The Experts below are selected from a list of 28839 Experts worldwide ranked by ideXlab platform
Pere Ridao - One of the best experts on this subject based on the ideXlab platform.
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Grasping for the Seabed: Developing a New Underwater Robot Arm for Shallow-Water Intervention
IEEE Robotics & Automation Magazine, 2013Co-Authors: J.j. Fernández, Mario Prats, Pedro J. Sanz, J.c. García, Raul Marin, Mike Robinson, David Ribas, Pere RidaoAbstract:A new underwater robot arm was developed through intensive cooperation between different academic institutions and an industrial company. The manipulator, which was initially designed to be teleoperated, was adapted for our autonomy needs. Its dimensions and weight were reduced, and its kinematic model was developed so that Autonomous control can be performed with it. We compare several commercially available underwater manipulators and describe the development of the new one, from its initial configuration to its mechanical adaptation, modeling, control, and final assembly on an Autonomous underwater vehicle (AUV). The feasibility and reliability of this arm is demonstrated in water tank conditions, where various innovative Autonomous Object-recovery operations are successfully performed, both in stand-alone operation and integrated in an AUV prototype.
Viktor V. Semenov - One of the best experts on this subject based on the ideXlab platform.
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NEW2AN - Application of an Autonomous Object Behavior Model to Classify the Cybersecurity State
Lecture Notes in Computer Science, 2019Co-Authors: Viktor V. Semenov, Ilya S. Lebedev, Mikhail Sukhoparov, Kseniya I. SalakhutdinovaAbstract:This paper considers the issues of ensuring the cybersecurity of Autonomous Objects. Prerequisites that determine the application of additional independent methods for assessing the state of Autonomous Objects were identified. Side channels were described, which enable the monitoring of the state of individual Objects. A transition graph was proposed to show the current state of the Object based on data from side channels. The type of sound signals used to analyze and classify the state of information security was also shown. An experiment intended to accumulate statistical information on the various types of unmanned Object maneuvers was conducted using two audio recorders. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. The Autonomous Object behavior model can be used as an additional element to determine the state of cybersecurity. Using a segmented model, it was possible to improve the accuracy of determining the cybersecurity state. The proposed model enabled the identification of differences in the states of Autonomous Object cybersecurity with probabilities that were, on average, more than 0.8.
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ICR - Approach to Side Channel-Based Cybersecurity Monitoring for Autonomous Unmanned Objects
Lecture Notes in Computer Science, 2019Co-Authors: Viktor V. Semenov, Mikhail Sukhoparov, Ilya LebedevAbstract:In this paper, problematic issues in ensuring the cybersecurity of Autonomous unmanned Objects were considered. Moreover, prerequisites that determine the need for external monitoring systems were identified. The type and statistical characteristics used for the analysis and classification of sound signals were also shown. The proposed approach to the analysis of the cybersecurity condition of an Autonomous Object is based on classification methods and allows the identification of the current status based on digitized acoustic information processing. An experiment aimed at obtaining statistical information on various types of unmanned Object maneuvers with various arrangements of an audio recorder was conducted. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. Hence, the problem of identifying the cybersecurity condition of Autonomous unmanned Objects on the basis of processing acoustic signal information obtained through side channels was solved. Digitized information from an acoustic sensor (microphone) located statically in the experiment area was classified more accurately than from the microphone located directly on the Autonomous Object. With a minimum time of statistical information accumulation using the proposed approach, it becomes possible to identify differences in maneuvers performed by the unmanned Object and, consequently, the cybersecurity condition of the Object with a probability close to 0.7. The proposed approach for processing signal information can be used as an additional independent element to determine the cybersecurity condition of Autonomous Objects of unmanned systems. This approach can be quickly adapted using various mathematical tools and machine learning methods to achieve a given quality probabilistic assessment.
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Application of an Autonomous Object Behavior Model to Classify the Cybersecurity State
Internet of Things Smart Spaces and Next Generation Networks and Systems, 2019Co-Authors: Viktor V. Semenov, Ilya S. Lebedev, Mikhail Sukhoparov, Kseniya I. SalakhutdinovaAbstract:This paper considers the issues of ensuring the cybersecurity of Autonomous Objects. Prerequisites that determine the application of additional independent methods for assessing the state of Autonomous Objects were identified. Side channels were described, which enable the monitoring of the state of individual Objects. A transition graph was proposed to show the current state of the Object based on data from side channels. The type of sound signals used to analyze and classify the state of information security was also shown. An experiment intended to accumulate statistical information on the various types of unmanned Object maneuvers was conducted using two audio recorders. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. The Autonomous Object behavior model can be used as an additional element to determine the state of cybersecurity. Using a segmented model, it was possible to improve the accuracy of determining the cybersecurity state. The proposed model enabled the identification of differences in the states of Autonomous Object cybersecurity with probabilities that were, on average, more than 0.8.
