scholarly journals Statistical Law and Predictive Analysis of Compressive Strength of Cemented Sand and Gravel

2020 ◽  
Vol 27 (1) ◽  
pp. 291-298
Author(s):  
Shoukai Chen ◽  
Yongqiwen Fu ◽  
Lei Guo ◽  
Shifeng Yang ◽  
Yajing Bie
Keyword(s):  
Compressive Strength ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Predictive Analysis ◽  
Distribution Law ◽  
Data Set ◽  
Cemented Sand ◽  
Mix Proportion ◽  
Kolmogorov Smirnov ◽  
Sand And Gravel

AbstractA data set of cemented sand and gravel (CSG) mix proportion and 28-day compressive strength was established, with outliers determined and removed based on the Boxplot. Then, the distribution law of compressive strength of CSG was analyzed using the skewness kurtosis and single-sample Kolmogorov-Smirnov tests. And with the help of Python software, a model based on Back Propagation neural network was built to predict the compressive strength of CSG according to its mix proportion. The results showed that the compressive strength follows the normal distribution law, the expected value and variance were 5.471 MPa and 3.962 MPa respectively, and the average relative error was 7.16%, indicating the predictability of compressive strength of CSG and its correlation with the mix proportion.

scholarly journals Statistical Law and Predictive Analysis of Compressive Strength of Cemented Sand and Gravel

2020 ◽  
Vol 27 (1) ◽  
pp. 366
Author(s):  
Shoukai Chen ◽  
Yongqiwen Fu ◽  
Lei Guo ◽  
Shifeng Yang ◽  
Yajing Bie
Keyword(s):  
Compressive Strength ◽  
Predictive Analysis ◽  
Cemented Sand ◽  
Sand And Gravel

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions

2016 ◽  
Vol 7 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Suruchi Chawla
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Web Search ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Information Need ◽  
Data Set ◽  
User Query ◽  
User Queries

In this paper novel method is proposed using hybrid of Genetic Algorithm (GA) and Back Propagation (BP) Artificial Neural Network (ANN) for learning of classification of user queries to cluster for effective Personalized Web Search. The GA- BP ANN has been trained offline for classification of input queries and user query session profiles to a specific cluster based on clustered web query sessions. Thus during online web search, trained GA –BP ANN is used for classification of new user queries to a cluster and the selected cluster is used for web page recommendations. This process of classification and recommendations continues till search is effectively personalized to the information need of the user. Experiment was conducted on the data set of web user query sessions to evaluate the effectiveness of Personalized Web Search using GA optimized BP ANN and the results confirm the improvement in the precision of search results.

THE STAG OIL FIELD FORMATION EVALUATION: A NEURAL NETWORK APPROACH

1999 ◽  
Vol 39 (1) ◽  
pp. 451 ◽  
Author(s):  
H. Crocker ◽  
C.C. Fung ◽  
K.W. Wong
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Oil Field ◽  
Training Data ◽  
Ann Model ◽  
Neural Network Approach ◽  
Data Set ◽  
Formation Evaluation ◽  
Core Data

The producing M. australis Sandstone of the Stag Oil Field is a bioturbated glauconitic sandstone that is difficult to evaluate using conventional methods. Well log and core data are available for the Stag Field and for the nearby Centaur–1 well. Eight wells have log data; six also have core data.In the past few years artificial intelligence has been applied to formation evaluation. In particular, artificial neural networks (ANN) used to match log and core data have been studied. The ANN approach has been used to analyse the producing Stag Field sands. In this paper, new ways of applying the ANN are reported. Results from simple ANN approach are unsatisfactory. An integrated ANN approach comprising the unsupervised Self-Organising Map (SOM) and the Supervised Back Propagation Neural Network (BPNN) appears to give a more reasonable analysis.In this case study the mineralogical and petrophysical characteristics of a cored well are predicted from the 'training' data set of the other cored wells in the field. The prediction from the ANN model is then used for comparison with the known core data. In this manner, the accuracy of the prediction is determined and a prediction qualifier computed.This new approach to formation evaluation should provide a match between log and core data that may be used to predict the characteristics of a similar uncored interval. Although the results for the Stag Field are satisfactory, further study applying the method to other fields is required.

Knowledge-driven business model innovation through the introduction of equity investment: evidence from China’s primary market

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiali Zheng ◽  
Han Qiao ◽  
Xiumei Zhu ◽  
Shouyang Wang
Keyword(s):  
Business Model ◽  
Business Models ◽  
Learning Algorithm ◽  
Empirical Studies ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Business Model Innovation ◽  
Data Set ◽  
Equity Investment ◽  
Content Type

Purpose This study aims to explore the role of equity investment in knowledge-driven business model innovation (BMI) in context of open modes according to the evidence from China’s primary market. Design/methodology/approach Based on the database of China’s private market and data set of news clouds, the statistic approach is applied to explore and explain whether equity investment promotes knowledge-driven BMI. Machine learning method is also used to prove and predict the performance of such open innovation. Findings The results of logistic regression show that explanatory variables are significant, providing evidence that knowledge management (KM) promotes BMI through equity investment. By further using back propagation neural network, the classification learning algorithm estimates the possibility of BMI, which can be regarded as a score to quantify the performance of knowledge-driven BMI Research limitations/implications The quality of secondhand big data is not very ideal, and future empirical studies should use first-hand survey data. Practical implications This study provides new insights into the link between KM and BMI by highlighting the important roles of external investments in open modes. Social implications From the perspective of investment, the findings of this study suggest the importance for stakeholders to share knowledge and strategies for entrepreneurs to manage innovation. Originality/value The concepts and indicators related to business models are difficult to quantify currently, while this study provides feasible and practical methods to estimate knowledge-driven BMI with secondhand data from the primary market. The mechanism of knowledge and innovation bridged by the experience from investors is introduced and analyzed.

