检索结果-内蒙古大学图书馆

Lecture Notes in Electrical engineering 2025年 1294卷 vii-viii页

作者： Kalam, Akhtar Mekhilef, Saad Williamson, Sheldon S. Institute for Sustainable Industries and Liveable Cities Victoria University FootscrayVIC Australia Department of Engineering Technologies Swinburne University of Technology MelbourneVIC Australia Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University OshawaON Canada

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient Behavior Modelling for Demand Side Recommender System in Solar Microgrid Applications Using Multi Agent Reinforcement Learning Model

SSRN

引用

SSRN 2022年

作者： Onile, Abiodun Emmanuel Belikov, Juri Levron, Yoash Petlenkov, Eduard Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia The Andrew and Erna Viterbi Faculty of Electrical Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia

Electricity consumers are often faced with challenges relating the choice of optimal energy saving plan, increasing integration of transient’s renewable sources of energy promises tantalizing solution which also constitutes emerging stability challenges for the electricity Grid. Demand side management using Battery storage as grid balancing measures are crucial towards extending physical limits of existing electricity grid. On the one hand, battery storage is assuming a critical role in decarbonization efforts partly due to its tendencies to store excess electricity obtained via renewable energy sources thus enabling unlimited potentials for seasonal storage. On the other hand, while solving the identified problem, other problems relating consumer behavior towards adoption of energy/battery efficiency technologies, consumers comfort-feedback and control loop integration exist. In this study, we presented electricity energy storage (EES) technologies which are embedded into the grid and further enhanced with the use of Reinforcement learning control and recommendation system technologies for improving the grid management, the grid reliability and sustainability, mitigated operational issues, improved BESS utilization and attain self-consumption goals. The result of the study shows that operating the BESS using the multi-agent RL control strategy achieved a maximum peak load reduction of about 24.5% alongside 94% comfort improvements in certain loads. © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

EC-GAN: Inferring brain effective connectivity via generative adversarial networks 34

EC-GAN: Inferring brain effective connectivity via generativ...

引用

34th AAAI Conference on Artificial Intelligence, AAAI 2020

作者： Liu, Jinduo Ji, Junzhong Xun, Guangxu Yao, Liuyi Huai, Mengdi Zhang, Aidong Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Computer Science and Engineering SUNY at Buffalo BuffaloNY14260 United States

ISBN: (纸本)9781577358350

Inferring effective connectivity between different brain regions from functional magnetic resonance imaging (fMRI) data is an important advanced study in neuroinformatics in recent years. However, current methods have limited usage in effective connectivity studies due to the high noise and small sample size of fMRI data. In this paper, we propose a novel framework for inferring effective connectivity based on generative adversarial networks (GAN), named as EC-GAN. The proposed framework EC-GAN infers effective connectivity via an adversarial process, in which we simultaneously train two models: a generator and a discriminator. The generator consists of a set of effective connectivity generators based on structural equation models which can generate the fMRI time series of each brain region via effective connectivity. Meanwhile, the discriminator is employed to distinguish between the joint distributions of the real and generated fMRI time series. Experimental results on simulated data show that EC-GAN can better infer effective connectivity compared to other state-of-the-art methods. The real-world experiments indicate that EC-GAN can provide a new and reliable perspective analyzing the effective connectivity of fMRI data. Copyright © 2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

LocalDrop: A hybrid regularization for deep neural networks

arXiv

引用

arXiv 2021年

作者： Lu, Ziqing Xu, Chang Du, Bo Ishida, Takashi Zhang, Lefei Sugiyama, Masashi The National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430079 China The School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia RIKEN Center for Advanced Intelligence Project Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Japan

