RISK CALCULATION AND THREAT ASSESSMENT OF THE USE OF VOICE CONTROL SYSTEMS IN TRUSTED DISTRIBUTED COMPUTER SYSTEMS

2021 ◽  
pp. 93-104
Author(s):  
Александр Александрович Хайдаров ◽  
Александр Сергеевич Шишлов ◽  
Николай Николаевич Толстых
Keyword(s):  
Control Systems ◽  
Voice Recognition ◽  
Threat Assessment ◽  
Distributed Computer System ◽  
Dynamic Transformation ◽  
Algorithmic Support ◽  
Voice Control ◽  
Recognition Systems ◽  
The Stability ◽  
The Voice

Цель исследования состоит в повышении защищенности элементов распределенной компьютерной системы автоматического распознавания голосовых команд от возможного неверного определения команды за счет создания алгоритмического обеспечения оценки и регулирования рисков неверной идентификации голосовой команды для сравнения реализации двух алгоритмов: алгоритм динамической трансформации временной шкалы и алгоритм на основе скрытых Марковских процессов.Полученные результаты могут быть использованы или адаптированы при необходимости повышения стойкости систем автоматического распознавания голосовых команд на этапах проектирования и модернизации, а также при необходимости восстановления эффективности функционирования после компрометации или взлома. The aim of the study is to increase the security of the elements of a distributed computer system for automatic recognition of voice commands from possible incorrect identification of the command by creating algorithmic support for assessing and managing the risks of incorrect identification of the voice command to compare the implementation of two algorithms: the algorithm for dynamic transformation of the timeline and the algorithm based on hidden Markov processes.The obtained results can be used or adapted if it is necessary to increase the stability of automatic voice recognition systems at the design and modernization stages, as well as if it is necessary to restore the efficiency of functioning after a compromise or hacking.

A Comparison of Three Methods for Controlling Aircraft Systems

1986 ◽  
Vol 30 (7) ◽  
pp. 638-641
Author(s):  
John P. Zenyuh ◽  
John M. Reising
Keyword(s):  
Control Systems ◽  
Relative Effectiveness ◽  
Voice Recognition ◽  
Recognition System ◽  
Control Device ◽  
Control Mode ◽  
Aircraft Systems ◽  
Remote Touch ◽  
The Voice ◽  
Speed And Accuracy

The objective of this study was to compare the relative effectiveness of three modes of subsystem control: a voice recognition system with visual feedback presented on the head-up display, a standard multifunction control device with tailored switching logic, and a remotely operated multifunction control with feedback presented on the head-up display. Comparisons were based on measures of interference with a loading task and overall speed and accuracy of the control operations performed. The working hypothesis was that the voice system and head-up multifunction control would manifest substantially lower interference with the primary task, while subsystem control operation times would remain unaffected by control mode. The results indicate that performance with the remote touch panel was significantly poorer than with the voice or standard multifunction control systems.

scholarly journals Comparison of CNNs and SVM for voice control wheelchair

2020 ◽  
Vol 9 (3) ◽  
pp. 387
Author(s):  
Mohammad Shahrul Izham Sharifuddin ◽  
Sharifalillah Nordin ◽  
Azliza Mohd Ali
Keyword(s):  
Feature Extraction ◽  
Processing Time ◽  
Voice Recognition ◽  
The Other ◽  
Raspberry Pi ◽  
Intelligent Wheelchair ◽  
Voice Control ◽  
Voice Data ◽  
The Voice ◽  
Feature Extraction Technique

In this paper, we develop an intelligent wheelchair using CNNs and SVM voice recognition methods. The data is collected from Google and some of them are self-recorded. There are four types of data to be recognized which are go, left, right, and stop. Voice data are extracted using MFCC feature extraction technique. CNNs and SVM are then used to classify and recognize the voice data. The motor driver is embedded in Raspberry PI 3B+  to control the movement of the wheelchair prototype. CNNs produced higher accuracy i.e. 95.30% compared to SVM which is only 72.39%. On the other hand, SVM only took 8.21 seconds while CNNs took 250.03 seconds to execute. Therefore, CNNs produce better result because noise are filtered in the feature extraction layer before classified in the classification layer. However, CNNs took longer time due to the complexity of the networks and the less complexity implementation in SVM give shorter processing time.

