检索结果-内蒙古大学图书馆

Enhancing Fault Diagnosis of Uncertain Grid-Connected Photovoltaic Systems using Deep GRU-based Bayesian optimization

引用

IFAC-PapersOnLine 2024年第4期58卷 449-454页

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine 1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir Tunisia

The efficacy of photovoltaic systems is significantly impacted by electrical production losses attributed to faults. Ensuring the rapid and cost-effective restoration of system efficiency necessitates robust fault detection and diagnosis (FDD) procedures. This study introduces a novel interval-gated recurrent unit (I-GRU) based Bayesian optimization framework for FDD in grid-connected photovoltaic (GCPV) systems. The utilization of an interval-valued representation is proposed to address uncertainties inherent in the systems, the GRU is employed for fault classification, while the Bayesian algorithm optimizes its hyperparameters. Addressing uncertainties through the proposed approach enhances monitoring capabilities, mitigating computational and storage costs associated with sensor uncertainties. The effectiveness of the proposed approach for FDD in GCPV systems is demonstrated using experimental application.

关键词： Bayesian optimization fault detection fault diagnosis gated recurrent units grid-connected PV systems interval-data representation uncertainties

来源：评论

学校读者我要写书评

暂无评论

An Efficient Model for Optimal Allocation of Renewable Energy Sources in Distribution Networks with Variable Loads

An Efficient Model for Optimal Allocation of Renewable Energ...

引用

IEEE Texas Power and Energy Conference (TPEC)

作者： Meisam Mahdavi Konrad Schmitt Stephen Bayne Manohar Chamana Department of Electrical Engineering University of Jaén Linares Jaén Spain Electrical and Computer Engineering Department Texas Tech University Lubbock TX USA Renewable Energy Program Texas Tech University Lubbock TX USA

Operational costs are affected by energy losses of distribution systems, in which these losses are higher than those of the transmission system. The placement of distributed generation (DG) units in power distribution networks is an efficient way of energy loss reduction. Today, DG penetration in distribution systems is increasing because of the great effect of renewable energy resources on environmental sustainability. Power demand variations have an essential role in the determination of energy losses amount and optimal generation of DGs. Besides, considering the variability of load level in the DG allocation problem raises the burden and computational time substantially. Therefore, this paper proposes an effective DG allocation strategy for energy loss minimization in the presence of variable power demand. The evaluation of numerical results indicates the desired performance of the presented framework for the optimal placement of DG units in distribution systems with time-varying loads.

关键词： Energy loss Renewable energy sources Power demand Costs Propagation losses Power systems Numerical models

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Art: Pattern Recognition of Wifi Detected Occupancy in Commercial Buildings

Fuzzy Art: Pattern Recognition of Wifi Detected Occupancy in...

引用

Annual Conference of the Canadian Society of Civil engineering, CSCE 2021

作者： Simma, Krishna Chaitanya Jagadeesh Caudell, Thomas P. Bogus, Susan M. Mammoli, Andrea Department of Civil Construction and Environmental Engineering University of New Mexico AlbuquerqueNMMSC01 1070 United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNMMSC01 1070 United States Customer Technologies Program Electric Power Research Institute 3420 Hillview Ave Palo AltoCA94304 United States

ISBN: (纸本)9789811905025

Research that predicts occupancy patterns in commercial buildings has gained in significance ever since the influence of occupants on building energy consumption became evident. Studies have employed a variety of sensory systems to collect the occupancy data and understand human behaviors throughout buildings. However, establishing a dedicated sensor network to collect occupancy data can become expensive for building owners. In this context, obtaining occupancy data from an existing WiFi network could eliminate the cost concerns. Data within the WiFi routers provide sufficient information for accurate estimates of occupancy. To estimate occupancy levels, this work proposes to learn and recognize WiFi connection using an Adaptive Resonance Theory (ART) artificial neural network. A detailed understanding of occupancy patterns using the WiFi data is helpful for developing heating and cooling schedules that optimize HVAC energy consumption. For this study, occupancy data was collected over a 17-week semester at the University of New Mexico using existing WiFi routers located in a large lecture hall used by multiple classes. This data was used to learn patterns of repetition using the neural network. The results show that if the 24-h occupancy profiles can be subdivided into smaller time segments defined by external schedules such as lecture start and end times or other constraints, significant patterns can be detected. A detailed understanding of these patterns can greatly facilitate occupancy load forecasting for effective building management (e.g. HVAC operation). © 2023, Canadian Society for Civil engineering.

关键词： Office buildings

来源：评论

学校读者我要写书评

暂无评论

Deformable Image Registration Using Vision Transformers for Cardiac Motion Estimation from Cine Cardiac MRI Images 12th

Deformable Image Registration Using Vision Transformers for...

