检索结果-内蒙古大学图书馆

3rd International Conference for Convergence in Technology (I2CT)

作者： Sahi, Geetanjali Lal Bahadur Shastri Inst Management Delhi India

ISBN: (纸本)9781538642733

In E-commerce, website is the bask point of contact between business and its customers, hence for online vendors, it is imperative that their B2C website be usable. The present study comprehensively explores the outstanding ability of Artificial Neural Networks (ANNs) to uncover knowledge hidden in data and thus discover relationships between input and output variables and entails questionnaire survey approach to assess usability of B2C websites. The performance of various ANN models has been evaluated by altering different parameters of a neural network. Three metrics viz. MSE, MAE and MAPE have been used to measure the performance of the neural network. The empirical results show that best result is obtained by using tansig purelin transfer function with trainlm training function having three nodes at hidden layer with 90 percent data for training. Further, in assessing the usability of E-commerce websites using ANN it was found that although all dimensions were important, System Quality followed by Trust were most significant.

关键词： Artificial Neural Networks back propagation algorithm transfer functions B2C E-commerce system quality trust extension quality propriety of content perceived usefulness

来源：评论

学校读者我要写书评

暂无评论

Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms

引用

Journal of Applied Research and Technology 2016年第3期14卷 206-214页

作者： Premalatha, Neelamegam Valan Arasu, Amirtham Department of Mechanical Engineering Kamaraj College of Engineering and Technology Virudhunagar Tamil Nadu 626001 India Department of Mechanical Engineering Thiagarajar College of Engineering Madurai Tamil Nadu 625015 India

Global solar radiation (GSR) is an essential parameter for the design and operation of solar energy systems. Long-standing records of global solar radiation data are not available in many places because of the cost and maintenance of the measuring instruments. The major objective of this work is to develop an ANN model for accurately predicting solar radiation. Two ANN models with four different algorithms are considered in the present study. Meteorological data collected for the last 10 years from five different locations across India have been used to train the models. The best ANN algorithm and model are identified based on minimum mean absolute error (MAE) and root mean square error (RMSE) and maximum linear correlation coefficient (R). Further, the present study confirms that prediction accuracy of the ANN model depends on the complete set of data being used for training the network for the intended application. The developed ANN model has a low mean absolute percentage error (MAPE) which ascertains the accuracy and suitability of the model to predict the monthly average global radiation so as to design or evaluate solar energy installations, where the meteorological data measuring facilities are not in place in India. © 2016 Universidad Nacional Autónoma de México, Centro de Ciencias Aplicadas y Desarrollo Tecnológico.

关键词： Artificial neural network back propagation algorithm Global solar radiation

来源：评论

学校读者我要写书评

暂无评论

Security assessment accounting uncertainties in line parameters and control variables with the considerations of transmission line unavailability

引用

Journal of Electrical Systems and Information Technology 2018年第3期5卷 576-593页

作者： Pushpendra Singh L.S. Titare S.C. Choube L.D. Arya Department of Electrical Engg. Rajkiya Engineering College Banda UP 210201 India Department of Electrical Engg. Govt. Engineering College Jabalpur MP 482 011 India Department of Electrical & Elex. UIT-RGPV Bhopal MP 462036 India Department of Electrical Engg. Medi-Caps University Indore MP 453331 India

This paper presents a new algorithm for voltage stability security assessment accounting uncertainties in the line parameters and control variables. Security index has been evaluated using Monte-Carlo simulation with & without consideration of unavailability of transmission lines, which is used to perform contingency selection. Further, probabilistic insecurity index at various loading conditions considering voltage stability limit has been obtained using cut-set method for single & double line outages. Static voltage stability limit for various sampled values of system parameters and control variables have been obtained using continuation power flow methodology. Few cases have been used to train back propagation algorithm (BPA). Obtained results for contingency selection based on security index have been compared with well-established methods.