In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas. We propose a new approach for the regularization of neural networks by the local Rademacher complexity called LocalDrop. A new regularization function for both fully-connected networks (FCNs) and convolutional neural networks (CNNs), including drop rates and weight matrices, has been developed based on the proposed upper bound of the local Rademacher complexity by the strict mathematical deduction. The analyses of dropout in FCNs and DropBlock in CNNs with keep rate matrices in different layers are also included in the complexity analyses. With the new regularization function, we establish a two-stage procedure to obtain the optimal keep rate matrix and weight matrix to realize the whole training model. Extensive experiments have been conducted to demonstrate the effectiveness of LocalDrop in different models by comparing it with several algorithms and the effects of different hyperparameters on the final performances. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Image-to-Image Translation Based Face Photo De-Meshing Using Gans

SSRN

引用

SSRN 2023年

作者： Jabbar, Abdul Assam, Muhammad Shloul, Tamara Al Hameed, Hala Abdel Al-Kahtani, Noor Alkahtani, Hend Khalid Ghamry, Nivin A. Tag El Din Bro, Wahii Elsayed M. Amin, Muhammad Shoib College of Computer Science and Technology Zhejiang University Hangzhou310027 China School of Software Engineering East China Normal University Shanghai200050 China Liwa College of Technology College of Business administration United Arab Emirates Khaybar Applied College Taibah University Saudi Arabia Department of Health Information Management and Technology College of Public Health Imam Abdulrahman Bin Faisal University Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Faculty of Computers and Artificial intelligence Cairo University Giza Egypt Faculty of Engineering and Technology Future University New Cairo11835 Egypt

Most of the existing face photo de-meshing methods have accomplished promising results;there are certain quality problems with these methods like the inpainted regions would appear blurry and unpleasant boundaries becoming visible. Such artifacts cause generated face photos unreal. Therefore, we propose an effective image-to-image translation framework called Face De-meshing Using Generative Adversarial Networks (De-mesh GANs). The De-mesh GANs is a two-stage model: (i) binary mask generating module, is a three convolution layers-based encoder-decoder network architecture that automatically generates a binary mask for the meshed region, and (ii) face photo de-meshing module, is a GANs-based network that eliminates the mesh mask and synthesizes the meshed area. An arrangement of careful losses (reconstruction loss, adversarial loss, and perceptual loss) is used to reassure the better quality of the de-mesh face photos. To facilitate the training of the proposed model, we have designed a dataset of clean/corrupted photo pairs using the CelebA dataset. Qualitative and quantitative evaluations of the De-mesh GANs on real-world corrupted face photo images show better performance than the previously proposed face photo de-meshing models. Furthermore, we also offer the ablation study for performance assessment of the additional network i.e., perceptual network. © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Collaboration Energy Efficiency with Mobile Edge Computing for Data Collection in IoT 7th

Collaboration Energy Efficiency with Mobile Edge Computing f...

引用

7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Zou, Guobin Zhang, Jian Tang, Jian Zhou, Junwu College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China BGRIMM Technology Group Co. Ltd. Beijing102600 China

ISBN: (纸本)9783030786144

In this paper, we investigated collaboration energy efficiency with mobile edge computing (MEC) mechanism in internet of things (IoT), which is a challenge issue. In order to prolong the lifetime of IoT, we adopt dynamic clustering methods to improve energy efficiency while guaranteeing energy balance. We deign the sensor selection scheme for collecting data according to Pareto optimality. Concretely, by considering the reality of random deployment and introducing the definition of node density for IoT, we develop a Pareto optimality for sensor nodes selection in terms of energy efficiency. Furthermore, we recruit voluntary mobile devices as mobile edge computing servers to offload the data from selected sensor nodes in the cluster and process them to the estimate the data state. Simulations demonstrate the efficiency for the performance on energy balance in terms of efficiently prolonging the IoT lifetime. © 2021, Springer Nature Switzerland AG.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Collaboration Energy Efficiency with Mobile Edge Computing for Target Tracking in IoT 7th

Collaboration Energy Efficiency with Mobile Edge Computing f...

