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A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy
چارچوب پیش بینی نیروی باد مکانی و مکانی رمان بر اساس ماشین بردار پشتیبانی چند خروجی و استراتژی بهینه سازی-2020 The integration of a large number of wind farms poses big challenges to the secure and
economical operation of power systems, and ultra-short-term wind power forecasting is an
effective solution. However, traditional approaches can only predict an individual wind farm
power at a time and ignore the spatio-temporal correlation of wind farms. In this paper, a novel
ultra-short-term forecasting framework based on spatio-temporal (ST) analysis, multi-output
support vector machine (MSVM) and grey wolf optimizer (GWO) which defined
ST-GWO-MSVM model is proposed to predict the output wind power from multiple wind farms;
the ST-GWO-MSVM model includes data analysis stage, parameters optimization stage, and
modeling stage. In the data analysis stage, the person correlation coefficient and partial
autocorrelation function are used to analyze the spatio-temporal correlation of wind power. In the
parameters optimization stage, to avoid obtaining the unreliable forecasting results due to the
parameters are chosen empirically, the GWO algorithm is used to optimize the kernel function
parameters of the MSVM model. In the modeling stage, an innovative forecasting model with
optimal parameter of MSVM is proposed to predict the output wind power of 15 wind farms.
Results show that the performance of ST-GWO-MSVM is better than other benchmark models in
terms of multiple-error metrics including fractional bias, direction accuracy, and improvement
percentages. Keywords: wind power forecasting | Spatio-temporal correlation | Multi-output support vector machine | Grey wolf optimizer | Combined forecasting approaches |
مقاله انگلیسی |
2 |
Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network
همجوشی امتیاز موازی ECG و اثر انگشت را برای احراز هویت انسان بر اساس شبکه های عصبی کانولوشن-2019 Biometrics have been extensively used in the past decades in various security systems and
have been deployed around the world. However, all unimodal biometrics have their own
limitations and disadvantages (e.g., fingerprint suffers from spoof attacks). Most of these
limitations can be addressed by designing a multimodal biometric system, which deploys
over one biometric modality to improve the performance and make the system robust to
spoof attacks. In this paper, we proposed a secure multimodal biometric system by fusing
electrocardiogram (ECG) and fingerprint based on convolution neural network (CNN). To the
best of our knowledge, this is the first study to fuse ECG and fingerprint using CNN for human
authentication. The feature extraction for individual modalities are performed using
CNN and then biometric templates are generated from these features. After that, we have
applied one of the cancelable biometric techniques to protect these templates. In the authentication
stage, we proposed a Q-Gaussian multi support vector machine (QG-MSVM) as
a classifier to improve the authentication performance. Dataset augmentation is successfully
used to increase the authentication performance of the proposed system. Our system
is tested on two databases, the PTB database from PhysioNet bank for ECG and LivDet2015
database for the fingerprint. Experimental results show that the proposed multimodal system
is efficient, robust and reliable than existing multimodal authentication algorithms.
According to the advantages of the proposed system, it can be deployed in real applications Keywords: Authentication | CNN | ECG | Fingerprint | Multimodal biometrics | MSVM |
مقاله انگلیسی |