ARTIFICIAL NEURAL NETWORK APPLICATIONS

Picture My Voice:Audio to Visual Speech Synthesis using Artificial Neural NetworksDominic W. Massaro , Jonas Beskow, Michael M. Cohen, Christopher L. Fry, and Tony RodriguezPerceptual Science Laboratory, University of California, Santa Cruz, Santa Cruz, CA 95064 U. S. A.ABSTRACTThis pa[r]

SVM applies for pattern classification even with large representation space. In this approach, we need to define the hyper-plane for pattern classification [13]. For example, if we need to classify the pattern into L classes, SVM methods will need to specify 1+ 2+ … + (L-1) = L (L-1) / 2 hyper-plane[r]

In this study, a feed-forward back-propagation Artificial Neural Network (ANN) is used to predict the stress relaxation and behavior of creep for bimaterial microcantilever beam for sensing device. Results obtained from ANSYS® 8.1 finite element (FE) simulations, which show good agreement with exper[r]

difficulties in attaining acceptable accuracy due to the sensitive dynamic interactions between vehicles and pavement surfaces. In Korea, a high-speed WIM system was developed by the Korea Highway Corporation, and is now operating on the Central Inland Highway for pre-selecting overweight vehicles.[r]

primary goal is outcome prediction and important interactions of complex nonlinearities exist in a data set like forbiomass gasification, because they can approximate arbitrarynonlinear functions. One of the characteristics of modelingbased on artificial neural networks is that it does[r]

There are many approaches apply for image classification. At the moment, the popular solution for this problem: using K-NN and K-Mean with the different measures, Support Vector Machine (SVM) and Artificial Neural Network (ANN). K-NN and K-Mean method is very suitable for classi[r]

Articles » General Programming » Algorithms & Recipes » Neural NetworksBackpropagation Artificial Neural Network in C++By Chesnokov Yuriy, 20 May 2008Download demo - 95.7 KBDownload source - 19.5 KBIntroductionI'd like to present a console based implementation of[r]

isubtractedfrom the weighted inputs. The Gaussian neurons work as ex-perts for certain areas of the d-dimensional input space. Ac-tivation of each neuron depends on its distance to the inputvector. Learning algorithms like back propagation (we used astochastic learning [9, 10]) can be used to adjust[r]

sparse matrices, the rows of which are tuples in a rela-tional database. Since MMC models can be easily cre-ated by a parser from existing computer programs, andthen refactored by algorithm, the MMC was proposed asa virtual machine for program evolution [1]. Subsequentwork [2] proved that any finitel[r]

cell arrays at output and 150 hidden layers. 3. Experimentswith TM and ANN 3.1 Template matching With 1600 prototypes we are able to recognize one page of printed text with Arial font with an accuracy of 82% up to 90%. Confidence level is much higher with clean printed texts and reduces significantl[r]

more information in data set.Despite some promising results of the proposedmethod, this article presents only a preliminary imple-mentation in complex process , which sheds light on thenov el idea of PC1DA RMF. The key concept of this ideais to achieve pattern matching between standard patternand un[r]

112 TRANG 6 DANH MỤC CÁC KÍ HIỆU, CÁC CHỮ VIẾT TẮT ANN - Artificial Neural Networks BNN - Biological Neural Networks E-Learning: Electronic Learning IJCNN International Joint Conference [r]

model increases the classified result more than the selection and original MANN method. VI. REFERENCES [1] Thai, L., Hai, S. T., Facial Expression Classification Based on Multi Artificial Neural Network, Volume of Extended Abstract, International conference on Advance Computin[r]

[1]. The massive use of the communication networks for various purposes in the past few years has posed new serious security threats and increased the potential damage that violations may cause. As organizations are increasing their reliance on computer network environments, they are becoming[r]

Laboratory for Computer Vision, Department of Computer Science, University of Calgary [14] Alexandra Boldyreva, “Efficient threshold signature, multi-signature and blind signature schemes based on the Gap-Diffie-Hellman-group signature scheme”, Dept. of Computer Science & Engineering, Univer[r]

Facial Expression Classification using Principal Component Analysis and Artificial Neural Network Thai Hoang Le, Computer Science Department, University of Science HCM City - Vietnam, lhthai@fit.hcmus.edu.vn Nguyen Thai Do Nguyen, Math and Computer Science Department, Universit[r]

Learning Objectives After completion of this module, you will be able to: Use the Parametric Utility to incorporate a Neural Network into a HYSYS model. Use the Parametric Unit Operation with tabular data to model a unit operation as a ‘black box’. Prerequisites Before starting this[r]

Nghiên cứu giới thiệu phương pháp dự báo độ rỗng bằng phương pháp truyền thống và sử dụng mạng neuron nhân tạo (Artificial Neural Network - ANN). Phương pháp nội suy truyền thống Kriging sẽ được áp dụng để tìm ra mối quan hệ trong không gian của thông số độ rỗng thông qua các mô hình 2D. Nghiên cứu[r]

However, these machines have limited use because of their insufficient motion freedom. In addition, these types of machines are not actively controlled and therefore can not accommodate complicated exercises required during rehabilitation. An interesting alternative to electric actuators for medical[r]

MẠNG BAYESIAN BELIEF NETWORKS (BBNs)VÀ GIỚI THIỆU MỘT SỐ NGHIÊN CỨU ỨNG DỤNG TRONG QUẢN LÝXÂY DỰNGKS. NGUYỄN VĂN TUẤN, ThS. LƯU TRƯỜNG VĂN - Trường Đại Học Bách Khoa Tp.HCMGS. LÊ KIỀU - Trường Đại Học Kiến Trúc Hà Nội1. GIỚI THIỆU Bayesian Belief Networks (BBNs) còngọi là Bayesian Networks (BNs) hay[r]