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Uncertainty

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DC Motor Control Technology Based on Multisensor Information Fusion.

Computational intelligence and neuroscience
To solve these uncertain problems by studying the motor fault diagnosis technology, so as to ensure the normal operation of the motor equipment is the primary problem to be solved in the field of motor fault diagnosis. The traditional DC motor is one...

Uncertainty Quantification for Deep Learning in Ultrasonic Crack Characterization.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Deep learning for nondestructive evaluation (NDE) has received a lot of attention in recent years for its potential ability to provide human level data analysis. However, little research into quantifying the uncertainty of its predictions has been do...

The Group Decision-Making Using Pythagorean Fuzzy Entropy and the Complex Proportional Assessment.

Sensors (Basel, Switzerland)
The Pythagorean fuzzy sets conveniently capture unreliable, ambiguous, and uncertain information, especially in problems involving multiple and opposing criteria. Pythagorean fuzzy sets are one of the popular generalizations of the intuitionistic fuz...

Effective Free-Driving Region Detection for Mobile Robots by Uncertainty Estimation Using RGB-D Data.

Sensors (Basel, Switzerland)
Accurate segmentation of drivable areas and road obstacles is critical for autonomous mobile robots to navigate safely in indoor and outdoor environments. With the fast advancement of deep learning, mobile robots may now perform autonomous navigation...

Self-normalized density map (SNDM) for counting microbiological objects.

Scientific reports
The statistical properties of the density map (DM) approach to counting microbiological objects on images are studied in detail. The DM is given by U[Formula: see text]-Net. Two statistical methods for deep neural networks are utilized: the bootstrap...

Using source data to aid and build variational state-space autoencoders with sparse target data for process monitoring.

Neural networks : the official journal of the International Neural Network Society
In industrial processes, different operating conditions and ratios of ingredients are used to produce multi-grade products in the same production line. Yet, the production grade changes so quickly as the demand from customers varies from time to time...

Development of a Relative Similarity Degree Based Engineering Construction Multi-Attribute Decision Model and Its Application.

Computational intelligence and neuroscience
Generally, there are large amounts of uncertain factors in the multi-attribute decision system. By using the gray relational degree and fuzzy gray relational degree, the weights of the comprehensive indexes are extracted. Then, a novel decision model...

Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification.

Sensors (Basel, Switzerland)
To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. However, the degradation inside the bearing is di...

Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency.

Medical image analysis
Despite that Convolutional Neural Networks (CNNs) have achieved promising performance in many medical image segmentation tasks, they rely on a large set of labeled images for training, which is expensive and time-consuming to acquire. Semi-supervised...

Uncertainty-Aware Deep Learning With Cross-Task Supervision for PHE Segmentation on CT Images.

IEEE journal of biomedical and health informatics
Perihematomal edema (PHE) volume, surrounding spontaneous intracerebral hemorrhage (SICH), is an important biomarker for the presence of SICH-associated diseases. However, due to irregular shapes and extremely low contrast of PHE on CT images, manual...