Ilya Lebedev - One of the best experts on this subject based on the ideXlab platform.
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ICR - Approach to Side Channel-Based Cybersecurity Monitoring for Autonomous Unmanned Objects
Lecture Notes in Computer Science, 2019Co-Authors: Viktor V. Semenov, Mikhail Sukhoparov, Ilya LebedevAbstract:In this paper, problematic issues in ensuring the cybersecurity of Autonomous unmanned Objects were considered. Moreover, prerequisites that determine the need for external monitoring systems were identified. The type and statistical characteristics used for the analysis and classification of sound signals were also shown. The proposed approach to the analysis of the cybersecurity condition of an Autonomous Object is based on classification methods and allows the identification of the current status based on digitized acoustic information processing. An experiment aimed at obtaining statistical information on various types of unmanned Object maneuvers with various arrangements of an audio recorder was conducted. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. Hence, the problem of identifying the cybersecurity condition of Autonomous unmanned Objects on the basis of processing acoustic signal information obtained through side channels was solved. Digitized information from an acoustic sensor (microphone) located statically in the experiment area was classified more accurately than from the microphone located directly on the Autonomous Object. With a minimum time of statistical information accumulation using the proposed approach, it becomes possible to identify differences in maneuvers performed by the unmanned Object and, consequently, the cybersecurity condition of the Object with a probability close to 0.7. The proposed approach for processing signal information can be used as an additional independent element to determine the cybersecurity condition of Autonomous Objects of unmanned systems. This approach can be quickly adapted using various mathematical tools and machine learning methods to achieve a given quality probabilistic assessment.
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Approach to Side Channel-Based Cybersecurity Monitoring for Autonomous Unmanned Objects
Interactive Collaborative Robotics, 2019Co-Authors: Viktor Semenov, Mikhail Sukhoparov, Ilya LebedevAbstract:In this paper, problematic issues in ensuring the cybersecurity of Autonomous unmanned Objects were considered. Moreover, prerequisites that determine the need for external monitoring systems were identified. The type and statistical characteristics used for the analysis and classification of sound signals were also shown. The proposed approach to the analysis of the cybersecurity condition of an Autonomous Object is based on classification methods and allows the identification of the current status based on digitized acoustic information processing. An experiment aimed at obtaining statistical information on various types of unmanned Object maneuvers with various arrangements of an audio recorder was conducted. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. Hence, the problem of identifying the cybersecurity condition of Autonomous unmanned Objects on the basis of processing acoustic signal information obtained through side channels was solved. Digitized information from an acoustic sensor (microphone) located statically in the experiment area was classified more accurately than from the microphone located directly on the Autonomous Object. With a minimum time of statistical information accumulation using the proposed approach, it becomes possible to identify differences in maneuvers performed by the unmanned Object and, consequently, the cybersecurity condition of the Object with a probability close to 0.7. The proposed approach for processing signal information can be used as an additional independent element to determine the cybersecurity condition of Autonomous Objects of unmanned systems. This approach can be quickly adapted using various mathematical tools and machine learning methods to achieve a given quality probabilistic assessment.
Mikhail Sukhoparov - One of the best experts on this subject based on the ideXlab platform.
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NEW2AN - Application of an Autonomous Object Behavior Model to Classify the Cybersecurity State
Lecture Notes in Computer Science, 2019Co-Authors: Viktor V. Semenov, Ilya S. Lebedev, Mikhail Sukhoparov, Kseniya I. SalakhutdinovaAbstract:This paper considers the issues of ensuring the cybersecurity of Autonomous Objects. Prerequisites that determine the application of additional independent methods for assessing the state of Autonomous Objects were identified. Side channels were described, which enable the monitoring of the state of individual Objects. A transition graph was proposed to show the current state of the Object based on data from side channels. The type of sound signals used to analyze and classify the state of information security was also shown. An experiment intended to accumulate statistical information on the various types of unmanned Object maneuvers was conducted using two audio recorders. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. The Autonomous Object behavior model can be used as an additional element to determine the state of cybersecurity. Using a segmented model, it was possible to improve the accuracy of determining the cybersecurity state. The proposed model enabled the identification of differences in the states of Autonomous Object cybersecurity with probabilities that were, on average, more than 0.8.
