scholarly journals Statistical Analysis and Neural Network in Detecting Steel Cord Failures in Conveyor Belts

2021 ◽  
Author(s):  
Dominika Olchówka ◽  
Aleksandra Rzeszowska ◽  
Leszek Jurdziak ◽  
Ryszard Błażej
Keyword(s):  
Neural Network ◽  
Statistical Analysis ◽  
Conveyor Belt ◽  
Data Sets ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Second Stage ◽  
Conveyor Belts ◽  
Steel Cord

The paper presents the identification and classification of steel cord failures in the conveyor belt core based on an analysis of a two-dimensional image of magnetic field changes recorded using the Diagbelt system around scanned failures in the test belt. The obtained set of identified changes in images obtained for numerous devices parameters settings were the base for statistical analysis. It makes it possible to determine the Pearson’s linear correlation coefficient between the parameters being changed and the image of the failures. In the second stage of the research, artificial intelligence methods were applied to construct a multilayer neural network (MLP) and to teach its appropriate identification of damage. In both methods were used the same data sets, which made it possible to compare methods.

scholarly journals Statistical Analysis and Neural Network in Detecting Steel Cord Failures in Conveyor Belts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3081
Author(s):  
Dominika Olchówka ◽  
Aleksandra Rzeszowska ◽  
Leszek Jurdziak ◽  
Ryszard Błażej
Keyword(s):  
Neural Network ◽  
Statistical Analysis ◽  
Conveyor Belt ◽  
Data Sets ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Second Stage ◽  
Conveyor Belts ◽  
Steel Cord

This paper presents the identification and classification of steel cord failures in the conveyor belt core based on an analysis of a two-dimensional image of magnetic field changes recorded using the Diagbelt system around scanned failures in the test belt. The obtained set of identified changes in images, obtained for numerous parameters settings of the device, were the base for statistical analysis. This analysis makes it possible to determine the Pearson’s linear correlation coefficient between the parameters being changed and the image of the failures. In the second stage of the research, artificial intelligence methods were applied to construct a multilayer neural network (MLP) and to teach it appropriate identification of damage. In both methods, the same data sets were used, which made it possible to compare methods.

Pattern Classification Based on Self-Organizing Feature Mapping Neural Network

2013 ◽  
Vol 448-453 ◽  
pp. 3645-3649 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Classification Performance ◽  
Adjustment Process ◽  
Data Sets ◽  
Two Dimensional ◽  
Sample Data ◽  
Sofm Network ◽  
Traditional Pattern

Traditional pattern classification methods are not always efficient because sample data sets are sometimes incomplete and there are exceptions and counter examples. In this paper, SOFM neural network is applied in pattern classification of two-dimensional vectors after analysis of its structure and algorithm. The method to establish SOFM network via MATLAB7.0 is introduced before the network is applied to classify two-dimensional vectors. The adjustment process of weight vectors together with classification performance of SOFM model are also tested in the condition of different number of training steps. The simulation results show that the classification approach based on SOFM model is effective because of its fast speed, high accuracy and strong generalization ability.

scholarly journals Analysis and classification of tem¬perature measurements during melting and casting of alloys using neural networks

2020 ◽  
Vol 63 (10) ◽  
pp. 856-861
Author(s):  
A. V. Fedosov ◽  
G. V. Chumachenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Human Factor ◽  
High Efficiency ◽  
Thermal Conditions ◽  
Second Stage ◽  
Three Stages ◽  
Fully Automatic ◽  
Artificial Neural

