ArticleSensors (Basel, Switzerland)2020
Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Research on Loosening Fault Diagnosis Method of Escalator Drive Mainframe Anchor Bolts Based on Improved High-Strength Denoising RCDAE Model.Sensors (Basel, Switzerland) · 2025Article
- Accuracy-Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection.Sensors (Basel, Switzerland) · 2025Article
- Collaborative multiview time series modeling for vehicle maintenance demand prediction.Scientific reports · 2025Article
- An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma.Scientific reports · 2023Article
- From genetic correlations of Alzheimer's disease to classification with artificial neural network models.Functional & integrative genomics · 2023Article
- GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems.Sensors (Basel, Switzerland) · 2023Article
- Benign and Malignant Breast Tumor Classification in Ultrasound and Mammography Images via Fusion of Deep Learning and Handcraft Features.Entropy (Basel, Switzerland) · 2023Article
- EDLM: Ensemble Deep Learning Model to Detect Mutation for the Early Detection of Cholangiocarcinoma.Genes · 2023Article
- Deep Learning with Attention Mechanisms for Road Weather Detection.Sensors (Basel, Switzerland) · 2023Article
- Optimization of Work Environment and Community Labor Health Based on Digital Model-Empirical Evidence from Developing Countries.International journal of environmental research and public health · 2022Article
- Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations.International journal of molecular sciences · 2022Article
- Computed Tomography Texture Features and Risk Factor Analysis of Postoperative Recurrence of Patients with Advanced Gastric Cancer after Radical Treatment under Artificial Intelligence Algorithm.Computational intelligence and neuroscience · 2022Article
- Psychological Mobilization of Innovative Teaching Methods for Students' Basic Educational Curriculum Reform Under Deep Learning.Frontiers in psychology · 2022Article
- Predictive Maintenance: An Autoencoder Anomaly-Based Approach for a 3 DoF Delta Robot.Sensors (Basel, Switzerland) · 2021Article
- Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series.Sensors (Basel, Switzerland) · 2021Article
- Deep Learning-Based Image Feature with Arthroscopy-Aided Early Diagnosis and Treatment of Meniscus Injury of Knee Joint.Journal of healthcare engineering · 2021Article
- Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals.Sensors (Basel, Switzerland) · 2020Article
- Cross-Entropy as a Metric for the Robustness of Drone Swarms.Entropy (Basel, Switzerland) · 2020Article
- Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges.Sensors (Basel, Switzerland) · 2020Review
- Reply.Ophthalmology scienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model's architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system's prognostics and diagnostics without modifying the models' architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.