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检索条件"任意字段=International Conference on Multisensor Fusion and Integration for Intelligent Systems"

共 2572 条记录，以下是31-40 订阅

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Extended Object Tracking and Shape Classification using Random Matrices and Virtual Measurement Models

Extended Object Tracking and Shape Classification using Rand...

引用

2023 IEEE Symposium Sensor Data fusion and international conference on multisensor fusion and integration, SDF-MFI 2023

作者： Hoher, Patrick Reuter, Johannes Govaers, Felix Koch, Wolfgang Konstanz Germany Fraunhofer Fkie Dept. Sensor Data and Information Fusion Wachtberg Germany

ISBN: (纸本)9798350382587

The random matrix approach is a robust algorithm to filter the mean and covariance matrix of noisy observations of a dynamic object. Afterward, virtual measurement models can be used to find iteratively the extent parameters of an object that would cause the same statistical moments within their measurements. In previous work, this was limited to elliptical targets and only contour *** this paper, we introduce the parallel use of an elliptical, triangular and rectangular-shaped virtual measurement model and a shape classification that selects the model that fits best to the measurements. The measurement likelihood is modeled either via ray tracing, a uniformly or normally spatial distribution over the object's extent or as a combination of *** results show that the extent estimation works precisely and that the classification accuracy highly depends on the measurement noise. © 2023 IEEE.

关键词： Iterative methods

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A Study of fusions of Multiple Estimates for Limit Cases

A Study of Fusions of Multiple Estimates for Limit Cases

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IEEE international conference on multisensor fusion and integration for intelligent systems (MFI)

作者： Ajgl, Jiri Straka, Ondrej Univ West Bohemia European Ctr Excellence New Technol Informat Soc Fac Appl Sci Plzen Czech Republic Univ West Bohemia Fac Appl Sci Dept Cybernet Plzen Czech Republic

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

Decentralised estimation often sacrifices optimality for solution simplicity, while within the fusion under unknown correlation, a worst-case type of optimality is adopted. This paper studies the gap between the simple solution and the optimal one for special cases. Namely, symmetric configurations are considered for infinite number of estimates and also for infinite dimension of the state to be estimated. In these academic cases, the optimal solution is better than the simple one by low tens percent, if the size of circumscribing balls is considered. In practice, much lower gap can be expected.

关键词： Correlation Estimation intelligent systems

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Revolutionizing Healthcare Intelligence multisensory Data fusion with Cutting-Edge Machine Learning and Deep Learning for Patients' Cognitive Knowledge

Revolutionizing Healthcare Intelligence Multisensory Data Fu...

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2024 international conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Swamy, Samatha R. Nandini Prasad, K.S. Dept. of Ise Bengaluru India Dept. of Ise Bengaluru India

ISBN: (纸本)9798350359688

The study explores the recent advancements in healthcare that have witnessed an unprecedented integration of multisensory data, ranging from wearable devices to medical imaging and electronic health records. This paper explores the transformative impact of cutting-edge machine learning algorithms and deep learning algorithms in processing and interpreting this multisensory data, offering unparalleled insights into patients' cognitive well-being. The review delves into the types and challenges of multisensory data, evaluating performance of supervised and supervised machine learning models for disease detection and pattern recognition respectively. Additionally, it scrutinizes the importance of deep learning applications that are based on convolutional neural networks as well as recurrent neural networks for medical image classification. The paper also mentions the importance of the integration of multisensor data fusion with these advanced algorithms, emphasizing real-time monitoring systems and ethical considerations. Summaries from research data shows effectiveness of remote patient care and predictive analysis and personalized treatment plans. The exploration of future directions and opportunities underscores the prime importance for further advancements in explainable AI, collaborative learning, and edge computing, consolidating the vision of a healthcare paradigm revolutionized by intelligent data fusion and cutting-edge technologies. © 2024 IEEE.

关键词： Electronic health record

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Enhanced Fault Detection in Semiconductor Wafers using multisensor Data fusion and Machine Learning Techniques 2

Enhanced Fault Detection in Semiconductor Wafers using Multi...

