IТ-диагностика болезни Паркинсона на основе анализа голосовых маркеров и машинного обучения

The results of studying the parameters of the spectra of speech signals by machine learning with the use of neural networks are presented. This study was carried out in order to confirm experimentally the possibility of performing an assessment of these parameters for the detection of Parkinson’s disease in the early stages (IT diagnostics). During the study, the public database was used, which systematized the spectra of vowel sounds uttered by patients with Parkinson’s disease. The applied method is binary data classification. In the course of the study, the speech data spectrum was first preprocessed, which consisted of filtering it in order to remove its noise components and eliminate bursts and gaps in it. Then the parameters of the processed spectrum of speech data were determined: average value, maximum and minimum, peak, wavelet coefficients, MFCC and TQWT. After that, the object was selected using the PCA algorithm. The model was trained using the Knn and Random Forest algorithms, as well as the Bayesian neural network. The Bayesian optimization algorithm and the GridSearch method were used to find the best model hyperparameters. It has been established that when using Knn, Random Forest and Bayesian neural network, it is possible to increase the accuracy of recognition of Parkinson’s disease by 94.7; 88.16 and 74.74 %, respectively. A similar study by other scientists showed that the recognition accuracy of data sets was only 86 %.Представлены результаты исследования параметров спектров речевых сигналов с помощью машинного обучения с применением нейронных сетей, проведенного в целях экспериментального подтверждения возможности выполнения оценки этих параметров для выявления болезни Паркинсона на ранних стадиях (IТ-диагностика). В ходе исследования использовали общедоступную базу данных, в которой систематизированы спектры гласных звуков, произнесенных пациентами с болезнью Паркинсона. Примененный метод – бинарная классификация данных. Сначала выполняли предварительную обработку спектра речевых данных, состоявшую в его фильтрации, для удаления из него шумов и устранения присутствующих в нем всплесков и пробелов. Затем определяли параметры обработанного спектра речевых данных: среднее значение, максимум, минимум, пик, вейвлет-коэффициенты, MFCC и TQWT. После этого выбирали объект с помощью алгоритма PCA. Для обучения модели использовали алгоритмы Knn и Random Forest и нейронной сети Байеса. Для нахождения наилучших гиперпараметров модели применяли алгоритм оптимизации Байеса и метод GridSearch. Установлено, что при использовании Knn, Random Forest и нейронной сети Байеса можно обеспечить увеличение точности распознавания болезни Паркинсона на 94,7; 88,16 и 74,74 % соответственно. Аналогичное исследование, проведенное другими учеными, показало, что точность распознавания наборов данных составила всего 86 %

IТ-диагностика болезни Паркинсона на основе анализа голосовых маркеров и машинного обучения

The results of studying the parameters of the spectra of speech signals by machine learning with the use of neural networks are presented. This study was carried out in order to confirm experimentally the possibility of performing an assessment of these parameters for the detection of Parkinson’s disease in the early stages (IT diagnostics). During the study, the public database was used, which systematized the spectra of vowel sounds uttered by patients with Parkinson’s disease. The applied method is binary data classification. In the course of the study, the speech data spectrum was first preprocessed, which consisted of filtering it in order to remove its noise components and eliminate bursts and gaps in it. Then the parameters of the processed spectrum of speech data were determined: average value, maximum and minimum, peak, wavelet coefficients, MFCC and TQWT. After that, the object was selected using the PCA algorithm. The model was trained using the Knn and Random Forest algorithms, as well as the Bayesian neural network. The Bayesian optimization algorithm and the GridSearch method were used to find the best model hyperparameters. It has been established that when using Knn, Random Forest and Bayesian neural network, it is possible to increase the accuracy of recognition of Parkinson’s disease by 94.7; 88.16 and 74.74 %, respectively. A similar study by other scientists showed that the recognition accuracy of data sets was only 86 %

Experimentally validated geometrically exact model for extreme nonlinear motions of cantilevers

