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3rd SICE International Symposium on Control Systems (SICE ISCS)

作者： El Hakim, Viktorio S. Bekooij, Marco J. G. Univ Twente Dept Comp Architectures Embedded Syst Enschede Netherlands NXP Semicond Dept Embedded Software & Signal Proc Eindhoven Netherlands

ISBN: (纸本)9784907764548

State estimation algorithms, such as the Kalman filter, are applied for conditioning and sensor fusion in digital control loops. It is desirable that these algorithms can be executed on embedded multiprocessor systems. However this results in large worst-case execution times with a consequence that a large sampling period must selected, which degrades the estimation and control performance. In this paper, we propose a free-running state-estimation approach in which the next sample is taken as soon as the current iteration completes. The approach utilizes the particle filter algorithm to mitigate the effects of sampling jitter, introduced by the variation in the execution times of tasks. As a result of the reduced interval between subsequent sampling moments the estimation accuracy is improved. The delay introduced by the estimator in a control loop is reduced by enabling execution of the prediction step in parallel with other control tasks. We compare simulation results obtained for our approach with a Kalman filter based approach, by estimating the state of a DC motor. These results show that our approach minimizes the estimation error, as a result of sampling jitter, by up to a factor of 10. Additionally we show that the approach does not require precise knowledge of the distribution of the execution times of the tasks.

关键词： Sampling jitter Particle filtering

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Advanced Deep Learning Approaches for Fault Detection and Diagnosis in Inverter-Driven PMSM Systems

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND applications 2024年第12期15卷 912-924页

作者： Bacha, Abdelkabir El Idrissi, Ramzi Lmai, Fatima EL Hassani, Hicham Idrissi, Khalid Janati Benhra, Jamal Hassan II Univ Casablanca Sch Elect & Mech Engn LARILE Lab OSIL Team Casablanca Morocco Hassan II Univ Casablanca Fac Sci Ain Chock Phys Dept LPMAT Casablanca Morocco Sidi Mohamed Ben Abdellah Univ Fez Fac Sci & Technol Fez Lab Mech Engn Mech Engn Team Fes Morocco

THIS paper presents a comprehensive approach to fault detection and diagnosis (FDD) in inverter-driven Permanent Magnet Synchronous Motor (PMSM) systems through the innovative integration of transformer-based architectures with physics- informed neural networks (PINNs). The methodology addresses critical challenges in power electronics reliability by incorporating domain-specific physical constraints into the learning process, enabling both high accuracy and physically consistent predictions. The proposed system combines advanced sensor fusion techniques with real-time monitoring capabilities, processing multiple input streams including phase currents, temperatures, and voltage measurements. The architecture's dual-objective optimization approach balances traditional classification metrics with physics- based constraints, ensuring predictions align with fundamental electromagnetic and thermal principles. Experimental validation using a comprehensive dataset of 10,892 samples across nine distinct fault scenarios demonstrates the system's exceptional performance, achieving 98.57% classification accuracy while maintaining physical consistency scores above 0.98. The model exhibits robust performance across varying operational conditions, including speed variations (97.45-98.57% accuracy range) and load fluctuations (97.91-98.12% accuracy range). Notable achievements include perfect detection rates for certain critical faults, such as high-side short circuits and thermal anomalies, with area under ROC curve (AUC) scores of 1.0. This research establishes new benchmarks in condition monitoring and fault diagnosis for power electronic systems, offering practical implications for predictive maintenance and system reliability enhancement.

关键词： -Fault detection and diagnosis PMSM deep learn- ing transformers physics-informed neural networks power elec- tronics

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Estimating bidirectional reflectance parameters by forward modeling and statistical inversion of remotely sensed data

Estimating bidirectional reflectance parameters by forward m...

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Statistical and Stochastic Methods in Image Processing ii

作者： Fletcher, Robin P. Grubb, Howard J. Godsalve, Christopher Department of Applied Statistics University of Reading Reading RG6 6FN United Kingdom Environmental Systems Science Centre University of Reading Reading RG6 6AB United Kingdom

We discuss the estimation of ground reflectance from remotely-sensed measurements made by a satellite-borne instrument. The particular sensor under study includes a spatially-varying weighting of the ground values within overlapping, large, low-resolution image pixels. Thus the problem is one of estimating sub-pixel information from a low resolution observation. The observation process involves interaction with an unknown atmosphere, realistic modeling of which requires sophisticated and computationally expensive algorithms. These make it difficult to specify likelihood functions for use in a fully Bayesian approach to the inversion and so we work with atmospherically-corrected data in a penalized least-squares framework. We formulate realistic physical models for the observation process which can then be inverted using a forward modeling approach. This can be solved by using a stochastic optimization algorithm on a suitably chosen energy function, which regularizes the accuracy of reconstruction of the observed satellite data with our prior beliefs about spatial smoothness of the ground reflectance. A further complication is introduced as the sensor views the same ground point from two viewing angles at closely-spaced times. As the surface property of reflectance intrinsically varies with viewing angle, depending on the vegetation or ground cover, these two views allow us to jointly estimate two parameters of a suitable physical model for this variation. From this we can also derive other functionals of interest to environmental applications. This estimation can be considered as a form of image fusion, via the forward model of the observation process. We consider practical aspects of the smoothness priors, the forward model and its implications for the design of an energy function and stochastic inversion algorithms in this application. We compare their performance with an existing method, on some synthetic data, generated with the important sensor properties. We d

关键词： Image fusion

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Ince-PD Model for Parkinson's Disease Prediction Using MDS-UPDRS I & ii and PDQ-8 Score 19th

Ince-PD Model for Parkinson's Disease Prediction Using MDS-U...

