检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Wang, Yingjie Yingxin, Li Weilong, Wang Peiyong, Duan Maradapu Vera Venkata Sai, Akshita Cai, Zhipeng School of Computer and Control Engineering Yantai University Yantai264005 China Department of Computer Science Georgia State University AtlantaGA30303 United States

With the rapid development of Internet of Things (IoT), 5G and 6G networks, and advancements in hardware and software devices has enabled mobile devices to be equipped with rich sensors (smart phones, smart bracelets, etc.). This led to devices with powerful sensing capabilities that can easily connect and communicate with other devices. As a result, ordinary users could efficiently complete various sensing data tasks and get paid accordingly, promoting the rapid development of Mobile Crowdsourcing (MCS) technology and effectively facilitating people's daily lives. However, the current MCS is still a long way from being complete and still faces several challenges. Therefore, many researchers have conducted extensive research on MCS in recent years. In this survey, we aim to provide a comprehensive summary of the recent research progress on MCS. With respect to the main research directions and real-world applications of MCS, we review the research in three main areas (task allocation, privacy protection and incentive mechanism) and real-world application scenarios of MCS. Although there is a lot of existing research on MCS, there are still some crucial problems to be solved. Therefore, we conclude with some lessons learned and suggest future research directions by analyzing the studies covered in this paper. © 2024, The Authors. All rights reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Ag...

引用

IEEE Conference on Decision and control

作者： M. H. W. Engelaar Z. Zhang E. E. Vlahakis D.V. Dimarogonas M. Lazar S. Haesaert Department of Electrical Engineering (Control Systems Group) Eindhoven University of Technology The Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into subspecifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing.

关键词： Vehicle routing Stochastic processes Probabilistic logic Prediction algorithms Real-time systems Robustness Resource management Vehicle dynamics Multi-agent systems Predictive control

来源：评论

学校读者我要写书评

暂无评论

Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining

Rock Mechanics Bulletin

引用

Rock Mechanics Bulletin 2025年第1期4卷 29-52页

作者： Yewuhalashet Fissha Prashanth Ragam Hajime Ikeda N.Kushal Kumar Tsuyoshi Adachi P.S.Paul Youhei Kawamura Department of Geosciences Geotechnologyand Materials Engineering for ResourcesGraduate School of International Resource SciencesAkita UniversityAkita010-8502Japan Department of Mining Engineering Aksum UniversityAksum7080TigrayEthiopia School of Computer Science and Engineering VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Systems Controland Information EngineeringNational Institute of TechnologyAsahikawa College2-2-1-6 Syunkodai Asahikawa city Hokkaido071-8142Japan Department of Mining Engineering Indian Institute of Technology(Indian School of Mines)Dhanbad826004India Faculty of Engineering Hokkaido UniversityKita 8Nishi 5Kita-KuSapporo0608628Japan

The vibrations generated by rock blasting are a serious and hazardous outcome of these activities,causing harmful effects on the surrounding environment as well as the nearby *** the local ecology and human communities suffer from the consequences of these *** the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity(PPV)and frequency,which are essential parameters for measuring vibration *** prediction of vibration occurrence is critically ***,this study employs five machine learning models for predicting the PPV and frequency resulting from quarry *** work compares five machine learning models(XGBoost,Catboost,Bagging,Gradient Boosting,and Random Forest Regression)to choose the most efficient performance *** performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase,there was a strong correlation observed between the actual and the predicted *** analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R^(2)=0.983,MSE=0.000078,RMSE=0.008,NRMSE=0.019,MAD=0.004,MAPE=35.197 in the PPV prediction,and R^(2)=0.975,MSE=0.000243,RMSE=0.015,NRMSE=0.031,MAD=0.008,MAPE=37.281 for the frequency *** study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration,and *** the context of the mining and civil industry,the application of this study offers significant potential for enhancing safety protocols and optimizing operational *** employing machine learning models,this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.

关键词： PPV Frequency Machine learning Blasting Regression Catboost XGboost

来源：评论

学校读者我要写书评

暂无评论

Explainable Artificial Intelligence Solution for Online Retail

引用

computers, Materials & Continua 2022年第6期71卷 4425-4442页

作者： Kumail Javaid Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Allah Ditta Muhammad Ahsan M.A.Khan Tariq Mahmood Muhammad Adnan Khan Department of Computer Science University of South AsiaLahore54000Pakistan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Division of Science and Technology University of EducationLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for *** the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the *** this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one *** main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the *** permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the *** performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC *** model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree ***,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying *** research also reveals the importance of page value as a key feature for capturing online purchasing *** may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.

关键词： Explainable artificial intelligence online retail neural network random forest regression

来源：评论

学校读者我要写书评

暂无评论

Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach 25

Real-Time Object Detection and Robotic Manipulation for Agri...

