2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)最新文献

Data analytics development of FDR (Flight Data Recorder) data for airline maintenance operations 为航空公司维修操作开发FDR(飞行数据记录器)数据分析

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-12-11 DOI: 10.1109/MFI.2017.8170443

Chang-hun Lee, Hyo-Sang Shin, A. Tsourdos, Z. Skaf

{"title":"Data analytics development of FDR (Flight Data Recorder) data for airline maintenance operations","authors":"Chang-hun Lee, Hyo-Sang Shin, A. Tsourdos, Z. Skaf","doi":"10.1109/MFI.2017.8170443","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170443","url":null,"abstract":"In this article, we propose a data analytics development to detect unusual patterns of flights from a vast amounts of FDR (flight data recorder) data for supporting airline maintenance operations. A fundamental rationale behind this development is that if there are potential issues on mechanical parts of an aircraft during a flight, evidences for these issues are most likely included in the FDR data. Therefore, the data analysis of FDR data enables us to detect the potential issues in the aircraft before they occur. To this end, in a data pre-processing step, a data filtering, a data sampling, and a data transformation are sequentially performed. And then, in this analysis, all time series data in the FDR are classified into three types: a continuous signal, a discrete signal, and a warning signal. For each type of signal, a high-dimensional vector by arranging the time series data is chosen as features. In the feature section process, a correlation analysis, a correlation relaxation, and a dimension reduction are sequentially conducted. Finally, a type of k-nearest neighbor approach is applied to automatically identify the FDR data in which the unusual flight patterns are recorded from a large amount of FDR data. The proposed method is tested with using a realistic FDR data from the NASA's open database.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121732754","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

Deep reinforcement learning algorithms for steering an underactuated ship 操纵欠驱动船舶的深度强化学习算法

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-12-11 DOI: 10.1109/MFI.2017.8170388

Le Pham Tuyen, Md. Abu Layek, Ngo Anh Vien, TaeChoong Chung

引用次数: 18

Underwater Terrain Navigation Using Standard Sea Charts and Magnetic Field Maps 使用标准海图和磁场图进行水下地形导航

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-12-11 DOI: 10.1109/MFI.2017.8170410

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

{"title":"Underwater Terrain Navigation Using Standard Sea Charts and Magnetic Field Maps","authors":"M. Lager, E. A. Topp, J. Malec","doi":"10.1109/MFI.2017.8170410","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170410","url":null,"abstract":"Many ships today rely on Global Navigation Satellite Systems (GNSS), for their navigation, where GPS (Global Positioning System) is the most well-known. Unfortunately, the GNSS systems make the ships dependent on external systems, which can be malfunctioning, be jammed or be spoofed. There are today some proposed techniques where, e.g., bottom depth measurements are compared with known maps using Bayesian calculations, which results in a position estimation. Both maps and navigational sensor equipment are used in these techniques, most often relying on high-resolution maps, with the accuracy of the navigational sensors being less important. Instead of relying on high-resolution maps and low accuracy navigation sensors, this paper presents an implementation of the opposite, namely using low-resolution maps, but compensating this by using high accuracy navigational sensors and fusing data from both bottom depth measurements and magnetic field measurements. The results from the simulated tests, described in this paper, show that the position error is below 25m throughout the whole test, and that the mean of the error is below 13m, which in most cases would be accurate enough to use for navigation.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124796705","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

Musculoskeletal model of a pregnant woman considering stretched rectus abdominis and co-contraction muscle activation 考虑拉伸腹直肌和共收缩肌激活的孕妇肌肉骨骼模型

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-12-07 DOI: 10.1109/MFI.2017.8170362

S. Morino, Masaki Takahashi

{"title":"Musculoskeletal model of a pregnant woman considering stretched rectus abdominis and co-contraction muscle activation","authors":"S. Morino, Masaki Takahashi","doi":"10.1109/MFI.2017.8170362","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170362","url":null,"abstract":"Weight gain and stretched and weakens abdominal muscles by an enlarging gravid uterus are remarkable features during pregnancy. These changes elicit postural and movement instability and place strain on various body segments. In general, agonist and antagonist muscles of body segments act simultaneously to increase joint stabilization during human movements. The co-contraction might be well observed in pregnant women because of their unstable body joints. Musculoskeletal models are well used to investigate muscle load. However, very few studies have been conducted on the model for pregnant women. Additionally, it is difficult to estimate the co-contraction of muscles by using musculoskeletal models. Therefore, the purpose of this study is to construct musculoskeletal model for pregnant women and estimate co-contraction of trunk muscles. At first, motion analysis of sit-to-stand for a pregnant woman was conducted to obtain the motion and force data for inputting to musculoskeletal model. Simultaneously, muscle activation of rectus abdominis and longissimus were measured by using surface electromyography. On the other hand, the size and mass of body segment of a musculoskeletal model were changed to meet pregnant women. Then, stretched abdominal muscle was modeled. At last, the co-contraction of rectus abdominis and longissimus was estimated from EMG data and joint torque that was calculated from the musculoskeletal model using genetic algorithm.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134628947","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