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2nd international conference on intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Mehta, Sourav Rao, Nadeem Chandigarh Univ Dept Math Mohali Punjab India

ISBN: (纸本)9798331540661;9798331540678

For electrical equipment to function properly, semiconductor wafer quality and dependability are essential. Even though they are helpful, traditional fault detection techniques frequently miss small flaws that could cause serious product failures. In order to improve detection accuracy, this research introduces an innovative fault detection system that combines machine learning approaches with multisensor data fusion. We create an extensive dataset for model training by using advanced data preprocessing and feature extraction techniques, as well as sensors like temperature, vibration, and audio sensors. Support vector machines (SVM), convolutional neural networks (CNN), and long short-term memory networks (LSTM) are examples of machine learning models that are put into practice and assessed. Our methodology achieves over 95% accuracy for some problem types, and experimental findings show significant increases in fault detection accuracy over conventional methods. This study demonstrates how sensor data and machine learning may be combined to enhance quality control in the semiconductor manufacturing process, which will ultimately result in higher productivity and lower costs.

关键词： Fault Detection Semiconductor Wafers Sensor Data Machine Learning Data fusion Support Vector Machines (SVM) Convolutional Neural Networks (CNN) Long Short-Term Memory (LSTM) Quality Control Manufacturing Efficiency

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Optimal Sensor Placement for Multilateration Using Alternating Greedy Removal and Placement

Optimal Sensor Placement for Multilateration Using Alternati...

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IEEE international conference on multisensor fusion and integration for intelligent systems (MFI)

作者： Frisch, Daniel Li, Kailai Hanebeck, Uwe D. Karlsruhe Inst Technol KIT Intelligent Sensor Actuator Syst Lab ISAS Inst Anthropomat & Robot Karlsruhe Germany

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

We present a novel algorithm for optimal sensor placement in multilateration problems. Our goal is to design a sensor network that achieves optimal localization accuracy anywhere in the covered region. We consider the discrete placement problem, where the possible locations of the sensors are selected from a discrete set. Thus, we obtain a combinatorial optimization problem instead of a continuous one. While at first, combinatorial optimization sounds like more effort, we present an algorithm that finds a globally optimal solution surprisingly quickly.

关键词： Multilateration Dilution of Precision Fisher Information Sensor Placement Combinatorial Optimization Greedy Algorithm

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Probabilistic Information Matrix fusion in a Multi-Object Environment

Probabilistic Information Matrix Fusion in a Multi-Object En...

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IEEE international conference on multisensor fusion and integration for intelligent systems (MFI)

作者： Yang, Kaipei Bar-Shalom, Yaakov Univ Connecticut Dept ECE Storrs CT 06269 USA

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

In distributed sensor fusion systems, each of the local sensors has its own tracker processing local measurements for measurement-to-track association and state estimation. Only the processed data, local tracks (LT) comprising state vector estimates and their covariance matrices are transmitted to the fusion center (FC). In this work, a multi-object hybrid probabilistic information matrix fusion (MO-HPIMF) is derived taking into account all association hypotheses. In MO-HPIMF, the association carried out is between the FC track states (prediction) and the LT state estimates from local sensors. When having a large number of objects and sensors in fusion, only the m-best FC-track-to-LT association hypotheses should be incorporated in MO-HPIMF to reduce the computational complexity. A Sequential m-best 2-D method is used for solving the multidimensional assignment problem in this work. It is shown in the simulations that MO-HPIMF can successfully track all targets of interest and is superior to track-to-track fusion (T2TF, a commonly used approach in distributed sensor fusion system) which relies on hard association decisions.

关键词： Target tracking Uncertainty Computational modeling Sensor fusion Probabilistic logic Covariance matrices Computational complexity

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Friction and Road Condition Estimation using Dynamic Bayesian Networks

Friction and Road Condition Estimation using Dynamic Bayesia...

