检索结果-内蒙古大学图书馆

A new approach for spatio-temporal interface treatment in fluid-solid interaction using artificial neural networks employing coupled partitioned fluid-solid solvers

引用

JOURNAL OF FLUIDS AND STRUCTURES 2024年 131卷

作者： Mazhar, Farrukh Javed, Ali Natl Univ Sci & Technol Coll Aeronaut Engn Dept Aerosp Engn Islamabad 44000 Pakistan

Partitioned fluid-solid interaction (FSI) problems involving non-conforming grids pose formidable challenge in interface treatment, especially for information exchange, interface tracking, and field variable interpolation between solvers in both space and time. These demand special considerations for accurate and efficient simulations. This paper presents an application of artificial neural networks (ANN) for the interface treatment in a coupled FSI problem employing partitioned solvers. A shallow time-series ANN (nonlinear auto-regressive model with exogenous inputs, NARX) scheme is proposed to handle the exchange of Neumann/Dirichlet information at the coupling interface. This scheme involves two interface treatment models that were developed and analysed. The proposed models interpolate and transfer loads from the fluid to the solid domains, and conversely, displacements from the solid to the fluid domains between non-collocated grids. To validate this approach, we tested it on a 3D FSI problem, which involved damped oscillations of a flexible flap submerged in a fluid cavity. Adequately trained NARX interface models demonstrate reliable input-output mapping and accurate prediction of transient behaviour at the interface. Additionally, we explored the concept of reduced-order modelling (ROM) in the time domain. This allowed us to reduce the model's complexity by half. Different training algorithms were evaluated to enhance the efficiency and performance of the proposed scheme. The study demonstrates that NARX networks trained with Bayesian Regularization (BR) and Levenberg-Marquardt (LM) algorithms exhibit the best accuracy, while the scaled conjugate gradient (SCG)-based training method provides better computational efficiency with acceptable accuracy. Overall, the NARX interface models provide precise performance and offer a viable potential for applications in FSI problems requiring accurate and faster computations.

关键词： Fluid-solid interaction (FSI) Artificial neural network (ANN) Interface treatment training algorithm NARX neural network

来源：评论

学校读者我要写书评

暂无评论

Solar air heaters performance prediction using multi-layer perceptron neural network- A systematic review

引用

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2021年第1期47卷 7682-7699页

作者： Ghritlahre, Harish Kumar Verma, Manoj Chhattisgarh Swami Vivekanand Tech Univ Dept Energy & Environm Engn Bhilai 491107 Chhattisgarh India

In solar thermal systems, solar air heater (SAH) is an important device for heating air using energy of the sun. The solar collector is the most important part of SAH which collects solar radiations as thermal energy (heat) and transmits it to the air. For efficient utilization of solar energy, the system performance needs to be optimized. There are many techniques that are used for optimization but Artificial Intelligence (AI) technique is very effective. Artificial neural networks (ANN) are coming up as valuable options in place of traditional statistical modeling techniques in many scientific disciplines. ANN technique is the most used one for modeling, prediction, and control. This technique is computes faster results and it can solve the complex and nonlinear problems which cannot be solved by other conventional approaches. Also this technique requires large data sets but not separate programming for solving the problems. In this paper, all the published research works from year 2009 to 2021 related to MLP model for performance prediction has been reviewed, which are applied on solar air heating systems. The main aim of current study is to review research work related to multi-layer perceptron (MLP) model applied in SAH and to find a gap for future research. This paper encompasses many research articles related to neural networks which focus particularly on MLP model. Review of some research papers has been included in detail for the proposed work. Also the present review article reported that MLP structure of neural model is the most commonly applicable model used for prediction of SAHs performances with satisfactory results.

