Reverse Engineering of Cam Design Based on BP NN

2014 ◽  
Vol 1039 ◽  
pp. 476-483
Author(s):  
Yan Zhao ◽  
Li Xin Lu ◽  
Gui Qin Li ◽  
Zheng Li ◽  
Xiao Yuan ◽  
...  
Keyword(s):  
Reverse Engineering ◽  
Curve Fitting ◽  
Demand Curve ◽  
Back Propagation ◽  
Learning Ability ◽  
Cam Design ◽  
Cubic Curve ◽  
Better Than ◽  
Model Fits

A three layer BP NN is created to design the cam. The data of cam contour ,which can be measured by CMM, has been used for the training where back propagation method is used.Advantage of solving nonliner problems gives BP netwok the ability to make out a more demand curve of cam. Taking advantage of its learning ability,NN model fits the actual cam contour gradually until the error fulfil the demand.Cam contour is ploted by Matlab,the result of which is better than cubic curve fitting,especially in the aspects of precise and velocity.

Using Connectionist Modules for Decision Support

1990 ◽  
Vol 29 (03) ◽  
pp. 167-181 ◽  
Author(s):  
G. Hripcsak
Keyword(s):  
Decision Support ◽  
Standard Deviation ◽  
Confidence Interval ◽  
Posterior Probability ◽  
Back Propagation ◽  
Connectionist Model ◽  
Test Set ◽  
The Third ◽  
Independent Test ◽  
Better Than

AbstractA connectionist model for decision support was constructed out of several back-propagation modules. Manifestations serve as input to the model; they may be real-valued, and the confidence in their measurement may be specified. The model produces as its output the posterior probability of disease. The model was trained on 1,000 cases taken from a simulated underlying population with three conditionally independent manifestations. The first manifestation had a linear relationship between value and posterior probability of disease, the second had a stepped relationship, and the third was normally distributed. An independent test set of 30,000 cases showed that the model was better able to estimate the posterior probability of disease (the standard deviation of residuals was 0.046, with a 95% confidence interval of 0.046-0.047) than a model constructed using logistic regression (with a standard deviation of residuals of 0.062, with a 95% confidence interval of 0.062-0.063). The model fitted the normal and stepped manifestations better than the linear one. It accommodated intermediate levels of confidence well.

Estimating ink mileage of dry toners in electrophotography

TAPPI Journal ◽  
2013 ◽  
Vol 12 (3) ◽  
pp. 9-14
Author(s):  
RENMEI XU ◽  
CELESTE M. CALKINS
Keyword(s):  
Experimental Data ◽  
Curve Fitting ◽  
Graphite Furnace ◽  
Atomic Absorption Spectrometer ◽  
Density Region ◽  
Coated Papers ◽  
Graphite Furnace Atomic Absorption ◽  
S Model ◽  
Better Than ◽  
Film Weight

This work investigates the ink mileage of dry toners in electrophotography (EP). Four different substrates were printed on a dry-toner color production Xerox iGen3 EP press. The print layout contained patches with different cyan, magenta, yellow, and black tonal values from 10% to 100%. Toner amounts on cyan patches were measured using an analytical method. Printed patches and unprinted paper samples, as well as dry toners, were dissolved in concentrated nitric acid. The copper concentrations in the dissolved solutions were analyzed by a Zeeman graphite furnace atomic absorption spectrometer. Analytical results were calculated to determine the toner amounts on paper for different tonal values. Their corresponding reflection densities were also measured. All data were plotted with OriginPro® 8 software, and four mathematical models were used for curve fitting. It was found that the C-S model fitted the experimental data of the two uncoated papers better than the other three models. None of the four models fitted the experimental data of the two coated papers, while the linear model was found to fit the data well. Linear fitting was the best in the practical density region for the two coated papers. Ink mileage curves obtained from curve fitting were used to estimate how much ink was required to achieve a target density for each paper; hence, the ink mileage was calculated. The highest ink mileage was 3.39 times the lowest ink mileage. The rougher the paper surface, the higher the requirement for ink film weight, and the lower ink mileage. No correlation was found between ink mileage and paper porosity.

scholarly journals Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098468
Author(s):  
Xianbin Du ◽  
Youqun Zhao ◽  
Yijiang Ma ◽  
Hongxun Fu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Adaptive Learning ◽  
High Speed ◽  
Back Propagation ◽  
Lateral Force ◽  
Learning Ability ◽  
Vehicle Performance ◽  
Neural Network Theory ◽  
Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

