检索结果-内蒙古大学图书馆

Torsional vibrations of pre-twisted blades using artificial neural network technology

ENGINEERING WITH COMPUTERS 2000年第1期16卷 10-15页

作者： Rao, MA Srinivas, J Andhra Univ Dept Mech Engn Visakhapatnam 530003 Andhra Pradesh India

The free torsional vibrations of a linearly tapered, twisted flexible blade, rotationally constrained at an arbitrary position along the length of blade, have been investigated using neural networks. The blade has a rectangular cross-section with equal taper in th horizontal and vertical planes, in addition to the flexibility at the root portion. The constraint is a rotational spring. The constraint on the blade at an optimum location is designed so as to increase the lowest natural frequency of the blades with considerable root flexibility. The optimum location is determined as the position of the node in the second mode shape of the unconstrained tapered blade with flexible roots. A trained neural network is used to identify the location of the nodal or optimum point for a given blade-taper ratio and root flexibility parameter. The minimum stiffness of the constraint at an optimum position for a maximum raise in the first eigenfrequency is evaluated. Results are presented in tabular and graphical form.

关键词： artificial neural networks back propagation algorithm mode shapes root flexibility taper-rotational constraint torsional frequencies

来源：评论

学校读者我要写书评

暂无评论

Adaptive basis function for artificial neural networks

引用

NEUROCOMPUTING 2002年第1-4期47卷 21-34页

作者： Philip, NS Joseph, KB Cochin Univ Sci & Technol Dept Phys Cochin 682022 Kerala India

It is shown that modifying the sigmoidal basis function of a multi-layer feedforward artificial neural network using a control parameter improves the network's ability to learn. The modification is rendered by a gradient descent algorithm similar to the back-propagation. In doing so, the method retains all the goodies of the sigmoidal function and causes the ANN to approximate the decision function faster and also with better accuracy. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： back propagation algorithm neural networks optimization of basis function

来源：评论

学校读者我要写书评

暂无评论

Experimental and neural model analysis of styrene removal from polluted air in a biofilter

引用

JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY 2009年第7期84卷 941-948页

作者： Rene, Eldon R. Veiga, Maria C. Kennes, Christian Univ A Coruna Chem Engn Lab E-15008 La Coruna Spain

backGROUND: Biofilters are efficient systems for treating malodorous emissions. The mechanism involved during pollutant transfer and subsequent biotransformation within a biofilm is a complex process. The use of artificial neural networks to model the performance of biofilters using easily measurable state variables appears to be an effective alternative to conventional phenomenological modelling. RESULTS: An artificial neural network model was used to predict the extent of styrene removal in a perlite-biofilter inoculated with a mixed microbial culture. After a 43 day biofilter acclimation period, styrene removal experiments were carried out by subjecting the bioreactor to different flow rates (0.15-0.9 m(3) h(-1)) and concentrations (0.5-17.2 g m(-3)), that correspond to inlet loading rates up to 1390 g m(-3) h(-1). During the different phases of continuous biofilter operation, greater than 92% styrene removal was achievable for loading rates up to 250 g m(-3) h(-1). A back propagation neural network algorithm was applied to model and predict the removal efficiency (%) of this process using inlet concentration (g m(-3)) and unit flow (h(-1)) as input variables. The data points were divided into training (115 x 3) and testing set (42 x 3). The most reliable condition for the network was selected by a trial and error approach and by estimating the determination coefficient (R-2) value (0.98) achieved during prediction of the testing set. CONCLUSION: The results showed that a simple neural network based model with a topology of 2-4-1 was able to efficiently predict the styrene removal performance in the biofilter. Through sensitivity analysis, the most influential input parameter affecting styrene removal was ascertained to be the flow rate. (C) 2009 Society of Chemical Industry

关键词： biofiltration back propagation algorithm modelling neural network reactor performance waste gas

来源：评论

学校读者我要写书评

暂无评论

An Inverse Halftoning algorithms Based on Neural Networks and Atomic Functions

引用

IEEE LATIN AMERICA TRANSACTIONS 2017年第3期15卷 488-495页

作者： Pelcastre, F. Miyatake, M. N. Toscano, K. Sanchez, G. Perez, H. Inst Politecn Nacl Mexico City DF Mexico Inst Politecn Nacl Escuela Super Ingn Mecan & Elect Mexico City DF Mexico

Halftoning and inverse halftoning algorithms are very important image processing tools, widely used in the development of digital printers, scanners, steganography and image authentication systems. Because such applications require to obtain high quality gray scale images from its halftone versions, the development of efficient inverse halftoning algorithms, that be able to provide gray scale images with Peak Signal to Noise Ratio (PSNR) higher than 25, have been research topic during the last several years. Although a PSNR of about 25dB may be enough for several applications, exist several other that require higher image quality. To reduce this problem, this paper proposes inverse halftoning algorithms based on Atomic Function and multi-layer perceptron neural network which provides gray scale images with PSNRs higher than 30dB independently of the method used to generate the halftone image.