The article considers the issues of monitoring the thermal conditions of alloys melting and casting at foundries. It is noted that the least reliable method is when the measurement and fixing the temperature is assigned to the worker. On the other hand, a fully automatic approach is not always available for small foundries. In this regard, the expediency of using an automated approach is shown, in which the measurement is assigned to the worker, and the values are recorded automatically. This method assumes implementation of an algorithm for automatic classification of temperature measurements based on an end-to-end array of data obtained in the production stream. The solving of this task is divided into three stages. Preparing of raw data for classification process is provided on the first stage. On the second stage, the task of measurement classification is solved by using neural network principles. Analysis of the results of the artificial neural network has shown its high efficiency and degree of their correspondence with the actual situation on the work site. It was also noted that the application of artificial neural networks principles makes the classification process flexible, due to the ability to easily supplement the process with new parameters and neurons. The final stage is analysis of the obtained results. Correctly performed data classification provides an opportunity not only to assess compliance with technological discipline at the site, but also to improve the process of identifying the causes of casting defects. Application of the proposed approach allows us to reduce the influence of human factor in the analysis of thermal conditions of alloys melting and casting with minimal costs for melting monitoring.

scholarly journals AI INFLUENCE IN COVID-19 DETECTION

2021 ◽  
Vol 23 (07) ◽  
pp. 1116-1120
Author(s):  
Cijil Benny ◽  
Keyword(s):  
Neural Network ◽  
Statistical Analysis ◽  
Convolutional Neural Network ◽  
Case Reports ◽  
Ct Scans ◽  
Data Sets ◽  
X Rays ◽  
Medical Field ◽  
Primary Key ◽  
Made In

This paper is on analyzing the feasibility of AI studies and the involvement of AI in COVID interrelated treatments. In all, several procedures were reviewed and studied. It was on point. The best-analyzing methods on the studies were Susceptible Infected Recovered and Susceptible Exposed Infected Removed respectively. Whereas the implementation of AI is mostly done in X-rays and CT- Scans with the help of a Convolutional Neural Network. To accomplish the paper several data sets are used. They include medical and case reports, medical strategies, and persons respectively. Approaches are being done through shared statistical analysis based on these reports. Considerably the acceptance COVID is being shared and it is also reachable. Furthermore, much regulation is needed for handling this pandemic since it is a threat to global society. And many more discoveries shall be made in the medical field that uses AI as a primary key source.

scholarly journals Performance of Cans Classification System for Different Conveyor Belt Speed using Naïve Bayes

2020 ◽  
Vol 5 (4) ◽  
pp. 111
Author(s):  
Yulia Resti ◽  
Firmansyah Burlian ◽  
Irsyadi Yani
Keyword(s):  
Classification System ◽  
Naive Bayes ◽  
Conveyor Belt ◽  
Original Model ◽  
Model Based ◽  
Accuracy Level ◽  
Conveyor Belts ◽  
Sorting Process ◽  
Input Variables

The classification system in the sorting process in the can recycling industry can be made based on digital images by exploring the basic color pixel values ​​of images such as R, G, and B as variable inputs. In real time, the classification of cans in the sorting process occurs when cans placed on a conveyor belt move at a certain speed. This paper discusses the performance of can classification systems using the Naïve Bayes method. This method can handle all types of variables, including when all variables are continuous. Two types of conveyor belts are designed to get different speeds, and all images of the cans are captured on both conveyor belts. Two models of Bayes naive are built on the basis of the different distribution assumptions; the original model (all Gaussian distributed) and the model based on the best distribution. Performance of the classification system is built by dividing data into the learning data and the testing data with a composition of 50:50 in which each data is designed into 50 groups with different percentages on each type of cans using sampling technique without replacement. The results obtained are, first, the speed of the conveyor belt when capturing an image affects the pixel values of red, green, and blue and ultimately affects the results of the classification of cans. Second, not all input variables are Gaussian distributed. The classification system was built using assumption the best distribution model for each input variable has the better average accuracy level than the model that assumes all input variables are Gaussian distributed, and the accuracy level of classification on the first speeds of conveyor belt with a gear ratio of 12:30 and a diameter of 35 mm has an accuracy that is better than the other speed, both on the original model and the model based on the best distribution. However, it is necessary to test more statistical distribution models to obtain significant results.

scholarly journals An Improved Skewness Decision Tree SVM Algorithm for the Classification of Steel Cord Conveyor Belt Defects

