检索结果-内蒙古大学图书馆

100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Raina, Rashika Simmons, Nidhi Simmons, David E. Yacoub, Michel Daoud Queen's University Centre for Wireless Innovation School of Electronics Electrical Engineering and Computer Science BelfastBT3 9DT United Kingdom Dhali Holdings Ltd. BelfastBT5 7HW United Kingdom University of Campinas Wireless Technology Laboratory School of Electrical and Computer Engineering Campinas13083-970 Brazil

ISBN: (纸本)9798331517786

In this paper, we explore the calibration of a machine learning (ML)-based outage predictor aimed at optimizing resource allocation to minimize outages in communication systems. We model the wireless channel using an auto-correlated time series with samples distributed in accordance with a Rayleigh fading process. Our novel contribution concerns proposing histogram-based reliability plots that employ logarithmic binning to assess its impact on the model calibration compared to the traditional linear binning technique. We train our ML model using an outage loss function (OLF) tailored to this system and the well-known binary cross entropy (BCE). Additionally, we analyze the effect of different model parameters on the calibration performance of this outage predictor. Our findings demonstrate that logarithmic binning reveals nuanced calibration traits ignored by linear binning, particularly at lower confidence levels. This finding is crucial for wireless systems for which understanding behavior at very low probabilities is essential. Additionally, we observe that our ML model trained with OLF becomes more overconfident as the classification threshold increases and in scenarios characterized with rare events, namely outages. This observation serves as a tool for improving the calibration properties of OLF. © 2024 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Degradation mechanism analysis of LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2) single crystal cathode materials through machine learning

引用

npj Computational Materials 2022年第1期8卷 2119-2127页

作者： Wuxin Sha Yaqing Guo Danpeng Cheng Qigao Han Ping Lou Minyuan Guan Shun Tang Xinfang Zhang Songfeng Lu Shijie Cheng Yuan-Cheng Cao School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan430074China State Grid Huzhou Electric Power Supply Company Huzhou313000China School of Cyber Science&Engineering Huazhong University of Science and TechnologyWuhan430074China

LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(NCM523)has become one of the most popular cathode materials for current lithium-ion batteries due to its high-energy density and cost ***,the rapid capacity fading of NCM severely hinders its development and ***,the single crystal NCM523 materials under different degradation states are characterized using scanning transmission electron microscopy(STEM).Then we developed a neural network model with a two-sequential attention block to recognize the crystal structure and locate defects in STEM *** number of point defects in NCM523 is observed to experience a trend of increasing first and then decreasing in the degradation *** space between the transition metal columns shrinks obviously,inducing dramatic capacity *** analysis sheds light on the defect evolution and chemical transformation correlated with layered material *** also provides interesting hints for researchers to regenerate the electrochemical capacity and design better battery materials with longer life.

关键词： battery crystal analysis

来源：评论

学校读者我要写书评

暂无评论

On a novel tracking differentiator design based on iterative learning in a moving window

引用

Control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

来源：评论

学校读者我要写书评

暂无评论

Lithographic printing inspired in-situ transfer of MXene-based films with localized topo-electro tunability for high-performance flexible pressure sensors

引用

Nano Research 2023年第11期16卷 12670-12679页

作者： Qiuyang Yan Yi Zhou Yin Cheng Liangjing Shi Ranran Wang Lian Gao Jing Sun State Key Laboratory of High Performance Ceramics and Superfine Microstructure Shanghai Institute of CeramicsChinese Academy of ScienceShanghai 200050China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore School of Chemistry and Materials Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

MXene-based films have been intensively explored for construction of piezoresistive flexible pressure sensors owing to their excellent mechanical and electrical *** pressure sensitivity relies on pre-molding a flexible substrate,or regulating the micromorphology of MXene sheets,to obtain a micro-structured ***,the two avenues usually require complicated and time-consuming microfabrication or wet chemical processing,and are limited to non-adjustable topographicelectrical(topo-electro)***,we propose a lithographic printing inspired in-situ transfer(LIPIT)strategy to fabricate MXene-ink films(MIFs).In LIPIT,MIFs not only inherit ridge-and-valley microstructure from paper substrate,but also achieve localized topo-electro tunability by programming ink-writing patterns and *** MIF-based flexible pressure sensor with periodical topo-electro gradient exhibits remarkably boosted sensitivity in a wide sensing range(low detection limit of 0.29 Pa and working range of 100 kPa).The MIF sensor demonstrates versatile applicability in both subtle and vigorous pressuresensing fields,ranging from pulse wave extraction and machine learning-assisted surface texture recognition to piano-training glove(PT-glove)for piano *** LIPIT is quick,low-cost,and compatible with free ink/substrate combinations,which promises a versatile toolbox for designing functional MXene films with tailored morphological-mechanical-electrical properties for extended application scenarios.

