检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Bothe, Shruti Masood, Usama Farooq, Hasan Imran, Ali AI4Networks Research Center Dept. of Electrical & Computer Engineering University of Oklahoma United States

Mobile cellular network operators spend nearly a quarter of their revenue on network maintenance and management. A significant portion of that budget is spent on resolving faults diagnosed in the system that disrupt or degrade cellular services. Historically, the operations to detect, diagnose and resolve issues were carried out by human experts. However, with diversifying cell types, increased complexity and growing cell density, this methodology is becoming less viable, both technically and financially. To cope with this problem, in recent years, research on self-healing solutions has gained significant momentum. One of the most desirable features of the self-healing paradigm is automated fault diagnosis. While several fault detection and diagnosis machine learning models have been proposed recently, these schemes have one common tenancy of relying on human expert contribution for fault diagnosis and prediction in one way or another. In this paper, we propose an AI-based fault diagnosis solution that offers a key step towards a completely automated self-healing system without requiring human expert input. The proposed solution leverages Random Forests classifier, Convolutional Neural Network and neuromorphic based deep learning model which uses RSRP map images of faults generated. We compare the performance of the proposed solution against state-of-the-art solution in literature that mostly use Naive Bayes models, while considering seven different fault types. Results show that neuromorphic computing model achieves high classification accuracy as compared to the other models even with relatively small training data. Copyright © 2020, The Authors. All rights reserved.

关键词： Failure analysis

来源：评论

学校读者我要写书评

暂无评论

Development of Fault Diagnosis Program for reducing Power Loss Cost of the Photovoltaic Power System using Actual Operation Data

引用

Transactions of the Korean Institute of electrical Engineers 2020年第11期69卷 1682-1688页

作者： Cho, Kyeong-Hee Kim, Eung-Sang Lee, Doug-Kyu Lee, Munsu Park, June Ho Dept. of Electrical and Computer Engineering Pusan National University Korea Republic of Digital Energy System Research Center Korea Electrotechnology Research Institute Korea Republic of

Countries are looking for a pathway toward a sustainable transition from fossil-based to less or zero-carbon sources in energy sector in order to reduce its impact from climate change. Among different renewable resources, photovoltaic power system (PV) is considered as one of the most promising technology, which has the biggest potential for increasing renewable energy. However, its profit can be varied depending on operation and management, which possibly causes performance degradation and safety issues due to faults. Therefore, we have collected actual operation data from the PV monitoring system which located in Changwon. Gyeong-nam province during one year. While most of the PV system has security function which is limited to its inverter protection, in this study, based on fault data software program is developed for fault diagnosis in order to increase robustness of the PV system and minimize operation cost. Also, the program comprises machine learning algorithm based on fault data to classify its types of faults. It also presents economic loss of each PV module considering mean time to repair (Mi IK) occurred from the event of faults in the PV system As a result, the program helps faster fault diagnosis of the PV system and decreasing overall operation cost for the system operator. © 2020 Korean Institute of electrical Engineers. All rights reserved.

关键词： Failure analysis

来源：评论

学校读者我要写书评

暂无评论

An Approach to Prevent Weight Manipulation by Minimum Adjustment and Maximum Entropy Method in Large-Scale Group Decision Making

SSRN

引用

SSRN 2022年

作者： Sun, Qi Wu, Jian Chiclana, Francisco Wang, Sha Herrera-Viedma, Enrique School of Economics and Management Shanghai Maritime University Shanghai201306 China Center for Artificial Intelligence and Decision Sciences Shanghai Maritime University Shanghai201306 China Institute of Artificial Intelligence Faculty of Computing Engineering and Media De Montfort University Leicester United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence Dept. of Computer Science and AI University of Granada Granada18071 Spain Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

This paper proposes a theoretical framework to prevent weight manipulation in large-scale group decision making (LSGDM). Firstly, a community detection based method is used to cluster the large group. The power relations of subgroups are measured by the rights index (RI), which depends on the subgroups size and cohesion. Then, a minimum adjustment feedback model with maximum entropy is proposed to prevent subgroups’manipulation behavior. The minimum adjustment rule aims for ‘effificiency’ while the maximum entropy rule aims for ‘justice’. Consequently, by keeping a balance between ‘effificiency’ and ‘justice’ in the mechanism of assigning weights, the rationality of weight allocation to reach consensus effificiently can be ensured. Detailed numerical and simulation analyses are carried out to verify the validity of the proposed method. © 2022, The Authors. All rights reserved.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Fast learning of graph neural networks with guaranteed generalizability: One-hidden-layer case 37

Fast learning of graph neural networks with guaranteed gener...