关键词： Solar air heaters multi-layer perceptron artificial neural network training algorithm artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Optimization of lattices for quantization

引用

IEEE TRANSACTIONS ON INFORMATION THEORY 1998年第5期44卷 1814-1828页

作者： Agrell, E Eriksson, T Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA AT&T Bell Labs Florham Pk NJ 07932 USA

A training algorithm for the design of lattices for vector quantization is presented, The algorithm uses a steepest descent method to adjust a generator matrix, in the search for a lattice whose Voronoi regions have minimal normalized second moment. The numerical elements of the found generator matrices are interpreted and translated into exact values. Experiments show that the algorithm is stable, in the sense that several independent runs reach equivalent lattices. The obtained lattices reach as low second moments as the best preciously reported lattices, or even lower. Specifically, we report lattices in nine and ten dimensions with normalized second moments of 0.0716 and 0.0708, respectively, and nonlattice tessellations in seven and nine dimensions with 0.0727 and 0.0711, which improves on previously known values, The new nine- and ten-dimensional lattices suggest that Conway and Sloane's conjecture on the duality between the optimal lattices for packing and quantization might be false. A discussion of the application of lattices in vector quantizer design for various sources, uniform and nonuniform, is included.

关键词： lattice design lattice quantization normalized second moment training algorithm Voronoi region

来源：评论

学校读者我要写书评

暂无评论

Dynamic Modeling Using Artificial Neural Network of Bacillus Velezensis Broth Cross-Flow Microfiltration Enhanced by Air-Sparging and Turbulence Promoter

引用

MEMBRANES 2020年第12期10卷 372-372页

作者： Jokic, Aleksandar Pajcin, Ivana Grahovac, Jovana Lukic, Natasa Ikonic, Bojana Nikolic, Nevenka Vlajkov, Vanja Univ Novi Sad Fac Technol Novi Sad Bulevar Cara Lazara 1 Novi Sad 21000 Serbia

Cross-flow microfiltration is a broadly accepted technique for separation of microbial biomass after the cultivation process. However, membrane fouling emerges as the main problem affecting permeate flux decline and separation process efficiency. Hydrodynamic methods, such as turbulence promoters and air sparging, were tested to improve permeate flux during microfiltration. In this study, a non-recurrent feed-forward artificial neural network (ANN) with one hidden layer was examined as a tool for microfiltration modeling using Bacillus velezensis cultivation broth as the feed mixture, while the Kenics static mixer and two-phase flow, as well as their combination, were used to improve permeate flux in microfiltration experiments. The results of this study have confirmed successful application of the ANN model for prediction of permeate flux during microfiltration of Bacillus velezensis cultivation broth with a coefficient of determination of 99.23% and absolute relative error less than 20% for over 95% of the predicted data. The optimal ANN topology was 5-13-1, trained by the Levenberg-Marquardt training algorithm and with hyperbolic sigmoid transfer function between the input and the hidden layer.

关键词： microbial biomass cultivation membrane separation permeate flux Kenics static mixer fouling mitigation ANN training algorithm transfer function architecture

来源：评论

学校读者我要写书评

暂无评论

Reduced symmetric self-constructing fuzzy neural network beamforming detectors

引用

IET MICROWAVES ANTENNAS & PROPAGATION 2011年第6期5卷 676-684页

作者： Chang, Y-J Ho, C-L Natl Cent Univ Dept Commun Engn Tao Yuan 32054 Taiwan

Beamforming technology has been widely used in smart antenna systems that can increase the user's capacity and coverage in modern communication products. In this study, a powerful reduced symmetric self-constructing fuzzy neural network (RS-SCFNN) beamforming detector is proposed for multi-antenna-assisted systems. A novel training algorithm for the RS-SCFNN beamformer is proposed based on clustering of array input vectors and an adaptive minimum bit-error rate method. An inherent symmetric property of the array input signal space is exploited to make training procedure of RS-SCFNN more efficient than that of standard SCFNN. In addition, the required amount of fuzzy rules can be greatly reduced in the RS-SCFNN structure. Simulation results demonstrate that RS-SCFNN beamformer provides superior performance to the classical linear ones and the other non-linear ones (including symmetric radial basis function, SCFNN and S-SCFNN), especially when supporting a large amount of users in the rank-deficient multi-antenna-assisted system.

