scholarly journals Localizer-to-Mapper Knowledge Transfer: Real-time Diagnosis of Deep SLAM in Everyday Navigation

2021 ◽  
Author(s):  
kanji tanaka
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Real Time ◽  
Deep Neural Networks ◽  
Black Box ◽  
Input Output ◽  
Diagnosis Algorithm ◽  
The Cost ◽  
Time Diagnosis

In this study, we address a novel "domain-shift localization (DSL)" problem, by which "user robots" of a deep SLAM system localize a domain-shifted region in the robot workspace during their daily navigation. Furthermore, we present a case study pertaining to a simple deep SLAM system comprising a visual place classifier and visual odometry as exteroceptive and proprioceptive modules, respectively. Such a DSL method enables "mapper robots" to focus on available resources (e.g., time, energy, and computation) in the domainshifted region, rather than the entire workspace, thereby significantly reducing the cost of per-domain DNN maintenance. Unlike conventional scenarios of SLAM diagnosis, the deep SLAM system comprises deep neural networks (DNNs) with black-box characteristics, which render it difficult to directly diagnose the internal signals of SLAM modules. Hence, we present a novel diagnosis algorithm that does not rely on internal signals but uses only the input/output signals of DNNs as input to DSL. Experiments demonstrate that, compared with a vanilla deep SLAM system that does not reflect fault diagnosis, the proposed deep SLAM framework can achieve a path that is more similar to the actual measured GPS path. This study is a first step towards a real-time approach to diagnose aging of deep SLAM systems.

scholarly journals Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

2021 ◽  
Vol 35 (2) ◽  
pp. 251-264
Author(s):  
Dimitrios Boursinos ◽  
Xenofon Koutsoukos
Keyword(s):  
Neural Networks ◽  
Distance Learning ◽  
Real Time ◽  
Speaker Recognition ◽  
Deep Neural Networks ◽  
Cyber Physical Systems ◽  
Error Rates ◽  
Traffic Sign ◽  
Conformal Prediction ◽  
Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

scholarly journals Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jianyu Long ◽  
Yanyang Zi ◽  
Shaohui Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Fault Diagnostics ◽  
Sparse Autoencoder

AbstractSupervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.

Multi-Constrained Real-Time Entry Guidance Using Deep Neural Networks

2021 ◽  
pp. 1-1 ◽  
Author(s):  
Lin Cheng ◽  
Fanghua Jiang ◽  
Zhenbo Wang ◽  
Junfeng Li
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Deep Neural Networks ◽  
Entry Guidance

scholarly journals Diagnostic data integration using deep neural networks for real-time plasma analysis

2021 ◽  
pp. 1-1
Author(s):  
A. Rigoni Garola ◽  
R. Cavazzana ◽  
M. Gobbin ◽  
R.S. Delogu ◽  
G. Manduchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Data Integration ◽  
Real Time ◽  
Deep Neural Networks ◽  
Plasma Analysis ◽  
Diagnostic Data

scholarly journals SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications

2020 ◽  
Vol 34 (07) ◽  
pp. 10901-10908 ◽  
Author(s):  
Abdullah Hamdi ◽  
Matthias Mueller ◽  
Bernard Ghanem
Keyword(s):  
Neural Networks ◽  
Recent Work ◽  
Autonomous Navigation ◽  
General Framework ◽  
Deep Neural Networks ◽  
Autonomous Driving ◽  
Black Box ◽  
Semantic Meaning ◽  
Safety Critical ◽  
Adversarial Attack

One major factor impeding more widespread adoption of deep neural networks (DNNs) is their lack of robustness, which is essential for safety-critical applications such as autonomous driving. This has motivated much recent work on adversarial attacks for DNNs, which mostly focus on pixel-level perturbations void of semantic meaning. In contrast, we present a general framework for adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task as well as pixel-level attacks. To do this, we re-frame the adversarial attack problem as learning a distribution of parameters that always fools the agent. In the semantic case, our proposed adversary (denoted as BBGAN) is trained to sample parameters that describe the environment with which the black-box agent interacts, such that the agent performs its dedicated task poorly in this environment. We apply BBGAN on three different tasks, primarily targeting aspects of autonomous navigation: object detection, self-driving, and autonomous UAV racing. On these tasks, BBGAN can generate failure cases that consistently fool a trained agent.

scholarly journals EP-2163: Deep Neural Networks vs Medical Physicists: An IMRT QA case study

2018 ◽  
Vol 127 ◽  
pp. S1194
Author(s):  
Y. Interian ◽  
G. Valdes ◽  
R. Vincent ◽  
C. Joey ◽  
K. Vasant ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Imrt Qa ◽  
Medical Physicists

Real-time strawberry detection using deep neural networks on embedded system (rtsd-net): An edge AI application

2022 ◽  
Vol 192 ◽  
pp. 106586
Author(s):  
Yanchao Zhang ◽  
Jiya Yu ◽  
Yang Chen ◽  
Wen Yang ◽  
Wenbo Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Embedded System ◽  
Real Time ◽  
Deep Neural Networks
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