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A framework for collaborative computing and multi-sensor data fusion in body sensor networks

INFORMATION fusion 2015年 22卷 50-70页

作者： Fortino, Giancarlo Galzarano, Stefano Gravina, Raffaele Li, Wenfeng Univ Calabria Dept Informat Modeling Elect & Syst Via P Bucci I-87036 Arcavacata Di Rende CS Italy Wuhan Univ Technol Sch Logist Engn Wuhan 430063 Peoples R China

Body sensor Networks (BSNs) have emerged as the most effective technology enabling not only new e-Health methods and systems but also novel applications in human-centered areas such as electronic health care, fitness/welness systems, sport performance monitoring, interactive games, factory workers monitoring, and social physical interaction. Despite their enormous potential, they are currently mostly used only to monitor single individuals. Indeed, BSNs can proactively interact and collaborate to foster novel BSN applications centered on collaborative groups of individuals. In this paper, C-SPINE, a framework for Collaborative BSNs (CBSNs), is proposed. CBSNs are BSNs able to collaborate with each other to fulfill a common goal. They can support the development of novel smart wearable systems for cyberphysical pervasive computing environments. Collaboration therefore relies on interaction and synchronization among the CBSNs and on collaborative distributed computing atop the collaborating CBSNs. Specifically, collaboration is triggered upon CBSN proximity and relies on service-specific protocols allowing for managing services among the collaborating CBSNs. C-SPINE also natively supports multi-sensor data fusion among CBSNs to enable joint data analysis such as filtering, time-dependent data integration and classification. To demonstrate its effectiveness, C-SPINE is used to implement e-Shake, a collaborative CBSN system for the detection of emotions. The system is based on a multisensor data fusion schema to perform automatic detection of handshakes between two individuals and capture of possible heart-rate-based emotion reactions due to the individuals' meeting. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Body sensor networks SPINE Collaborative computing multi-sensor data fusion Emotion detection Handshake detection

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Performance evaluation on contour extraction using Hough transform and RANSAC for multi-sensor data fusion applications in industrial food inspection

Performance evaluation on contour extraction using Hough tra...

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IEEE Signal Processing Algorithms, Architectures, Arrangements, and Applications Conference

作者： M. Kroger W. Sauer-Greff R. Urbansky M. Lorang M. Siegrist Chair for Communications Engeneering University of Kaiserslautern WIPOTEC GmbH

ISBN: (纸本)9781509026616

For multi-sensor data fusion applications the accurate alignment of different sensor data is essential for the proper combination of matching features. In food inspection system the boxing often is in a rectangular shape. This knowledge can be used to rectify the image data, an important step in the alignment stage. In case of low contrast between boxing and background, the detected contour may differ significantly from the actual values. In this paper the performance of the Hough transform and the RANdom SAmple Consensus (RANSAC)-algorithm are evaluated relating to the correct extraction of the boxing contour out of contour data distorted by position errors of the outer shape. The evaluation results indicate the superiority of the RANSAC algorithm with respect to scalability, robustness and execution time.

关键词： Hough transform Food inspection Edge detection boxing multi-sensor data fusion sensor data Performance Evaluation location error contour image data execution time

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The multi-sensor-based measurement of machining signals and data fusion to develop predictive tool wear models for TiAlN-PVD coated carbide inserts during end milling of Inconel 617

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2024年第6-7期238卷 886-903页

作者： Adishesha, Pramod Lawrence, K. Deepak Mathew, Jose Natl Inst Technol Calicut Dept Mech Engn NIT Calicut Campus Kozhikode 673601 Kerala India

Machining signals from the Force dynamometer, Acoustic Emission (AE), and Accelerometer are acquired and fused to develop Machine Learning (ML) models for tool wear monitoring of TiAlN-PVD coated carbide inserts. Milling experiments were performed on Inconel 617 with varied process parameter combinations until the tool flank wear met the failure criterion. Support Vector Regression, Random Forest Regression, and Long Short-Term Memory models are developed and compared based on a combination of sensor data fusion for tool wear predictions. It is observed that the Random Forest Regression approach can predict the tool wear with 94% accuracy compared to Support Vector Regression (85%) and Long Short-Term Memory (84%) models while using three-sensor data fusion. Further, the two-sensor data combination was used to test the relative efficacy of all the three developed machine learning tool wear models and found that the force dynamometer and the AE sensor fared better for Random Forest Regression and Long Short-Term Memory models in comparison to Support Vector Regression. For Support Vector Regression-based tool wear predictive models, force dynamometer and accelerometer data fusion performed better.