引用

37th International Conference on Machine Learning, ICML 2020

作者： Zhang, Shuai Wang, Meng Liu, Sijia Chen, Pin-Yu Xiong, Jinjun Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute NY United States MIT-IBM Waston AI Lab CambridgeMA United States IBM Thomas J. Watson Research Center Yorktown HeightsNY United States

ISBN: (纸本)9781713821120

Although graph neural networks (GNNs) have made great progress recently on learning from graph-structured data in practice, their theoretical guarantee on generalizability remains elusive in the literature. In this paper, we provide a theoretically-grounded generalizability analysis of GNNs with one hidden layer for both regression and binary classification problems. Under the assumption that there exists a groundtruth GNN model (with zero generalization error), the objective of GNN learning is to estimate the ground-Truth GNN parameters from the training data. To achieve this objective, we propose a learning algorithm that is built on tensor initialization and accelerated gradient descent. We then show that the proposed learning algorithm converges to the ground-Truth GNN model for the regression problem, and to a model sufficiently close to the ground-Truth for the binary classification problem. Moreover, for both cases, the convergence rate of the proposed learning algorithm is proven to be linear and faster than the vanilla gradient descent algorithm. We further explore the relationship between the sample complexity of GNNs and their underlying graph properties. Lastly, we provide numerical experiments to demonstrate the validity of our analysis and the effectiveness of the proposed learning algorithm for GNNs. © 2020 by the Authors All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

(Invited) Printed Electrodes for All-Solution-Processed Inverted-Structure OLEDs

(Invited) Printed Electrodes for All-Solution-Processed Inve...

引用

International Conference on Display Technology, ICDT 2019

作者： Hong, Yongtaek Park, Jongjang Kim, Geonhee Yoon, Hyungsoo Jeong, Sujin Lee, Seunghwan Dept. Electrical and Computer Engineering Inter-University Semiconductor Research Center Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781510896161

A key enabling technology for all-printed organic light-emitting diodes is printing electrodes that can be used for anodes or cathodes for conventional or inverted device structures. In both device structures, the first bottom electrodes, which are either transparent or opaque depending on the device structure, are typically printed while the last top electrodes have been deposited by using a vacuum-based evaporation process because when the last top electrodes are fabricated by using a solution-based process, under organic layers are easily damaged during the fabrication process. Although so-called transfer printing process has been often reported for organic devices such as organic solar cells, it has been rarely applied for OLEDs. In addition, no fine patterning has been adopted in such printing processes. As part of our efforts towards all-printed OLEDs, inkjet-printed and/or transfer-printed, finely-patterned PEDOT:PSS electrodes have been investigated in our lab and most recent achievement will be reported in detail. In order to implement all-solution-processed OLEDs, we also studied the inverted-structure OLEDs, which showed comparable results to the conventional structure OLEDs with the vacuum-based deposited electrodes. © 2019, John Wiley and Sons Inc. All rights reserved.

关键词： Electrodes

来源：评论

学校读者我要写书评

暂无评论

Generation of polarization entanglement via the quantum Zeno effect

arXiv

引用

arXiv 2022年

作者： Nodurft, Ian C. Shaw, Harry Glasser, Ryan T. Kirby, Brian T. Searles, Thomas A. Dept. of Electrical & Computer Engineering University of Illinois Chicago ChicagoIL60607 United States Peraton GreenbeltMD20770 United States NASA Goddard Space Flight Center GreenbeltMD20771 United States Department of Physics and Engineering Physics Tulane University New OrleansLA70118 United States DEVCOM US Army Research Laboratory AdelphiMD20783 United States

The quantum Zeno effect reveals that the continuous observation of a quantum system can result in significant alterations to its evolution. Here, we present a method for establishing polarization entanglement between two initially unentangled photons in coupled waveguides via the quantum Zeno effect. We support our analytical investigation with numerical simulations of the underlying Schrodinger equation describing the system. Further, we extend our technique to three coupled waveguides in a planar configuration and determine the parameter regime required to generate three-qubit W-states. Our findings offer a powerful quantum state engineering approach for photonic quantum information technologies. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum optics

