scholarly journals Mitigation of Nonlinear Impairments by Using Support Vector Machine and Nonlinear Volterra Equalizer

2019 ◽  
Vol 9 (18) ◽  
pp. 3800
Author(s):  
Rebekka Weixer ◽  
Jonas Koch ◽  
Patrick Plany ◽  
Simon Ohlendorf ◽  
Stephan Pachnicke
Keyword(s):  
Support Vector Machine ◽  
Phase Noise ◽  
Nonlinear Effects ◽  
Single Mode ◽  
Single Mode Fiber ◽  
Kernel Functions ◽  
Training Data ◽  
Support Vector ◽  
Data Set ◽  
Nonlinear Phase

A support vector machine (SVM) based detection is applied to different equalization schemes for a data center interconnect link using coherent 64 GBd 64-QAM over 100 km standard single mode fiber (SSMF). Without any prior knowledge or heuristic assumptions, the SVM is able to learn and capture the transmission characteristics from only a short training data set. We show that, with the use of suitable kernel functions, the SVM can create nonlinear decision thresholds and reduce the errors caused by nonlinear phase noise (NLPN), laser phase noise, I/Q imbalances and so forth. In order to apply the SVM to 64-QAM we introduce a binary coding SVM, which provides a binary multiclass classification with reduced complexity. We investigate the performance of this SVM and show how it can improve the bit-error rate (BER) of the entire system. After 100 km the fiber-induced nonlinear penalty is reduced by 2 dB at a BER of 3.7 × 10 − 3 . Furthermore, we apply a nonlinear Volterra equalizer (NLVE), which is based on the nonlinear Volterra theory, as another method for mitigating nonlinear effects. The combination of SVM and NLVE reduces the large computational complexity of the NLVE and allows more accurate compensation of nonlinear transmission impairments.

scholarly journals Automatic Task Classification via Support Vector Machine and Crowdsourcing

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Hyungsik Shin ◽  
Jeongyeup Paek
Keyword(s):  
Support Vector Machine ◽  
Mobile Devices ◽  
Prediction Accuracy ◽  
Training Data ◽  
Amazon Mechanical Turk ◽  
Support Vector ◽  
Data Set ◽  
English Sentence ◽  
Task Classification ◽  
Personal Assistant

Automatic task classification is a core part of personal assistant systems that are widely used in mobile devices such as smartphones and tablets. Even though many industry leaders are providing their own personal assistant services, their proprietary internals and implementations are not well known to the public. In this work, we show through real implementation and evaluation that automatic task classification can be implemented for mobile devices by using the support vector machine algorithm and crowdsourcing. To train our task classifier, we collected our training data set via crowdsourcing using the Amazon Mechanical Turk platform. Our classifier can classify a short English sentence into one of the thirty-two predefined tasks that are frequently requested while using personal mobile devices. Evaluation results show high prediction accuracy of our classifier ranging from 82% to 99%. By using large amount of crowdsourced data, we also illustrate the relationship between training data size and the prediction accuracy of our task classifier.

scholarly journals Prediction of Collapsibility of Loess of Construction Sites in Xining Based on Machine Learning Methods

2021 ◽  
Author(s):  
Qifei Zhao ◽  
Xiaojun Li ◽  
Yunning Cao ◽  
Zhikun Li ◽  
Jixin Fan
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Training Data ◽  
Support Vector ◽  
Engineering Practice ◽  
Burial Depth ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods ◽  
North East

Abstract Collapsibility of loess is a significant factor affecting engineering construction in loess area, and testing the collapsibility of loess is costly. In this study, A total of 4,256 loess samples are collected from the north, east, west and middle regions of Xining. 70% of the samples are used to generate training data set, and the rest are used to generate verification data set, so as to construct and validate the machine learning models. The most important six factors are selected from thirteen factors by using Grey Relational analysis and multicollinearity analysis: burial depth、water content、specific gravity of soil particles、void rate、geostatic stress and plasticity limit. In order to predict the collapsibility of loess, four machine learning methods: Support Vector Machine (SVM), Random Subspace Based Support Vector Machine (RSSVM), Random Forest (RF) and Naïve Bayes Tree (NBTree), are studied and compared. The receiver operating characteristic (ROC) curve indicators, standard error (SD) and 95% confidence interval (CI) are used to verify and compare the models in different research areas. The results show that: RF model is the most efficient in predicting the collapsibility of loess in Xining, and its AUC average is above 80%, which can be used in engineering practice.

