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Lecture Notes in Networks and Systems 2023年 606卷 v-vi页

作者： Srujan Raju, K. Sellathurai, Mathini Ashoka Reddy, K. George, Boby Rama Devi, B. Kakatiya Institute of Technology and Science Warangal India Department of Electrical and Electronics Engineering Indian Institute of Technology Tamil Nadu Chennai India Department of Signal Processing Heriot-Watt University Edinburgh United Kingdom Department of Electronics and Communication Engineering Kakatiya Institute of Technology and Science Warangal India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India

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Enhancing reliability and safety of uncertain grid-connected photovoltaic systems based on intelligent transient regime analysis

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Applied Energy 2025年 397卷

作者： Amal Hichri Mansour Hajji Majdi Mansouri Kais Bouzrara Noureddine Elboughdiri Hatem Gasmi Research Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kairouan Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat 123 Oman Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Monastir 5019 Tunisia Chemical Engineering Department College of Engineering University of Ha’il P.O. Box 2440 Ha’il 81441 Saudi Arabia Civil Engineering Department College of Engineering University of Ha’il P.O. Box 2440 Ha’il 81441 Saudi Arabia

Ensuring the uninterrupted operation of Grid-Connected Photovoltaic (GCPV) systems is crucial, as these systems are highly susceptible to faults and downtime caused by various factors, which can lead to significant system damage. To address these challenges, fault detection and diagnosis (FDD) methods are essential to maintain the reliability and safety of GCPV systems. This paper presents a transient regime based on the FDD approach of uncertain GCPV systems, employing deep learning techniques to detect and classify faults effectively. Furthermore, the proposed method takes advantage of the transition phase between healthy and faulty states in renewable energy systems to enable early fault detection by identifying anomalies in performance signals. By combining transient regime analysis with deep learning techniques, the approach facilitates rapid and accurate fault detection, thereby enhancing the reliability and extending the lifespan of photovoltaic systems. To handle uncertainties in the measured data, an interval-valued data representation is utilized, ensuring robust fault analysis under varying conditions. However, the hyperparameters of the proposed techniques are optimized using the Genetic Algorithm, improving their adaptability to diverse operating scenarios. The robustness of the methodology is further validated by introducing varying levels of noise into the data, simulating real-world perturbations and dynamic variations. The processed outputs are used to train deep learning classifiers to distinguish between various operating modes in GCPV systems. Experimental validation with real-world data demonstrates the efficacy and robustness of the proposed approach, enabling immediate decision-making and preventing fault propagation. The results highlight the strategy’s high accuracy and computational efficiency, contributing to improved reliability and safety of GCPV systems.

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Converting Neural Networks to Rule Foam

Converting Neural Networks to Rule Foam

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Akash Kumar Panda Bart Kosko Department of Electrical and Computer Engineering Signal and Image Processing Institute University of Southern California Los Angeles California

ISBN: (数字)9781728155845

ISBN: (纸本)9781728155852

A system of rules can approximate a trained neural classifier after sampling from that classifier. The rules define a generalized probability mixture that then describes the classifier. The size or granularity of the rule if-parts defines a foam-like structure with a few large rule if-part set bubbles in patternclass centers and many smaller if-part sets near class borders. The rule foam's mixture gives a Bayesian posterior over the rules. The posterior describes the relative importance of each rule for each observed input and output. The foam's mixture also gives the conditional variance that measures the uncertainty in its output. So the rule base is statistically interpretable as well as modular and adaptive. A rule foam with 1000 Gaussian rules approximated a 96.85% accurate MNIST neural classifier and had itself 95.66% classification accuracy. Foams can also approximate other foams. Some approximator foams out-performed the target foam that generated their training data. The rule foam's granularity mitigates the rule explosion inherent in the rule-based approximator's graph-covering structure.

关键词： Additives Neural networks Bayes methods Dispersion Uncertainty Fuzzy systems Vector quantization

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Bayes Theorem Extends to Overlapping Hypotheses

Bayes Theorem Extends to Overlapping Hypotheses

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Bart Kosko Department of Electrical and Computer Engineering Signal and Image Processing Institute University of Southern California Los Angeles California

ISBN: (数字)9781728155845

ISBN: (纸本)9781728155852

Bayes theorem extends to arbitrary overlapping hypotheses if a local probability describes the inherent uncertainty of each element in the overlap. This local probability avoids over-counting the overlap regions. It arises by normalizing the indicator function of each set hypothesis in the partition by the sum or integral of all the partition indicator functions. The default probability is that an element belongs equally likely to each overlapping hypothesis that contains it. This reduces to a trivial binary distribution in the extreme case of an exact partition with no overlap. The resulting fuzzy or uncertainty-weighted set events give a generalized theorem on total probability. This result gives in turn a Bayes theorem for any partition or collection of overlapping hypotheses. Applications include the overlapping pattern data that trains deep classification networks.

