Prediction of SOx–NOx emission from a coal-fired CFB power plant with machine learning: Plant data learned by deep neural network and least square support vector machine

2020 ◽  
Vol 270 ◽  
pp. 122310 ◽  
Author(s):  
Derrick Adams ◽  
Dong-Hoon Oh ◽  
Dong-Won Kim ◽  
Chang-Ha Lee ◽  
Min Oh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Power Plant ◽  
Deep Neural Network ◽  
Least Square ◽  
Nox Emission ◽  
Support Vector ◽  
Plant Data

scholarly journals Evaluating structural safety of trusses using Machine Learning

2021 ◽  
Vol 15 (58) ◽  
pp. 308-318
Author(s):  
Tran-Hieu Nguyen ◽  
Anh-Tuan Vu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Roc Curve ◽  
Deep Neural Network ◽  
Structural Safety ◽  
Training Dataset ◽  
Support Vector ◽  
Machine Model ◽  
Adaptive Boosting

In this paper, a machine learning-based framework is developed to quickly evaluate the structural safety of trusses. Three numerical examples of a 10-bar truss, a 25-bar truss, and a 47-bar truss are used to illustrate the proposed framework. Firstly, several truss cases with different cross-sectional areas are generated by employing the Latin Hypercube Sampling method. Stresses inside truss members as well as displacements of nodes are determined through finite element analyses and obtained values are compared with design constraints. According to the constraint verification, the safety state is assigned as safe or unsafe. Members’ sectional areas and the safety state are stored as the inputs and outputs of the training dataset, respectively. Three popular machine learning classifiers including Support Vector Machine, Deep Neural Network, and Adaptive Boosting are used for evaluating the safety of structures. The comparison is conducted based on two metrics: the accuracy and the area under the ROC curve. For the two first examples, three classifiers get more than 90% of accuracy. For the 47-bar truss, the accuracies of the Support Vector Machine model and the Deep Neural Network model are lower than 70% but the Adaptive Boosting model still retains the high accuracy of approximately 98%. In terms of the area under the ROC curve, the comparative results are similar. Overall, the Adaptive Boosting model outperforms the remaining models. In addition, an investigation is carried out to show the influence of the parameters on the performance of the Adaptive Boosting model.

scholarly journals Performance Comparison of Oil Spill and Ship Classification from X-Band Dual- and Single-Polarized SAR Image Using Support Vector Machine, Random Forest, and Deep Neural Network

2021 ◽  
Vol 13 (16) ◽  
pp. 3203
Author(s):  
Won-Kyung Baek ◽  
Hyung-Sup Jung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Performance Improvement ◽  
Oil Spill ◽  
Deep Neural Network ◽  
Support Vector ◽  
Sar Image ◽  
X Band

It is well known that the polarization characteristics in X-band synthetic aperture radar (SAR) image analysis can provide us with additional information for marine target classification and detection. Normally, dual-and single-polarized SAR images are acquired by SAR satellites, and then we must determine how accurate the marine mapping performance from dual-polarized (pol) images is versus the marine mapping performance from the single-pol images in a given machine learning model. The purpose of this study is to compare the performance of single- and dual-pol SAR image classification achieved by the support vector machine (SVM), random forest (RF), and deep neural network (DNN) models. The test image is a TerraSAR-X dual-pol image acquired from the 2007 Kerch Strait oil spill event. For this, 824,026 pixels and 1,648,051 pixels were extracted from the image for the training and test, respectively, and sea, ship, oil, and land objects were classified from the image by using the three machine learning methods. The mean f1-scores of the SVM, RF, and DNN models resulting from the single-pol image were approximately 0.822, 0.882, and 0.889, respectively, and those from the dual-pol image were about 0.852, 0.908, and 0.898, respectively. The performance improvement achieved by dual-pol was about 3.6%, 2.9%, and 1% in SVM, RF, and DNN, respectively. The DNN model had the best performance (0.889) in the single-pol test while the RF model was best (0.908) in the dual-pol test. The performance improvement was approximately 2.1% and not noticeable. If the condition that dual-pol images have two-times lower spatial resolution versus single-pol images in the azimuth direction is considered, a small improvement may not be valuable. Therefore, the results show that the performance improvement by X-band dual-pol image may be not remarkable when classifying the sea, ships, oil spills, and sea and land surfaces.