Applying Artificial Neural Networks. I. Estimating Nicotine in Tobacco from near Infrared Data

1995 ◽  
Vol 3 (3) ◽  
pp. 133-142 ◽  
Author(s):  
M. Hana ◽  
W.F. McClure ◽  
T.B. Whitaker ◽  
M. White ◽  
D.R. Bahler
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Near Infrared ◽  
Back Propagation ◽  
Linear Network ◽  
Data Set ◽  
Input Layer ◽  
Propagation Network ◽  
Better Than

Two artificial neural network models were used to estimate the nicotine in tobacco: (i) a back-propagation network and (ii) a linear network. The back-propagation network consisted of an input layer, an output layer and one hidden layer. The linear network consisted of an input layer and an output layer. Both networks used the generalised delta rule for learning. Performances of both networks were compared to the multiple linear regression method MLR of calibration. The nicotine content in tobacco samples was estimated for two different data sets. Data set A contained 110 near infrared (NIR) spectra each consisting of reflected energy at eight wavelengths. Data set B consisted of 200 NIR spectra with each spectrum having 840 spectral data points. The Fast Fourier transformation was applied to data set B in order to compress each spectrum into 13 Fourier coefficients. For data set A, the linear regression model gave better results followed by the back-propagation network which was followed by the linear network. The true performance of the linear regression model was better than the back-propagation and the linear networks by 14.0% and 18.1%, respectively. For data set B, the back-propagation network gave the best result followed by MLR and the linear network. Both the linear network and MLR models gave almost the same results. The true performance of the back-propagation network model was better than the MLR and linear network by 35.14%.

scholarly journals Communicative efficiency and differential case marking: a reverse-engineering approach

2021 ◽  
Vol 7 (s3) ◽  
Author(s):  
Natalia Levshina
Keyword(s):  
Reverse Engineering ◽  
Conditional Probabilities ◽  
Case Marking ◽  
Poisson Models ◽  
Linguistic Patterns ◽  
Cue Reliability ◽  
Communicative Efficiency ◽  
Cue Availability ◽  
Probabilistic Measures ◽  
Better Than

Abstract The use of differential case marking of A and P has been explained in terms of efficiency (economy) and markedness. The present study tests predictions based on these accounts, using conditional probabilities of a particular feature given the syntactic role (cue availability), and conditional probabilities of a particular syntactic role given the feature in question (cue reliability). Cue availability serves as a measure of markedness, whereas cue reliability is central for the efficiency account. Similar to reverse engineering, we determine which of the probabilistic measures could have been responsible for the recurrent cross-linguistic patterns described in the literature. The probabilities are estimated from spontaneous informal dialogues in English and Russian (Indo-European), Lao (Tai-Kadai), N||ng (Tuu) and Ruuli (Bantu). The analyses, which involve a series of mixed-effects Poisson models, clearly demonstrate that cue reliability matches the observed cross-linguistic patterns better than cue availability. Thus, the results support the efficiency account of differential marking.

Polynomial Moving Fitting Method for Edge Identification

2011 ◽  
Vol 308-310 ◽  
pp. 2560-2564 ◽  
Author(s):  
Xiang Rong Yuan
Keyword(s):  
Edge Detection ◽  
Curve Fitting ◽  
Polynomial Function ◽  
High Order ◽  
Polynomial Fitting ◽  
Fitting Method ◽  
Order Polynomial ◽  
Better Than ◽  
Low Order

A moving fitting method for edge detection is proposed in this work. Polynomial function is used for the curve fitting of the column of pixels near the edge. Proposed method is compared with polynomial fitting method without sub-segment. The comparison shows that even with low order polynomial, the effects of moving fitting are significantly better than that with high order polynomial fitting without sub-segment.

scholarly journals Forecasting Gold Prices Based on Extreme Learning Machine

2016 ◽  
Vol 11 (3) ◽  
pp. 372 ◽  
Author(s):  
Kumar Chandar Sivalingam ◽  
Sumathi Mahendran ◽  
Sivanandam Natarajan
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Learning Ability ◽  
Stock Index ◽  
Feed Forward Back Propagation ◽  
Crude Oil Prices ◽  
The Future ◽  
Good Learning ◽  
Learning Machine

