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2nd International Conference on Energy Transition and Security, ICETS 2023

作者： Younes, Djellouli Ahmed El Mehdi, Ardjoun Sid Houcine, Chafouk Mouloud, Denai Djilali Liabes University Irecom Laboratory Faculty of Electrical Engineering Sidi Bel-Abbes22000 Algeria Normandy University Unirouen Esigelec Irseem Rouen76000 France University of Hertfordshire School of Physics Engineering and Computer Science HatfieldAL10 9AB United Kingdom

ISBN: (纸本)9798350372182

Electric Vehicles (EVs) are anticipated to play a pivotal role in addressing global climate change objectives and offering a solution for improved fuel efficiency. However, enhancing the performance, energy efficiency and cost remain the major challenges for EV technology. Identifying the operating points of the EVs motor drive system is crucial for optimizing its performance. Sensorless electric motor control for EVs is one of the promising research areas for cost reduction and performance improvement. This work's primary objective is to develop, implement and validate experimentally a torque estimator based on Luenberger observer on an EV driven by an open-end winding induction motor (OEWIM) fed by two inverters. The proposed observer structure uses the complete OEWIM mathematical model. The rotor fluxes are estimated using the electrical model whilst the torque is observed using the mechanical model. This observer has been implemented using a board signified for a 1.5 kW OEWIM. The obtained experimental findings demonstrate the efficacy of the method put forward. © 2023 IEEE.

关键词： Torque

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UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks 39

UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Qian, Yuanbin Ye, Shuhan Wang, Chong Cai, Xiaojie Qian, Jiangbo Wu, Jiafei Faculty of Electrical Engineering and Computer Science Ningbo University China Merchants’ Guild Economics and Cultural Intelligent Computing Laboratory Ningbo University China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

ISBN: (纸本)157735897X

Video anomaly detection plays a significant role in intelligent surveillance systems. To enhance model’s anomaly recognition ability, previous works have typically involved RGB, optical flow, and text features. Recently, dynamic vision sensors (DVS) have emerged as a promising technology, which capture visual information as discrete events with a very high dynamic range and temporal resolution. It reduces data redundancy and enhances the capture capacity of moving objects compared to conventional camera. To introduce this rich dynamic information into the surveillance field, we created the first DVS video anomaly detection benchmark, namely UCF-Crime-DVS. To fully utilize this new data modality, a multi-scale spiking fusion network (MSF) is designed based on spiking neural networks (SNNs). This work explores the potential application of dynamic information from event data in video anomaly detection. Our experiments demonstrate the effectiveness of our framework on UCF-Crime-DVS and its superior performance compared to other models, establishing a new baseline for SNN-based weakly supervised video anomaly detection. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Real-Time Implementation of the Luenberger Torque Observer for an Induction Motor 2

Real-Time Implementation of the Luenberger Torque Observer f...

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2nd International Conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Younes, Djellouli El Mehdi, Ardjoun Sid Ahmed Mouloud, Denai Houcine, Chafouk Djillali Liabes University Irecom Laboratory Faculty of Electrical Engineering Sidi Bel-Abbes22000 Algeria School of Physics Engineering and Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom Normandy University Unirouen Esigelec Irseem Rouen76000 France

ISBN: (纸本)9798350314243

Currently, the technology of high-performance variable speed drives without mechanical sensors is widely used in the industrial environment. This preference arises from the exorbitant costs and fragility associated with mechanical sensors. This investigation aims to establish, implement, and empirically validate a Luenberger Observer for wheel torque within an electric propulsion framework that employs induction motors. The proposed observer structure focuses on the complete dynamic model of an induction motor. Concerning the rotor fluxes, they are estimated using the electrical model. On the other hand, the torque is observed using the mechanical model. A real-time implementation was performed on a DSpace 1104 board designed for a 1.5 kW induction motor to verify the proposed observer's effectiveness. The experiment results show that the proposed method exhibits satisfactory performance, affirming its effectiveness and robustness and giving valuable insights into its practical applicability. © 2023 IEEE.