关键词： Halftoning inverse halftoning atomic functions multilayer perceptron back propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Neural network aided Kalman filtering for multitarget tracking applications

引用

COMPUTERS & ELECTRICAL ENGINEERING 2001年第2期27卷 217-228页

作者： Vaidehi, V Chitra, N Chokkalingam, M Krishnan, CN Madras Inst Technol Sch Instrumentat & Elect Madras 600044 Tamil Nadu India

The adaptive capability of the Kalman filtering is known to increase by incorporating a neural network into the normal Kalman filter. Current work extends this fact and proposes the neural network aided Kalman filtering scheme for tracking multitargets that are highly manoeuvring. The improvement in the tracking accuracy due to the proposed scheme is presented For various tracking scenarios. (C) 2000 Published by Elsevier Science Ltd.

关键词： Kalman filtering manoeuvring targets multitarget tracking neural networks back propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Fault-cause identification method based on adaptive deep belief network and time-frequency characteristics of travelling wave

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第5期13卷 724-732页

作者： Liang, Hanqing Liu, Yadong Sheng, Gehao Jiang, Xiuchen Shanghai Jiao Tong Univ Dept Elect Informat & Elect Engn Shanghai Peoples R China

Accurate fault-cause identification is highly important to the fault analysis of overhead transmission lines (OTLs). In order to improve the efficiency and accuracy of fault identification, this study proposes a fault identification method based on the ADBN (adaptive deep belief network) model and the time-frequency characteristics of a travelling wave. According to the mechanisms of different OTL faults, the appropriate time-frequency characteristic parameters of the fault current travelling wave were selected as the input of the ADBN model, and the fault-type labels were selected as the output. The ADBN model introduces the idea of adaptive learning rate into CD (contrastive divergence) algorithm and improves its performance with self-adjusting learning rate. The parameters of the ADBN model were pre-trained with the improved CD algorithm and adjusted by back propagation algorithm with the labels of the samples. The performance of the ADBN model was verified by field data, and the accuracy of fault identification was analysed under different model parameters, characteristic parameters, and sample sizes. The results showed that the model helps to characterise the inherent relationship between characteristic parameters and fault causes, and the proposed method can effectively identify different fault causes in OTLs.

关键词： backpropagation fault diagnosis power transmission faults power overhead lines belief networks fault location feature extraction neural nets power engineering computing time-frequency analysis characteristic parameters fault-cause identification method adaptive deep belief network time-frequency characteristics fault analysis overhead transmission lines fault current travelling wave ADBN model fault-type labels adaptive learning rate self-adjusting learning rate improved CD algorithm OTL faults travelling wave characteristics contrastive divergence algorithm back propagation algorithm field data

来源：评论

学校读者我要写书评

暂无评论

ANN model for prediction of the effects of composition and process parameters on tensile strength and percent elongation of Si-Mn TRIP steels

引用

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING 2004年第1-2期374卷 122-128页

作者： Hosseini, SMK Zarei-Hanzaki, A Panah, MJY Yue, S McGill Univ Dept Min Met & Mat Engn Montreal PQ H3A 2B2 Canada Univ Tehran Dept Met & Mat Engn Tehran Iran Univ Tehran Dept Elect & Comp Engn Tehran Iran

The effects of composition and intercritical heat treatment parameters on tensile strength and percentage elongation of Si-Mn TRIP steels were modeled, using a neural network with a feed forward topology and a back propagation algorithm. It was found that a committee of nets models the experimental data more accurately than a single model. The trained network was then applied to a low-carbon low-silicon steel in order to estimate the appropriate heat treatment process conditions. To explain variations in the mechanical properties, the material was subjected to a typical two-stages intercritical annealing and bainitic holding treatment. According to the results of model, tempering of material for a shorter time results in higher tensile strength and percentage elongation values. This behavior was later confirmed by microstructural studies and was attributed to both higher austenite volume fraction and higher martensite content in the samples tempered for a shorter bainitic holding. (C) 2004 Published by Elsevier B.V.