关键词： Contingency ranking Probabilistic insecurity index Artificial neural network back propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Neural network modelling of Abbott-Firestone roughness parameters in honing processes

引用

INTERNATIONAL JOURNAL OF SURFACE SCIENCE AND ENGINEERING 2017年第6期11卷 512-530页

作者： Sivatte-Adroer, Maurici Buj-Corral, Irene Llanas-Parra, Xavier Univ Politecn Cataluna Dept Mech Engn Sch Engn Vilanova I la Geltru EPSEVG Av Victor Balaguer 1 Barcelona 08800 Spain Univ Politecn Cataluna Dept Mech Engn Sch Engn Barcelona ETSEIB Av Diagonal 647 E-08028 Barcelona Spain Univ Politecn Cataluna Sch Engn Vilanova I la Geltru EPSEVG Automat Control Dept Av Victor Balaguer 1 Barcelona 08800 Spain

In present study, three roughness parameters defined in the Abbott-Firestone or bearing area curve, Rk, Rpk and Rvk, were modelled for rough honing processes by means of artificial neural networks (ANN). Input variables were grain size and density of abrasive, pressure of abrasive stones on the workpiece's surface, tangential or rotation speed of the workpiece and linear speed of the honing head. Two strategies were considered, either use of one network for modelling the three parameters at the same time or use of three networks, one for each parameter. Overall best neural network consists of three networks, one for each roughness parameter, with one hidden layer having 25, nine and five neurons for Rk, Rpk and Rvk respectively. However, use of one network for the three roughness parameters would allow addressing an indirect model. In this case, best solution corresponds to two hidden layers having 26 and 11 neurons.

关键词： surface roughness honing artificial neural networks ANN backpropagation algorithm Abbott-Firestone roughness parameters back propagation algorithm grain size density of abrasive linear speed tangential speed pressure

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

Soft Sensor for Capacitance Tracking in Quality Assurance of Fruit Ripening Process

引用

Materials Today: Proceedings 2018年第10期5卷 20785-20792页

作者： K. Sujatha V. Srividhya R.S. Ponmagal T. Godhavari Dept. of EEE/CSE/ECE Center for Electronics Automation & Industrial Research (CEAIR) Dr.MGR Educational & Research Institute Chennai India Dept. of EEE Center for Electronics Automation & Industrial Research (CEAIR) Dr.MGR Educational & Research Institute Chennai India

The highlight of this research work is to discover an efficient capacitance tracking for generation of ethylene gas (C 2 H 4 in ppm) used for ripening of fruits by employing soft sensor built using image processing and Artificial Neural Networks (ANN) algorithms. The proposed method relies on the statistical analysis of the color which denotes the various stages in ripening and in turn indicates the amount of ethylene gas required. The changes in color, texture, intensity variation, mean, variance and standard deviation extracted from the images are the features which enable the personnel to determine the amount of ethylene gas. The Feed Forward Neural Network (FFNN) is used for ethylene gas and the corresponding capacitance value estimation. This is made possible using back propagation algorithm (BPA) for training the FFNN. As a part of image processing the intensity values in color images and its variation are tracked by dithering which is used as a unique feature input to train the FFNN. The major findings of the proposed method depends on the FFNN estimating the ethylene gas needed for ripening process in a feed forward fashion thereby providing the precision and recall values spontaneously for every instance. The improvement made in application side denotes that earlier a circuit with capacitance is used to generate ethylene gas for this purpose which is on other hand replaced by using a soft sensor. Nearly 51 images of banana are considered for training and testing respectively. Testing and confirmation result shows the required precision and recall values are in range of 80 to 89% and 100% respectively .

关键词： Feature Extraction Dithering Fruit Ripening back propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Determining the Content of Nitrogen in Rubber Trees by the Method of NIR Spectroscopy

引用

JOURNAL OF APPLIED SPECTROSCOPY 2017年第4期84卷 627-632页

作者： Tang, R. Chen, K. Jiang, C. Li, Ch Hainan Univ Sch Mech & Elect Engn Haikou 570228 Hainan Peoples R China

Foliar nitrogen (N) is an important index to evaluate the content of nitrogen in rubber trees. Hence, a fast way utilizing the spectroscopy to estimate the nitrogen content level of the rubber tree under natural environment has been preliminarily investigated in this paper. The combined features strategy is adopted in this study. The evaluation model is developed by back propagation (BP) algorithm and Adaboost algorithm. The results of simulation experiment show that satisfactory classification performance has been achieved using the proposed model and the percentage of samples correctly classified by the proposed model in the validation set is 92.98%.