Voice Recognition for STEM Education Using Robotics

2017 ◽  
Author(s):  
Shubo Chen ◽  
Binsen Qian ◽  
Harry Cheng
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Stem Education ◽  
Basic Concept ◽  
Voice Recognition ◽  
Voice Control ◽  
K 12 ◽  
The Voice ◽  
Recognition Technique ◽  
Machine Learning Tool

In this paper, we provide a new voice recognition framework which allows K-12 students to write programs to solve problems using voice control. The framework contains the voice recognition module SPHINX which is based on an open source machine learning tool developed by Carnegie Mellon University and a wrapper function which is written in C/C++ interpreter Ch. The wrapper function allows students to interact the module in Ch. Along with Ch programming and robotic coursework, students will get the chance to learn the basic concept of machine learning and voice recognition technique. In order to bring students attention and interest in machine learning, various tasks have been designed for students to accomplish based on the framework. The framework is also flexible for them to explore other interesting projects.

scholarly journals Automatic Vehicle Turn Indicator using Speech Recognition

2019 ◽  
Vol 8 (3) ◽  
pp. 6697-6700
Keyword(s):  
Regular Language ◽  
Voice Recognition ◽  
Human Communication ◽  
Signaling Systems ◽  
Turn Indicator ◽  
Voice Control ◽  
Language Control ◽  
Equipment Manufacture ◽  
Manual Tasks ◽  
The Voice

Voice-controlled innovation is an energizing region of research that is utilized to help people in the mechanical control of manual frameworks. It is a part of human-communication that builds the advances in programmed discourse acknowledgment or ASR with the inventive advances of characteristic language handling or NLP. Wise frameworks, for example, Automatic Vehicle Signaling Systems, can likewise take into consideration adaptability in manual activities. In late investigations, analysts have investigated regular language control of manual tasks. In this paper we will research interfacing voice control activities with Arduino-based equipment stages that are utilized to structure the programmed sign highlights in a vehicle. The control system includes a voice recognition circuit for activating turn signal devices within the vehicle. The voice recognition circuit takes in input from the voice from the Google maps voice assistant. In some formats, a wireless hardware is provided while in other embodiments original equipment manufacture is accommodated

The Design and Realization of Remote Voice Control System Based on the Intelligent Home

2014 ◽  
Vol 530-531 ◽  
pp. 1112-1118
Author(s):  
Ye Fen Yang ◽  
Jun Zhang ◽  
Dong Hai Zeng
Keyword(s):  
Control System ◽  
Mobile Phone ◽  
Control Function ◽  
Voice Recognition ◽  
Security Requirements ◽  
Intelligent Home ◽  
Design Program ◽  
Wide Range ◽  
Voice Control ◽  
The Voice

A design program of remote voice control system is presented based on the intelligent home on Android mobile phone platform. Via the voice recognition of Android mobile phone, the intelligent home can have a remote voice control function by this program, which greatly improves the security requirements of the intelligent home. This system is tested and proved its real-time, effectiveness and stability. Meanwhile, it can also provide a practical reference solution for human-computer interaction, having a wide range of application.

Lampu Sein Helm Sepeda Berbasis Voice Recognition

2019 ◽  
Vol 11 (01) ◽  
pp. 20-25
Author(s):  
Indra Saputra ◽  
Parulian Silalahi ◽  
Bayu Cahyawan ◽  
Imam Akbar
Keyword(s):  
Voice Recognition ◽  
Driving Safety ◽  
Bicycle Helmet ◽  
Turn Signal ◽  
Voice Command ◽  
The Voice ◽  
Indicator Signal

Bicycles are not equipped with the turn signal. For driving safety, a bicycle helmet with a turn signal is designed with voice rrecognition. It is using the Arduino Nano as a controller to control the ON and OFF of turn signal lights with voice commands. This device uses a Voice Recognition sensor and microphone that placed on a bicycle helmet. When the voice command is mentioned in the microphone, the Voice Recognition sensor will detect the command specified, the sensor will automatically read and send a signal to Arduino, then the turn signal will light up as instructed, the Arduino on the helmet will send an indicator signal via the Bluetooth Module. The device is able to detect sound with a percentage of 80%. The tool can work with a distance of <2 meters with noise <71 db.

