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Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

computers, Materials & Continua 2023年第9期76卷 3623-3651页

作者： Yang Yang Yuhan Long Yijing Lin Zhipeng Gao Lanlan Rui Peng Yu State Key Laboratory ofNetworking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China School of Computer Science and Engineering Nanyang Technological University639798Singapore Science and Technology on Communication Networks Laboratory The 54th Research Institute of China Electronics Technology Group CorporationShijiazhuang050051China

With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant *** existing fault diagnosismethods have the characteristics of heavy computing and storage load,and most of them have computational redundancy,which is not suitable for deployment on edge devices with limited resources and *** paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge ***,we offer a clustering-based self-knowledge distillation approach(Cluster KD),which takes the mean value of the sample diagnosis results,clusters them,and takes the clustering results as the terms of the loss *** utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high ***,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side *** propose a two-stage edge-side fault diagnosismethod(TSM)that separates fault detection and fault diagnosis into different stages:in the first stage,a fault detection model based on a denoising auto-encoder(DAE)is adopted to achieve fast fault responses;in the second stage,a diverse convolutionmodel with variance weighting(DCMVW)is used to diagnose faults in detail,extracting features frommicro andmacro *** comparison experiments conducted on two fault datasets,it is proven that the proposed method has high accuracy,low delays,and small computation,which is suitable for intelligent edge-side fault *** addition,experiments show that our approach has a smooth training process and good balance.

关键词： Fault diagnosis knowledge distillation edge-side lightweight model high similarity

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Efficient production of hydrogen-rich syngas by pyrolysis coupled with catalytic steam reforming of degradable polylactic acid waste plastics over high-entropy oxide catalyst

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International Journal of Hydrogen Energy 2025年 142卷 1102-1112页

作者： Wu, Jiaxuan He, Xi Xu, Liujie Song, Qingbin Tian, Zhipeng Wang, Chao Zhao, Ming Zhou, Guopeng Gao, Yajing National Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research Institute Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR 999078 China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR 999078 China Guangdong Provincial Key Laboratory on Functional Soft Condensed Matter School of Materials and Energy Guangdong University of Technology Guangzhou510006 China Key Laboratory for Solid Waste Management and Environment Safety Ministry of Education School of Environment Tsinghua University Beijing100084 China China Huaneng Group Energy Research Institute Beijing100031 China

To mitigate the solid waste crisis, converting waste plastics into valuable products through thermochemical processes, such as through pyrolysis, is emerging. Polylactic acid (PLA) plastics are difficult to separate from other plastics in solid waste, and mixed waste is usually directly landfilled or incinerated. Moreover, studies focused solely on the pyrolysis of PLA plastic remain relatively scarce. The large amount of tar produced during plastic pyrolysis can reduce the high-value production of hydrogen, and high-entropy oxide (HEO) catalyst exhibit excellent performance in tar conversion and anti-carbon deposition. In this research, HEO catalyst was synthesized by the sol-gel method for the pyrolysis of waste PLA plastic volatiles, aiming to generate H2-rich syngas in a two-stage integrated reactor system. During catalytic reforming, the highest hydrogen production was 124.1 mmol∙gPLA−1, and the gas yield based on plastic mass reached 259.8 wt%. The superiority of HEO catalyst stems from the synergistic interaction of various elements, enhancing the structural stability of the high-entropy system and increasing oxygen vacancies, resulting in strong C–C and C–H bond-breaking capabilities in the Ce, Mg, Fe, Co, and Ni HEO catalyst. In addition, this study investigated the mechanism of PLA's simple pyrolysis and the role of the HEO catalyst in the catalytic steam reforming of PLA. This study developed a novel high-entropy aluminate catalyst demonstrating efficient hydrogen-rich syngas production, providing an innovative approach for generating hydrogen-rich syngas from high-molecular-weight polymer plastics through pyrolysis coupled with catalytic steam reforming. © 2025 Hydrogen Energy Publications LLC

关键词： Sol-gel process

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On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods 38

On Partial Optimal Transport: Revising the Infeasibility of ...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Nguyen, Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Toh, Kim-Chuan Department of Mathematics National University of Singapore Singapore Department of Computer and Information Science University of Pennsylvania United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States IBM Research Thomas J. Watson Research Center United States Institute of Operations Research and Analytics National University of Singapore Singapore

ISBN: (纸本)1577358872

This paper studies the Partial Optimal Transport (POT) problem between two unbalanced measures with at most n supports and its applications in various AI tasks such as color transfer or domain adaptation. There is hence a need for fast approximations of POT with increasingly large problem sizes in arising applications. We first theoretically and experimentally investigate the infeasibility of the state-of-the-art Sinkhorn algorithm for POT, which consequently degrades its qualitative performance in real world applications like point-cloud registration. To this end, we propose a novel rounding algorithm for POT, and then provide a feasible Sinkhorn procedure with a revised computation complexity of Oe(n2/Ε4). Our rounding algorithm also permits the development of two first-order methods to approximate the POT problem. The first algorithm, Adaptive Primal-Dual Accelerated Gradient Descent (APDAGD), finds an Ε-approximate solution to the POT problem in Oe(n2.5/Ε). The second method, Dual Extrapolation, achieves the computation complexity of Oe(n2/Ε), thereby being the best in the literature. We further demonstrate the flexibility of POT compared to standard OT as well as the practicality of our algorithms on real applications where two marginal distributions are unbalanced. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gradient methods

