scholarly journals Bayesian Feature Fusion Using Factor Graph in Reduced Normal Form

2021 ◽  
Vol 11 (4) ◽  
pp. 1934
Author(s):  
Amedeo Buonanno ◽  
Antonio Nogarotto ◽  
Giuseppe Cacace ◽  
Giovanni Di Gennaro ◽  
Francesco A. N. Palmieri ◽  
...  
Keyword(s):  
Normal Form ◽  
Information Fusion ◽  
Missing Values ◽  
Feature Fusion ◽  
Information Source ◽  
Factor Graph ◽  
Probabilistic Framework ◽  
Great Flexibility ◽  
Probabilistic Network ◽  
Identity Document

In this work, we investigate an Information Fusion architecture based on a Factor Graph in Reduced Normal Form. This paradigm permits to describe the fusion in a completely probabilistic framework and the information related to the different features are represented as messages that flow in a probabilistic network. In this way we build a sort of context for observed features conferring to the solution a great flexibility for managing different type of features with wrong and missing values as required by many real applications. Moreover, modifying opportunely the messages that flow into the network, we obtain an effective way to condition the inference based on the different reliability of each information source or in presence of single unreliable signal. The proposed architecture has been used to fuse different detectors for an identity document classification task but its flexibility, extendibility and robustness make it suitable to many real scenarios where the signal can be wrongly received or completely missing.

scholarly journals Application of Two-Level Joint Information Fusion Model in Intelligent Vehicle

2016 ◽  
Vol 12 (03) ◽  
pp. 77
Author(s):  
Yan Ting Lan ◽  
Jiinying Huang ◽  
Xiaodong Chen
Keyword(s):  
Information Fusion ◽  
Feature Fusion ◽  
Target Identification ◽  
Information Source ◽  
Evidence Theory ◽  
Intelligent Vehicles ◽  
Fusion Model ◽  
Multiple Features ◽  
Model Combining ◽  
Real Vehicle

This paper proposes a two-level joint information fusion model combining BP neural network and D-S evidence theory. The model of great practical value reduces target identification error probability by multiple features of the target information, shows good scalability with its two steps of information fusion model, and conveniently increases/reduces feature fusion information source according to different situations and different objects. The method used for intelligent vehicles has good flexibility and robustness in tracking and avoiding obstacle. The simulation and real vehicle tests have verified effectiveness of the method.

scholarly journals Variable Selection and Parameter Tuning for BART Modeling in the Fragile Families Challenge

2019 ◽  
Vol 5 ◽  
pp. 237802311982588 ◽  
Author(s):  
Nicole Bohme Carnegie ◽  
James Wu
Keyword(s):  
Variable Selection ◽  
Missing Values ◽  
Linear Models ◽  
Parameter Tuning ◽  
Black Box ◽  
Machine Learning Algorithms ◽  
Outcome Variable ◽  
Fragile Families ◽  
Great Flexibility ◽  
Additive Regression

Our goal for the Fragile Families Challenge was to develop a hands-off approach that could be applied in many settings to identify relationships that theory-based models might miss. Data processing was our first and most time-consuming task, particularly handling missing values. Our second task was to reduce the number of variables for modeling, and we compared several techniques for variable selection: least absolute selection and shrinkage operator, regression with a horseshoe prior, Bayesian generalized linear models, and Bayesian additive regression trees (BART). We found minimal differences in final performance based on the choice of variable selection method. We proceeded with BART for modeling because it requires minimal assumptions and permits great flexibility in fitting surfaces and based on previous success using BART in black-box modeling competitions. In addition, BART allows for probabilistic statements about the predictions and other inferences, which is an advantage over most machine learning algorithms. A drawback to BART, however, is that it is often difficult to identify or characterize individual predictors that have strong influences on the outcome variable.

