2021 IEEE 24th International Conference on Information Fusion (FUSION)最新文献_第6页

Handling Traceability in Graph Fusion for a Trustworthy Framework 基于可信框架的图融合可追溯性处理

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627035

Charlotte Jacobé de Naurois, C. Laudy

引用次数: 0

Improving the performance of Transformer Context Encoders for NER 改进面向NER的变压器上下文编码器的性能

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627061

Avi Chawla, Nidhi Mulay, Vikas Bishnoi, Gaurav Dhama

{"title":"Improving the performance of Transformer Context Encoders for NER","authors":"Avi Chawla, Nidhi Mulay, Vikas Bishnoi, Gaurav Dhama","doi":"10.23919/fusion49465.2021.9627061","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627061","url":null,"abstract":"Large Transformer based models have provided state-of-the-art results on a variety of Natural Language Processing (NLP) tasks. While these Transformer models perform exceptionally well on a wide range of NLP tasks, their usage in Sequence Labeling has been mostly muted. Although pretrained Transformer models such as BERT and XLNet have been successfully employed as input representation, the use of the Transformer model as a context encoder for sequence labeling is still minimal, and most recent works still use recurrent architecture as the context encoder. In this paper, we compare the performance of the Transformer and Recurrent architecture as context encoders on the Named Entity Recognition (NER) task. We vary the character-level representation module from the previously proposed NER models in literature and show how the modification can improve the NER model’s performance. We also explore data augmentation as a method for enhancing their performance. Experimental results on three NER datasets show that our proposed techniques established a new state-of-the-art using the Transformer Encoder over the previously proposed models in the literature using only non-contextualized embeddings.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116674294","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

A Gaussian-inverse Gamma mixture Distributions and Expectation-Maximization Based Robust Kalman Filter 基于期望最大化的高斯-逆伽玛混合分布鲁棒卡尔曼滤波

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626831

Hongpo Fu, Yong-mei Cheng, Cheng Cheng

{"title":"A Gaussian-inverse Gamma mixture Distributions and Expectation-Maximization Based Robust Kalman Filter","authors":"Hongpo Fu, Yong-mei Cheng, Cheng Cheng","doi":"10.23919/fusion49465.2021.9626831","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626831","url":null,"abstract":"In the study of the state estimation for the systems with unknown time-varying non-Gaussian noises, the existing robust Kalman filters (RKFs) perform well. However, the calculation loads of these RKFs usually are large and their performance is easily affected by the roughly preselected initial process noise covariance matrix (PNCM). To solve the problems, a new RKF is proposed. Firstly, a Gaussian-inverse Gamma mixture distribution is developed to model the inaccurate noises and a simple hierarchical Gaussian (HG) model is constructed. Then, the expectation-maximization (EM) method is applied to realize the adaptive adjustment of the prior scale matrix of the prediction error covariance. Based on the HG model and EM, a robust KF is derived, where the variational Bayesian (VB) approach is used to jointly estimate model parameters and an alternate iteration method is employed to reduce the computation time. Finally, our filter performance is tested. Compared with the existing state-of-the-art robust filters, the proposed filter has slightly better estimation accuracy and significantly less computation load. Meanwhile, the filter performance is almost not affected by the selection accuracy of initial PNCM.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123503033","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

Filtering and sensor optimization applied to angle-only navigation 滤波和传感器优化在纯角度导航中的应用

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626988

C. Musso, F. Dambreville, C. Chahbazian

引用次数: 1

Track-before-detect Bernoulli filters for combining passive and active sensors 结合无源和有源传感器的检测前跟踪伯努利滤波器

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626922

M. J. Ransom, M. Hernandez, J. Ralph, S. Maskell

{"title":"Track-before-detect Bernoulli filters for combining passive and active sensors","authors":"M. J. Ransom, M. Hernandez, J. Ralph, S. Maskell","doi":"10.23919/fusion49465.2021.9626922","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626922","url":null,"abstract":"This paper is concerned with the implementation of track-before-detect (TkBD) algorithms for a range of single-target multi-sensor scenarios with only intermittently visible targets. Visible targets generate measurements from sensors characterised by data rate and clutter density. Bernoulli filters implementing multiple hypothesis tracking (MHT) strategies are deployed to infer both the target location and existence probability. Various Bernoulli filter configurations are compared, including integrated probabilistic data association filters (IPDAF) and integrated expected likelihood particle filters (IELPF) using both prior and Gaussian mixture proposal distributions for the latter. Performance is evaluated against the clutter density in scenarios featuring one low data rate active sensor or two sensors, complimenting the former with a high data rate passive sensor with opposing measurement resolutions. The performance measures used are the area under the receiver operating characteristic (ROC) curve, localisation root mean squared error (RMSE) compared with the posterior Cramér-Rao lower bound (PCRLB), and computation time. Simulation results show that Kalman filters provide an effective solution at low computational expense in less cluttered and comparatively easy scenarios, whereas particle filters implementing Gaussian mixture proposal distributions provide performance benefits relative to computational costs as scenarios become more cluttered and comparatively challenging.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126180931","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

Counting Technique versus Single-Time Test for Track-to-Track Association 赛道对赛道关联的计数技术与单次测试

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626911

Jonas Åsnes Sagild, Audun Gullikstad Hem, E. Brekke

引用次数: 4

An IMM-Enabled Adaptive 3D Multi-Object Tracker for Autonomous Driving 一种支持imm的自动驾驶自适应3D多目标跟踪器

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626913

Peng Liu, Z. Duan

引用次数: 0

Forecasting pharmacy purchases orders 预测药房采购订单

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627017

B. K. Almentero, Jiye Li, C. Besse

引用次数: 3

Accelerated Design of a Conformal Strongly Coupled Magnetic Resonance Wireless Power Transfer 共形强耦合磁共振无线电力传输的加速设计

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626981

Makhetha Molefi, E. Markus, A. Abu-Mahfouz

引用次数: 2

Multi-Criteria Information Fusion for Storm Prediction Based on Belief Functions 基于信念函数的风暴预报多准则信息融合

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626835

J. Dezert, A. Bouchard, M. Buguet

引用次数: 1