关键词： smart antenna systems rank-deficient multiantenna-assisted system nonlinear ones mobile communication error statistics RS-SCFNN beamformer Communications computing Neural computing techniques Digital signal processing Mobile radio systems Other topics in statistics adaptive antenna arrays Signal detection radial basis function networks symmetric radial basis function RS-SCFNN structure inherent symmetric property telecommunication computing RS-SCFNN beamforming detector beamforming technology reduced symmetric self-constructing fuzzy neural network beamforming detectors communication products Antenna arrays array signal processing array input signal space fuzzy rules training algorithm array input vectors multiantenna-assisted systems adaptive minimum bit-error rate method fuzzy neural nets training procedure

来源：评论

学校读者我要写书评

暂无评论

LiSSA: Localized Stochastic Sensitive Autoencoders

引用

IEEE TRANSACTIONS ON CYBERNETICS 2021年第5期51卷 2748-2760页

作者： Wang, Ting Ng, Wing W. Y. Pelillo, Marcello Kwong, Sam South China Univ Technol Sch Comp Sci & Engn Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Peoples R China Univ Venice Dept Environm Sci Informat & Stat I-30172 Venice Italy Univ Venice European Ctr Living Technol I-30172 Venice Italy City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

The training of autoencoder (AE) focuses on the selection of connection weights via a minimization of both the training error and a regularized term. However, the ultimate goal of AE training is to autoencode future unseen samples correctly (i.e., good generalization). Minimizing the training error with different regularized terms only indirectly minimizes the generalization error. Moreover, the trained model may not be robust to small perturbations of inputs which may lead to a poor generalization capability. In this paper, we propose a localized stochastic sensitive AE (LiSSA) to enhance the robustness of AE with respect to input perturbations. With the local stochastic sensitivity regularization, LiSSA reduces sensitivity to unseen samples with small differences (perturbations) from training samples. Meanwhile, LiSSA preserves the local connectivity from the original input space to the representation space that learns a more robustness features (intermediate representation) for unseen samples. The classifier using these learned features yields a better generalization capability. Extensive experimental results on 36 benchmarking datasets indicate that LiSSA outperforms several classical and recent AE training methods significantly on classification tasks.

关键词： training Sensitivity Noise reduction Stochastic processes Perturbation methods Image reconstruction Data models Autoencoder (AE) stochastic sensitivity training algorithm

来源：评论

学校读者我要写书评

暂无评论

Bandwidth Enhancement by Direct Coupled Antenna for WLAN/GPS/WiMax Applications and Feed Point Coordinate Analysis through ANN

引用

WIRELESS PERSONAL COMMUNICATIONS 2016年第1期91卷 9-32页

作者： Ayub, Shahanaz Srivastava, Rajat Bundelkhand Inst Engn & Technol Jhansi Uttar Pradesh India

In the present work first of all the bandwidth of rectangular Microstrip antenna is enhanced by direct coupling at the operating frequency 2.4 GHz using IE3D software and then it is analyzed through IE3D simulation software for maximum bandwidth for different antenna dimensions at different operating frequencies (ranging from 1.2 to 2.5 GHz). The rectangular line feed is used to energize the proposed antenna (at 2.4 GHz operating frequency), the feed point coordinate is varied as the antenna dimensions varied so it is required to analyze the antenna for the feed point coordinates where it provides the maximum bandwidth. The simulation of antenna for its maximum bandwidth through most of the antenna simulation software's is a time taking process. In order to reduce this time, the Artificial Neural Network (ANN) approach is adopted here. For this purpose a Feed Forward Neural Network with Levenberg-Marquardt training algorithm is used. Determining the feed point coordinates through proposed ANN takes only 1-2 s where as antenna simulation softwares (like IE3D) take time in hours. The proposed antenna has dual frequency band. In first frequency band (1.392-1.44 GHz) the fractional bandwidth is 3.38 % and in second frequency band (1.733-3.58 GHz) the fractional bandwidth is 69.5 % which is suitable for broad band applications.