Application of Support Vector Machine in Determination of Real Estate Price

2012 ◽  
Vol 461 ◽  
pp. 818-821
Author(s):  
Shi Hu Zhang
Keyword(s):  
Support Vector Machine ◽  
Real Estate ◽  
Learning Algorithm ◽  
Predictive Ability ◽  
Training Data ◽  
Small Samples ◽  
Support Vector ◽  
Data Set ◽  
Real Estate Price

The problem of real estate prices are the current focus of the community's concern. Support Vector Machine is a new machine learning algorithm, as its excellent performance of the study, and in small samples to identify many ways, and so has its unique advantages, is now used in many areas. Determination of real estate price is a complicated problem due to its non-linearity and the small quantity of training data. In this study, support vector machine (SVM) is proposed to forecast the price of real estate price in China. The experimental results indicate that the SVM method can achieve greater accuracy than grey model, artificial neural network under the circumstance of small training data. It was also found that the predictive ability of the SVM outperformed those of some traditional pattern recognition methods for the data set used here.

scholarly journals DoS attack detection method on application layer for publish-subscribe networks

2020 ◽  
pp. 50-60
Author(s):  
Dmitrii Dikii
Keyword(s):  
Support Vector Machine ◽  
New Technologies ◽  
Denial Of Service ◽  
Attack Detection ◽  
Training Data ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Cyberphysical Systems ◽  
Dos Attack ◽  
Data Set

Introduction: For the development of cyberphysical systems, new technologies and data transfer protocols are being developed, in order to reduce the energy costs of communication devices. One of the modern approaches to data transmission in cyberphysical systems is the publish-subscribe model, which is subject to a denial-of-service attack. Purpose: Development of a model for detecting a DoS attack implemented at the application level of publish-subscribe networks based on the analysis of their traffic using machine learning methods. Results: A model is developed for detecting a DoS attack, operating with three classifiers depending on the message type: connection, subscription, and publication. This approach makes it possible to identify the source of an attack. That can be a network node, a particular device, or a user account. A multi-layer perceptron, the random forest algorithm, and a support vector machine of various configurations were considered as classifiers. Training and test data sets were generated for the proposed feature vector. The classification quality was evaluated by calculating the F1 score, the Matthews correlation coefficient, and accuracy. The multilayer perceptron model and the support vector machine with a polynomial kernel and SMO optimization method showed the best values of all metrics. However, in the case of the support vector machine, a slight decrease in the prediction quality was detected when the width of the traffic analysis window was close to the longest period of sending legitimate messages from the training data set. Practical relevance: The results of the research can be used in the development of intrusion detection features for cyberphysical systems using the publish-subscribe model, or other systems based on the same approach

scholarly journals Beam-hardening correction by a surface fitting and phase classification by a least square support vector machine approach for tomography images of geological samples

2015 ◽  
Vol 7 (4) ◽  
pp. 3383-3408 ◽  
Author(s):  
F. Khan ◽  
F. Enzmann ◽  
M. Kersten
Keyword(s):  
Support Vector Machine ◽  
Image Data ◽  
Least Square ◽  
Training Data ◽  
Support Vector ◽  
Beam Hardening ◽  
Reconstruction Procedure ◽  
Data Set ◽  
Corrected Image ◽  
Computed Microtomography

Abstract. In X-ray computed microtomography (μXCT) image processing is the most important operation prior to image analysis. Such processing mainly involves artefact reduction and image segmentation. We propose a new two-stage post-reconstruction procedure of an image of a geological rock core obtained by polychromatic cone-beam μXCT technology. In the first stage, the beam-hardening (BH) is removed applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. The final BH-corrected image is extracted from the residual data, or the difference between the surface elevation values and the original grey-scale values. For the second stage, we propose using a least square support vector machine (a non-linear classifier algorithm) to segment the BH-corrected data as a pixel-based multi-classification task. A combination of the two approaches was used to classify a complex multi-mineral rock sample. The Matlab code for this approach is provided in the Appendix. A minor drawback is that the proposed segmentation algorithm may become computationally demanding in the case of a high dimensional training data set.

