2020 IEEE 23rd International Conference on Information Fusion (FUSION)最新文献_第9页

Towards Neural Situation Evolution Modeling: Learning a Distributed Representation for Predicting Complex Event Sequences 面向神经情境演化建模:学习一种预测复杂事件序列的分布式表示

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190165

Andrea Salfinger, L. Snidaro

{"title":"Towards Neural Situation Evolution Modeling: Learning a Distributed Representation for Predicting Complex Event Sequences","authors":"Andrea Salfinger, L. Snidaro","doi":"10.23919/FUSION45008.2020.9190165","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190165","url":null,"abstract":"In real-world monitoring tasks, a situation can be understood as a sequence of causally related events of interest. In road traffic control, such a situation could be a rear-end collision at the end of a traffic jam, which worsens congestion and requires clearing operations and potentially rerouting. Whereas conventional event sequence prediction focuses on sequences of individual events $langle e_{1}, ldots, e_{n}rangle$, evolving situations thus can be conceived as sequences of states composed of multiple concurrent events, i.e., complex events: $langle{e_{1},ldots, e_{m}}, ldots, {e_{l},ldots, e_{n}}rangle$. Situation (evolution) prediction thus requires learning a transition model for these complex events to provide the expectations for potential successor event types. In previous work, this was represented by a Markov Chain defined on the observed complex events. However, using the entire event composite as “atomic” situation state representation does not allow capturing patterns between its individual events (e.g., events of type “accident” share similar successor event types across different event composites), nor generalizing behaviors between similar event types or incorporating additional features. Hence, we propose a neural modeling approach to learn a distributed representation of a given situation dataset. By encoding the input states as conjunction of their individual comprised events, the devised model can learn associations (i.e., enable an “information flow”) between individual event types, allowing to capture similar behaviors across different situations. We test our approach on both synthetic and real-world datasets.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"68 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":"124449201","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

Covariance Estimation for Factor Graph Based Bayesian Estimation 基于贝叶斯估计的因子图协方差估计

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190223

O. A. Vanli, Clark N. Taylor

引用次数: 1

Computationally Efficient Methods for Estimating Unknown Input Forces on Structural Systems 结构系统未知输入力估计的高效计算方法

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190270

Hoa Van Nguyen, D. Ranasinghe, A. Skvortsov, S. Arulampalam

{"title":"Computationally Efficient Methods for Estimating Unknown Input Forces on Structural Systems","authors":"Hoa Van Nguyen, D. Ranasinghe, A. Skvortsov, S. Arulampalam","doi":"10.23919/FUSION45008.2020.9190270","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190270","url":null,"abstract":"We consider the problem of estimating unknown input forces on structural systems using only noisy acceleration measurement data. This is an important task for condition monitoring, for example, to predict fatigue damage in a structure's body or to reduce transmission of vibrations in marine vessels. In this paper, we propose a new idea to estimate an input force with a sinusoidal form by formulating a force identification problem without a direct feed-through system. Consequently, the minimum variance unbiased (MVU) filter can be implemented coupled with a fast Fourier transform algorithm to estimate unknown input forces accurately in real-time. Moreover, when the input force is completely unknown, the ensemble sampling method combined with an augmented Kalman filter can be formulated to significantly reduce computation time. Experimental results confirm the effectiveness of our proposed methods and show that the formulations investigated outperform other state-of-the-art methods in term of computational cost whilst not compromising estimation performance.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"20 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":"121303904","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: 1

Representing and updating objects' identities in semantic SLAM 语义SLAM中对象身份的表示和更新

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190524

Or Tslil, Amit Elbaz, Tal Feiner, Avishy Carmi

引用次数: 1

Development of the UFRJ Nautilus' AUV: A Multisensor Data Fusion case study UFRJ Nautilus水下航行器的发展:多传感器数据融合案例研究

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190478

S. Simplicio, H. Ferreira, Gustavo Villela, Felipe B. Costa, Vitor Pavani, Luma Rodrigues, C. Farias

引用次数: 1

Position and Speed Estimation for BLDC Motors Using Fourier-Series Regression 基于傅里叶级数回归的无刷直流电机位置和速度估计

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190271

Ajit Basarur, Jana Mayer, Antonio Zea, U. Hanebeck

{"title":"Position and Speed Estimation for BLDC Motors Using Fourier-Series Regression","authors":"Ajit Basarur, Jana Mayer, Antonio Zea, U. Hanebeck","doi":"10.23919/FUSION45008.2020.9190271","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190271","url":null,"abstract":"The control of brushless DC motors requires high-resolution angular position and accurate speed information. However, available sensor-based solutions only measure either the position or the speed directly, and then approximate the other numerically. In this work, a novel technique is presented to estimate both of these values simultaneously by sensing the stray magnetic field of the internal permanent magnets of the motor. However, achieving this requires the following two challenges to be addressed. First, the relationship between the magnetic field and the motor position is distorted by the rotational speed in a non-intuitive way, requiring careful modeling of these dependencies. Second, the derived model needs to consider that the angular position data is periodic by nature, but the magnetic field data and the angular speed data are linear (i.e., non-periodic). To achieve this, we introduce two different multidimensional regression models based on the Fourier series. Both models are first trained offline using reference data, and then used as a measurement function in a nonlinear estimator such as the EKF for online estimation. Evaluations show that both models outperform state-of-the-art techniques.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"45 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":"126845387","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

Passive sensor planning for TDOA/FDOA geolocation under communication constraints 通信约束下TDOA/FDOA定位的无源传感器规划

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190509

Hugo Seuté, L. Ratton, Antoine Fagette

引用次数: 2

Unique Animal Identification using Deep Transfer Learning For Data Fusion in Siamese Networks 在暹罗网络中使用深度迁移学习进行数据融合的独特动物识别

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190426

Terence L van Zyl, M. Woolway, B. Engelbrecht

引用次数: 13

A Hyperhemispherical Grid Filter for Orientation Estimation 一种用于方向估计的超半球网格滤波器

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190611

F. Pfaff, Kailai Li, U. Hanebeck

引用次数: 7

Robust Terrain-Aided Navigation through Sensor Fusion 基于传感器融合的鲁棒地形辅助导航

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190578

M. Lager, E. A. Topp, J. Malec

引用次数: 2