Compressive sensing based data collection in wireless sensor networks 基于压缩感知的无线传感器网络数据采集

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-18 DOI: 10.1109/MFI.2017.8170360

A. Masoum, N. Meratnia, P. Havinga

引用次数: 1

fNIRS motion artifact correction for overground walking using entropy based unbalanced optode decision and wavelet regression neural network 基于熵不平衡光电判决和小波回归神经网络的近红外运动伪影校正

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-16 DOI: 10.1109/MFI.2017.8170427

Gihyoun Lee, S. Jin, Seung Hyun Lee, B. Abibullaev, J. An

{"title":"fNIRS motion artifact correction for overground walking using entropy based unbalanced optode decision and wavelet regression neural network","authors":"Gihyoun Lee, S. Jin, Seung Hyun Lee, B. Abibullaev, J. An","doi":"10.1109/MFI.2017.8170427","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170427","url":null,"abstract":"Functional near-infrared spectroscopy (fNIRS) can be employed to investigate brain activation by measuring the absorption of near-infrared light through an intact skull. fNIRS can measure hemoglobin signals, which are similar to functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) signals. The general linear model (GLM), which is a standard method for fMRI imaging, has been applied for fNIRS imaging analysis. However, when the subject moves, the fNIRS signal can contain artifacts during the measurement. These artifacts are called motion artifacts. However, the GLM has a drawback of failure because of motion artifacts. Recently, wavelet and hemodynamic response function based algorithms are popular detrending methods of motion artifact correction for fNIRS signals. However, these methods cannot show impressive performance in harsh environments such as overground walking tasks. This paper suggests a new motion artifact correction method that uses an entropy based unbalanced optode decision rule and a wavelet regression based back propagation neural network. Through the experiments, the performance of the proposed method was proven using graphic results, a brain activation map, and an objective performance index when compared with conventional detrending algorithms.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127105595","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

Explainable sleep quality evaluation model using machine learning approach 使用机器学习方法的可解释睡眠质量评估模型

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-16 DOI: 10.1109/MFI.2017.8170377

Rock-Hyun Choi, Won-Seok Kang, C. Son

引用次数: 6

State estimation in networked control systems with delayed and lossy acknowledgments 具有延迟和有损确认的网络控制系统的状态估计

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-16 DOI: 10.1109/MFI.2017.8170359

Florian Rosenthal, B. Noack, U. Hanebeck

引用次数: 7

Relevance and redundancy as selection techniques for human-autonomy sensor fusion 相关度和冗余度作为人类自主传感器融合的选择技术

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-16 DOI: 10.1109/MFI.2017.8170409

Justin D. Brody, Anna M. R. Dixon, Daniel Donavanik, R. Robinson, W. Nothwang

{"title":"Relevance and redundancy as selection techniques for human-autonomy sensor fusion","authors":"Justin D. Brody, Anna M. R. Dixon, Daniel Donavanik, R. Robinson, W. Nothwang","doi":"10.1109/MFI.2017.8170409","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170409","url":null,"abstract":"Human-autonomy teaming using physiological sensors poses a novel sensor fusion problem due to the dynamic nature of the sensor models and the difficulty of modeling their temporal and inter-subject variability. Developing analytical models therefore requires defining objective criteria for selection and weighting of sensors under an appropriate fusion paradigm. We investigate a selection methodology grounded in two intuitions: 1) that maximizing the relevance between sensors and target classes will enhance overall performance within a given fusion scheme; and 2) that minimizing redundancy amongst the selected sensors will not harm fusion performance and may improve precision and recall. We apply these intuitions to a human-autonomy image classification task. Preliminary results indicate strong support for the relevance hypothesis and weaker effects for the redundancy hypothesis. This relationship and its application to human-autonomy sensor fusion are explored within a framework employing three common fusion methodologies: Naive Bayes fusion, Dempster-Shafer theory, and Dynamic Belief Fusion.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125412752","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

F-formation based navigation planner for a mobile servant robot 基于f队形的移动服务型机器人导航规划器

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170398

Sujeong You, S. Ji

引用次数: 1