06/2021, Letnik:
4, Številka:
12
Journal Article
Recenzirano
Odprti dostop
Актуальність теми - інтеграція технологій машинного навчання в системи кібербезпеки. Ознайомившись з технічною літературою було сформульовано основні технології машинного навчання які реалізуються в ...організації кібербезпеки. Ознайомлено з основним типом штучної нейронної мережі, яка використовуються під час попередження і виявлення кіберзагрози та встановлено, що основною для розгляду загального застосування технологій машинного навчання є штучні нейронні мережі, засновані на багатошаровому персептроні із зворотним поширенням помилок. Запропоновано використовувати індикатори компромісних кібератак як початкової інформації для систем автоматичного машинного навчання . Акцентовано увагу на основні типи даних, які можуть бути використані підсистемами спостерігання засобів захисту інформації та організації кібербезпеки для виконання завдань і попередження, класифікації та прогнозування подій кібербезпеки. За результатами аналізу визначено основні проблемні напрямки щодо їх реалізації в системах інформаційної безпеки. Проблему використання машинного навчаня (ML) в кібербезпеці складно вирішити, оскільки досягнення в цій області відкривають багато можливостей, з яких складно обрати дієві засоби реалізації та прийняття рішень. Окрім цього, ця технологія також може використотуватись хакерами для створення кібератаки. Метою дослідження є реалізація машинного навчання в технології інформаційної безпеки та кібербезпеки, та зобразити модель на основі самонавчання.
The problems that arise during implementation of electronic document management systems at Ukrainian universities were analyzed. Functional incompleteness of such systems and the difficulties that ...arise during their deployment were shown. For this reason, it was proposed extending the scope of such systems to include the processes of information management of universities. The project approach to deployment of university information management systems was proposed.
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