2018 ◽  
Vol 8 (12) ◽  
pp. 2574 ◽  
Author(s):  
Qinghua Mao ◽  
Hongwei Ma ◽  
Xuhui Zhang ◽  
Guangming Zhang
Keyword(s):  
Decision Tree ◽  
Classification Accuracy ◽  
Pso Algorithm ◽  
Conveyor Belt ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Data Sets ◽  
Classification Problems ◽  
Steel Cord

Skewness Decision Tree Support Vector Machine (SDTSVM) algorithm is widely known as a supervised learning model for multi-class classification problems. However, the classification accuracy of the SDTSVM algorithm depends on the perfect selection of its parameters and the classification order. Therefore, an improved SDTSVM (ISDTSVM) algorithm is proposed in order to improve the classification accuracy of steel cord conveyor belt defects. In the proposed model, the classification order is determined by the sum of the Euclidean distances between multi-class sample centers and the parameters are optimized by the inertia weight Particle Swarm Optimization (PSO) algorithm. In order to verify the effectiveness of the ISDTSVM algorithm with different feature space, experiments were conducted on multiple UCI (University of California Irvine) data sets and steel cord conveyor belt defects using the proposed ISDTSVM algorithm and the conventional SDTSVM algorithm respectively. The average classification accuracies of five-fold cross-validation were obtained, based on two kinds of kernel functions respectively. For the Vowel, Zoo, and Wine data sets of the UCI data sets, as well as the steel cord conveyor belt defects, the ISDTSVM algorithm improved the classification accuracy by 3%, 3%, 1% and 4% respectively, compared to the SDTSVM algorithm. The classification accuracy of the radial basis function kernel were higher than the polynomial kernel. The results indicated that the proposed ISDTSVM algorithm improved the classification accuracy significantly, compared to the conventional SDTSVM algorithm.

Fetal cardiotocography monitoring using Legendre neural networks

2019 ◽  
Vol 64 (6) ◽  
pp. 669-675 ◽  
Author(s):  
Abdulaziz Alsayyari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Uterine Contraction ◽  
High Efficiency ◽  
Fetal Monitoring ◽  
Data Sets ◽  
Legendre Series ◽  
Electronic Fetal Monitoring ◽  
A New Technique

Abstract A new technique for electronic fetal monitoring (EFM) using an efficient structure of neural networks based on the Legendre series is presented in this paper. Such a structure is achieved by training a Legendre series-based neural network (LNN) to classify the different fetal states based on recorded cardiotocographic (CTG) data sets given by others. These data sets consist of measurements of fetal heart rate (FHR) and uterine contraction (UC). The applied LNN utilizes a Legendre series expansion for the input vectors and, hence, has the capability to produce explicit equations describing multi-input multi-output systems. Simulations of the proposed technique in EFM demonstrate its high efficiency. Training the LNN requires a few number of iterations (5–10 epochs). The applied technique makes the classification of the fetal state available through equations combining the trained LNN weights and the current measured CTG record. A comparison of performance between the proposed LNN and other popular neural network techniques such as the Volterra neural network (VNN) in EFM is provided. The comparison shows that, the LNN outperforms the VNN in case of less computational requirements and fast convergence with a lower mean square error.

scholarly journals Data Mining Techniques for Identification and Classification of Various Diseases in Plants

2019 ◽  
Vol 9 (2S) ◽  
pp. 676-680
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Nearest Neighbors ◽  
Crop Productivity ◽  
Vital Role ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbors ◽  
Data Mining Techniques

Data mining is currently being used in various applications; In research community it plays a vital role. This paper specify about data mining techniques for the preprocessing and classification of various disease in plants. Since various plants has different diseases based on that each of them has different data sets and different objectives for knowledge discovery. Data Mining Techniques applied on plants that it helps in segmentation and classification of diseased plants, it avoids Oral Inspection and helps to increase in crop productivity. This paper provides various classification techniques Such as K-Nearest Neighbors, Support Vector Machine, Principle component Analysis, Neural Network. Thus among various techniques neural network is effective for disease detection in plants.

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