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19th International Conference on Artificial Intelligence applications and Innovations (AIAI)

作者： Tsolakis, Nikos Maga-Nteve, Christoniki Meditskos, Georgios Vrochidis, Stefanos Kompatsiaris, Ioannis Informat Technol Inst Ctr Res & Technol Hellas Maroussi Greece Aristotle Univ Thessaloniki Sch Informat Thessaloniki 54124 Greece

ISBN: (纸本)9783031341137;9783031341113;9783031341106

Parkinson's disease (PD) is one of the most prevalent and complex neurodegenerative disorders. Timely and accurate diagnosis is essential for the effectiveness of the initial treatment and improvement of the patients' quality of life. Since PD is an incurable disease, the early intervention is important to delay the progression of symptoms and severity of the disease. This paper aims to present Ince-PD, a new, highly accurate model for PD prediction based on Inception architectures for time-series classification, using wearable data derived from IoT sensor-based recordings and surveys from the mPower dataset. The feature selection process was based on the clinical knowledge shared by the medical experts through the course of the EU funded project ALAMEDA. The algorithm predicted total MDS-UPDRS I & ii scores with a mean absolute error of 1.97 for time window and 2.27 for patient, as well as PDQ-8 scores with a mean absolute error of 2.17 for time window and 2.96 for patient. Our model demonstrates a more effective and accurate method to predict Parkinson Disease, when compared to some of the most significant deep learning algorithms in the literature.

关键词： Deep Learning models Parkinson's Disease MDS-UPDRS PDQ-8 Wearable sensors Convolution Networks

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Superresolution Image Reconstruction Using Panchromatic and Multispectral Image fusion

Superresolution Image Reconstruction Using Panchromatic and ...

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Conference on Optics and Photonics for Information Processing ii

作者： Elbakary, M. I. Alam, M. S. Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

ISBN: (纸本)9780819472922

Hyperspectral imagery is used for a wide variety of applications, including target detection, tacking, agricultural monitoring and natural resources exploration. The main reason for using hyperspectral imagery is that these images reveal spectral information about the scene that is not available in a single band. Unfortunately, many factors such as sensor noise and atmospheric scattering degrade the spatial quality of these images. Recently, many algorithms are introduced in the literature to improve the resolution of hyperspectral images using co-registered high special-resolution imagery such as panchromatic imagery. In this paper, we propose a new algorithm to enhance the spatial resolution of low resolution hyperspectral bands using strongly correlated and co-registered high special-resolution panchromatic imagery. The proposed algorithm constructs the superresolution bands corresponding to the low resolution bands to enhance the resolution using a global correlation enhancement technique. The global enhancement is based on the least square regression and the histogram matching to improve the estimated interpolation of the spatial resolution. The introduced algorithm is considered as an improvement for Price's algorithm which uses the global correlation only for the spatial resolution enhancement. Numerous studies are conducted to investigate the effect of the proposed algorithm for achieving the enhancement compared to the traditional algorithm for superresolution enhancement. Experiments results obtained using hyperspectral data derived from airborne imaging sensor are presented to verify the superiority of the proposed algorithm.

关键词： Hyperspectral multispectral panchromatic image resolution enhancement superresolution

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Multi-sensor, multi-source information fusion: architecture, algorithms, and applications - a panoramic overview

Multi-sensor, multi-source information fusion: architecture,...

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International Conference on Computational Cybernetics (ICCC)

作者： B.V. Dasarathy Huntsville AL USA

This plenary presentation offers a panoramic overview of the field of multi-sensor, and/or multi-source information fusion from three complementary perspectives, namely, architectures, algorithms, and applications. This overview is developed through addressing the questions of 'what', 'why', 'when', and last but not least 'how' of information fusion and illustrating the answers with appropriate algorithmic and applicative examples

关键词： Feature extraction Iron Taxonomy Data processing Data mining Biosensors sensor fusion Security Bibliographies

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DSP architectures of Covariance Intersection fusion Algorithm for Automotive sensor fusion

DSP Architectures of Covariance Intersection Fusion Algorith...