TO THE STABILITY OF TANK VEHICLES IN THE BRAKE MODE

2019 ◽  
pp. 27-32
Author(s):  
Denys Popelysh ◽  
Yurii Seluk ◽  
Sergyi Tomchuk
Keyword(s):  
Mathematical Model ◽  
Control Systems ◽  
Distribution Systems ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Center Of Mass ◽  
Dynamic Processes ◽  
Course Stability ◽  
The Stability ◽  
Tank Vehicles

This article discusses the question of the possibility of improving the roll stability of partially filled tank vehicles while braking. We consider the dangers associated with partially filled tank vehicles. We give examples of the severe consequences of road traffic accidents that have occurred with tank vehicles carrying dangerous goods. We conducted an analysis of the dynamic processes of fluid flow in the tank and their influence on the basic parameters of the stability of vehicle. When transporting a partially filled tank due to the comparability of the mass of the empty tank with the mass of the fluid being transported, the dynamic qualities of the vehicle change so that they differ significantly from the dynamic characteristics of other vehicles. Due to large displacements of the center of mass of cargo in the tank there are additional loads that act vehicle and significantly reduce the course stability and the drivability. We consider the dynamics of liquid sloshing in moving containers, and give examples of building a mechanical model of an oscillating fluid in a tank and a mathematical model of a vehicle with a tank. We also considered the method of improving the vehicle’s stability, which is based on the prediction of the moment of action and the nature of the dynamic processes of liquid cargo and the implementation of preventive actions by executive mechanisms. Modern automated control systems (anti-lock brake system, anti-slip control systems, stabilization systems, braking forces distribution systems, floor level systems, etc.) use a certain list of elements for collecting necessary parameters and actuators for their work. This gives the ability to influence the course stability properties without interfering with the design of the vehicle only by making changes to the software of these systems. Keywords: tank vehicle, roll stability, mathematical model, vehicle control systems.

ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

2019 ◽  
Vol 12 ◽  
Author(s):  
Basavaraj N Hiremath ◽  
Malini M Patilb
Keyword(s):  
Feature Extraction ◽  
Computer Program ◽  
Principle Component Analysis ◽  
Voice Recognition ◽  
Component Analysis ◽  
Recognition System ◽  
Principle Component ◽  
Whole Process ◽  
The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

scholarly journals The Application of an Impedance-Passivity Controller in Haptic Stability Analysis

2021 ◽  
Vol 11 (4) ◽  
pp. 1618
Author(s):  
Ping-Nan Chen ◽  
Yung-Te Chen ◽  
Hsin Hsiu ◽  
Ruei-Jia Chen
Keyword(s):  
Control Systems ◽  
Force Feedback ◽  
Training System ◽  
Considerable Time ◽  
Virtual Training ◽  
Control Gain ◽  
Negative Damping ◽  
The Stability ◽  
Time Required ◽  
Stable Control

This paper proposes a passivity theorem on the basis of energy concepts to study the stability of force feedback in a virtual haptic system. An impedance-passivity controller (IPC) was designed from the two-port network perspective to improve the chief drawback of haptic systems, namely the considerable time required to reach stability if the equipment consumes energy slowly. The proposed IPC can be used to achieve stability through model parameter selection and to obtain control gain. In particular, haptic performance can be improved for extreme cases of high stiffness and negative damping. Furthermore, a virtual training system for one-degree-of-freedom sticking was developed to validate the experimental platform of our IPC. To ensure consistency in the experiment, we designed a specialized mechanical robot to replace human operation. Finally, compared with basic passivity control systems, our IPC could achieve stable control rapidly.

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