关键词： Tool wear monitoring multi-sensor data fusion condition monitoring machine learning

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Low-Cost sensors based multi-sensor data fusion Techniques for RPAS Navigation and Guidance

Low-Cost Sensors based Multi-sensor Data Fusion Techniques f...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Cappello, Francesco Ramasamy, Subramanian Sabatini, Roberto Liu, Jing RMIT Univ Sch Aerosp Mech & Mfg Engn Melbourne Vic 3000 Australia

ISBN: (纸本)9781479960101

In order for Remotely Piloted Aircraft Systems (RPAS) to coexist seamlessly with manned aircraft in non-segregated airspace, enhanced navigational capabilities are essential to meet the Required Navigational Performance (RNP) levels in all flight phases. A multi-sensor data fusion (MSDF) framework is adopted to improve the navigation capabilities of an integrated Navigation and Guidance System (NGS) designed for small-sized RPAS. The MSDF architecture includes low-cost and low weight/volume navigation sensors suitable for various classes of RPAS. The selected sensors include Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based sensors (VBS). A loosely integrated navigation architecture is presented where an Unscented Kalman Filter (UKF) is used to combine the navigation sensor measurements. The presented UKF based VBS-INS-GNSS-ADM (U-VIGA) architecture is an evolution of previous research performed on Extended Kalman Filter (EKF) based VBS-INS-GNSS (E-VIGA) systems. An Aircraft Dynamics Model (ADM) is adopted as a virtual sensor and acts as a knowledge-based module providing additional position and attitude information, which is pre-processed by an additional/local UKF. The E-VIGA and U-VIGA performances are evaluated in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) by exploring a representative cross-section of this RPAS operational flight envelope. The position and attitude accuracy comparison shows that the E-VIGA and U-VIGA systems fulfill the relevant RNP criteria, including precision approach in CAT-II. A novel Human Machine Interface (HMI) architecture is also presented, whose design takes into consideration the coordination tasks of multiple human operators. In addition, the interface scheme incorporates the human operator as an integral part of the control loop providing a higher level of situational awareness.

关键词： Remotely Piloted Aircraft Systems Aircraft Dynamics Model Unscented Kalman Filter Low-cost sensors Navigation and Guidance System multi-sensor data fusion Human Machine Interface

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A comparative study of multi-sensor data fusion methods for highly accurate assessment of manufactured parts 12

A comparative study of multi-sensor data fusion methods for ...

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12th International Conference on Quality Control by Artificial Vision

作者： Hannachi, Ammar Kohler, Sophie Lallement, Alex Hirsch, Ernest Univ Strasbourg Lab Sci Ingn Informat & Imagerie CNRS ICUBEUMR 7357 F-67412 Illkirch Graffenstaden France UHA Lab Modelisat Intelligence Proc Syst MIPS EA 2332 F-68093 Mulhouse France