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International Conference on Computing and Networking Technology (ICCNT)

作者： D.S.L. Praharshita Achala G Pathipati Srihari U. Shripathi Acharya Bethi Pardhasaradhi Engineering Development Group (EDG) India ECE Dept. National Institute of Technology Karnataka India ADAS Dept. Continental Autonomous Mobility India Pvt Ltd India

The data fusion from sensors within the automotive vehicle is vital for improved accuracy and safety. The centralized and information matrix fusion (IMF) algorithms are famous for providing an optimal fusion estimate. However, the IMF is not viable in automotive sensor fusion applications due to the limited bandwidth and low hardware resources. Hence, distributed fusion technology is widely adopted in the automotive sensor applications to achieve high-speed and low-area realizations. This paper proposes three digital signal processing (DSP) architectures for covariance intersection (CI) fusion algorithm: Pipelined-traditional CI, adder-ladder CI, and pipelined adder-ladder CI. The proposed DSP architectures are evaluated with hardware resource consumption (multipliers, adders, and delays), maximum achievable frequency, and latency of the architecture. In addition, proposed CI algorithms for Digital Signal Processing (DSP) architectures are compared with IMF DSP architectures. The hardware resources and optimal pipeline stages required for CI with respect to N number of sensors are provided. The traditional pipeline algorithm requires N number of stages where as the proposed pipelined version of adder-ladder CI requires a N−1 pipeline stage with additional 7N−1 and 7N−3 delay elements for even and odd number of sensors to achieve the overall system operating frequency to an operation of multiplier. The proposed DSP architectures are suitable for automotive sensor fusion due to their high operating frequency and low hardware resources.

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Fault detection and isolation in multisensor train navigation systems

Fault detection and isolation in multisensor train navigatio...

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UKACC International Conference on Control 98

作者： Mirabadi, A Mort, N Schmid, F Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England

ISBN: (纸本)085296708X

In new Automatic Train Control (ATC) and Automatic Train Protection (ATP) systems, the onboard navigation system plays a vital role. Applying multisensor fusion algorithms in the vehicle navigation field has made it possible to make inexpensive and accurate positioning systems which will satisfy the requirements of railway systems. Kalman filtering, as one of the well known state estimation methods, has proved to be the most effective technique in multisensor data fusion for navigation applications. On the other hand reliability of the navigation system is of great concern which necessitates the design of a fault tolerant navigation system in which the detection and isolation of the faults is possible. This paper will first address different multisensor integration methods and later will discuss some of the fault detection methods for navigation systems using Kalman filtering and some simulation results will be presented. Fault isolation using the proposed sensor architecture is another topic which will be discussed in more detail.

关键词： multisensor fusion Kalman filter navigation railways fault detection

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Networked filtering and fusion in wireless sensor networks 1

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2014年

作者： Magdi S Mahmoud

ISBN: (数字)9781482250961

ISBN: (纸本)1482250969;9781482250961;9781482250978

By exploiting the synergies among available data, information fusion can reduce data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. Networked Filtering and fusion in Wireless sensor Networks introduces the subject of multi-sensor fusion as the method of choice for implementing distributed *** book examines the state of the art in information fusion. It presents the known methods, algorithms, architectures, and models of information fusion and discusses their applicability in the context of wireless sensor networks (WSNs). Paying particular attention to the wide range of topics that have been covered in recent literature, the text presents the results of a number of typical case *** with research supported elements and comprehensive references, this teaching-oriented volume uses standard scientific terminology, conventions, and notations throughout. It applies recently developed convex optimization theory and highly efficient algorithms in estimation fusion to open up discussion and provide researchers with an ideal starting point for further research on distributed estimation and fusion for *** book supplies a cohesive overview of the key results of theory and applications of information-fusion-related problems in networked systems in a unified framework. Providing advanced mathematical treatment of fundamental problems with information fusion, it will help you broaden your understanding of prospective applications and how to address such problems in *** reading the book, you will gain the understanding required to model parts of dynamic systems and use those models to develop distributed fusion control algorithms that are based on feedback control theory.

关键词： Computer Science Engineering & Technology

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Nodes deployment optimization algorithm based on improved evidence theory 18th

Nodes deployment optimization algorithm based on improved ev...

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18th International Conference on algorithms and architectures for Parallel Processing - ICA3PP 2018 Collocated Workshops: ICA3PP 2018 Workshop on Intelligent algorithms for Large-scale Complex Optimization Problems, IALCOP 2018, ICA3PP 2018 Workshop on Security and Privacy in Data Processing, SPDP 2018

作者： Song, Xiaoli Gong, Yunzhan Jin, Dahai Li, Qiangyi Jing, Hengchang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Henan University of Science and Technology HenanLuoyang471023 China

ISBN: (纸本)9783030052331

Underwater wireless sensor networks (UWSNs) applications for ocean monitoring, deep sea surveillance, and locating natural resources are gaining popularity. To monitor the underwater environment or any object of interest, these applications are required to deploy underwater connected node sensors for obtaining useful data. For thriving UWSNs, it is essential that an efficient and secure node deployment mechanism is in place. In this article, we are presenting a novel nodes deployment scheme which is based on evidence theory approach and cater-for 3D-UWSNs. This scheme implements sonar probability perception and an enhanced data fusion model to improve prior probability deployment algorithm of D-S evidence theory. The viability of our algorithm is verified by performing multiple simulation experiments. The simulation results reveal that as compared to other schemes, our algorithm deploys fewer nodes with enhanced network judgment criteria and expanded detection capabilities for a relatively large area. © Springer Nature Switzerland AG 2018.

关键词： sensor nodes

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