引用

25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Zhao, Hongyu Tang, Zezhi Li, Zhenhong Dong, Yi Si, Yuancheng Lu, Mingyang Panoutsos, George Imperial College The Department Of Physics London United Kingdom University Of Sheffield The Department Of Automatic Control And Systems Engineering SheffieldS1 3JD United Kingdom University Of Manchester The Department Of Electrical And Electronic Engineering Manchester United Kingdom University Of Southampton The Department Of Electronics And Computer Science Southampton United Kingdom Fudan University The Department Of Economics Shanghai200433 China AmesIA50011 United States

ISBN: (纸本)9798350340266

The optimization of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialization. Nowadays, the utilization of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of Convolutional Neural Networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The You Only Look Once(YOLO) algorithmic framework is employed with traditional Rectangular Bounding Boxes (R-Bbox) for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation. © 2024 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

A nano-sheet graphene-based enhanced thermal radiation composite for passive heat dissipation from vehicle batteries

引用

Nano Materials science 2024年第4期6卷 443-455页

作者： Ji-Xiang Wang Christopher Salmean Jiaxin Li Chaojie Lei Jun Li Mingliang Zhong Bo Qi Yufeng Mao Hebei Key Laboratory of Man-machine Environmental Thermal Control Technology and Equipment Hebei Vocational University of Technology and EngineeringHebei 054000PR China College of Electrical Energy and Power EngineeringYangzhou UniversityYangzhou225009PR China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and TechnologyKowloonHong Kong SARPR China China Academy of Launch Vehicle Technology Beijing100076PR China Beijing Sino-Spark Technology Co. Ltd.Beijing100191PR China School of Environmental Science and Engineering Suzhou University of Science and TechnologySuzhou215009PR China Institute of Optics and Electronics Chinese Academy of SciencesChengdu610209PR China National Key Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of SciencesChengdu610209PR China Taizhou Wavexploration Energy Ltd. Taizhou225513PR China Department of Electrical and Computer Engineering University of TorontoTorontoM5S3G4Canada

In response to thermal runaway(TR)of electric vehicles,recent attention has been focused on mitigation strategies such as efficient heat dredging in battery thermal *** management with particular focus on battery cooling has been becoming increasingly *** usually happened when an electric vehicle is unpowered and *** this state,traditional active battery cooling schemes are disabled,which can easily lead to dangerous incidents due to loss of cooling ability,and advanced passive cooling strategies are therefore gaining ***,we developed an enhanced thermal radiation material,consisting of~1μm thick multilayered nano-sheet graphene film coated upon the heat dissipation surface,thereby enhancing thermal radiation in the *** surface was characterized on the nanoscale,and tested in a battery-cooling *** found that the graphene-based coating's spectral emissivity is between 91% and 95% in the mid-infrared region,and thermal experiments consequently illustrated that graphene-based radiative cooling yielded up to15.1% temperature reduction when compared to the uncoated *** the novel graphene surface to augment a heat pipe,the temperature reduction can be further enlarged to 25.6%.The new material may contribute to transportation safety,global warming mitigation and carbon neutralization.

关键词： Battery thermal management Nano-sheet graphene Functional material Passive cooling Thermal radiation

来源：评论

学校读者我要写书评

暂无评论

Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method 32

Texture Analysis of Breast US Images Using Morphological Tra...

引用

32nd Mediterranean Conference on control and Automation, MED 2024

作者： Tabacaru, Gigi Moldovanu, Simona Barbu, Marian 'Dunarea de Jos' University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Automatic Control and Electrical Engineering Romania 'Dunarea de Jos' University of Galati Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Romania Dunarea de Jos University of Galati The Modelling & Simulation Laboratory Galati800008 Romania

ISBN: (纸本)9798350395440

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer. © 2024 IEEE.

关键词： Fractal dimension

来源：评论

学校读者我要写书评

暂无评论

False Data Injection Attack Detection Based on Wavelet Packet Decomposition and Random Forest in Smart Grid 23

False Data Injection Attack Detection Based on Wavelet Packe...

引用

23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Zhenyu Yuan, Shuai Wu, Longfei Guan, Zhitao Du, Xiaojiang School of Control and Computer Engineering North China Electric Power University China Brock University Department of Finance Operations and Information Systems ON Canada Fayetteville State University Department of Mathematics and Computer Science Fayetteville United States Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9781665494571

As one of the critical infrastructures, the safety and reliability of the smart grid are directly associated with the development and stability of society. However, studies have shown that the power grid is at risk when the parameters are manipulated and cyber-attacks are generated against the state estimation, i.e., under false data injection attack (FDIA). Currently, a rich body of literature has studied on the FDIA defense methods, but most of them focus on the direct current (DC) scenario. This paper proposes a novel detection model that combines the wavelet packet decomposition (WPD) technique with the random forest (RF) algorithm. The WPD is able to capture the deviation of parameters from the normal conditions, whereas the RF is developed to classify these features and effectively identify the malicious data. The proposed model is also evaluated using real-world data on IEEE 118-bus power system. The results demonstrate excellent performance on precision rate and recall rate under varying scenarios. © 2021 IEEE.

关键词： Radio frequency Training data Interference Power system stability Feature extraction Data models Wavelet packets

来源：评论

学校读者我要写书评

暂无评论

A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

引用

Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

来源：评论

学校读者我要写书评

暂无评论

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced control Applications

引用

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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：