关键词： MXene film transfer printing surface tunability flexible pressure sensor

来源：评论

学校读者我要写书评

暂无评论

Improving Small License Plate Detection with Bidirectional Vehicle-Plate Relation 1

引用

30th International Conference on MultiMedia Modeling, MMM 2024

作者： Dai, Songkang Chen, Song-Lu Liu, Qi Zhu, Chao Liu, Yan Chen, Feng Yin, Xu-Cheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China EEasy Technology Company Ltd. Zhuhai China

ISBN: (数字)9783031533082

ISBN: (纸本)9783031533075

License plate detection is a critical component of license plate recognition systems. A challenge in this domain is detecting small license plates captured at a considerable distance. Previous researchers have proved that pre-detecting the vehicle can enhance small license plate detection. However, this approach only utilizes the one-way relation that the presence of a vehicle can enhance license plate detection, potentially resulting in error accumulation if the vehicle fails to be detected. To address this issue, we propose a unified network that can simultaneously detect the vehicle and the license plate while establishing bidirectional relationships between them. The proposed network can utilize the vehicle to enhance small license plate detection and reduce error accumulation when the vehicle fails to be detected. Extensive experiments on the SSIG-SegPlate, AOLP, and CRPD datasets prove our method achieves state-of-the-art detection performance, achieving an average detection AP0.5 of 99.5% on these three datasets, especially for small license plates. When incorporating a license plate recognizer that relies on character detection, we can achieve an average recognition accuracy of 95.9%, surpassing all comparative methods. Moreover, we have manually annotated the vehicles within the CRPD dataset and have made these annotations publicly available at https://***/kiki00007/CRPDV. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： License plates (automobile)

来源：评论

学校读者我要写书评

暂无评论

Synthesis of wafer-scale graphdiyne/graphene heterostructure for scalable neuromorphic computing and artificial visual systems

引用

Nano Research 2021年第12期14卷 4591-4600页

作者： Zhi-Cheng Zhang Yi Li Jing-Jing Wang De-Han Qi Bin-Wei Yao Mei-Xi Yu Xu-Dong Chen Tong-Bu Lu Ministry of Education International Joint Laboratory of Materials Microstructure Institute for New Energy Materials and Low Carbon TechnologiesSchool of Material Science and EngineeringTianjin University of TechnologyTianjin300384China Key Laboratory of Computer Vision and Systems(Ministry of Education) School of Computer Science and EngineeringTianjin University of TechnologyTianjin300384China

Graphdiyne(GDY)is emerging as a promising material for various applications owing to its unique structure and fascinating ***,the application of GDY in electronics and optoelectronics are still in its infancy,primarily owing to the huge challenge in the synthesis of large-area and uniform GDY film for scalable *** a modified van der Waals epitaxy strategy is proposed to synthesize wafer-scale GDY film with high uniformity and controllable thickness directly on graphene(Gr)surface,providing an ideal platform to construct large-scale GDY/Gr-based optoelectronic synapse *** synaptic behaviors have been realized,and the linear and symmetric conductance-update characteristics facilitate the implementation of neuromorphic computing for image recognition with high accuracy and strong fault *** functions including“NAND”and“NOR”are integrated into the synapse which can be executed in an optical ***,a visible information sensing-memory-processing system is constructed to execute real-time image acquisition,in situ image memorization and distinction tasks,avoiding the time latency and energy consumption caused by data conversion and transmission in conventional visual *** results highlight the potential of GDY in applications of neuromorphic computing and artificial visual systems.

关键词： graphdiyne optoelectronic synapses neuromorphic computing sensing-memory-processing optical logic functions

来源：评论

学校读者我要写书评

暂无评论

Robust Adversarial Attacks Detection Based on Explainable Deep Reinforcement Learning for UAV Guidance and Planning

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2023年第10期8卷 4381-4394页

作者： Hickling, Thomas Aouf, Nabil Spencer, Phillippa University of London Northampton Square School of Mathematics Computer Science and Engineering Department of Electrical and Electronic Engineering City LondonEC1V 0HB United Kingdom Porton Down Defence Science and Technology Laboratory SalisburySP4 0LF United Kingdom

