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Force Myography-Based Human Robot Interactions via Deep Domain Adaptation and Generalization.

Sensors (Basel, Switzerland)
Estimating applied force using force myography (FMG) technique can be effective in human-robot interactions (HRI) using data-driven models. A model predicts well when adequate training and evaluation are observed in same session, which is sometimes t...

A Transfer Learning Framework with a One-Dimensional Deep Subdomain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions.

Sensors (Basel, Switzerland)
Accurate and fast rolling bearing fault diagnosis is required for the normal operation of rotating machinery and equipment. Although deep learning methods have achieved excellent results for rolling bearing fault diagnosis, the performance of most me...

A New Method of Image Classification Based on Domain Adaptation.

Sensors (Basel, Switzerland)
Deep neural networks can learn powerful representations from massive amounts of labeled data; however, their performance is unsatisfactory in the case of large samples and small labels. Transfer learning can bridge between a source domain with rich s...

Inferring RNA-binding protein target preferences using adversarial domain adaptation.

PLoS computational biology
Precise identification of target sites of RNA-binding proteins (RBP) is important to understand their biochemical and cellular functions. A large amount of experimental data is generated by in vivo and in vitro approaches. The binding preferences det...

Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics.

PLoS biology
The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to em...

Online Domain Adaptation for Rolling Bearings Fault Diagnosis with Imbalanced Cross-Domain Data.

Sensors (Basel, Switzerland)
Traditional machine learning methods rely on the training data and target data having the same feature space and data distribution. The performance may be unacceptable if there is a difference in data distribution between the training and target data...

Adaptive Contrastive Learning with Label Consistency for Source Data Free Unsupervised Domain Adaptation.

Sensors (Basel, Switzerland)
Unsupervised domain adaptation, which aims to alleviate the domain shift between source domain and target domain, has attracted extensive research interest; however, this is unlikely in practical application scenarios, which may be due to privacy iss...

MSGAN: Multi-Stage Generative Adversarial Networks for Cross-Modality Domain Adaptation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Domain adaptation has become an important topic because the trained neural networks from the source domain generally perform poorly in the target domain due to domain shifts, especially for cross-modality medical images. In this work, we present a ne...

Stiffness Adaptation of a Hybrid Soft Surgical Robot for Improved Safety in Interventional Surgery.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Minimally invasive instruments are inserted per-cutaneously and are steered toward the desired anatomy. The low stiffness of instruments is an advantage; however, once the target is reached, the instrument usually is required to transmit force to the...

Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey.

Sensors (Basel, Switzerland)
Sensors are devices that output signals for sensing physical phenomena and are widely used in all aspects of our social production activities. The continuous recording of physical parameters allows effective analysis of the operational status of the ...