Real-time pixel-wise grasp affordance prediction based on multi-scale context information fusion

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yongxiang Wu ◽  
Yili Fu ◽  
Shuguo Wang
Keyword(s):  
Information Fusion ◽  
Real World ◽  
Feature Fusion ◽  
Detection Performance ◽  
Context Information ◽  
Data Sets ◽  
Robotic Grasping ◽  
Content Type ◽  
Scale Feature ◽  
Multi Scale

Purpose This paper aims to use fully convolutional network (FCN) to predict pixel-wise antipodal grasp affordances for unknown objects and improve the grasp detection performance through multi-scale feature fusion. Design/methodology/approach A modified FCN network is used as the backbone to extract pixel-wise features from the input image, which are further fused with multi-scale context information gathered by a three-level pyramid pooling module to make more robust predictions. Based on the proposed unify feature embedding framework, two head networks are designed to implement different grasp rotation prediction strategies (regression and classification), and their performances are evaluated and compared with a defined point metric. The regression network is further extended to predict the grasp rectangles for comparisons with previous methods and real-world robotic grasping of unknown objects. Findings The ablation study of the pyramid pooling module shows that the multi-scale information fusion significantly improves the model performance. The regression approach outperforms the classification approach based on same feature embedding framework on two data sets. The regression network achieves a state-of-the-art accuracy (up to 98.9%) and speed (4 ms per image) and high success rate (97% for household objects, 94.4% for adversarial objects and 95.3% for objects in clutter) in the unknown object grasping experiment. Originality/value A novel pixel-wise grasp affordance prediction network based on multi-scale feature fusion is proposed to improve the grasp detection performance. Two prediction approaches are formulated and compared based on the proposed framework. The proposed method achieves excellent performances on three benchmark data sets and real-world robotic grasping experiment.

scholarly journals Upper Stage Visual Inertial Integrated Navigation Method Based on Factor Graph

2021 ◽  
Vol 2085 (1) ◽  
pp. 012018
Author(s):  
Peng Wu ◽  
Rongjun Mu ◽  
Bingli Liu
Keyword(s):  
Information Fusion ◽  
Inertial Sensors ◽  
Joint Probability ◽  
Graph Model ◽  
Factor Graph ◽  
Joint Probability Distribution ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Upper Stage ◽  
Navigation Information

Abstract In the working process of the upper stage integrated navigation information fusion system, the multi-source navigation information fusion algorithm based on factor graph Bayesian estimation is used to fuse the information of inertial sensors, visual sensors and other sensors. The overall joint probability distribution of the system is described in the form of probability graph model with the dependence of local variables, so as to reduce the complexity of the system, adjust the data structure of information fusion to improve the efficiency of information fusion and smoothly switch the sensor configuration.

scholarly journals Research Based on Multimodal Deep Feature Fusion for the Auxiliary Diagnosis Model of Infectious Respiratory Diseases

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Jingyuan Zhao ◽  
Liyan Yu ◽  
Zhuo Liu
Keyword(s):  
Respiratory Diseases ◽  
Pathogenic Bacteria ◽  
Missing Values ◽  
Feature Fusion ◽  
Clinical Manifestations ◽  
Test Results ◽  
Multimodal Data ◽  
Deep Feature ◽  
Laboratory Test Results ◽  
Diagnosis Model

Pulmonary infection is a common clinical respiratory tract infectious disease with a high incidence rate and a severe mortality rate as high as 30%–50%, which seriously threatens human life and health. Accurate and timely anti-infective treatment is the key to improving the cure rate. NGS technology provides a new, fast, and accurate method for pathogenic diagnosis, which can provide effective clues to the clinic, but determining the true pathogenic bacteria is a problem that needs to be solved urgently, and a comprehensive judgment must be made by the clinician combining the laboratory results, clinical information, and epidemiology. This paper intends to effectively collect and process the missing values of NGS data, clinical manifestations, laboratory test results, imaging test results, and other multimodal data of patients with infectious respiratory diseases. It also studies the deep feature fusion algorithm of multimodal data, couples the private and shared features of different modal data of infectious respiratory diseases, and digs into the hidden information of different modalities to obtain efficient and robust shared features that are conducive to auxiliary diagnosis. The establishment of an auxiliary diagnosis model for the infectious respiratory diseases can intelligentize and automate the diagnosis process of infectious respiratory, which has important significance and application value when applied to clinical practice.

scholarly journals Data Analysis of Magnetic Flux Leakage Detection Based on Multi-Source Information Fusion

2020 ◽  
Author(s):  
Weilin Shao ◽  
Ming Sun ◽  
Yilai Ma ◽  
Jinzhong Chen ◽  
Xiaowei Kang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Magnetic Flux ◽  
Information Fusion ◽  
Information Source ◽  
Magnetic Flux Leakage ◽  
Leakage Detection ◽  
Source Information ◽  
Source Data ◽  
Fusion Analysis ◽  
Flux Leakage