scholarly journals Automatic volcanic ash detection from MODIS observations using a back-propagation neural network

2015 ◽  
Vol 8 (12) ◽  
pp. 5089-5097 ◽  
Author(s):  
T. M. Gray ◽  
R. Bennartz
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Volcanic Eruptions ◽  
High Water ◽  
Back Propagation Neural Network ◽  
Aleutian Islands ◽  
Hysplit Model ◽  
Data Set ◽  
Skill Scores ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Due to the climate effects and aviation threats of volcanic eruptions, it is important to accurately locate ash in the atmosphere. This study aims to explore the accuracy and reliability of training a neural network to identify cases of ash using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Satellite images were obtained for the following eruptions: Kasatochi, Aleutian Islands, 2008; Okmok, Aleutian Islands, 2008; Grímsvötn, northeastern Iceland, 2011; Chaitén, southern Chile, 2008; Puyehue-Cordón Caulle, central Chile, 2011; Sangeang Api, Indonesia, 2014; and Kelut, Indonesia, 2014. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to obtain ash concentrations for the same archived eruptions. Two back-propagation neural networks were then trained using brightness temperature differences as inputs obtained via the following band combinations: 12–11, 11–8.6, 11–7.3, and 11 μm. Using the ash concentrations determined via HYSPLIT, flags were created to differentiate between ash (1) and no ash (0) and SO2-rich ash (1) and no SO2-rich ash (0) and used as output. When neural network output was compared to the test data set, 93 % of pixels containing ash were correctly identified and 7 % were missed. Nearly 100 % of pixels containing SO2-rich ash were correctly identified. The optimal thresholds, determined using Heidke skill scores, for ash retrieval and SO2-rich ash retrieval were 0.48 and 0.47, respectively. The networks show significantly less accuracy in the presence of high water vapor, liquid water, ice, or dust concentrations. Significant errors are also observed at the edge of the MODIS swath.

Coal Mine Disaster Warning Internet of Things Intrusion Detection System Based on Back Propagation Neural Network Improved by Genetic Algorithms

2013 ◽  
Vol 441 ◽  
pp. 343-346 ◽  
Author(s):  
Ying Hu ◽  
Li Min Sun ◽  
Sheng Chen Yu ◽  
Jiang Lan Huang ◽  
Xiao Ju Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Internet Of Things ◽  
Coal Mine ◽  
Intrusion Detection System ◽  
Detection System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Threshold Values ◽  
Data Set

In order to improve the detection rate of intruders in coal mine disaster warning internet of things, and to solve the problem that the back propagate neural network (BPNN) is invalid when these initial weight and threshold values of BPNN are chosen impertinently, Genetic Algorithms (GA) s characteristic of getting whole optimization value was combined with BPNNs characteristic of getting local precision value with gradient method. After getting an approximation of whole optimization value of weight and threshold values of BPNN by GA, the approximation was used as first parameter of BPNN, to train (educate) the BPNN again (in other words, learning). The educated BPNN was used to recognize intruders in internet of things. Experiment results shown that this method was useful and applicable, and the detection right rate of intruders was above 95% for the KDD CUP 1999 data set.

scholarly journals Machine Learning Model for GSM BSC Control Plane Units

2019 ◽  
Vol 8 (6S) ◽  
pp. 219-223
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Model Parameters ◽  
Large Set ◽  
Data Set ◽  
Wide Acceptance ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Accuracy Of Prediction

At maximum traffic intensity i.e. during the busy hour, the GSM BSC signalling units (BSU) measured CPU load will be at its peak. The BSUs CPU load is a function of the number of transceivers (TRXs) mapped to it and hence the volume of offered traffic being handled by the unit. The unit CPU load is also a function of the nature of the offered load, i.e. with the same volume of offered load, the CPU load with the nominal traffic profile would be different as compared to some other arbitrary traffic profile. To manage future traffic growth, a model to estimate the BSU unit CPU load is an essential need. In recent times, using Machine Learning (ML) to develop such a model is an approach that has gained wide acceptance. Since, the estimation of CPU load is difficult as it depends on large set of parameters, machine learning approach is more scalable. In this paper, we describe a back-propagation neural network model that was developed to estimate the BSU unit CPU load. We describe the model parameters and choices and implementation architecture, and estimate its accuracy of prediction, based on an evaluation data set. We also discuss alternative ML architectures and compare their relative prediction accuracies, to the primary ML model

scholarly journals DEVELOPMENT OF BIOMETRIC METHODS AND INFORMATION SECURITY TOOLS

2021 ◽  
Vol 2 (336) ◽  
pp. 121-124
Author(s):  
T. Zh. Mazakov ◽  
D. N. Narynbekovna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Principal Component ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Future Perspective ◽  
Data Set

Now a day’s security is a big issue, the whole world has been working on the face recognition techniques as face is used for the extraction of facial features. An analysis has been done of the commonly used face recognition techniques. This paper presents a system for the recognition of face for identification and verification purposes by using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) and the implementation of face recognition system is done by using neural network. The use of neural network is to produce an output pattern from input pattern. This system for facial recognition is implemented in MATLAB using neural networks toolbox. Back propagation Neural Network is multi-layered network in which weights are fixed but adjustment of weights can be done on the basis of sigmoidal function. This algorithm is a learning algorithm to train input and output data set. It also calculates how the error changes when weights are increased or decreased. This paper consists of background and future perspective of face recognition techniques and how these techniques can be improved.

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