引用

7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Zou, Guobin Yu, Gang Zhang, Jian Tang, Jian Zhou, Junwu College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Process Automation in Mining and Metallurgy Beijing102600 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Beijing Key Laboratory of Process Automation in Mining and Metallurgy Beijing102600 China BGRIMM Technology Group Co. Ltd. Beijing102600 China

ISBN: (纸本)9783030786205

In this paper, the target tracking problem is investigated with mobile edge computing (MEC) mechanism in internet of things (IoT), where the challenge of energy efficiency is a significant issue when the target tracking event is driven. In order to prolong the lifetime of IoT, we adopt dynamic clustering methods to improve energy efficiency while guaranteeing target tracking effects. We deign the sensor selection scheme for carrying out the tracking tasks according to energy distribution of the sensor node. Concretely, by considering the reality of random deployment and introducing the definition of node density for IoT, we develop a Pareto optimality for sensor nodes selection in terms of energy efficiency without reducing the accuracy of target tracking. Furthermore, we recruit voluntary mobile devices as mobile edge computing servers to offload the data from selected sensor nodes in the cluster and process them to the estimate the target state. Simulations demonstrate the efficiency for tracking performance on energy balance in terms of efficiently prolonging the IoT lifetime. © 2021, Springer Nature Switzerland AG.

关键词： Target tracking

来源：评论

学校读者我要写书评

暂无评论

Cardiac Detection Using YOLO-v5 with Data Preprocessing

Cardiac Detection Using YOLO-v5 with Data Preprocessing

引用

International Conference on Computational science and Computational Intelligence (CSCI)

作者： Sungjin Lee Jia Xibin Ahyoung Lee Hyo-Wook Gil Sungyeup Kim Min Hong Dept. of software convergence Soonchunhyang university Asan Korea Faculty of information technology Beijng university of technology Beijing China Dept. of computer science Kennesaw state university Marietta USA Dept. of internal Medicine Cheonan hospital Soonchunhyang university Cheonan Korea Dept. of research All for one Asan Korea Dept. of software engineering Soonchunhyang university Asan Korea

Currently, interest in medical-related deep learning is dramatically increasing. Although this interest in deep learning is widely used in other fields, but it is very effective in medical image processing such as CT and MRI, which takes a lot of time for simple medical tests and analysis. In general, in deep learning using such image processing, it is possible to determine which algorithm is the most efficient by collecting data, preprocessing data, and using various models This paper conducted a research on cardiovascular CT images collected from Soonchunhyang University Hospital in Korea, and all of them used data collected for 3 years by professional medical staff. In the case of medical data, the number of data is very limited, so the results can vary greatly depending on how it is processed. Therefore, in this paper, research on an efficient deep learning method was conducted through image data preprocessing using Yolo.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Short-time simulation of quantum dynamics by Pauli measurements

arXiv

引用

arXiv 2024年

作者： Faehrmann, Paul K. Eisert, Jens Kieferová, Mária Kueng, Richard Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Integrated Circuits and Quantum Computing Johannes Kepler University Linz Austria Helmholtz-Zentrum Berlin für Materialien und Energie Hahn-Meitner-Platz 1 Berlin14109 Germany Fraunhofer Heinrich Hertz Institute Berlin10587 Germany Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia

Simulating the dynamics of complex quantum systems is a central application of quantum devices. Here, we propose leveraging the power of measurements to simulate short-time quantum dynamics of physically prepared quantum states in classical post-processing using a truncated Taylor series approach. While limited to short simulation times, our hybrid quantum-classical method is equipped with rigorous error bounds. It is extendable to estimate low-order Taylor approximations of smooth, time-dependent functions of tractable linear combinations of measurable operators. These insights can be made use of in the context of Hamiltonian learning and device verification, short-time imaginary time evolution, or the application of intractable operations to sub-universal quantum simulators in classical post-processing. © 2024, CC BY.

关键词： Hamiltonians

来源：评论

学校读者我要写书评

暂无评论

SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection

arXiv

引用

arXiv 2024年

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Co. Limerick CastletroyV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems Tuam GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © 2024, CC BY.

关键词： Automobile simulators

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：