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Impact of Music Signals on the Human Mind and Body: Need of Natural/Green Music Signals 5

Impact of Music Signals on the Human Mind and Body: Need of ...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Batra, Neha Lakra, Sachin Department of Computer Science & Technology Manav Rachna University Department of Computer Science & Engineering Manav Rachna International Institute of Research & Studies Haryana Faridabad India Department of Computer Science & Technology Manav Rachna University Haryana Faridabad India

ISBN: (纸本)9798400709418

Music not only gives pleasure to the mind or brain but also helps in relaxing the whole body. Studies have shown the impact of music on the Human Mind, Soul, and Body. Music has an unusual capability to conjure influential sentiments. It is mainly interesting because, unlike many other stimuli that induce emotions, such as aroma, flavor or facial manifestation, music has no evident core living or subsistence value. Further, fluctuations in certain physiological and psychological methods have been categorized in response to music signals. The paper reviews the impact of music of different genres and analyzes its impact on the human mind and body. Based on the impact of music reviewed, it is proposed to further study natural/green music produced by music instruments that are made of natural material as this natural music will be more amenable to the human mind and body. © 2023 ACM.

关键词： Physiological models

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A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

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Intelligent Automation & Soft Computing 2023年第8期37卷 1275-1291页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation *** classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS ***,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual *** ability to extract important features is vital in making RST classification more *** work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction *** used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification *** experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art *** proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt ***,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.

关键词： Road surface type classification deep learning inertial sensor deep pyramidal residual network squeeze-and-excitation module

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A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach to Trainable Freebie Optimization 3

A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Vinitha, A. Jeevitha, S. Kumar, N.B. Mahesh Sathish Kumar, L. Saravanan, D. Karpagam Academy of Higher Education Department of Computer Applications Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil Tamilnadu Virudhunagar India Hindusthan Institute of Technology Computer Science and Engineering Tamil Nadu Coimbatore India Madhya Pradesh Sehore India

ISBN: (纸本)9798331509675

Significant progress has been made in real-time object detection, with YOLOv7 emerging as a state-of-the-art model that achieves the highest levels of accuracy and speed. This study combines Bag-of-Freebies (BoF) strategies with YOLOv7 to improve performance without incurring extra computational expenses. Our tests show that adding BoF preserves real-time inference capabilities while increasing mAP@0.5 by 3.5%. The accuracy and FPS of YOLOv7 with BoF are superior to those of the current YOLO-based models (YOLOv4, YOLOv5, YOLOv6). According to the results, YOLOv7 with BoF sets a new standard for real-time object detection by achieving a mAP@0.5 of 55.3% and 83 FPS on the COCO dataset. © 2025 IEEE.

关键词： Contrastive Learning

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Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

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computer Systems science & engineering 2023年第3期44卷 1939-1950页

作者： Dhanalakshmi R Sovan Samanta Arun Kumar Sivaraman Jeong Gon Lee Balasundaram A Sanamdikar Sanjay Tanaji Priya Ravindran Department of Computer Science and Engineering KCG College of TechnologyChennai600097Tamil NaduIndia Department of Mathematics Tamralipta MahavidyalayaWest Bengal721636India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127Tamil NaduIndia Division of Applied Mathematics Wonkwang UniversityIksan-SiJeonbuk54538Korea School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of TechnologyChennai600127Tamil NaduIndia Department of Instrumentation Engineering PDEAs College of EngineeringManjari412307PuneIndia Engineering and Research Services HCL TechnologiesChennai600119India

Fuzzy sets have undergone several expansions and generalisations in the literature,including Atanasov’s intuitionistic fuzzy sets,type 2 fuzzy sets,and fuzzy multisets,to name a *** can be regarded as fuzzy multisets from a formal standpoint;nevertheless,their interpretation differs from the two other approaches to fuzzy multisets that are currently *** fuzzy sets(HFS)are very useful if consultants have hesitation in dealing with group decision-making problems between several possible ***,these possible memberships can be not only crisp values in[0,1],but also interval values during a practical evaluation *** bipolar valued fuzzy set(HBVFS)is a generalization of *** paper aims to introduce a general framework of multi-attribute group decision-making using social *** propose two types of decision-making processes:Type-1 decision-making process and Type-2 decision-making *** the Type-1 decision-making process,the experts’original opinion is proces for thefinal ranking of *** Type-2 decision making processs,there are two major aspects we ***,consistency tests and checking of consensus models are given for detecting that the judgments are logically ***,the framework demands(partial)decision-makers to review their ***,the coherence and consensus of several HBVFSs are established forfinal ranking of *** proposed framework is clarified by an example of software packages selection of a university.