关键词： Uncertainty Fuzzy sets Weight measurement Machine learning Probability distribution Measurement uncertainty Scientific computing

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DOA estimation for transmit beamspace MIMO radar via tensor decomposition with Vandermonde factor matrix

arXiv

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arXiv 2021年

作者： Xu, Feng Morency, Matthew W. Vorobyov, Sergiy A. School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Signal Processing and Acoustics Aalto University Espoo02150 Finland Dept. Microelectronics School of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

We address the problem of tensor decomposition in application to direction-of-arrival (DOA) estimation for transmit beamspace (TB) multiple-input multiple-output (MIMO) radar. A general 4-order tensor model that enables computationally efficient DOA estimation is designed. Whereas other tensor decomposition-based methods treat all factor matrices as arbitrary, the essence of the proposed DOA estimation method is to fully exploit the Vandermonde structure of the factor matrices to take advantage of the shift-invariance between and within different subarrays. Specifically, the received signal of TB MIMO radar is expressed as a 4-order tensor. Depending on the target Doppler shifts, the constructed tensor is reshaped into two distinct 3-order tensors. A computationally efficient tensor decomposition method is proposed to decompose the Vandermonde factor matrices. The generators of the Vandermonde factor matrices are computed to estimate the phase rotations between subarrays, which can be utilized as a look-up table for finding target DOA. It is further shown that our proposed method can be used in a more general scenario where the subarray structures can be arbitrary but identical. The proposed DOA estimation method requires no prior information about the tensor rank and is guaranteed to achieve precise decomposition result. Simulation results illustrate the performance improvement of the proposed DOA estimation method as compared to conventional DOA estimation techniques for TB MIMO Radar. © 2021, CC0.

关键词： Tensors

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Secure Optical Wireless Links with Dynamic Beam and Diversity Receiver

Secure Optical Wireless Links with Dynamic Beam and Diversit...

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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Jupeng Ding Chih Lin I Jintao Wang Hui Yang Lili Wang Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region School of Information Science and Engineering Xinjiang University China China Mobile Research Institute Beijing China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China School of Information and Electrical Engineering Ludong University Yantai China

ISBN: (纸本)9781665405836

Optical wireless communication (OWC) has emerged as one promising candidate to mitigate the frequency spectrum crisis. Due to the broadcast nature of OWC channel, physical layer security (PLS) techniques have been considered and explored to improve the transmission confidentiality of OWC links. However, almost all current schemes focus on scenarios including multiple transmitters and fail to match the scenarios with limited transmitters, even single transmitter. For addressing this issue, in this work, the dynamic optical beam based PLS enhancement scheme is proposed. Unlike conventional Lambertian beam based technique paradigm, the above scheme tentatively utilize the commercially available non-Lambertian beam to configure the secure OWC links. Numerical results show that, compared with about 1.10 bps/Hz average secrecy capacity (SC) of the conventional configuration, up to 1.93 bps/Hz average SC gain could be provided by the proposed dynamic beam scheme. Moreover, this potential gain will be increased to about 2.39 bps/Hz when the diversity combing is available for the legitimate user receiver.

关键词： Wireless communication Integrated optics Measurement Conferences Optical fiber networks Physical layer security Optical receivers

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Optimum elements selection in IRS-assisted UAV communications with phase errors

TechRxiv

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TechRxiv 2021年

作者： Jangsher, Sobia Al-Dweik, Arafat Al-Jarrah, Mohammd Ahmad Alusa, Emad Alouini, Mohamed-Slim Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Wireless and Signal Processing Lab Institute of Space Technology Islamabad Pakistan School of Electrical and Electronic Engineering University of manchester ManchesterM13 9PL United Kingdom Center for Cyber Physical Systems Khalifa University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering Western University LondonON Canada Thuwal Makkah Province Saudi Arabia

This letter considers minimizing the bit error rate (BER) of unmanned aerial vehicle (UAV) communications assisted by intelligent reflecting surfaces (IRSs). By noting that increasing the number of IRS elements in the presence of phase errors does not necessarily improve the BER, it is crucial to use only the elements that contribute to reducing the BER. Consequently, we propose an efficient algorithm to activate only the elements that improve the BER. The proposed algorithm has lower complexity and comparable BER to the optimum selection process, which is an NP-hard problem. The accuracy of the estimated phase is evaluated by deriving the probability distribution function (PDF) of the least-square (LS) channel estimator, and showing that the PDF can be closely approximated by the von Mises distribution at high signal-to-noise ratios (SNRs). The obtained analytical and simulation results show that using all the available reflectors can significantly deteriorate the BER, and thus, elements’ selection is necessary. In particular scenarios, using about 26% of the reflectors provides more than 10 fold BER reduction. © 2021, CC BY.

