2020 IEEE 23rd International Conference on Information Fusion (FUSION)最新文献_第10页

An IoT Inspired Distributed Data Fusion Architecture for Coastal Surveillance Applications 基于物联网的海岸监测分布式数据融合架构

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190591

J. F. B. Brancalion, S. Dias

引用次数: 0

Progressive Bayesian Filtering with Coupled Gaussian and Dirac Mixtures 高斯和狄拉克混合耦合的渐进贝叶斯滤波

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190540

Daniel Frisch, U. Hanebeck

{"title":"Progressive Bayesian Filtering with Coupled Gaussian and Dirac Mixtures","authors":"Daniel Frisch, U. Hanebeck","doi":"10.23919/FUSION45008.2020.9190540","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190540","url":null,"abstract":"Nonlinear filtering is the most important aspect in state estimation with real-world systems. While the Kalman filter provides a simple though optimal estimate for linear systems, feasible filters for general systems are still subject of intensive research. The previously proposed Progressive Gaussian Filter PGF42 marked a new milestone, as it was able to efficiently compute an optimal Gaussian approximation of the posterior density in nonlinear systems [1]. However, for highly nonlinear systems where true posteriors are “banana-shaped” (e.g., cubic sensor problem) or multimodal (e.g., extended object tracking), even an optimal Gaussian approximation is an inadequate representation. Therefore, we generalize the established framework around the PGF42 from Gaussian to Gaussian mixture densities that are better able to approximate arbitrary density functions. Our filter simultaneously holds approximate Gaussian mixture and Dirac mixture representations of the same density, what we call coupled discrete and continuous densities (CoDiCo). For conversion between discrete and continuous representation, we employ deterministic sampling and the expectation-maximization (EM) algorithm, which we extend to deal with weighted particles.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115556481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Use cases for social data analysis with URREF criteria 使用URREF标准进行社会数据分析的用例

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190619

C. Laudy, V. Dragos

{"title":"Use cases for social data analysis with URREF criteria","authors":"C. Laudy, V. Dragos","doi":"10.23919/FUSION45008.2020.9190619","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190619","url":null,"abstract":"Social data analysis has gained prominence in a wide range of domains as it provides users with the opportunity to communicate and share posts and topics. Automated analysis and reasoning about such data potentially derive meaningful insights, with tremendous potential for applications. However, the sheer volume, noise, and high dynamics of social data impose challenges that hinder the efficacy of algorithms. Automated approaches and classification models require then significant resources to be developed and prove to be often relevant to only a limited number of tasks. Imperfections of inputs, precision of techniques and accuracy of results need to be accounted and assessed as the process runs. This paper discuses two use cases allowing the investigation of implicit and explicit uncertainty arising when processing data gleaned on social media. The objective of this paper is twofold. The first objective is to set up the ETUR use case on social media analysis by adopting two tasks on opinion mining for cyberspace surveillance and information extraction for crisis analysis, respectively. The second objective is to discuss an overall methodology allowing the identification and assessment of uncertainties underlying each task The paper introduces two illustrations of social data analysis, investigates various sources of uncertainty and describes a methodology to select criteria for uncertainty assessment.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121357881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Multimodal Fusion with Co-attention Mechanism 基于共同注意机制的多模态融合

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190483

Pei Li, Xinde Li

引用次数: 6

Real-Time Manufacturing Drilling Operations Analysis by Utilization of Data-Fusion 基于数据融合的实时制造钻井作业分析

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190248

M. Zare, A. Visa, Ville Pärssinen, Hesam Jafarian, Henri Oksman, Liisa Aha

{"title":"Real-Time Manufacturing Drilling Operations Analysis by Utilization of Data-Fusion","authors":"M. Zare, A. Visa, Ville Pärssinen, Hesam Jafarian, Henri Oksman, Liisa Aha","doi":"10.23919/FUSION45008.2020.9190248","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190248","url":null,"abstract":"In mining and construction operations, the protection, safety and machinery's lifetime hold a crucial concern that can impose unwelcoming costs on the projects. The motivation behind this work is to deliver a model capable of addressing these apprehensions besides managing the potential risks and costs of the types of machinery. The presented model in this article aims to increase the quality and reliability of the products and their operations by utilizing sensor information for real-time prediction and categorization of drilling operations. This model works based on the time analyses on the sensory fused data. We applied the model on the three-axis acceleration and angular velocity signals (generated from a simulated system) to extract features and categorize three different rock drilling operations. For each operation, we measured the Median Absolute Deviation (MAD) and dynamic range parameters of the acceleration signals. In addition, we succeeded to calculate the Root Mean Square (RMS) parameter as a feature from angular velocity signals. The obtained results in this study approve the real-time prediction and categorization potential of the introduced approach for the different rock drilling operations. However, the limitation of this work can be the source of the data which is originating from the simulated normal operations. As an extending future work in future publications, we will include the faulty operation data, the real data from measurements and present data analysis of abnormal operations.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122428065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conceptual Space Modeling for Space Event Characterization 空间事件表征的概念空间建模