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2023 IEEE Symposium Sensor Data fusion and international conference on multisensor fusion and integration, SDF-MFI 2023

作者： Volkmann, Björn Kortmann, Karl-Philipp Seel, Thomas Leibniz University Hannover Institute of Mechatronic Systems An der Universität 1 Garbsen30823 Germany

ISBN: (纸本)9798350382587

An essential factor in ensuring driving stability and traffic safety of vehicles is knowledge about road condition and maximum tire-road friction potential. In previous research systems that either measure friction-related parameters or estimate the friction coefficient via a mathematical model have been proposed. However, the performance of these systems can be negatively impacted by environmental factors or require sufficient excitation in the form of tire slip, which is often too low under practical conditions. Therefore, this paper investigates if a more robust estimation can be achieved by fusing the information of multiple sources using a dynamic Bayesian network, which models the statistical relationship between different heterogeneous sources of information and the maximum friction coefficient. The algorithm is applied to data from a passenger vehicle to demonstrate the performance under real conditions. By using the dynamic Bayesian network the error can be reduced by up to 50%. To our knowledge this is the first time a dynamic Bayesian network is used to estimate road condition and friction coefficient. © 2023 IEEE.

关键词： Friction

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Low-Illumination Lane Detection by fusion of Multi-light Information

Low-Illumination Lane Detection by Fusion of Multi-light Inf...

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IEEE international conference on multisensor fusion and integration for intelligent systems (MFI)

作者： Zhao, Wenbo Tian, Wei Han, Yi Yu, Xianwang Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

Lane detection based on the visual sensor is of great significance for the environmental perception of the intelligent vehicle. Current mature lane detection algorithms are trained and implemented in good visual conditions. However, the low-light environment such as in the night is much more complex, easily causing misdetections and even perception failures, which are harmful to the downstream tasks such as behavior decision and control of ego-vehicle. To tackle this problem, we propose a new lane detection algorithm that introduces the multi-light information into lane detection task. The proposed algorithm adopts a multi-exposure image processing module, which generates and fuses multi-exposure information from the source image data. By integrating this module, mainstream lane detection models can jointly learn the extraction of lane features as well as the enhancement of low-exposed image, thus improving both the performance and robustness of lane detection in the night.

关键词： lane detection image enhancement multi-exposure fusion

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Design of Automotive Anti-Theft System Based on Iot and Multi Sensor fusion

Design of Automotive Anti-Theft System Based on Iot and Mult...

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2024 international conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Hong, Cheng Guo, Shun College of Automobile and Rail Anhui Technical College of Mechanical and Electrical Engineering Wuhu241002 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

With the increase in the number of motor vehicles, the number of stolen cars has also been increasing year by year, and vehicle networking anti-theft has become one of the hot topics and research topics. This article analyzes the behavioral characteristics of stolen vehicles based on several common situations of car theft, and then proposes the overall requirements for anti-theft systems. A car intelligent anti-theft system combining multiple sensors and the Internet of Things is proposed by comparing and designing the performance of several key technical solutions according to the requirements. Secondly, the hardware system design was carried out using modular design principles, including sensor acquisition and transmission node design and vehicle controller design. On this basis, we designed a frame time slot ALOHA algorithm based on time grouping to optimize information collection and improve system efficiency. After simulation testing, this solution can provide new ideas and technical paths for automotive anti-theft design. © 2025 SPIE.

关键词： Crime

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Enhancing Reinforcement Learning in Sensor fusion: A Comparative Analysis of Cubature and Sampling-based integration Methods for Rover Search Planning

Enhancing Reinforcement Learning in Sensor Fusion: A Compara...

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2024 international conference on intelligent Robots and systems

作者： Ewers, Jan-Hendrik Swinton, Sarah Anderson, David McGookin, Euan Thomson, Douglas Univ Glasgow Aerosp Sci Res Div Glasgow Lanark Scotland

ISBN: (纸本)9798350377712;9798350377705

This study investigates the computational speed and accuracy of two numerical integration methods, cubature and sampling-based, for integrating an integrand over a 2D polygon. Using a group of rovers searching the Martian surface with a limited sensor footprint as a test bed, the relative error and computational time are compared as the area was subdivided to improve accuracy in the sampling-based approach. The results show that the sampling-based approach exhibits a 14:75% deviation in relative error compared to cubature when it matches the computational performance at 100%. Furthermore, achieving a relative error below 1% necessitates a 10000% increase in relative time to calculate due to the O(N-2) complexity of the sampling-based method. It is concluded that for enhancing reinforcement learning capabilities and other high iteration algorithms, the cubature method is preferred over the sampling-based method.

关键词： Contrastive Learning

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