关键词： rubber tree nitrogen spectral discrimination back propagation algorithm Adaboost algorithm

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

The Journal of Photographic Science 2018年第2期66卷 69-83页

ABSTRACTABSTRACTText 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

Soft Sensor for Flame Temperature Measurement and IoT based Monitoring in Power Plants

引用

Materials Today: Proceedings 2018年第4期5卷 10755-10762页

作者： K. Sujatha Nallamilli.P.G Bhavani Su-Qun Cao K.S. Ram Kumar Mechanical & Material Engineering Faculty /I yr ECE Dept. of EEE/ECECenter for Electronics Automation & Industrial Research (CEAIR) Dr.MGR Educational & Research Institute/ Huaiyin Institute of Technology China J.N.N Institute of Engineering Chennai India

Investigation of temperature measurement from the flame colour in thermal and gas turbine power plants is of enormous significance in the realm of vision machine technology. The primary objective for this work relies on detection, recognition and understanding of colour image processing for flame colour analysis. In this effort, soft computing methods using Artificial Neural Network (ANN) model with back propagation algorithm (BPA) and Ant Colony Optimisation (ACO) are used for this purpose. The central theme of this work uses the fact that the colour of the flame images is dependent on the temperature. The initial move is to describe a facet quantity for each flame image together with 10 facet rudiments, which are the brightness of flame, the area of the high temperature flame, the brightness of high temperature flame, the rate of area of the high temperature flame, the flame centroid about X and Y, orientation and the two discriminant vectors correspondingly. The superiority of the images used is improved using Curvelet transform. The conception of flame detection and classification is conceded to compute the temperature from its colour. The specimen incorporates 51 flame images, a portion of which is used for trail and testing the ANN and ACO model. Ultimately, the whole specimen flame images are recognized and classified based on the temperatures corresponding to the core of the fire ball. The results are being validated by comparing with the conventional Euclidean classifier. Demonstrations establish an effective and indigenous system for flame temperature measurement. The elucidation states that the Internet of Things (IoT) with the proposed intelligent temperature sensor is connected to the embedded computing system to monitor the fluctuation in flame temperature with respect to colour changes in order to ensure complete combustion. This scheme utilizes wearable electronics technology which constantly monitors and controls the improvement of productivity in p

关键词： Soft Sensor back propagation algorithm Ant Colony Optimization Euclidean classifier Flame Temperature Feature extraction Curvelet transform Fisher’s Linear Discriminant Analysis Arduino UNO and Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Support Vector Machines versus back propagation algorithm for Oil Price Prediction

Support Vector Machines versus Back Propagation Algorithm fo...

引用

8th International Symposium on Neural Networks

作者： Khashman, Adnan Nwulu, Nnamdi I. Near East Univ ISRG Lefkosa Turkey

ISBN: (纸本)9783642211102

The importance of crude oil in the world economy has made it imperative that efficient models be designed for predicting future prices. Neural networks can be used as prediction models, thus, in this paper we investigate and compare the use of a support vector machine and a back propagation neural network for the task of predicting oil prices. We also present a novel method of representing the oil price data as input data to the neural networks by defining input economic and seasonal indicators which could affect the oil price. The oil price database is publicly available online and can be obtained from the West Texas Intermediate crude oil price dataset spanning a period of 24 years. Experimental results suggest the neural networks can be efficiently used to predict future oil prices with minimal computational expense.

关键词： Crude Oil Price Prediction Neural Networks back propagation algorithm Support Vector Machines Radial Basis Function

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

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

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：