引用

Functional Imaging and Modeling of the Heart - 12th International Conference, FIMH 2023, Proceedings

作者： Upendra, Roshan Reddy Simon, Richard Shontz, Suzanne M. Linte, Cristian A. Center for Imaging Science Rochester Institute of Technology RochesterNY United States Biomedical Engineering Rochester Institute of Technology RochesterNY United States Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Bioengineering Program University of Kansas LawrenceKS United States Institute for Information Sciences University of Kansas LawrenceKS United States

ISBN: (纸本)9783031353017

Accurate cardiac motion estimation is a crucial step in assessing the kinematic and contractile properties of the cardiac chambers, thereby directly quantifying the regional cardiac function, which plays an important role in understanding myocardial diseases and planning their treatment. Since the cine cardiac magnetic resonance imaging (MRI) provides dynamic, high-resolution 3D images of the heart that depict cardiac motion throughout the cardiac cycle, cardiac motion can be estimated by finding the optical flow representation between the consecutive 3D volumes from a 4D cine cardiac MRI dataset, thereby formulating it as an image registration problem. Therefore, we propose a hybrid convolutional neural network (CNN) and Vision Transformer (ViT) architecture for deformable image registration of 3D cine cardiac MRI images for consistent cardiac motion estimation. We compare the image registration results of our proposed method with those of the VoxelMorph CNN model and conventional B-spline free form deformation (FFD) non-rigid image registration algorithm. We conduct all our experiments on the open-source Automated Cardiac Diagnosis Challenge (ACDC) dataset. Our experiments show that the deformable image registration results obtained using the proposed method outperform the CNN model and the traditional FFD image registration method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image registration

来源：评论

学校读者我要写书评

暂无评论

Antithetic Multilevel Particle Filters

arXiv

引用

arXiv 2023年

作者： Jasra, Ajay Maama, Mohamed Ombao, Hernando Applied Mathematics and Computational Science Program United States Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the filtering of partially observed multi-dimensional diffusion processes that are observed regularly at discrete times. This is a challenging problem which requires the use of advanced numerical schemes based upon time-discretization of the diffusion process and then the application of particle filters. Perhaps the state-of-the-art method for moderate dimensional problems is the multilevel particle filter of [12]. This is a method that combines multilevel Monte Carlo and particle filters. The approach in that article is based intrinsically upon an Euler discretization method. We develop a new particle filter based upon the antithetic truncated Milstein scheme of [10]. We show that for a class of diffusion problems, for ϵ > 0 given, that the cost to produce a mean square error (MSE) in estimation of the filter, of O(ϵ2) is O(ϵ−2 log(ϵ)2). In the case of multidimensional diffusions with non-constant diffusion coefficient, the method of [12] has a cost of O(ϵ−2.5) to achieve the same MSE. We support our theory with numerical results in several examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

CleftGAN: Adapting A Style-Based Generative Adversarial Network To Create Images Depicting Cleft Lip Deformity

arXiv

引用

arXiv 2023年

作者： Hayajneh, Abdullah Serpedin, Erchin Shaqfeh, Mohammad Glass, Graeme Stotland, Mitchell A. Electrical and Computer Engineering Department Texas A&M University College StationTX United States Electrical and Computer Engineering Program Texas A&M University Doha Qatar Division of Plastic Craniofacial and Hand Surgery Sidra Medicine Weill Cornell Medical College Doha Qatar

A major obstacle when attempting to train a machine learning system to evaluate facial clefts is the scarcity of large datasets of high-quality, ethics board-approved patient images. In response, we have built a deep learning-based cleft lip generator designed to produce an almost unlimited number of artificial images exhibiting high-fidelity facsimiles of cleft lip with wide variation. We undertook a transfer learning protocol testing different versions of StyleGAN-ADA (a generative adversarial network image generator incorporating adaptive data augmentation (ADA)) as the base model. Training images depicting a variety of cleft deformities were pre-processed to adjust for rotation, scaling, color adjustment and background blurring. The ADA modification of the primary algorithm permitted construction of our new generative model while requiring input of a relatively small number of training images. Adversarial training was carried out using 514 unique frontal photographs of cleft-affected faces to adapt a pre-trained model based on 70,000 normal faces. The Frechet Inception Distance (FID) was used to measure the similarity of the newly generated facial images to the cleft training dataset, while Perceptual Path Length (PPL) and the novel Divergence Index of Severity Histograms (DISH) measures were also used to assess the performance of the image generator that we dub CleftGAN. CleftGAN demonstrates an ability to automatically generate vast numbers of unique faces depicting a wide range of cleft lip deformity including a variation of ethnic/racial background. We found that StyleGAN3 with translation invariance (StyleGAN3-t) performed optimally as a base model. Generated images achieved a low FID reflecting a close similarity to our training input dataset of genuine cleft images. Low PPL and DISH measures reflected a smooth and semantically valid interpolation of images through the transfer learning process and a similar distribution of severity in the training and gen

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems

arXiv

引用

arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science graduate program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages a fractional exponent to capture long-memory effects in optimization, yet its practical impact is often constrained by the difficulty of tuning and stabilizing this exponent. In this work, we introduce 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), a novel method that synergistically combines the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to automatically calibrate the fractional exponent in a data-driven manner. By continuously gauging model sensitivity and effective dimensionality, 2SED dynamically adjusts the exponent to curb erratic oscillations and enhance convergence rates. Theoretically, we demonstrate how this dimension-aware adaptation retains the benefits of fractional memory while averting the sluggish or unstable behaviors frequently observed in naive fractional SGD. Empirical evaluations across multiple benchmarks confirm that our 2SED-driven fractional exponent approach not only converges faster but also achieves more robust final performance, suggesting broad applicability for fractional-order methodologies in large-scale machine learning and related domains. © 2025, CC BY.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

A weighted multivariate fuzzy time series method for multiple output high-dimensional time series forecasting in IoT applications

A weighted multivariate fuzzy time series method for multipl...

