scholarly journals Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 959 ◽  
Author(s):  
Yuehjen E. Shao ◽  
Shih-Chieh Lin
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Rapid Development ◽  
Control Signal ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Normal Process ◽  
Multivariate Normal ◽  
Statistical Process ◽  
Control Signals

With the rapid development of advanced sensor technologies, it has become popular to monitor multiple quality variables for a manufacturing process. Consequently, multivariate statistical process control (MSPC) charts have been commonly used for monitoring multivariate processes. The primary function of MSPC charts is to trigger an out-of-control signal when faults occur in a process. However, because two or more quality variables are involved in a multivariate process, it is very difficult to diagnose which one or which combination of quality variables is responsible for the MSPC signal. Though some statistical decomposition methods may provide possible solutions, the mathematical difficulty could confine the applications. This study presents a time delay neural network (TDNN) classifier to diagnose the quality variables that cause out-of-control signals for a multivariate normal process (MNP) with variance shifts. To demonstrate the effectiveness of our proposed approach, a series of simulated experiments were conducted. The results were compared with artificial neural network (ANN), support vector machine (SVM) and multivariate adaptive regression splines (MARS) classifiers. It was found that the proposed TDNN classifier was able to accurately recognize the contributors of out-of-control signal for MNPs.

scholarly journals Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yuehjen E. Shao ◽  
Po-Yu Chang ◽  
Chi-Jie Lu
Keyword(s):  
Neural Network ◽  
Process Control ◽  
Control Chart ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Two Stage ◽  
Accurate Identification ◽  
Statistical Process ◽  
Control Chart Patterns ◽  
Chart Patterns

The effective controlling and monitoring of an industrial process through the integration of statistical process control (SPC) and engineering process control (EPC) has been widely addressed in recent years. However, because the mixture types of disturbances are often embedded in underlying processes, mixture control chart patterns (MCCPs) are very difficult for an SPC-EPC process to identify. This can result in problems when attempting to determine the underlying root causes of process faults. Additionally, a large number of categories of disturbances may be present in a process, but typical single-stage classifiers have difficulty in identifying large numbers of categories of disturbances in an SPC-EPC process. Therefore, we propose a two-stage neural network (NN) based scheme to enhance the accurate identification rate (AIR) for MCCPs by performing dimension reduction on disturbance categories. The two-stage scheme includes a combination of a NN, support vector machine (SVM), and multivariate adaptive regression splines (MARS). Experimental results reveal that the proposed scheme achieves a satisfactory AIR for identifying MCCPs in an SPC-EPC system.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

scholarly journals ATNN and SVM for Autonomous Mobile Robot

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Time Delay ◽  
Information Needs ◽  
Support Vector ◽  
Uncertain Information ◽  
Autonomous Mobile Robot ◽  
Vector Machines ◽  
Adaptive Time ◽  
Absolute Measurements

If a robot does not know where it is, it can be difficult to determine what to do next. In order to localizeitself, a robot has access to relative and absolute measurements giving the robot feedback about its driving actions andthe situation of the environment around the robot. Given this information, the robot has to determine its location asaccurately as possible. What makes this difficult is the existence of uncertainty in both the driving and the sensing ofthe robot. The uncertain information needs to be combined in an optimal way. The Kalman Filter is a technique fromestimation theory that combines the information of different uncertain sources to obtain the values of variables ofinterest together with the uncertainty in these. In this work we provide a thorough discussion of the robot localizationproblem resolved by Kalman Filter, Adaptive Time Delay Neural Network and Support Vector machines.

scholarly journals A Framework for Diagnosing the Out-of-Control Signals in Multivariate Process Using Optimized Support Vector Machines

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-fu Li ◽  
Sheng Hu ◽  
Zheng-yuan Wei ◽  
Zhi-qiang Liao
Keyword(s):  
Support Vector Machines ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Support Vector ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Control Signals ◽  
Vector Machines ◽  
Multivariate Control