ISBN: (纸本)9781628416992

3D modeling of scene contents takes an increasing importance for many computer vision based applications. In particular, industrial applications of computer vision require efficient tools for the computation of this 3D information. Routinely, stereo-vision is a powerful technique to obtain the 3D outline of imaged objects from the corresponding 2D images. As a consequence, this approach provides only a poor and partial description of the scene contents. On another hand, for structured light based reconstruction techniques, 3D surfaces of imaged objects can often be computed with high accuracy. However, the resulting active range data in this case lacks to provide data enabling to characterize the object edges. Thus, in order to benefit from the positive points of various acquisition techniques, we introduce in this paper promising approaches, enabling to compute complete 3D reconstruction based on the cooperation of two complementary acquisition and processing techniques, in our case stereoscopic and structured light based methods, providing two 3D data sets describing respectively the outlines and surfaces of the imaged objects. We present, accordingly, the principles of three fusion techniques and their comparison based on evaluation criterions related to the nature of the workpiece and also the type of the tackled application. The proposed fusion methods are relying on geometric characteristics of the workpiece, which favour the quality of the registration. Further, the results obtained demonstrate that the developed approaches are well adapted for 3D modeling of manufactured parts including free-form surfaces and, consequently quality control applications using these 3D reconstructions.

关键词： Stereo-vision structured light multi-sensor data fusion 3D reconstruction quality control

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Design of a new nonlinear model predictive fault tolerant control system using multi-sensor data fusion technique based on UKF algorithm

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2015年第4期34卷 1286-1301页

作者： Ghesmat, Mohammad Khalkhali, Akbar Petr Univ Technol Instrumentat & Automat Dept Ahvaz Iran Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

Purpose - There are high expectations for reliability, safety and fault tolerance are high in chemical plants. Control systems are capable of potential faults in the plant processing systems. This paper proposes is a new Fault Tolerant Control (FTC) system to identify the probable fault occurrences in the plant. Design/methodology/approach - A Fault Diagnosis and Isolation (FDI) module has been devised based on the estimated state of system. An Unscented Kalman Filter (UKF) is the main innovation of the FDI module to identify the faults. A multi-sensor data fusion algorithm is utilized to integrate the UKF output data to enhance fault identification. The UKF employs an augmented state vector to estimate system states and faults simultaneously. A control mechanism is designed to compensate for the undesirable effects of the detected faults. Findings - The performance of the Nonlinear Model Predictive Controller (NMPC) without any fault compensation is compared with the proposed FTC scheme under different fault scenarios. Analysis of the simulation results indicates that the FDI method is able to identify the faults accurately. The proposed FTC approach facilitates recovery of the closed loop performance after the faults have been isolated. Originality/value - A significant contribution of the paper is the design of an FTC system by using UKF to estimate faults and enhance the accuracy of data. This is done by applying a data fusion algorithm and controlling the system by the NMPC after eliminating the effects of faults.

关键词： Non-linear control systems Fault analysis Optimal control Fault tolerance Filters Fault tolerant control system Unscented Kalman Filter multi-sensor data fusion Nonlinear model predictive control

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multi-sensor data fusion FOR REALISTIC AND ACCURATE 3D RECONSTRUCTION 5

MULTI-SENSOR DATA FUSION FOR REALISTIC AND ACCURATE 3D RECON...

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5th European Workshop on Visual Information Processing (EUVIP)

作者： Hannachi, Ammar Kohler, Sophie Lallement, Alex Hirsch, Ernest ICUBE CNRS UdS UMR 7357 CNRS Lab Sci Ingn Informat & Imager F-67412 Illkirch Graffenstaden France Univ Haute Alsace MIPS Lab Modelisat Intelligence Proc Syst EA 2332 F-68093 Mulhouse France

ISBN: (纸本)9781479945726

In this paper, the problem of multi-sensor data fusion for 3D dimensional reconstruction/assessment of manufactured parts is investigated. For this, a method relying on cooperation of two acquisition/processing techniques (stereoscopic and structured light sensors) is presented. The stereoscopic system reconstructs the outline of the object, whereas the structured light system represents the workpiece as points that characterize its 3D surfaces. The developed approach exploits the complementarity of the two acquired data sets. To represent the whole set of measurements in a common reference frame, a fusion method is developed based on the geometric characteristics of the workpiece used to define a cost function, which allows perfect alignment of the two point sets. This is further favored by use of a camera common to the two sensory systems. Experimental results demonstrate that the proposed method outputs results, which can be meaningfully used in applications such as quality control.

关键词： Structured light stereo system multi-sensor data fusion geometric cost function optimization

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multi-sensor data fusion Technology Based on BP Neural Network Application in the Coal Mine Equipment Fault Diagnosis 3

Multi-sensor Data Fusion Technology Based on BP Neural Netwo...

