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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Wang, Li Zhang, Xinyu Zhao, Fachuan Wu, Chuze Wang, Yichen Song, Ziying Yang, Lei Xu, Bin Li, Jun Ge, Shuzhi Sam School of Machanical Engineering Beijing Institute of Technology Beijing China School of Vehicle and Mobility Tsinghua University Beijing China School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering Interactive Digital Media Institute Social Robotics Laboratory National University of Singapore Singapore

Non-maximum suppression (NMS) is an essential post-processing module in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overreliance on classification scores and difficulties in determining appropriate thresholds can affect the resulting accuracy directly. To address these issues, we introduce fuzzy learning into NMS and propose a novel generalized Fuzzy-NMS module to achieve finer candidate bounding box filtering. The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process. Adequate validation experiments use the mainstream KITTI and large-scale Waymo 3D object detection benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS module can improve the accuracy of numerous recently NMS-based detectors significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is particularly evident for small objects such as pedestrians and bicycles. As a plug-and-play module, Fuzzy-NMS does not need to be retrained and produces no obvious increases in inference time. IEEE

关键词： Object detection

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Convergence of Monte Carlo Exploring Starts with TD-Learning 63

Convergence of Monte Carlo Exploring Starts with TD-Learning

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Winnicki, Anna Srikant, R. University of Illinois at Urbana-Champaign Coordinated Science Laboratory Department of Electrical and Computer Engineering United States Uiuc C3.ai Digital Transformation Institute United States

ISBN: (纸本)9798350316339

The use of TD-learning has been widely employed in reinforcement learning algorithms due to its efficiency and practicality. Herein, we study the convergence of a variant of Monte Carlo Exploring Starts when operatornameTD(λ) is used in policy evaluation and policy improvement, and lookahead is used in the policy improvement step. Our results provide a threshold for the amount of lookahead that ensures convergence of Monte Carlo Exploring Starts with T D(λ) as a function of λ in[0,1]. © 2024 IEEE.

关键词： Adversarial machine learning

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An Enhancing Stability Control Strategy of VSG-Based Power Source Under PWM Rectifier Load

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 808-818页

作者： Li, Gaoxiang Liu, Bowen Song, Wenhao Jining, Chen Liu, Xiao Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology School of Electrical Engineering Nanning530004 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530200 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing School of Computer Electronics and Information Nanning530004 China

Under PWM rectifier load, the power source based on virtual synchronous generator (VSG) is prone to cause mid-frequency oscillation. Firstly, the system oscillation mechanism and characteristic of the VSG-based power source are analyzed based on impedance method. Due to the negative resistance and capacitive characteristic in the middle frequency range, the PWM rectifier load is prone to interact with the VSG-based power source, which can lead to impedance resonance. Secondly, to improve the stability of the VSG-based power source under the PWM rectifier load, a voltage differentiation feedforward control is proposed. In addition, since the mid-frequency impedance magnitude decrease obviously, the improved VSG-based power source is closer to the ideal voltage source, which can automatically suppress the middle harmonics induced by uncontrollable rectifier load. Finally, the proposed control and the corresponding theoretical analysis are verified by simulations and experiments. © 1975-2011 IEEE.

关键词： Synchronous generators

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DUAL-STEP OPTIMIZATION FOR BINARY SEQUENCES WITH HIGH MERIT FACTORS

arXiv

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arXiv 2024年

作者： Pšeničnik, Blaž Mlinarič, Rene Brest, Janez Bošković, Borko Computer Architecture and Languages Laboratory Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia

MSC Codes 68T20The problem of finding aperiodic low auto-correlation binary sequences (LABS) presents a significant computational challenge, particularly as the sequence length increases. Such sequences have important applications in communication engineering, physics, chemistry, and cryptography. This paper introduces a novel dual-step algorithm for long binary sequences with high merit factors. The first step employs a parallel algorithm utilizing skew-symmetry and restriction classes to generate sequence candidates with merit factors above a predefined threshold. The second step uses a priority queue algorithm to refine these candidates further, searching the entire search space unrestrictedly. By combining GPU-based parallel computing and dual-step optimization, our approach has successfully identified new best-known binary sequences for all lengths ranging from 450 to 527, with the exception of length 518, where the previous best-known value was matched with a different sequence. This hybrid method significantly outperforms traditional exhaustive and stochastic search methods, offering an efficient solution for finding long sequences with good merit factors. © 2024, CC BY.