引用

2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Bitencourt, Hugo Vinicius De Oliveira Lucas, Patricia Orang, Omid Silva, Petronio Guimaraes, Frederico Gadelha Universidade Federal de Minas Gerais Graduate Program in Electrical Engineering Belo Horizonte31270-901 Brazil Instituto Federal Do Norte de Minas Gerais Salinas Brazil Universidade Federal de Minas Gerais Department of Computer Science Belo Horizonte31270-901 Brazil Instituto Federal Do Norte de Minas Gerais Januária Brazil

ISBN: (纸本)9798350374575

In Internet of Things (IoT) applications, data flows are continuous streams of high-dimensional time series that aggregate various data sources. In this context, decision-making processes frequently encompass multiple factors and criteria that demand forecasting these time series. This paper introduces MO-WMVFTS, a novel multiple-input multiple-output (MIMO) fuzzy time series (FTS) method for tackling this complex scenario. MO-WMVFTS is a hybrid forecasting method that fuses weighted multivariate FTS (WMVFTS) with embedding transformations, designed for IoT applications. To assess the performance of the proposed method, it was applied to the prediction of energy consumption in smart homes and air quality in smart cities. Here, three real-world datasets are used to assess the validity of our proposed approach, and the results obtained are highly competitive when compared to other existing methods. © 2024 IEEE.

关键词： Smart homes

来源：评论

学校读者我要写书评

暂无评论

Ball Position Transformation with Artificial Intelligence Based on Tensorflow Libraries 23

Ball Position Transformation with Artificial Intelligence Ba...

引用

23rd International Electronics Symposium, IES 2021

作者： Setiawardhana DIkairono, Rudy Dewantara, Bima Sena Bayu Pratama, Afis Asryullah Politeknik Elektronika Negeri Surabaya Dept of Informatic and Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Dept of Electrical Engineering Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Postgraduate Program Dept of Electrical Engineering Surabaya Indonesia

ISBN: (纸本)9781665443463

Research on wheeled soccer robots has been carried out by several researchers. This is due to the existence of national and international competitions. Previous research was to create a ball position transformation system with a modified method of neural network architecture. This research was developed by building an intelligent transformation system with the Tensorflow library. This transformation system aims to be able to directly measure the distance of objects in real terms without first changing the environmental image from an omni field to a flat plane with conventional camera calibration techniques. This process can replace manual calibration with a variety of field size changes The system can transform with mean error 0.0000026 on epoch 10000 using 'conda-tensorflowneural network' libraries. It can transform the position of the ball from the omni space to the cartesian space. This system was implemented on wheeled soccer robot as keeper. © 2021 IEEE.

关键词： Sports

来源：评论

学校读者我要写书评

暂无评论

Thermal energy dependent transient permittivity of epsilon-near-zero material

引用

Science China(Physics,Mechanics & Astronomy) 2022年第8期65卷 96-101页

作者： Heng Wang Lixun Sun Kang Du Wending Zhang SooJin Chua Guixin Li Ting Mei MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical UniversityXi’an710129China Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen518055China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen518055China Department of Electrical and Computer Engineering National University of SingaporeSingapore117583Singapore LEES Program Singapore-MIT Alliance for Research&Technology(SMART)Singapore138602Singapore

Transparent conductive oxides exhibit attractive optical nonlinearity with ultrafast response and giant refractive index change near the epsilon-near-zero(ENZ) wavelength, originating from the intraband dynamics of conduction electrons. The optical nonlinearity of ENZ materials has been explained by using the overall-effective-mass and the overall-scattering-time of electrons in the extended Drude model. However, their response to optical excitation is yet the last building block to complete the theory. In this paper, the concept of thermal energy is theoretically proposed to account for the total energy of conduction electrons exceeding their thermal equilibrium value. The time-varying thermal energy is adopted to describe the transient optical response of indium-tin-oxide(ITO), a typical ENZ material. A spectrally-resolved femtosecond pump-probe experiment was conducted to verify our theory. By correlating the thermal energy with the pumping density, both the giant change and the transient response of the permittivity of ITO can be predicted. The results in this work provide a new methodology to describe the transient permittivities of ENZ materials, which will benefit the design of ENZ-based nonlinear photonic devices.

关键词： epsilon-near-zero optical nonlinearity transient response thermal energy hot electron indium tin oxide

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：