关键词： Group decision-making aggregation operators hesitant bipolar-valued fuzzy set

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Crafting Customer Segmentation Strategies for Advanced Recommendation System 3

Crafting Customer Segmentation Strategies for Advanced Recom...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Seethalakshmi, K. Bhagyalakshmi, A. Anitha, G. Sheela, E. Sandeep, Varada Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Tami Nadu Chennai India S.A Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai India St Peter's Institute of Higher Education and Research Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350369083

With the static nature of recommender systems, it is crucial to take into account the intricate details of customer behaviour to better improve operating efficiencies. This paper is about original methods to augment recommendation frameworks by employing spectral clustering methods, to be more specific. Limited segmentation techniques now cannot catch up with the growing complexity and variety of customer conduct. To address this need, we are proposing a new way in which the spectral clustering technique, a supervised learning technique, can be applied. Revealing in-depth relationships inside customer data will enable an upgrade in the segmentation approach beyond the existing norms. Spectral clustering stands out as a really amazing tool for accomplishing extraordinarily precise customer segments, which help understand the tastes of users on a deeper level. Through discovering separate customer groups, we are aiming for a meaningful contribution in the field of recommendation systems. This project combines the effectiveness of spectral clustering with the convenience of a recommendation system that aims to provide a holistic, meaningful, and user-oriented approach to enhancing recommendation systems in the area of personalised recommendation. Our aim is to create recommendation systems that are not only attuned to the heterogeneity of customer preferences but also adopt new trends in user experience design beforehand. The segmentation is done with K-Means along with the proposed algorithm and the clusters are hence compared and Implementation results shows that spectral clustering is much more efficient than the existing K-Means algorithm © 2024 IEEE.

关键词： Sales

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Fast target-aware learning for few-shot video object segmentation

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science China(Information sciences) 2022年第8期65卷 71-86页

作者： Yadang CHEN Chuanyan HAO Zhi-Xin YANG Enhua WU Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and Technology School of Education Science and Technology Nanjing University of Posts and Telecommunications State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of Macau State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of Sciences Faculty of Science and Technology University of Macau

Few-shot video object segmentation(FSVOS) aims to segment a specific object throughout a video sequence when only the first-frame annotation is given. In this study, we develop a fast target-aware learning approach for FSVOS, where the proposed approach adapts to new video sequences from its firstframe annotation through a lightweight procedure. The proposed network comprises two models. First, the meta knowledge model learns the general semantic features for the input video image and up-samples the coarse predicted mask to the original image size. Second, the target model adapts quickly from the limited support set. Concretely, during the online inference for testing the video, we first employ fast optimization techniques to train a powerful target model by minimizing the segmentation error in the first frame and then use it to predict the subsequent frames. During the offline training, we use a bilevel-optimization strategy to mimic the full testing procedure to train the meta knowledge model across multiple video *** proposed method is trained only on an individual public video object segmentation(VOS) benchmark without additional training sets and compared favorably with state-of-the-art methods on DAVIS-2017, with a J &F overall score of 71.6%, and on YouT ubeVOS-2018, with a J &F overall score of 75.4%. Meanwhile,a high inference speed of approximately 0.13 s per frame is maintained.

关键词： video object segmentation few-shot target-aware meta knowledge bilevel-optimization

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Secrecy Capacity Analysis with Imperfect Channel State Information and Varying Interference for 6G C-V2X Communication 10

Secrecy Capacity Analysis with Imperfect Channel State Infor...

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10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Ayaz, Hina Abbas, Ghulam Abbas, Ziaul Haq Waqas, Muhammad Gik Institute of Engineering Sciences and Technology Facutly of Computer Science and Engineering Topi23460 Pakistan Research Center Topi23460 Pakistan University of Bahrain College of Information Technology Computer Engineering Department 32038 Bahrain

ISBN: (纸本)9798350329674

It has been observed that the use of radio-frequency fingerprinting (RF-FP) for location estimation (LE) of vehicles can significantly improve secrecy capacity (SC) for urban scenarios in 6th generation cellular vehicle-to-everything (6G C-V2X) communication. However, in most of the literature, interference is considered constant for simplicity. Thus, there is a need to investigate the impact of varying levels of interference and incomplete channel state information (CSI) on the performance of SC. This study examines the impact of shadowing and interference in the presence of heavy traffic and obstructions. We have proposed a technique that analyzes the effects of varying levels of interference for LE via RF-FP to improve SC with incomplete CSI. Simulation results demonstrate that interference has a significant impact on SC which depends on the distance and location of the eavesdropping vehicle. However, a decrease in SC is not solely caused by the increase in interference, since other factors, such as the speed and relative position of the illegitimate vehicles as well as incomplete CSI, can also significantly affect the SC performance, as demonstrated through simulations. © 2023 IEEE.

关键词： Vehicle to Everything

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