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3rd International Conference on Mechatronics and Control Engineering (ICMCE)

作者： Meng, Xiangzhong Liu, Huilong Hou, Zisheng Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266042 Shandong Peoples R China

ISBN: (纸本)9783038352914

In this paper, for the frequent faults problems of the mine air compressor main motor, we use the BP neural network learning algorithms on the basis of the theory of multi-sensor data fusion. The collected characteristic signals were processed by the method of data fusion, and we could get the current motor fault state value. Compared to the experimental results, it can realize the fault diagnosis of mine equipment obviously.

关键词： BP neural network multi-sensor data fusion fault diagnosis

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multi-sensor data fusion in Wireless sensor Network for Target Detection

Multi-sensor Data Fusion in Wireless Sensor Network for Targ...

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4th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Su, Wang Bo, Yang Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781467349338

Wireless sensor network has advantages of rapid deployment, self-organization, strong concealment, and high fault tolerance, etc. and it is very suitable for military reconnaissance. Considering the requirements of battlefield detection environment for men, gunmen and vehicles, a small-scale wireless sensor network(WSN) system is designed under laboratory conditions to simulate the target detection. According to the characteristics of different targets and zoning, the target feature extraction and detection methods are proposed for the signals of infrared sensors, magnetic sensors and sound sensors. A simple and efficient fixed threshold algorithm based on variance for infrared and magnetic resistance signals is put forward and a data fusion algorithm of D-S evidence theory is used to identify the targets. The binary detection algorithm of multi-sensor data fusion is presented for single target location and tracking. Finally, the experimental results have shown that the designed system can achieve reliable target detection, recognition, location and tracking in the laboratory.

关键词： wireless sensor network multi-sensor data fusion target recognition target location and tracking

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multi-sensor data fusion for Checking Plausibility of V2V Communications by Vision-based multiple-Object Tracking

Multi-Sensor Data Fusion for Checking Plausibility of V2V Co...

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IEEE Vehicular Networking Conference (VNC)

作者： Obst, Marcus Hobert, Laurens Reisdorf, Pierre BASELABS GmbH Ebertstr 10 D-09126 Chemnitz Germany Hitachi Europe SAS F-06560 Sophia Antipolis France Tech Univ Chemnitz D-09126 Chemnitz Germany

ISBN: (纸本)9781479976607

Automated vehicles require an adequate and reliable perception of the surrounding world in order to make good decisions. Using vehicle-to-vehicle (V2V) communication to exchange location data (i.e. time, position, heading and speed) can improve the perception beyond the capabilities of traditional on-board sensors (e.g. radar, lidar). However, it is vital to trust the data before it is being used. Cryptographic mechanisms can protect the exchange and authenticity of data but do not guarantee the correctness of the content. In this paper we present a vision-based multi-object tracking system for checking the plausibility of V2V communication. The system is addressing the challenge of fusing relative sensor observations as provided by a MobilEye vision-system with time-delayed absolute GNSS-based measurements from Cooperative Awareness Messages (CAMs) as provided by V2V. The plausibility check is implemented in a prototype and based on a state-of-the-art multiple-object tracking algorithm. The proposed system is evaluated and validated under real-world conditions by conducting several test drives under urban conditions.

关键词： computer vision driver information systems image fusion image sensors mobile communication object tracking satellite navigation CAM MobilEye vision-system V2V communications automated vehicles cooperative awareness messages driver support location data exchange multisensor data fusion plausibility checking relative sensor observation fusion time-delayed absolute GNSS-based measurements vehicle-to-vehicle communication vision-based multiple-object tracking system Cams Computer aided manufacturing data integration Radar tracking sensor fusion Vehicles Computer aided manufacturing sensor data fusion Automated vehicles Vehicle to vehicle communications driver information systems Satellite navigation sensors (image) data integration V2V sensor fusion image fusion mobile communication multi-sensor data fusion computer vision tracking objects

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