A unique feature of flexible cantilevered beams, which is used in a range of applications from energy harvesting to bio-inspired actuation, is their capability to undergo motions of extremely large amplitudes. The well-known third-order nonlinear cantilever model is not capable of capturing such a behaviour, hence requiring the application of geometrically exact models. This study, for the first time, presents a thorough experimental investigation on nonlinear dynamics of a cantilever under base excitation in order to capture extremely large oscillations to validate a geometrically exact model based on the centreline rotation. To this end, a state-of-the-art in vacuo base excitation experimental set-up is utilised to excite the cantilever in the primary resonance region and drive it to extremely large amplitudes, and a high-speed camera is used to capture the motion. A robust image processing code is developed to extract the deformed state of the cantilever at each frame as well as the tip displacements and rotation. For the theoretical part, a geometrically exact model is developed based on the Euler–Bernoulli beam theory and inextensibility condition, while using Kelvin–Voigt material damping. To ensure accurate predictions, the equation of motion is derived for the centreline rotation and all terms are kept geometrically exact throughout the derivation and discretisation procedures. Thorough comparisons are conducted between experimental and theoretical results in the form of frequency response diagrams, time histories, motion snapshots, and motion videos. It is shown that the predictions of the geometrically exact model are in excellent agreement with the experimental results at both relatively large and extremely large oscillation amplitudes

Rank, strain, and corruption among Chinese public officials: A general strain theory perspective

The problem of corruption has long been a societal focus in China. Agnew’s general strain theory (GST) offers a good explanation of the drive to engage in corruption; that is, offenders are likely to be driven by various types of strains and engage in corrupt behavior as a coping mechanism. However, whether and how an official’s rank moderates the effect of strain on corrupt behavior has yet to be explored. The current study surveyed 687 inmates from 60 prisons in China who had been convicted of corrupt behaviors. The results show that although different levels of officials experience different types of strain, nearly all types of strains are significantly and positively associated with the frequency of corrupt behavior. As for the conditional effect, officials’ ranks significantly moderate the relationship between work-related strain and the frequency of corrupt behavior; that is, work-related strain is shown to have a more significant effect on officials at the clerk level (a higher rank) than on officials at non-clerk level (a lower rank). This research is believed to further expand on the applicability of GST to corruption in non-Western societies

Учебная сеть интернета вещей для ИТ-диагностики

This report presents a comprehensive solution for IoT data collection, storage, analysis, and visualization using EMQX MQTT message middleware, ClickHouse OLAP database, and Grafana data visualization. The proposed stack enables seamless integration and efficient processing of large-scale IoT data, facilitating real-time monitoring and valuable insights

Design of school bell automatic control system based on single-chip microcomputer

This article introduces the basic components of the school's automatic control system, and makes a detailed introduction and comparison of the functions, application scenarios, and advantages of each part. The hardware design of the automatic control system is based on the STC89C52 single-chip control circuit as the core, supplemented by sensor circuits, clock circuits, bell circuits and human-computer interaction circuits to complete various functions. The human-computer interaction circuits include keyboard input circuits and liquid crystal display circuits. The software design of this system mainly includes sensor detection, button setting, and bell output part. The sensor detection part is composed of a temperature detection subprogram, the key setting part is composed of an independent key subprogram and a liquid crystal display subprogram, and the bell output part is composed of a voice recording and playback subprogram. The program and clock subroutine constitute

Design of smart code lock

This design uses MCS-51 single-chip microcomputer and the corresponding interface chip to complete the design of a smart password lock. The matrix key input module is used as the input channel for passwords and related information, and the display screen LCD1602 is used to display the prompt words through a stepping motor. The rotation of the door lock can be opened and closed, and the buzzer and LED are used to realize the sound and light alarm when the password is wrong. In addition, the uniqueness of this design is that the proximity switch is used to detect whether the door is closed or not, which is more intelligent