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190163

Jeremy R. Chapman, David Kasmier, David Limbaugh, Steph Gagnon, J. Crassidis, J. Llinas, Barry Smith, Alexander P. Cox

{"title":"Conceptual Space Modeling for Space Event Characterization","authors":"Jeremy R. Chapman, David Kasmier, David Limbaugh, Steph Gagnon, J. Crassidis, J. Llinas, Barry Smith, Alexander P. Cox","doi":"10.23919/FUSION45008.2020.9190163","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190163","url":null,"abstract":"This paper provides a method for characterizing space events using the framework of conceptual spaces. We focus specifically on estimating and ranking the likelihood of collisions between space objects. The objective is to design an approach for anticipatory decision support for space operators who can take preventive actions on the basis of assessments of relative risk. To make this possible our approach draws on the fusion of both hard and soft data within a single decision support framework. Contextual data is also taken into account, for example data about space weather effects, by drawing on the Space Domain Ontologies, a large system of ontologies designed to support all aspects of space situational awareness. The framework is coupled with a mathematical programming scheme that frames a mathematically optimal approach for decision support, providing a quantitative basis for ranking potential for collision across multiple satellite pairs. The goal is to provide the broadest possible information foundation for critical assessments of collision likelihood.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128400926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Marginal Association Probabilities for Multiple Extended Objects without Enumeration of Measurement Partitions 不列举度量分区的多扩展对象的边际关联概率

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190500

Shishan Yang, Laura M. Wolf, M. Baum

引用次数: 6

Second-Order Learning and Inference using Incomplete Data for Uncertain Bayesian Networks: A Two Node Example 不确定贝叶斯网络中使用不完全数据的二阶学习与推理:一个双节点示例

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190472

Lance M. Kaplan, Federico Cerutti, Murat Sensoy, K. Mishra

引用次数: 3

Data Fusion and Artificial Neural Networks for Modelling Crop Disease Severity 作物病害严重程度数据融合与人工神经网络建模

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190211

Priyamvada Shankar, A. Johnen, M. Liwicki

{"title":"Data Fusion and Artificial Neural Networks for Modelling Crop Disease Severity","authors":"Priyamvada Shankar, A. Johnen, M. Liwicki","doi":"10.23919/FUSION45008.2020.9190211","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190211","url":null,"abstract":"This paper analyzes the possibility of applying data fusion combined with artificial neural networks (ANN) on a dataset combining hard and soft data for prediction of one of the most devastating crop diseases of winter wheat, i.e., Septoria Tritici (Zymoseptoria tritici). In advanced decision support systems for crop protection choices, disease models form a major component. They reproduce the biophysical processes of disease development and temporal spread as a set of rules or processes to predict disease risk value. However, the adaptation of these rules or processes to incorporate the effects of climate change is complex and requires extensive rework. To remedy this issue, statistical machine learning techniques have been introduced to model disease severity percentage for some diseases. However, the use of artificial neural networks has been limited (mainly to image data) and is unexplored for Septoria Tritici. This paper explores the use of Feed Forward neural networks on fused tabular data for the task of disease severity modelling. First, ten years of trial data ranging from 2008 to 2018 across Europe is used for the creation of the new tabular dataset with a fusion of all important data sources baring impact on disease development: Field-specific data, weather data, crop growth stages, and disease severity observation made by human trial operators (response variable). Next, two implementation architectures of Feed Forward neural networks on tabular data are employed: a) standard architecture with backpropagation, drop out regularization, and batch normalization and b) advanced architecture with improvements such as cyclic learning rate and cosine annealing. The advanced architecture is able to better model the data and make estimations of disease severity with a difference of +-10% giving a better quantifiable estimate of disease stress. For better outreach to farmers, a technique to incorporate such modelling techniques into the well established Decision Support Systems is also presented.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121929613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A New Leader-follower Model for Bayesian Tracking 一种新的贝叶斯跟踪的领导者-追随者模型

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190329

Qing Li, S. Godsill

{"title":"A New Leader-follower Model for Bayesian Tracking","authors":"Qing Li, S. Godsill","doi":"10.23919/FUSION45008.2020.9190329","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190329","url":null,"abstract":"This paper introduces a novel leader-follower model for tracking a group of manoeuvring objects under a probabilistic framework. The proposed model develops on the conventional leader-follower model in which the followers are driven stochastically towards the velocity and position of the leader. Here we consider the dynamic of followers as a mean-reverting process and express it in a continuous-time stochastic differential equation. Instead of using a standard global Cartesian or polar system, an intrinsic coordinate model is utilised for the leader where piecewise constant forces are applied relative to the heading of the leader. Followers then mean revert towards the heading angle and speed of the leader, leading to a more realistic behavioural modelling than the more conventional global coordinate systems. Such a dynamical model is readily incorporated into tracking algorithms using for example the variable rate particle filtering framework which can accurately capture and estimate the manoeuvres of the leader and followers. The simulation results verify its efficacy under challenging group tracking scenarios and future work will explore automatic identification of group structure and leadership from measurements of groups of moving objects.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115811580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3