Applying matrix factorization to consistency-based direct diagnosis

AbstractConfiguration systems must be able to deal with inconsistencies which can occur in different contexts. Especially in interactive settings, where users specify requirements and a constraint solver has to identify solutions, inconsistencies may more often arise. In inconsistency situations, there is a need of diagnosis methods that support the identification of minimal sets of constraints that have to be adapted or deleted in order to restore consistency. A diagnosis algorithm’s performance can be evaluated in terms of time to find a diagnosis (runtime) and diagnosis quality. Runtime efficiency of diagnosis is especially crucial in real-time scenarios such as production scheduling, robot control, and communication networks. However, there is a trade off between diagnosis quality and the runtime efficiency of diagnostic reasoning. In this article, we deal with solving the quality-runtime performance trade off problem of direct diagnosis. In this context, we propose a novel learning approach based on matrix factorization for constraint ordering. We show that our approach improves runtime performance and diagnosis quality at the same time.

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Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-378 ◽

2020 ◽

Vol 2020 (14) ◽

pp. 378-1-378-7

Author(s):

Tyler Nuanes ◽

Matt Elsey ◽

Radek Grzeszczuk ◽

John Paul Shen

Keyword(s):

Real Time ◽

Mobile Device ◽

Computational Cost ◽

False Positives ◽

Compact Model ◽

High Quality ◽

False Negatives ◽

Trade Off ◽

Depth Reconstruction ◽

Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

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Industrial communication networks. Profiles. Additional Fieldbus profiles for real-time networks based on ISO/IEC 8802-3. SNpTYPE

10.3403/30208742u ◽

2015 ◽

Keyword(s):

Real Time ◽

Communication Networks

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A Novel Ensemble Learning Approach of Deep Learning Techniques to Monitor Distracted Driver Behaviour in Real Time

2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA) ◽

10.1109/caida51941.2021.9425243 ◽

2021 ◽

Author(s):

Hafiz Umer Draz ◽

Muhammad Zeeshan Khan ◽

Muhammad Usman Ghani Khan ◽

Amjad Rehman ◽

Ibrahim Abunadi

Keyword(s):

Deep Learning ◽

Real Time ◽

Ensemble Learning ◽

Learning Approach ◽

Driver Behaviour ◽

Learning Techniques

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A Real-time Pothole Detection Based on Deep Learning Approach

Journal of Physics Conference Series ◽

10.1088/1742-6596/1828/1/012001 ◽

2021 ◽

Vol 1828 (1) ◽

pp. 012001

Author(s):

Yeoh Keng Yik ◽

Nurul Ezaila Alias ◽

Yusmeeraz Yusof ◽

Suhaila Isaak

Keyword(s):

Deep Learning ◽

Real Time ◽

Learning Approach ◽

Pothole Detection

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Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

Scientific Reports ◽

10.1038/s41598-021-90688-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Simon Tam ◽

Mounir Boukadoum ◽

Alexandre Campeau-Lecours ◽

Benoit Gosselin

Keyword(s):

Real Time ◽

Transfer Learning ◽

Control Strategy ◽

High Density ◽

Fine Tuning ◽

Mode Switching ◽

Correct Prediction ◽

Learning Approach ◽

Predictive Values ◽

Hand Prosthesis

AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

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