Fault Detection Techniques for Robotic Surgery Using Sensors


Fault Detection Techniques for Robotic Surgery Using Sensors – Deep learning approaches have recently demonstrated great success in detecting various types of malware in web applications. In this work, we present a novel method, Deep Neural Networks (DNNs), for detecting malware in web applications. Deep-CNNs can efficiently process and summarize malicious actions in Web pages, while being more robust to the local changes of web page elements, e.g., images and text. We develop a Deep-CNN framework, based on a Convolutional Neural Network (CNN) and deep learning to automatically process Web pages, detect malicious actions and detect malicious entities. Our CNN has been trained and compared to a baseline CNN for malware detection and detection, with the aim of detecting malicious entities. In particular, we developed a two-stage CNN architecture, which contains a 3D CNN model with CNN layers, and a 2D CNN model with CNN layers. Our learned CNN system detects malicious entities with an accuracy of over 90% on a publicly available benchmark of malware detection data from the web applications. The detection accuracy is comparable to state-of-the-art detection in web applications of malware detection.

Autonomous vehicles must use the environment to be used. We consider the problem of avoiding conflicts between a robot and a human driver and the presence of such conflict. The agent should avoid situations that arise while using a vehicle, for example conflict between human drivers and humans. As we argue, this issue lacks theoretical support. We study this issue via several empirical measures. We show that for a robot to evade conflicts, it would need to model both situations explicitly for the robot to know whether the conflict has happened or not. We build on prior work and show how to do so using a deep neural network. These findings, based on a novel approach we describe, can be applied to a variety of real-world scenarios, but are based on the human behavior. We provide a theoretical underpinning for both the human behavior and the robot behavior which is needed in order to implement the learned behavior.

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Fault Detection Techniques for Robotic Surgery Using Sensors

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  • Dynamic Perturbation for Deep Learning

    On the construction of the network that enables autonomous driving: design and simulationAutonomous vehicles must use the environment to be used. We consider the problem of avoiding conflicts between a robot and a human driver and the presence of such conflict. The agent should avoid situations that arise while using a vehicle, for example conflict between human drivers and humans. As we argue, this issue lacks theoretical support. We study this issue via several empirical measures. We show that for a robot to evade conflicts, it would need to model both situations explicitly for the robot to know whether the conflict has happened or not. We build on prior work and show how to do so using a deep neural network. These findings, based on a novel approach we describe, can be applied to a variety of real-world scenarios, but are based on the human behavior. We provide a theoretical underpinning for both the human behavior and the robot behavior which is needed in order to implement the learned behavior.


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