scholarly journals Nonparameter Nonlinear Phase Noise Mitigation by Using M-ary Support Vector Machine for Coherent Optical Systems

2013 ◽  
Vol 5 (6) ◽  
pp. 7800312-7800312 ◽  
Author(s):  
Minliang Li ◽  
Song Yu ◽  
Jie Yang ◽  
Zhixiao Chen ◽  
Yi Han ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Phase Noise ◽  
Optical Systems ◽  
Support Vector ◽  
Noise Mitigation ◽  
Nonlinear Phase

scholarly journals Application of Machine Learning to a Medium Gaussian Support Vector Machine in the Diagnosis of Motor Bearing Faults

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2266
Author(s):  
Shih-Lin Lin
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithms ◽  
Kernel Functions ◽  
Fault Detection And Diagnosis ◽  
Machine Learning Algorithms ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Data Set ◽  
Motor Bearing

In recent years, artificial intelligence technology has been widely used in fault prediction and health management (PHM). The machine learning algorithm is widely used in the condition monitoring of rotating machines, and normal and fault data can be obtained through the data acquisition and monitoring system. After analyzing the data and establishing a model, the system can automatically learn the features from the input data to predict the failure of the maintenance and diagnosis equipment, which is important for motor maintenance. This research proposes a medium Gaussian support vector machine (SVM) method for the application of machine learning and constructs a feature space by extracting the characteristics of the vibration signal collected on the spot based on experience. Different methods were used to cluster and classify features to classify motor health. The influence of different Gaussian kernel functions, such as fine, medium, and coarse, on the performance of the SVM algorithm was analyzed. The experimental data verify the performance of various models through the data set released by the Case Western Reserve University Motor Bearing Data Center. As the motor often has noise interference in the actual application environment, a simulated Gaussian white noise was added to the original vibration data in order to verify the performance of the research method in a noisy environment. The results summarize the classification results of related motor data sets derived recently from the use of motor fault detection and diagnosis using different machine learning algorithms. The results show that the medium Gaussian SVM method improves the reliability and accuracy of motor bearing fault estimation, detection, and identification under variable crack-size and load conditions. This paper also provides a detailed discussion of the predictive analytical capabilities of machine learning algorithms, which can be used as a reference for the future motor predictive maintenance analysis of electric vehicles.

scholarly journals Prediksi Risiko Perjalanan Transportasi Online Dari Data Telematik Menggunakan Algoritma Support Vector Machine

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Christ Memory Sitorus ◽  
Adhi Rizal ◽  
Mohamad Jajuli
Keyword(s):  
Support Vector Machine ◽  
User Satisfaction ◽  
Internet Technology ◽  
Training Data ◽  
Support Vector ◽  
Data Set ◽  
K Value ◽  
Movement Data ◽  
Svm Algorithm ◽  
Rbf Kernel

The ride-hailing service is now booming because it has been helped by internet technology, therefore many call this service online transportation. The magnitude of the potential for growth in online transportation service users also increases the risk of user satisfaction which could have declined therefore the company is increasing in its service. Both in terms of application and services provided by partners/drivers of the company. During each trip, the online transportation application will record device movement data and send it to the server. This data set is usually called telematic data. This telematics data if processed can have enormous benefits. In this study, an analysis will be conducted to predict the risk of online transportation trips using the Support Vector Machine (SVM) algorithm based on the obtained telematic data. The data obtained is telematic data so it must be processed first using feature engineering to obtain 51 features, then trained using the SVM algorithm with RBF kernel and modified C values. Every C value that is changed will be used K-Fold cross-validation first to separate the testing data and training data. The specified k value is 5. The results for each trial obtained accuracy, Receiver Operating Characteristic (ROC) and Area Under the Curves (AUC), for the best that is at C = 100 while the worst at C = 0.001.

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