来源：评论

学校读者我要写书评

暂无评论

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

arXiv

引用

arXiv 2022年

作者： Moridian, Parisa Ghassemi, Navid Jafari, Mahboobeh Salloum-Asfar, Salam Sadeghi, Delaram Khodatars, Marjane Shoeibi, Afshin Khosravi, Abbas Ling, Sai Ho Subasi, Abdulhamit Alizadehsani, Roohallah Gorriz, Juan M. Abdulla, Sara A. Acharya, U. Rajendra Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Neurological Disorders Research Center Qatar Biomedical Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Deakin University GeelongVIC Australia UltimoNSW Australia Institute of Biomedicine Faculty of Medicine University of Turku Turku Finland Dept. of Computer Science College of Engineering Effat University Jeddah Saudi Arabia Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming;therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Stackelberg Differential Game-based Optimal Fault Estimation and Accommodation for Continuous-time Linear Systems

引用

IFAC-PapersOnLine 2020年第2期53卷 13751-13756页

作者： Yuhang Xu Hao Yang Bin Jiang Marios M. Polycarpou The College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China KIOS Research and Innovation Center of Excellence and the Dept. of Electrical and Computer Engineering University of Cyprus Nicosia 1678 Cyprus

This paper deals with the optimal fault estimation and accommodation problem for a class of linear systems in the framework of Stackelberg differential game theory. In this framework, the observer plays the role of the follower, while the system plays the role of the leader in making sequential decisions. A dual controller approach is used to design an auxiliary controller for the observer such that it can non-cooperate with the controller of the system to achieve the Stackelberg equilibrium. To achieve the online updating of the fault-tolerant controller, an adaptive dynamic programming methodology is used by establishing two critic neural networks for the observer and system respectively. Finally, a simulation is presented to illustrate the efficiency and applicability of the theoretical results.

关键词： Optimal fault estimation optimal fault-tolerant control Stackelberg differential game adaptive dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Tailored learning-based scheduling for kubernetes-oriented edge-cloud system

arXiv

引用

arXiv 2021年

作者： Han, Yiwen Shen, Shihao Wang, Xiaofei Wang, Shiqiang Leung, Victor C.M. TANKLab College of Intelligence and Computing Tianjin University Tianjin China IBM T. J. Watson Research Center Yorktown HeightsNY United States College of Computer Science and Software Engineering Shenzhen University Shenzhen China Dept. of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

Kubernetes (k8s) has the potential to merge the distributed edge and the cloud but lacks a scheduling framework specifically for edge-cloud systems. Besides, the hierarchical distribution of heterogeneous resources and the complex dependencies among requests and resources make the modeling and scheduling of k8s-oriented edge-cloud systems particularly sophisticated. In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud systems to improve the long-term throughput rate of request processing. First, we design a coordinated multi-agent actor-critic algorithm to cater to decentralized request dispatch and dynamic dispatch spaces within the edge cluster. Second, for diverse system scales and structures, we use graph neural networks to embed system state information, and combine the embedding results with multiple policy networks to reduce the orchestration dimensionality by stepwise scheduling. Finally, we adopt a two-time-scale scheduling mechanism to harmonize request dispatch and service orchestration, and present the implementation design of deploying the above algorithms compatible with native k8s components. Experiments using real workload traces show that KaiS can successfully learn appropriate scheduling policies, irrespective of request arrival patterns and system scales. Moreover, KaiS can enhance the average system throughput rate by 14.3% while reducing scheduling cost by 34.7% compared to baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Scheduling

来源：评论

学校读者我要写书评

暂无评论

Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

引用

Saudi Arabia Smart Grid Conference (SASG)

作者： Yiannis Kamarianakis Yannis Pantazis Evangelia Kalligiannaki Theodoros D. Katsaounis Konstantinos Kotsovos Issam Gereige Marwan Abdullah Aqil Jamal Athanasios Tzavaras Data Science Group Institute of Applied and Computational Mathematics Foundation for Research and Technology-Hellas Greece Dept. of Mathematics and Applied Mathematics University of Crete Greece Carbon Management Division Saudi Aramco R&D Center Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average.

关键词：

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：