Results 271 to 280 of about 285,439 (304)

Guidelines for applied machine learning in construction industry—A case of profit margins estimation

Advanced Engineering Informatics, 2020
Abstract The progress in the field of Machine Learning (ML) has enabled the automation of tasks that were considered impossible to program until recently. These advancements today have incited firms to seek intelligent solutions as part of their enterprise software stack. Even governments across the globe are motivating firms through policies to tape
Muhammad Bilal 0005, Lukumon O. Oyedele
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Estimation of Load Margin by Machine Learning based on Synchrophasor data

2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2019
In the modern power systems, precise estimation of load margin limits becomes an important guiding the system operator to determine the operation ranges before reaching the instability. This paper proposes a model of machine learning based method to improve the load margin estimator performance.
Ansaya Treeworawet, Achara Pichetjamroen, Sanchai Dechanupaprittha   +2 more
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Secondary Reserve Marginal Band Price Prediction with Classical and Machine Learning Based Techniques

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, 2023
As a consequence of the continuous growth being experienced by renewable energy systems, the role of the different ancillary services is becoming essential for the reliable operation of the electric system. This paper develops a methodology for estimating the secondary reserve marginal band price in the Iberian electricity market using four forecasting
Javier Cardo-Miota, Hector Beltran, Emilio Pérez, Emilio Sansano-Sansano   +3 more
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Multi-view learning based on maximum margin of twin spheres support vector machine

Journal of Intelligent & Fuzzy Systems, 2021
Multi-view learning utilizes information from multiple representations to advance the performance of categorization. Most of the multi-view learning algorithms based on support vector machines seek the separating hyperplanes in different feature spaces, which may be unreasonable in practical application.
Huiru Wang, Zhijian Zhou
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Sparse Learning for Linear Twin Parameter-margin Support Vector Machine

Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning
Twin Parameter-margin support vector machine (TPMSVM) is a recent very powerful binary classifier. To improve its sparsity, a linear sparse TPMSVM (Lin-STPMSVM) is proposed in this paper. In the primal problem, the vectors defining the hyperplane are replaced with their expression in terms of the dual variables as derived from Karush Khun Tucker (KKT ...
Shuanghong Qu, Renato De Leone, Min Huang   +2 more
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Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning

IEEE Transactions on Neural Networks and Learning Systems, 2019
Positive and unlabeled learning (PU learning) aims to train a binary classifier based on only PU data. Existing methods usually cast PU learning as a label noise learning problem or a cost-sensitive learning problem. However, none of them fully take the data distribution information into consideration when designing the model, which hinders them from ...
Chen Gong 0002, Tongliang Liu, Jian Yang 0003, Dacheng Tao   +3 more
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Large-Margin Extreme Learning Machines With Hybrid Features for Wafer Map Defect Recognition

IEEE Transactions on Instrumentation and Measurement
Zhengkun Yi, Wanfeng Shang, Dong Wang
exaly   +2 more sources

Monitoring of the power system load margin based on a machine learning technique

Electrical Engineering, 2021
The voltage stability margin is an important load margin measure used in power system operating centers to prevent a voltage collapse. However, oscillatory problems that arise with increasing load can also compromise the performance and stability of the power system.
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Supervised and semi-supervised twin parametric-margin regularized extreme learning machine

Pattern Analysis and Applications, 2020
Twin extreme learning machine (TELM) has attracted considerable attention and achieved great success in the machine learning field. However, its performance will be severely affected when outliers exist in the dataset since TELM does not consider heteroscedasticity in practical applications.
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Voltage stability margin prediction by ensemble based extreme learning machine

2013 IEEE Power & Energy Society General Meeting, 2013
Voltage stability margin (VSM) evaluation is one of the essential tasks of power system voltage stability analysis. Conventional methods for VSM calculation is based on continuation-power flow technique. Recently, there is growing interest to apply artificial neural network (ANN) techniques to rapidly predict the VSM.
null Rui Zhang, null Yan Xu, null Zhao Yang Dong, null Pei Zhang, null Kit Po Wong   +4 more
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