关键词： artificial neural network back propagation algorithm supervised learning modeling Si-Mn TRIP steel intercritical annealing

来源：评论

学校读者我要写书评

暂无评论

Automatic text classification using BPLion-neural network and semantic word processing

引用

IMAGING SCIENCE JOURNAL 2018年第2期66卷 69-83页

作者： Ranjan, Nihar M. Prasad, Rajesh S. Sinhgad Inst Technol & Sci Pune Maharashtra India NBN Sinhgad Sch Engn Pune Maharashtra India

Text mining has become a major research topic in which text classification is the important task for finding the relevant information from the new document. Accordingly, this paper presents a semantic word processing technique for text categorization that utilizes semantic keywords, instead of using independent features of the keywords in the documents. Hence, the dimensionality of the search space can be reduced. Here, the back propagation Lion algorithm (BP Lion algorithm) is also proposed to overcome the problem in updating the neuron weight. The proposed text classification methodology is experimented over two data sets, namely, 20 Newsgroup and Reuter. The performance of the proposed BPLion is analysed, in terms of sensitivity, specificity, and accuracy, and compared with the performance of the existing works. The result shows that the proposed BPLion algorithm and semantic processing methodology classifies the documents with less training time and more classification accuracy of 90.9%.

关键词： Text classification back propagation algorithm Lion optimization algorithm semantic words hyponymy

来源：评论

学校读者我要写书评

暂无评论

Rock mass quality classification based on deep learning:A feasibility study for stacked autoencoders

引用

Journal of Rock Mechanics and Geotechnical Engineering 2023年第7期15卷 1749-1758页

作者： Danjie Sheng Jin Yu Fei Tan Defu Tong Tianjun Yan Jiahe Lv Faculty of Engineering China University of GeosciencesWuhan430074China CCCC Second Harbor Engineering Compasny Ltd. Wuhan430040China

Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility *** develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net *** rock parameters of rock mass rating(RMR)system are calibrated in this *** model is trained using 75%of the total database for training sample *** SAEs trained model achieves a nearly 100%prediction *** comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion ***,this model is further employed to predict the slope risk level of an abandoned *** proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human *** findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.

关键词： Rock mass quality classification Deep learning Stacked autoencoder(SAE) back propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning

引用

IEEE ACCESS 2021年 9卷 53979-53995页

作者： Sowmya, N. Ponnusamy, Vijayakumar SRM Inst Sci & Technol Dept ECE Chennai 603203 Tamil Nadu India

Adulteration in milk is a common scenario for gaining extra profit, which may cause severe harmful effects on humans. The qualitative spectroscopic technique provides a better solution for detecting the toxic contents of milk and foodstuffs. All the available spectroscopic methods for milk adulterant detection are based on laboratory-based with costly equipment. This laboratory-based detection takes a long time and is more expensive, which may not be afforded by a common man. To overcome this issue, this research work involves the design and development of a low-cost, portable, multispectral, AI-based, non-destructive spectroscopic sensor system that can be used to detect the milk adulterant in real-time. The designed sensor system uses the spectroscopic method with wavelength ranges from (410-940nm) which consists of three different bands Ultraviolet (UV), visible, and Infra-Red(IR) spectrum to improve the accuracy of detection. The sensor system is connected to the internet via the developed IoT application module, which displays the detected adulterant results in a dedicated web page designed for this purpose. This IoT application enables the adulterant detected results published on the internet immediately with location information for bringing transparency. Adulterant detection problem is formulated as a classification problem and solved by machine learning algorithms of a decision tree, Naive Bayes, linear discriminant analysis, support vector machine and neural network model. The average accuracy of linear discriminant analysis, support vector machine, Naive Bayes, decision tree and neural network model are obtained as 88.1%, 90%, 90%, 91.7% and 92.7% respectively. Genetic algorithm framework is formulated for hyperparameter tuning of neural network model which improved the accuracy from 92.7% to 100%. The model is trained for five different classes of four adulterants, namely Sodium Salicylate, Dextrose, Hydrogen Peroxide, Ammonium Sulphate, and one pure mil

关键词： Dairy products Cows Powders Spectroscopy Sensor systems Internet of Things Hydrogen back propagation algorithm K-means clustering machine learning milk adulteration multispectral spectroscopy neural network

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：