关键词： Bit error rate

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Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

arXiv

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arXiv 2025年

作者： Imran, Muhammad Krebs, Jonathan R. Sivaraman, Vishal Balaji Zhang, Teng Kumar, Amarjeet Ueland, Walker R. Fassler, Michael J. Huang, Jinlong Sun, Xiao Wang, Lisheng Shi, Pengcheng Rokuss, Maximilian Baumgartner, Michael Kirchhof, Yannick Maier-Hein, Klaus H. Isensee, Fabian Liu, Shuolin Han, Bing Nguyen, Bong Thanh Shin, Dong-Jin Ji-Woo, Park Choi, Mathew Uhm, Kwang-Hyun Ko, Sung-Jea Lee, Chanwoong Chun, Jaehee Kim, Jin Sung Zhang, Minghui Zhang, Hanxiao You, Xin Gu, Yun Pan, Zhaohong Liu, Xuan Liang, Xiaokun Tiefenthaler, Markus Almar-Munoz, Enrique Schwab, Matthias Kotyushev, Mikhail Epifanov, Rostislav Wodzinski, Marek Müller, Henning Qayyum, Abdul Mazher, Moona Niederer, Steven A. Wang, Zhiwei Yang, Kaixiang Ren, Jintao Korreman, Stine Sofia Gao, Yuchong Zeng, Hongye Zheng, Haoyu Zheng, Rui Yue, Jinghua Zhou, Fugen Liu, Bo Cosman, Alexander Liang, Muxuan Zhao, Chang Upchurch, Gilbert R. Ma, Jun Zhou, Yuyin Cooper, Michol A. Shao, Wei Department of Medicine University of Florida GainesvilleFL32611 United States Department of Surgery University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Information Science and Engineering University of Florida GainesvilleFL32611 United States Shanghai Jiao Tong University Shanghai China Electronic & Information Engineering School Harbin Institute of Technology [Shenzhen Shenzhen China Division of Medical Image Computing German Cancer Research Center [DKFZ Heidelberg Germany CO. LTD Beijing China MedAI Seoul Korea Republic of Korea University Seoul Korea Republic of Department of Radiation Oncology Yonsei Cancer Center Heavy Ion Therapy Research Institute Yonsei University College of Medicine Seoul Korea Republic of Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Medical University of Innsbruck Innsbruck Austria Novosibirsk State University Novosibirsk Russia AGH University of Krakow Krakow Poland Sierre Switzerland University of Geneva Geneva Switzerland The Sense Innovation and Research Center Sion Switzerland National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan China Aarhus University Department of Clinical Medicine Nordre Palle Juul-Jensens Blvd. 11 Aarhus8200 Denmark Shanghaitech University Pudong Shanghai China Image Processing Center Beihang University Beijing China Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Biostatistics University of Florida GainesvilleFL32611 United States University Health Network ON Canada Agronomy Department University of F

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://***. © 2025, CC BY-NC-SA.

关键词： Computerized tomography

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Yule-Walker Equations Using Higher Order Statistics for Nonlinear Autoregressive Model

Yule-Walker Equations Using Higher Order Statistics for Nonl...

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2019 signal processing Symposium, SPSympo 2019

作者： Libal, Urszula Johansson, Karl H. School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Signal Processing Systems Department Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9781728117157

The paper presents the derivation of Yule-Walker equations for the nonlinear autoregressive model NAR(p) of a time-series. The proposed method allows for easy calculation of parameters of the model. In the determined equations, higher order statistics are used, instead of autocovariances. For the linear autoregressive model AR(p), the standard Yule-Walker equations are directly based on autocovariances of time-series, or equivalently autocorrelations if the equations are rescaled. Unfortunately, it does not apply for nonlinear model. The authors show a compact matrix notation of Yule-Walker equations for the nonlinear autoregressive model with the nonlinearity of polynomial type of degree 2, with the use of higher order statistics up to 4th order, and numerical examples for electromyography signals for different hand movements. © 2019 IEEE.

关键词： Time series

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Estimation Of Gravimetric Vegetation Moisture In The Western United States Using A Multi-Sensor Approach

Estimation Of Gravimetric Vegetation Moisture In The Western...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： David Chaparro Thomas Jagdhuber Maria Piles François Jonard Mercè Vall-Llossera Adriano Camps Carlos López-Martínez Anke Fluhrer Roberto Fernández-Morán Martin J. Baur Andrew F. Feldman Dara Entekhabi German Aerospace Center Microwaves and Radar Institute Wessling Germany Institute of Geography University of Augsburg Augsburg Germany Image Processing Lab Universitat de València València Spain Earth Observation and Ecosystem Modelling Laboratory SPHERES Research Unit Université de Liège (ULiege) Liège Belgium Department of Signal Theory and Communications and Institut d’Estudis Espacials de Catalunya CommSensLab Universitat Politècnica de Catalunya (UPC) Barcelona Spain ASPIRE Visiting International Professor UAE University CoE Al Ain UAE Department of Geography University of Cambridge Cambridge UK NASA Postdoctoral Program Biospheric Sciences Laboratory NASA Goddard Space Flight Center Greenbelt MD USA Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge MA USA

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (m g ) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of m g with help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

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