Multivariate statistical process control is the continuation and development of unitary statistical process control. Most multivariate statistical quality control charts are usually used (in manufacturing and service industries) to determine whether a process is performing as intended or if there are some unnatural causes of variation upon an overall statistics. Once the control chart detects out-of-control signals, one difficulty encountered with multivariate control charts is the interpretation of an out-of-control signal. That is, we have to determine whether one or more or a combination of variables is responsible for the abnormal signal. A novel approach for diagnosing the out-of-control signals in the multivariate process is described in this paper. The proposed methodology uses the optimized support vector machines (support vector machine classification based on genetic algorithm) to recognize set of subclasses of multivariate abnormal patters, identify the responsible variable(s) on the occurrence of abnormal pattern. Multiple sets of experiments are used to verify this model. The performance of the proposed approach demonstrates that this model can accurately classify the source(s) of out-of-control signal and even outperforms the conventional multivariate control scheme.

scholarly journals Deep Learning Based-Phishing Attack Detection

2019 ◽  
Vol 8 (3) ◽  
pp. 8428-8432
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Human Life ◽  
Rapid Development ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector

Due to the rapid development of the communication technologies and global networking, lots of daily human life activities such as electronic banking, social networks, ecommerce, etc are transferred to the cyberspace. The anonymous, open and uncontrolled infrastructure of the internet enables an excellent platform for cyber attacks. Phishing is one of the cyber attacks in which attackers open some fraudulent websites similar to the popular and legal websites to steal the user’s sensitive information. Machine learning techniques such as J48, Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB) and Artificial Neural Network (ANN) were widely to detect the phishing attacks. But, getting goodquality training data is one of the biggest problems in machine learning. So, a deep learning method called Deep Neural Network (DNN) is introduced to detect the phishing Uniform Resource Locators (URLs). Initially, a feature extractor is used to construct a 30-dimension feature vector based on URL-based features, HTML-based features and domain-based features. These features are given as input to the DNN classifier for phishing attack detection. It consists of one input layer, multiple hidden layers and one output layer. The multiple hidden layers in DNN try to learn high-level features in an incremental manner. Finally, the DNN returns a probability value which represent the phishing URLs and legitimate URLs. By using DNN the accuracy, precision and recall of phishing attack detection is improved.

scholarly journals Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5529 ◽  
Author(s):  
Xiaopeng Tan ◽  
Shaojing Su ◽  
Zhen Zuo ◽  
Xiaojun Guo ◽  
Xiaoyong Sun
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Simulated Annealing Algorithm ◽  
Rapid Development ◽  
Pso Algorithm ◽  
Deep Belief Network ◽  
Classification Model ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Belief Network

With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlinear data, this paper proposes an intrusion detection method based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). First, a classification model based on the DBN is constructed, and the PSO algorithm is then used to optimize the number of hidden layer nodes of the DBN, to obtain the optimal DBN structure. The simulations are conducted on a benchmark intrusion dataset, and the results show that the accuracy of the DBN-PSO algorithm reaches 92.44%, which is higher than those of the support vector machine (SVM), artificial neural network (ANN), deep neural network (DNN), and Adaboost. It can be seen from comparative experiments that the optimization effect of PSO is better than those of the genetic algorithm, simulated annealing algorithm, and Bayesian optimization algorithm. The method of PSO-DBN provides an effective solution to the problem of intrusion detection of UAV networks.

scholarly journals Recognition of Process Disturbances for an SPC/EPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yuehjen E. Shao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Process Control ◽  
Support Vector ◽  
Statistical Process ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Process Disturbances ◽  
Network Approaches

Because of the excellent performance on monitoring and controlling an autocorrelated process, the integration of statistical process control (SPC) and engineering process control (EPC) has drawn considerable attention in recent years. Both theoretical and empirical findings have suggested that the integration of SPC and EPC can be an effective way to improve the quality of a process, especially when the underlying process is autocorrelated. However, because EPC compensates for the effects of underlying disturbances, the disturbance patterns are embedded and hard to be recognized. Effective recognition of disturbance patterns is a very important issue for process improvement since disturbance patterns would be associated with certain assignable causes which affect the process. In practical situations, after compensating by EPC, the underlying disturbance patterns could be of any mixture types which are totally different from the original patterns. This study proposes the integration of support vector machine (SVM) and artificial neural network (ANN) approaches to recognize the disturbance patterns of the underlying disturbances. Experimental results revealed that the proposed schemes are able to effectively recognize various disturbance patterns of an SPC/EPC system.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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