For the analysis of the magnetic flux leakage detection data in pipelines, a single information source data analysis method is used to determine the pipeline characteristics with uncertainty. A multi-source information fusion data analysis technology is proposed. This paper makes full use of the information collected by the multi-source sensors of the magnetic leakage internal detector, and adopts distributed and centralized multi-source information fusion analysis technology. First, pre-analyze and judge the information data of the auxiliary sensors (speed, pressure, temperature) of the internal magnetic flux leakage detector. Then, the data of the main sensor, ID / OD sensor, axial mileage sensor, and circumferential clock sensor of the magnetic flux leakage detector are analyzed separately. Finally, the RBF neural network + least squares support vector machine (LSSVM)fusion analysis technology is adopted to realize the fusion analysis of multi-source information. The results show that this method can effectively improve the quality and reliability of data analysis compared with traditional single information source data analysis.

A probabilistic framework for image information fusion with an application to mammographic analysis

2012 ◽  
Vol 16 (4) ◽  
pp. 865-875 ◽  
Author(s):  
Marina Velikova ◽  
Peter J.F. Lucas ◽  
Maurice Samulski ◽  
Nico Karssemeijer
Keyword(s):  
Information Fusion ◽  
Probabilistic Framework ◽  
Image Information

scholarly journals A Novel Sleep Staging Network Based on Data Adaptation and Multimodal Fusion

2021 ◽  
Vol 15 ◽  
Author(s):  
Lijuan Duan ◽  
Mengying Li ◽  
Changming Wang ◽  
Yuanhua Qiao ◽  
Zeyu Wang ◽  
...  
Keyword(s):  
Single Channel ◽  
Feature Fusion ◽  
Multimodal Fusion ◽  
Computer Assisted ◽  
Sleep Staging ◽  
Time Frequency ◽  
Probabilistic Network ◽  
Sleep Analysis ◽  
High Layer ◽  
3D Cnn

Sleep staging is one of the important methods to diagnosis and treatment of sleep diseases. However, it is laborious and time-consuming, therefore, computer assisted sleep staging is necessary. Most of the existing sleep staging researches using hand-engineered features rely on prior knowledges of sleep analysis, and usually single channel electroencephalogram (EEG) is used for sleep staging task. Prior knowledge is not always available, and single channel EEG signal cannot fully represent the patient’s sleeping physiological states. To tackle the above two problems, we propose an automatic sleep staging network model based on data adaptation and multimodal feature fusion using EEG and electrooculogram (EOG) signals. 3D-CNN is used to extract the time-frequency features of EEG at different time scales, and LSTM is used to learn the frequency evolution of EOG. The nonlinear relationship between the High-layer features of EEG and EOG is fitted by deep probabilistic network. Experiments on SLEEP-EDF and a private dataset show that the proposed model achieves state-of-the-art performance. Moreover, the prediction result is in accordance with that from the expert diagnosis.

scholarly journals Optimized realization of Bayesian networks in reduced normal form using latent variable model

2021 ◽  
Author(s):  
Giovanni Di Gennaro ◽  
Amedeo Buonanno ◽  
Francesco A. N. Palmieri
Keyword(s):  
Normal Form ◽  
Bayesian Networks ◽  
Latent Variable ◽  
Learning Algorithm ◽  
Latent Variable Model ◽  
Factor Graph ◽  
Variable Model ◽  
Multi Level ◽  
Online Learning Algorithm ◽  
Direct Use

AbstractBayesian networks in their Factor Graph Reduced Normal Form are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In this work, through a detailed algorithmic and structural analysis, various solutions for cost reduction are proposed. Moreover, an online version of the classic batch learning algorithm is also analysed, showing very similar results in an unsupervised context but with much better performance; which may be essential if multi-level structures are to be built. The solutions proposed, together with the possible online learning algorithm, are included in a C++ library that is quite efficient, especially if compared to the direct use of the well-known sum-product and Maximum Likelihood algorithms. The results obtained are discussed with particular reference to a Latent Variable Model structure.

Sign in / Sign up

Export Citation Format

Share Document