scholarly journals A Survey and Analysis of Extreme Machine Learning Models and Its Techniques

2021 ◽  
Author(s):  
Shubhangi Pande ◽  
Neeraj Kumar Rathore ◽  
Anuradha Purohit
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Global Optimum ◽  
Least Square ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Feed Forward

Abstract Machine learning applications employ FFNN (Feed Forward Neural Network) in their discipline enormously. But, it has been observed that the FFNN requisite speed is not up the mark. The fundamental causes of this problem are: 1) for training neural networks, slow gradient descent methods are broadly used and 2) for such methods, there is a need for iteratively tuning hidden layer parameters including biases and weights. To resolve these problems, a new emanant machine learning algorithm, which is a substitution of the feed-forward neural network, entitled as Extreme Learning Machine (ELM) introduced in this paper. ELM also come up with a general learning scheme for the immense diversity of different networks (SLFNs and multilayer networks). According to ELM originators, the learning capacity of networks trained using backpropagation is a thousand times slower than the networks trained using ELM, along with this, ELM models exhibit good generalization performance. ELM is more efficient in contradiction of Least Square Support Vector Machine (LS-SVM), Support Vector Machine (SVM), and rest of the precocious approaches. ELM’s eccentric outline has three main targets: 1) high learning accuracy 2) less human intervention 3) fast learning speed. ELM consider as a greater capacity to achieve global optimum. The distribution of application of ELM incorporates: feature learning, clustering, regression, compression, and classification. With this paper, our goal is to familiarize various ELM variants, their applications, ELM strengths, ELM researches and comparison with other learning algorithms, and many more concepts related to ELM.

scholarly journals PEMODELAN PENGARUH SENTIMEN PUBLIK DI TWITTER TERKAIT POLITIK TERHADAP KURS RUPIAH DAN INDEKS HARGA SAHAM DENGAN PENDEKATAN MACHINE LEARNING

2021 ◽  
Vol 2020 (1) ◽  
pp. 989-999
Author(s):  
Epan Mareza Primahendra ◽  
Budi Yuniarto
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Least Square ◽  
Support Vector ◽  
Hidden Layer ◽  
Hidden Neurons

Kurs Rupiah dan indeks harga saham (IHS) berpengaruh terhadap perekonomian Indonesia. Pergerakan kurs Rupiah dan IHS dipengaruhi oleh, informasi publik, kondisi sosial, dan politik. Kejadian politik banyak menimbulkan sentimen dari masyarakat. Sentimen tersebut banyak disampaikan melalui media sosial terutama Twitter. Twitter merupakan sumber big data yang jika datanya tidak dimanfaatkan akan menjadi sampah. Pengumpulan data dilakukan pada periode 26 September 2019 - 27 Oktober 2019. Pola jumlah tweets harian yang sesuai dengan pergerakan kurs Rupiah dan IHS mengindikasikan bahwa terdapat hubungan antara sentimen di Twitter terkait situasi politik terhadap kurs Rupiah dan IHS. Penelitian ini menggunakan pendekatan machine learning dengan algoritma Neural Network dan Least Square Support Vector Machine. Penelitian ini bertujuan untuk mengetahui pengaruh sentimen terhadap kurs Rupiah dan IHS sekaligus mengkaji kedua algoritmanya. Hasilnya menjelaskan bahwa model terbaik untuk estimasi IHS yaitu NN dengan 1 hidden layer dan 2 hidden neurons. Modelnya menunjukan bahwa terdapat pengaruh antara sentimen tersebut terhadap IHS karena volatilitas estimasi IHS sudah cukup mengikuti pola pergerakan IHS aktual. Model terbaik untuk estimasi kurs Rupiah yaitu LSSVM. Pola pergerakan estimasi kurs Rupiah cenderung stagnan di atas nilai aktual. Ini mengindikasikan bahwa modelnya masih belum memuaskan dalam mengestimasi pengaruh sentimen publik terhadap kurs Rupiah.

scholarly journals An Analysis of Extreme Machine Learning Models

2021 ◽  
Author(s):  
Shubhangi Pande ◽  
Neeraj Rathore ◽  
Anuradha Purohit
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Global Optimum ◽  
Least Square ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Feed Forward