<p>In recent years, the investors pay major attention to invest in gold market ecause of huge profits in the future. Gold is the only commodity which maintains ts value even in the economic and financial crisis. Also, the gold prices are closely elated with other commodities. The future gold price prediction becomes the warning ystem for the investors due to unforeseen risk in the market. Hence, an accurate gold rice forecasting is required to foresee the business trends. This paper concentrates on orecasting the future gold prices from four commodities like historical data’s of gold rices, silver prices, Crude oil prices, Standard and Poor’s 500 stock index (S&amp;P500) ndex and foreign exchange rate. The period used for the study is from 1st January 000 to 31st April 2014. In this paper, a learning algorithm for single hidden layered eed forward neural networks called Extreme Learning Machine (ELM) is used which as good learning ability. Also, this study compares the five models namely Feed orward networks without feedback, Feed forward back propagation networks, Radial asis function, ELMAN networks and ELM learning model. The results prove that he ELM learning performs better than the other methods.</p>

scholarly journals Rational Irrationality Across Institutional Contexts

2015 ◽  
Vol 21 (1-2) ◽  
Author(s):  
Ryan H. Murphy
Keyword(s):  
Demand Curve ◽  
Institutional Arrangements ◽  
Governance Structures ◽  
Institutional Contexts ◽  
Better Than

AbstractThis paper considers how Bryan Caplan’s concept of rational irrationality may manifest in various political institutional arrangements, building off the demand curve for irrationality. Mob democracy, anarchy, autocracy, and constitutionally constrained democracy are the governance structures addressed. While anarchy is strictly better than mob democracy, under certain conditions, democracy, anarchy, or constitutionally constrained democracy may yield the best outcomes depending on the circumstances.

Pengaruh Metode Pembelajaran dan Kecerdasan Linguistik Terhadap Kemampuan Bahasa Arab Siswa Kelas II SDIT Anak Sholeh

2016 ◽  
Vol 18 (2) ◽  
pp. 77
Author(s):  
Ayif Royidi
Keyword(s):  
Hypothesis Testing ◽  
Experimental Methods ◽  
Arabic Language ◽  
Learning Ability ◽  
Learning Method ◽  
Tukey Test ◽  
Student Ability ◽  
And Linguistics ◽  
Better Than ◽  
Arabic Students

The purpose of this research is to find out the influence of Three-ber method and linguistic intelligence implementation on Arabic students learning Ability of Arabic language. The research is comparative quantitative with the experimental methods and 2 x 2 by level design .A test is the instrument, used to gather the linguistics data intelligence and student Ability of Arabic language. ANAVA is applied for hypothesis testing two lanes continued to Tukey Test .The results of the study (1) .The Students who learn Arabic trough Three-ber method achieve better than the students who is being taught conventionally(2) There is an interaction between learning method and linguistics intelligence(3)The students whose high linguistic intelligence and learn using Three-ber method achieve better than the students who learn conventionally(4). Arabic students learning Ability of Arabic language whose low linguistic intelligence and learn using Three-ber method achieve lower than student who is being taught conventionally.

scholarly journals Analysis of Language Skills Competencies through the Intrinsic Elements of the Short Story with the Learning Motivation and Discovery Learning

2018 ◽  
Vol 7 (4.9) ◽  
pp. 258
Author(s):  
Yetty Morelent ◽  
Hendra Hidayat ◽  
Susi Herawati ◽  
Marsis . ◽  
Riche Karnilla
Keyword(s):  
High School ◽  
Short Stories ◽  
Short Story ◽  
Language Skills ◽  
Discovery Learning ◽  
Learning Motivation ◽  
Learning Ability ◽  
Learning Method ◽  
Design Data ◽  
Better Than

The purpose of this study is to analyze language skills competencies using the intrinsic element of the short stories and its impact on students’ learning motivation using discovery learning method in senior high school. The research was Quasi-Experiment with a 2x2 factorial design. Data collection was conducted through two instruments items; non-test (questionnaire) for the learning motivation and test (essay) for the ability to identify intrinsic elements of short stories. The results of the research indicated that firstly, the ability to identify intrinsic elements of short stories of the students who were taught by using discovery learning method is better than students ability who were taughtconventionally. Secondly, it means that the ability to identify the intrinsic elements of short stories of the highly motivated students who were taught by using discovery learning method was higher than highly motivated students who are taught by using conventional method.. Thirdly, the ability to identify intrinsic elements of theshort story of students who have low learning motivation taught by using discovery learning method is higher than the students have low learning motivation taught by using conventional methods. Finally, there is no interaction between discovery learning method and learning motivation on the ability to identify the intrinsic elements of the short stories.From the result, it can be concluded that discovery learning methods can be used in learning ability to identify the intrinsic element of short stories. 

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