ArticleEntropy (Basel, Switzerland)2021
Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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
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Who cites it
13 citing papers in PubMed.
- Causality, Machine Learning, and Feature Selection: A Survey.Sensors (Basel, Switzerland) · 2025Review
- A GM-JMNS-CPHD Filter for Different-Fields-of-View Stochastic Outlier Selection for Nonlinear Motion Tracking.Sensors (Basel, Switzerland) · 2024Article
- An Efficient Implementation Method for Distributed Fusion in Sensor Networks Based on CPHD Filters.Sensors (Basel, Switzerland) · 2023Article
- Gaussian Mixture Cardinalized Probability Hypothesis Density(GM-CPHD): A Distributed Filter Based on the Intersection of Parallel Inverse Covariances.Sensors (Basel, Switzerland) · 2023Article
- The metabolic pathway regulation in kidney injury and repair.Frontiers in physiology · 2023Review
- Special Issue "Complex Dynamic System Modelling, Identification and Control".Entropy (Basel, Switzerland) · 2022Article
- A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting.Entropy (Basel, Switzerland) · 2022Article
- An Information Gain-Based Model and an Attention-Based RNN for Wearable Human Activity Recognition.Entropy (Basel, Switzerland) · 2021Article
- Design and Implementation of an Atmospheric Anion Monitoring System Based on Beidou Positioning.Sensors (Basel, Switzerland) · 2021Article
- Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm.Sensors (Basel, Switzerland) · 2021Article
- Multi-Channel Fusion Classification Method Based on Time-Series Data.Sensors (Basel, Switzerland) · 2021Article
- Error Prediction of Air Quality at Monitoring Stations Using Random Forest in a Total Error Framework.Sensors (Basel, Switzerland) · 2021Article
- The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement's causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network's over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system's big measurement data to improve prediction performance.
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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.