Abstract Machine learning applications employ FFNN (Feed Forward Neural Network) in their discipline enormously. But, it has been observed that the FFNN requisite speed is not up the mark. The fundamental causes of this problem are: 1) for training neural networks, slow gradient descent methods are broadly used and 2) for such methods, there is a need for iteratively tuning hidden layer parameters including biases and weights. To resolve these problems, a new emanant machine learning algorithm, which is a substitution of the feed-forward neural network, entitled as Extreme Learning Machine (ELM) introduced in this paper. ELM also come up with a general learning scheme for the immense diversity of different networks (SLFNs and multilayer networks). According to ELM originators, the learning capacity of networks trained using backpropagation is a thousand times slower than the networks trained using ELM, along with this, ELM models exhibit good generalization performance. ELM is more efficient in contradiction of Least Square Support Vector Machine (LS-SVM), Support Vector Machine (SVM), and rest of the precocious approaches. ELM’s eccentric outline has three main targets: 1) high learning accuracy 2) less human intervention 3) fast learning speed. ELM consider as a greater capacity to achieve global optimum. The distribution of application of ELM incorporates: feature learning, clustering, regression, compression, and classification. With this paper, our goal is to familiarize various ELM variants, their applications, ELM strengths, ELM researches and comparison with other learning algorithms, and many more concepts related to ELM.

scholarly journals Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning

2021 ◽  
Vol 11 (22) ◽  
pp. 10682
Author(s):  
Pham-The Hien ◽  
Ic-Pyo Hong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Deep Neural Network ◽  
Dielectric Constants ◽  
Classification Model ◽  
Support Vector ◽  
Scattering Parameters ◽  
Systematic Monitoring ◽  
Material Thickness

Wall-thinning in building structures due to corrosion and surface erosion occurs due to the severe operating conditions and the changing of the surrounding environment, or it can result from poor workmanship and a lack of systematic monitoring during construction. Hence, the continuous monitoring of structures plays an important role in decreasing unexpected accidents. In this paper, a novel method based on the deep neural network and support vector machine approaches is investigated to build up a thickness classification model by incorporating different input features, including the dielectric constants of the material under test, which are extracted from the scattering parameters proceeded by the National Institute of Standards and Technology iterative method. The attained classification results from both machine learning algorithms are then compared and show that both of the models have a good prediction ability. While the deep neural network is the better solution with a large amount of data, the support vector machine is the more appropriate solution when employing small dataset. It can be stated that the proposed method is able to support systematic monitoring as it can help to improve the accuracy of the prediction of material thickness.

scholarly journals Predicting S&P 500 Market Price by Deep Neural Network and Enemble Model

2020 ◽  
Vol 214 ◽  
pp. 02040
Author(s):  
Feiyu Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Deep Neural Network ◽  
Market Price ◽  
Support Vector ◽  
Learning Models ◽  
Conventional Machine ◽  
Machine Learning Models

The method to predict the movement of stock market has appealed to scientists for decades. In this article, we use three different models to tackle that problem. In particular, we propose a Deep Neural Network (DNN) to predict the intraday direction of SP500 index and compare the DNN with two conventional machine learning models, i.e. linear regression, support vector machine. We demonstrate that DNN is able to predict SP500 index with relatively highest accuracy.

Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine

2011 ◽  
Vol 130-134 ◽  
pp. 2047-2050 ◽  
Author(s):  
Hong Chun Qu ◽  
Xie Bin Ding
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Coefficient Matrix ◽  
Least Square ◽  
Support Vector ◽  
Influence Coefficient ◽  
Structural Risk ◽  
Better Than

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

scholarly journals HARTH: A Human Activity Recognition Dataset for Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7853
Author(s):  
Aleksej Logacjov ◽  
Kerstin Bach ◽  
Atle Kongsvold ◽  
Hilde Bremseth Bårdstu ◽  
Paul Jarle Mork
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Human Activity Recognition ◽  
Support Vector ◽  
Free Living

Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera’s video signal and achieved high inter-rater agreement (Fleiss’ Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.

Sign in / Sign up

Export Citation Format

Share Document