关键词： Induction motors

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Bilateral Multi-Subarray Padded Coprime Array Configuration for DOA Estimation in MIMO Radar

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IEEE Transactions on Vehicular Technology 2025年

作者： Chen, Hua Li, Jiajie Yang, Songjie Liu, Wei Yuen, Chau So, Hing Cheung Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu610054 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Kowloon Hong Kong Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Simultaneous consideration of uniform degrees of freedom (uDOF) and mutual coupling is a key focus in sparse array design. To reduce mutual coupling and increase degrees of freedom (DOF), a bilateral multi-subarray coprime array (BMSCA) is proposed with its specific hole locations in the difference co-array (DCA). To address the holes in the central DCA, an additional subarray is incorporated, resulting in the bilateral multi-subarray padded coprime array (BMSPCA), which substantially increases both DOF and uDOF. Additionally, the first three weight functions and the optimal structure are determined for BMSPCA. As an application, BMSPCA is applied to multiple-input multiple-output (MIMO) radar with sum-difference co-array, where the holes in DCA and virtual sparse sensors can be divided into similar layers and exhibit symmetry;specific sparse arrays that exhibit similar symmetrical properties are then identified, which can fundamentally enhance the uDOF of MIMO radar. Simulation results demonstrate the superior performance of both the proposed array and the sparse MIMO array in scenarios with strong mutual coupling. © 1967-2012 IEEE.

关键词： Direction of arrival

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RoRankNet: Modeling Ordinal Progression in Alzheimer’s Disease with Plackett-Luce Ranking and Hard Instance Learning

RoRankNet: Modeling Ordinal Progression in Alzheimer’s Dise...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Minghang Yu Linlin Gao Dan Wu Jianhua Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China College of Biomedical Engineering and Instrument Science Zhejiang University Zhejiang China Department of Radiology The First Affiliated Hospital of Xiamen University Xiamen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder characterized by progression from normal control (NC) to mild cognitive impairment (MCI), and ultimately to AD. These stages reflect ordinal deterioration of specific brain tissues. Timely and accurately identifying these stages is crucial for effective treatment. While existing multi-classification methods using structural magnetic resonance imaging show promise, they often overlook the inherently ordinal progressive relations. Addressing this, our study introduces RoRankNet, a robust ranking network designed to model the ordinal progression of AD. Specifically, we propose a distance-based Plackett-Luce ranking module to effectively represent the distance relations of these stages, enhancing the alignment between extracted features and disease progression. Additionally, we present a hard instance rectification module to identify and correct challenging instances, enhancing classification robustness. Our extensive experiments demonstrate RoRankNet’s superiority in classifying NC, MCI, and AD. Notably, our approach not only preserves the ordinal nature in feature space but also excels in localizing discriminative biomarkers essential for accurate stage prediction in clinical practice. Source codes will be publicly available soon.

关键词： Accuracy Source coding Magnetic resonance imaging Biomarkers Feature extraction Brain modeling Robustness Alzheimer's disease Bioinformatics

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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A Pilot Study of Attenuation Correction for Static and Dynamic13N-Ammonia Cardiac PET Using a Conditional Generative Adversarial Network

A Pilot Study of Attenuation Correction for Static and Dynam...

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2022 IEEE Nuclear science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Sun, Hao Wang, Fanghu Yang, Yuling Hong, Xiaotong Xu, Weiping Mok, Greta S. P. Lu, Lijun Southern Medical University School of Biomedical Engineering 1023 Shatai Road Guangzhou510515 China Department of Electrical and Computer Engineering Faculty of Science and Technology China Guangdong Provincial People's Hospital Department of Nuclear Medicine Guangzhou China