关键词： Stochastic systems

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engineering CsPbI2Br Perovskite Thin Film Morphology with Ionic Liquid Additives for Enhanced Performance Solar Cells 52

Engineering CsPbI2Br Perovskite Thin Film Morphology with Io...

引用

52nd IEEE Photovoltaic Specialist Conference, PVSC 2024

作者： Zhumadil, Gulzhan Pavlenko, Vladimir Nigmetova, Gaukhar Yelzhanova, Zhuldyz Balanay, Mannix P. Ng, Annie School of Engineering and Digital Sciences Nazarbayev University Department of Electrical and Computer Engineering Astana010000 Kazakhstan Nazarbayev University National Laboratory of Astana Astana010000 Kazakhstan Nazarbayev University Department of Chemistry Sciences and Humanities Astana010000 Kazakhstan

ISBN: (纸本)9781665464260

Ionic liquids (ILs) have emerged as a promising class of additive for modulating perovskite crystallization behavior and thin film morphology. However, the influence of incorporating ILs directly within the cesium lead iodide bromide (CsPbI2Br) perovskite precursor system is less explored. This work aims to investigate the impact of using various IL additives in the bulk of the CsPbI2Br perovskite material and how it affects thin film formation. We study the morphology of CsPbI2Br when incorporated with five different ILs based on the common 1-ethyl-3-methylimidazolium [EMIM]+cation, but varying the anion: bis(pentafluoroethylsulfonyl)imide ([EMIM]+[PFSI]-), bis(trifluoromethylsulfonyl)imide ([EMIM]+[TFSI]-), diethyl phosphate ([EMIM]+[DEP]-), dimethyl phosphate ([EMIM]+[DMP]-), and hexafluorophosphate ([EMIM]+[PF6]-). Through characterization of the perovskite films, we found that the morphology of CsPbI2Br is largely different depending on the choice of IL used. These morphological changes provide insights into how different IL-perovskite interactions affect crystallization and thus thin film quality. The experimental results help in understanding of IL-mediated crystallization, facilitating the development of additive engineering for high-performance perovskite solar cells (PSCs). © 2024 IEEE.

关键词： Perovskite

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Rockburst prediction using artificial intelligence techniques:A review

Rock Mechanics Bulletin

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Rock Mechanics Bulletin 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

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Sound of Reason: Using AI to build a digital Twin after the Kanon of Pythagoras in school settings 10

Sound of Reason: Using AI to build a Digital Twin after the ...

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10th IEEE Smart World Congress, SWC 2024

作者： Anthis, Zach Basiakoulis, Alexandros Efstathiou, Kyriakos Efstathiou, Marianna Department of Communication Culture and Media London United Kingdom Aristotle University of Thessaloniki Laboratory for Machine Tools and Manufacturing Engineering Department of Mechanical Engineering Thessaloniki Greece Cyprus University of Technology Digital Heritage Research Lab Department of Electrical Engineering Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798331520861

This paper presents ongoing work on kanON, an AI-driven EdTech tool designed to foster interactive learning and support digital Cultural Heritage (DCH) preservation. kanON facilitates the digital reconstruction of the infamous Pythagorean monochord, by allowing users to seamlessly manipulate parameters such as origin (location and epoch), materials, and shape/dimensions. Through the integration of Machine Learning (ML) algorithms, the tool predicts missing archaeological data and generates real-time 3D renderings based on user input, accompanied by corresponding confidence metrics. A mixed-methods (pilot) study was conducted in classroom environments to evaluate its educational impact via quantitative Likert scale assessments and qualitative content analysis of educator feedback. Preliminary results demonstrate high levels of student engagement and reflect positive educator responses, highlighting its potential to enhance the in-class accessibility and understanding of complex historical concepts, as well as to enable the preservation and exploration of cultural artifacts through digital means. Future work will focus on refining kanON and expanding its applicability within both educational and DCH contexts, by introducing the capability of outputting a fully operational digital twin, that integrates mixed reality functionalities. The idea is to enable users to later engage with their (personalized) model dynamically (e.g., readjusting string length and tension), thus allowing them to produce different soundscapes in real-time. This advancement aims to deepen the pedagogical impact by merging tactile interaction with visual and auditory feedback, further enhancing the educational experience via immersive, experiential learning. © 2024 IEEE.