The dangers of adversarial attacks on Uncrewed Aerial Vehicle (UAV) agents operating in public are increasing. Adopting AI-based techniques and, more specifically, Deep Learning (DL) approaches to control and guide these UAVs can be beneficial in terms of performance but can add concerns regarding the safety of those techniques and their vulnerability against adversarial attacks. Confusion in the agent's decision-making process caused by these attacks can seriously affect the safety of the UAV. This article proposes an innovative approach based on the explainability of DL methods to build an efficient detector that will protect these DL schemes and the UAVs adopting them from attacks. The agent adopts a Deep Reinforcement Learning (DRL) scheme for guidance and planning. The agent is trained with a Deep Deterministic Policy Gradient (DDPG) with Prioritised Experience Replay (PER) DRL scheme that utilises Artificial Potential Field (APF) to improve training times and obstacle avoidance performance. A simulated environment for UAV explainable DRL-based planning and guidance, including obstacles and adversarial attacks, is built. The adversarial attacks are generated by the Basic Iterative Method (BIM) algorithm and reduced obstacle course completion rates from 97% to 35%. Two adversarial attack detectors are proposed to counter this reduction. The first one is a Convolutional Neural Network Adversarial Detector (CNN-AD), which achieves accuracy in the detection of 80%. The second detector utilises a Long Short Term Memory (LSTM) network. It reaches an accuracy of 91% with faster computing times compared to the CNN-AD, allowing for real-time adversarial detection. © 2016 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes

引用

Neural Computing and Applications 2025年第16期37卷 9295-9308页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

This paper presents a comprehensive framework for activity recognition and anomaly detection in smart home environments, targeting applications in convenience, efficiency, responsiveness, and healthcare. The proposed framework incorporates explainable artificial intelligence (XAI) to interpret feature impacts on learning models and optimize feature selection for activity recognition and anomaly detection. The study examines the recognition of two activity sets: Activities Set I, comprising 8 activities, and Activities Set II, comprising 11 activities. Various machine learning models (random forest (RF), decision tree (DT), and XGBoost (XGB)) are employed, along with an ensemble voting classifier (EVC) with these machine learning. EVC achieved 91% accuracy for Activities Set I and 90–91% accuracy for Activities Set II. The model demonstrated strong precision, recall, and F1-score across most classes. For anomaly detection, the Isolation Forest and H2O Isolation Forest Estimator algorithms are utilized to uncover irregularities in daily routines. The Isolation Forest identified anomalies in activities such as chores and desk-related tasks, while the H2O Isolation Forest Estimator detected no anomalies in either activity set. This study underscores the potential of the proposed framework to enhance smart home functionality and highlights avenues for future research and system improvements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Insights into Non-uniform Aging Effects on Broadband Dielectric Behaviors for High-voltage XLPE Cable Insulation: A Novel Fractional-order Circuit Modeling Approach

引用

IEEE Transactions on Dielectrics and electrical Insulation 2024年第3期32卷 1793-1801页

作者： Dai, Xize Hao, Jian Jian, Zheng Hosani, Khalifa Hassan Al Liao, Ruijin Chongqing University State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing400044 China Aalborg University Department of Energy Aalborg9220 Denmark State Grid Chongqing Electric Power Company Shinan Power Supply Branch Chongqing401336 China Khalifa University APEC Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates

A non-uniform aging pattern inside insulation systems is a significant contributor to the degradation of high-voltage (HV) power equipment. Therefore, this article presents a comprehensive investigation into the effects of non-uniform aging patterns on broadband dielectric properties of polymer insulation, with a focus on XLPE power cables, aiming to provide a deeper understanding of these effects. By examining two uniformly aged and five non-uniformly aged XLPE insulation cases, the study comparatively analyzes the influence of diverse aging patterns on broadband dielectric properties. Furthermore, a novel fractional-order circuit model is proposed to characterize broadband dielectric behaviors influenced by insulation structure heterogeneity caused by non-uniform aging patterns. The results indicate that non-uniform aging effects on real part of capacitance in XLPE insulation are highly sensitive, though the analysis remains qualitative. Subsequently, the proposed fractional-order circuit model following power-law behaviors successfully reconstructs broadband dielectric properties of non-uniformly aged XLPE insulation systems with average relative errors of less than 10%, making it a potential quantification tool to unveil underlying dynamic behaviors. This model offers a rational physical explanation and characterization of XLPE dielectric properties subject to diverse aging patterns, providing theoretical support for the aging analysis of HV cable equipment. © 1994-2012 IEEE.

关键词： Capacitance

来源：评论

学校读者我要写书评

暂无评论

Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

引用

Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：