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ICR - Approach to Side Channel-Based Cybersecurity Monitoring for Autonomous Unmanned Objects
Lecture Notes in Computer Science, 2019Co-Authors: Viktor V. Semenov, Mikhail Sukhoparov, Ilya LebedevAbstract:In this paper, problematic issues in ensuring the cybersecurity of Autonomous unmanned Objects were considered. Moreover, prerequisites that determine the need for external monitoring systems were identified. The type and statistical characteristics used for the analysis and classification of sound signals were also shown. The proposed approach to the analysis of the cybersecurity condition of an Autonomous Object is based on classification methods and allows the identification of the current status based on digitized acoustic information processing. An experiment aimed at obtaining statistical information on various types of unmanned Object maneuvers with various arrangements of an audio recorder was conducted. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. Hence, the problem of identifying the cybersecurity condition of Autonomous unmanned Objects on the basis of processing acoustic signal information obtained through side channels was solved. Digitized information from an acoustic sensor (microphone) located statically in the experiment area was classified more accurately than from the microphone located directly on the Autonomous Object. With a minimum time of statistical information accumulation using the proposed approach, it becomes possible to identify differences in maneuvers performed by the unmanned Object and, consequently, the cybersecurity condition of the Object with a probability close to 0.7. The proposed approach for processing signal information can be used as an additional independent element to determine the cybersecurity condition of Autonomous Objects of unmanned systems. This approach can be quickly adapted using various mathematical tools and machine learning methods to achieve a given quality probabilistic assessment.
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Approach to Side Channel-Based Cybersecurity Monitoring for Autonomous Unmanned Objects
Interactive Collaborative Robotics, 2019Co-Authors: Viktor Semenov, Mikhail Sukhoparov, Ilya LebedevAbstract:In this paper, problematic issues in ensuring the cybersecurity of Autonomous unmanned Objects were considered. Moreover, prerequisites that determine the need for external monitoring systems were identified. The type and statistical characteristics used for the analysis and classification of sound signals were also shown. The proposed approach to the analysis of the cybersecurity condition of an Autonomous Object is based on classification methods and allows the identification of the current status based on digitized acoustic information processing. An experiment aimed at obtaining statistical information on various types of unmanned Object maneuvers with various arrangements of an audio recorder was conducted. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. Hence, the problem of identifying the cybersecurity condition of Autonomous unmanned Objects on the basis of processing acoustic signal information obtained through side channels was solved. Digitized information from an acoustic sensor (microphone) located statically in the experiment area was classified more accurately than from the microphone located directly on the Autonomous Object. With a minimum time of statistical information accumulation using the proposed approach, it becomes possible to identify differences in maneuvers performed by the unmanned Object and, consequently, the cybersecurity condition of the Object with a probability close to 0.7. The proposed approach for processing signal information can be used as an additional independent element to determine the cybersecurity condition of Autonomous Objects of unmanned systems. This approach can be quickly adapted using various mathematical tools and machine learning methods to achieve a given quality probabilistic assessment.
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Application of an Autonomous Object Behavior Model to Classify the Cybersecurity State
Internet of Things Smart Spaces and Next Generation Networks and Systems, 2019Co-Authors: Viktor V. Semenov, Ilya S. Lebedev, Mikhail Sukhoparov, Kseniya I. SalakhutdinovaAbstract:This paper considers the issues of ensuring the cybersecurity of Autonomous Objects. Prerequisites that determine the application of additional independent methods for assessing the state of Autonomous Objects were identified. Side channels were described, which enable the monitoring of the state of individual Objects. A transition graph was proposed to show the current state of the Object based on data from side channels. The type of sound signals used to analyze and classify the state of information security was also shown. An experiment intended to accumulate statistical information on the various types of unmanned Object maneuvers was conducted using two audio recorders. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. The Autonomous Object behavior model can be used as an additional element to determine the state of cybersecurity. Using a segmented model, it was possible to improve the accuracy of determining the cybersecurity state. The proposed model enabled the identification of differences in the states of Autonomous Object cybersecurity with probabilities that were, on average, more than 0.8.
J.j. Fernández - One of the best experts on this subject based on the ideXlab platform.
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Grasping for the Seabed: Developing a New Underwater Robot Arm for Shallow-Water Intervention
IEEE Robotics & Automation Magazine, 2013Co-Authors: J.j. Fernández, Mario Prats, Pedro J. Sanz, J.c. García, Raul Marin, Mike Robinson, David Ribas, Pere RidaoAbstract:A new underwater robot arm was developed through intensive cooperation between different academic institutions and an industrial company. The manipulator, which was initially designed to be teleoperated, was adapted for our autonomy needs. Its dimensions and weight were reduced, and its kinematic model was developed so that Autonomous control can be performed with it. We compare several commercially available underwater manipulators and describe the development of the new one, from its initial configuration to its mechanical adaptation, modeling, control, and final assembly on an Autonomous underwater vehicle (AUV). The feasibility and reliability of this arm is demonstrated in water tank conditions, where various innovative Autonomous Object-recovery operations are successfully performed, both in stand-alone operation and integrated in an AUV prototype.