ISBN: (纸本)9781665488723

Attenuation correction (AC) of myocardial perfusion PET remains challenging in routine clinical practice due to the potential misalignment of PET and CT data, which results in image artifacts and inaccurate quantitative parameters in PET/CT. In this work, a 3D conditional generative adversarial network (cGAN) was used to directly generate attenuation-corrected PET (PET-AC) images from non-attenuation-corrected PET (PET-NAC) images. This work included 60 patients who underwent rest13N-ammonia PET/CT examinations. For each patient, one static PET and twenty-one dynamic PET frames were acquired with both AC and NAC. The network was firstly trained by 3D paired static13N-ammonia PET-NAC and CT-based AC PET (PET-CTAC) images in an end-to-end fashion (PET-DLAC). Then the pre-trained network was fine-tuned (FT) by 3D paired dynamic PET-NAC and PET-CTAC frames (PET-DLAC-FT). Five-fold cross-validation was adopted to train and test the network. Qualitative and quantitative analyses were performed by voxel-wise analysis on the standard intake values (SUV), e.g., RMSE, PSNR, SSIM, and joint histogram analysis versus the PET-CTAC images as reference. Furthermore, quantitative kinetic parameters and time activity curves of the dynamic PET were performed to evaluate the PET-DLAC-FT method. Qualitative and quantitative results show the proposed PET-DLAC and PET-DLAC-FT methods reduce attenuation artifacts significantly and achieve comparable performances with clinical CTAC. Transfer learning-based AC is feasible for dynamic cardiac PET. © 2022 IEEE.

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Multi-view semi-supervised feature selection based on adaptive graph and tensor learning

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Applied Intelligence 2025年第10期55卷 1-16页

作者： Chen, Hangyu Xie, Xijiong Chao, Guoqing Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Key Laboratory of Mobile Network Application Technology of Zhejiang Province Ningbo China School of Computer Sciences and Technolog Harbin Institute of Technology at Weihai Weihai China

Multi-view feature selection has gained popularity as a study area in the machine learning field, because it can effectively reduce the dimensionality of data. Due to the scarcity of labeled information, numerous algorithms for unsupervised feature selection have surfaced, while supervised or semi-supervised feature selection algorithms are relatively few. However, when there is a certain amount of labeled information, unsupervised learning algorithms fail to explore the true structure of samples due to their inherent limitations. A multi-view semi-supervised feature selection method based on adaptive graph and tensor learning is proposed to effectively reduce the dimension of data with few labels. The method completes the feature selection task by exploring high-order connections between views and discriminative label information. An efficient algorithm is designed to solve the resulted optimization problem. Compared with other mainstream methods, we have achieved a certain improvement in classification accuracy on multiple basic datasets which proves the superiority and universality of our method. We also conducted experiments such as convergence analysis to prove the effectiveness of our proposed method.

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An intelligent solar flare prediction model based on X-ray flux curves using Long Short-Term Memory

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Astronomical Techniques and Instruments 2025年第2期2卷 65-72页

作者： Yan Gao Li Zhang Long Xu College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang 550025China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causing magnetic ***,it is very important to accurately predict the time period of solar *** paper proposes a flare prediction model,based on physical images of active solar *** employ X-ray flux curves recorded directly by the Geostationary Operational Environmental Satellite,used as input data for the model,allowing us to largely avoid the influence of accidental errors,effectively improving the model prediction efficiency.A model based on the X-ray flux curve can predict whether there will be a flare event within 24 *** reverse can also be verified by the peak of the X-ray flux curve to see if a flare has occurred within the past 24 *** True Positive Rate and False Positive Rate of the prediction model,based on physical images of active regions are 0.6070 and 0.2410 respectively,and the accuracy and True Skill Statistics are 0.7590 and *** model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records,providing a simple method for solar flare prediction.

关键词： Neural Network Long Short-Term Memory Solar flare prediction X-ray flux curve

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SQAT-LD: SPeech Quality Assessment Transformer Utilizing Listener Dependent Modeling for Zero-Shot Out-of-Domain MOS Prediction

SQAT-LD: SPeech Quality Assessment Transformer Utilizing Lis...

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IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Kailai Shen Diqun Yan Li Dong Ying Ren Xiaoxun Wu Jing Hu Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang Key Laboratory of Mobile Network Application Technology

In this paper, we propose the speech quality assessment transformer utilizing listener dependent modeling (SQAT-LD) mean opinion score (MOS) prediction system, which was submitted to the 2023 VoiceMOS Challenge. The system is based on a combination of self-supervised learning (SSL) models and listener-dependent modeling. Due to this challenge’s emphasis on real-world and challenging zero-shot out-of-domain MOS prediction in three different voice evaluation scenarios, we specifically designed a two-branch module to predict scores and weights for each frame, aiming to achieve better generalization. In the challenge, our system achieved fourth place in Track 1a, second place in Track 1b and first place in Track 2. Additionally, we conducted an ablation study to investigate the effectiveness of our proposed method.

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