关键词： Adversarial machine learning

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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A Novel Deep Multi-Instance Convolutional Neural Network for Disaster Classification From High-Resolution Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 2098-2114页

作者： Li, Chengfan Zhang, Zixuan Liu, Lan Kim, Jung Yoon Sangaiah, Arun Kumar the School of Computer Engineering and Science Shanghai University Shanghai200444 China the Key Laboratory of National Geographic Census and Monitoring Ministry of Natural Resources Wuhan University Wuhan430079 China the Key Laboratory for Digital Land and Resources of Jiangxi Province East China University of Technology Nanchang330013 China the School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China the College of Future Industry Gachon University Seongnam-Si Gyeonggi-do13120 Korea Republic of the International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu64002 Taiwan the Department of Electrical and Computer Engineering Lebanese American University Byblos11020 Lebanon

—The fully supervised deep convolutional neural network (CNN) cannot detect the discriminant local information that is responsible for spatial transformations in high-resolution remote sensing images. To address the various types and missing labels of natural disasters, a new deep multi-instance convolutional neural network (DMCNN) model for disaster classification in high-resolution remote sensing image is presented in this article. Specifically, based on sample enhancement and atrous spatial pyramid pooling, we first extract and integrate the features via the CNN structure to obtain the instance feature of bags in the image. Besides, introducing a prototype learning layer with distance measure, the instance features extracted from pretrained CNN are mapped into a series of prototype instance features with bag-level. Subsequently, all instance features from prototype and bag take part in disaster detection and image classification. Finally, we conduct extensive experiments on xBD dataset and discussions from qualitative and quantitative aspects. Experimental results show that the proposed DMCNN model achieves better classification accuracy of natural disaster from high-resolution remote sensing images compared to traditional CNNs, and effectively improves the disaster classification performance with weakly supervised from high-resolution remote sensing images. © 2023 The Authors.

关键词： Remote sensing

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Intelligent Maturity Detection of Daylily Based on Improved YOLOv8 16th

Intelligent Maturity Detection of Daylily Based on Improved ...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Chen, Le Wu, Ligang Shi, Jianhua Wu, Yeqiu College of Coal Engineering Shanxi Datong University Datong037003 China College of Mechanical and Electrical Engineering Shanxi Datong University Datong037003 China Intelligent Manufacturing and Digital Twin Technology Innovation Laboratory of Shanxi Datong037003 China Datong Key Laboratory of Intelligent Manufacturing Digital Twin Synchronous Virtual and Physical Technology Datong037003 China Shanxi Engineering Research Center Based On Twin Operation and Control Technology for Digital Production Lines Datong037003 China College of Architecture and Geomatics Engineering Shanxi Datong University Datong037003 China

ISBN: (纸本)9789819617760

Aiming at the problems of low precision and slow detection of daylily maturity, an intelligent detection method based on YOLOv8 daylily maturity is proposed. This intelligent detection method incorporates the SimAM into the C2f module to enhance the model's attention towards daylily. The utilization of the CARAFE module aims to enhance the extraction of crucial features. Furthermore, the study employs the Inner-loU as a loss function to enhance the network's bounding box regression performance. The findings from the experiment indicate that the YOLOv8 Daily algorithm enhances both detection precision and recall by 1.55% and 1.00%, respectively, in comparison to the baseline model. The mAP@0.5 achieved by the YOLOv8 Daily algorithm is 88.28%, and the average reconciliation value is enhanced to 0.86. Furthermore, the algorithm demonstrates a real-time detection speed of 283.2 frames per second. These results offer valuable insights for further exploration in the field of intelligent and real-time detection of daylily. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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