关键词： Direct coupling Bandwidth enhancement Artificial neural network training algorithm Feed forward networks Gain Feed point coordinates IE3D

来源：评论

学校读者我要写书评

暂无评论

Overcoming Hardware Imperfections in Optical Neural Networks Through a Machine Learning-Driven Self-Correction Mechanism

IEEE PHOTONICS JOURNAL

引用

IEEE PHOTONICS JOURNAL 2024年第2期16卷 1页

作者： Kim, Minjoo Kim, Beomju Kim, Yelim Handriani, Lia Saptini Jang, Suhee Jeong, Dae Yeop Yang, Sung Ik Park, Won Il Hanyang Univ Div Mat Sci & Engn Seoul 04763 South Korea Kyung Hee Univ Dept Appl Chem Yongin 17104 South Korea

We developed an optical neural network (ONN) for efficient processing and recognition of 2-dimensional (2D) images, employing a conventional liquid crystal display panel as optical neurons and synapses. This configuration allowed for optical signal outputs proportional to matrix-vector multiplication for 2D image inputs. However, our experimental results revealed a 26.6% decrease in the optical classification accuracy, despite utilizing digitally pre-trained parameters with 100% accuracy for 500 handwritten digits. This decline can be attributed to system imperfections associated with non-ideal functions of optical components and optical alignment. Rather than pursuing an elusive, imperfection-free ONN or attempting to calibrate these defects individually, we addressed these challenges by introducing a self-correction mechanism that utilizes a machine learning algorithm. This approach effectively restored the recognition accuracy and minimized loss of our ONN to levels comparable to the digitally pre-trained model. This study underscores the potential of constructing defect-tolerant hardware in ONNs through the application of machine learning techniques.

关键词： Hardware imperfections matrix-vector multiplication (MVM) optical neural networks (ONNs) recognition accuracy self-correction approach training algorithm

来源：评论

学校读者我要写书评

暂无评论

A competitive learning process for hyperspherical classifiers

引用

NEUROCOMPUTING 1997年第2期17卷 99-110页

作者： Yen, CWV Liu, TZ Department of Mechanical Engineering National Sun-Yat Sen University Kaohsiung Taiwan80024 ROC

Based on the minimum cost associative processor training algorithm proposed by Telfer and Casasent, this paper introduces a new training method for hyperspherical classifiers. Designed specifically for the hyperspherical neural networks, a novel feature of the proposed approach is a clustering method which can simplify the classification problem by adaptively removing redundant samples from the training set. The other distinct feature of the proposed method is the use of an adaptive hypersphere placement method. Specifically, in constructing a new hypersphere, a candidate hypersphere is generated for each class of the training samples. The hypersphere that has successfully classified the largest number of samples is selected as the new hypersphere. As demonstrated by the experimental results, the proposed method can achieve better classification accuracy with less training time and fewer number of hyperspheres. The proposed approach can also solve some difficult classification problems which can not be effectively handled by the previous method.

关键词： neural networks hyperspherical classifier neural classifier training algorithm

来源：评论

学校读者我要写书评

暂无评论

Model-wise and point-wise random sample consensus for robust regression and outlier detection

引用

NEURAL NETWORKS 2014年 59卷 23-35页

作者： El-Melegy, Moumen T. Assiut Univ Dept Elect Engn Assiut 71516 Egypt

Popular regression techniques often suffer at the presence of data outliers. Most previous efforts to solve this problem have focused on using an estimation algorithm that minimizes a robust M-estimator based error criterion instead of the usual non-robust mean squared error. However the robustness gained from M-estimators is still low. This paper addresses robust regression and outlier detection in a random sample consensus (RANSAC) framework. It studies the classical RANSAC framework and highlights its model-wise nature for processing the data. Furthermore, it introduces for the first time a point-wise strategy of RANSAC. New estimation algorithms are developed following both the model-wise and point-wise RANSAC concepts. The proposed algorithms' theoretical robustness and breakdown points are investigated in a novel probabilistic setting. While the proposed concepts and algorithms are generic and general enough to adopt many regression machineries, the paper focuses on multilayered feed-forward neural networks in solving regression problems. The algorithms are evaluated on synthetic and real data, contaminated with high degrees of outliers, and compared to existing neural network training algorithms. Furthermore, to improve the time performance, parallel implementations of the two algorithms are developed and assessed to utilize the multiple CPU cores available on nowadays computers. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Multi-layered feed-forward neural networks training algorithm Robust statistics Regression Outliers

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：