Deep Learning for Short-Term Voltage Stability Assessment of Power Systems

Zhang, Meng; Li, Jiazheng; Li, Yang; Xu, Runnan

doi:10.1109/access.2021.3057659

Cited by 65 publications

(47 citation statements)

References 56 publications

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“…On the other hand, the specific category of botnet attack is identified in multi-class classification. Thus far, Artificial Intelligence (AI) techniques have achieved good performance in handling classification tasks in different application areas including voltage stability assessment of power systems among many others [ 17 ]. Specifically, various Machine Learning (ML) and Deep Learning (DL) models have been developed to classify network traffic data in IoT networks.…”

Section: Introductionmentioning

confidence: 99%

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Popoola

Adebisi

Ande

et al. 2021

Sensors

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Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.

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Section: Introductionmentioning

confidence: 99%

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Popoola

Adebisi

Ande

et al. 2021

Sensors

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“…A study [14] stated that, in order to utilize the dynamic route of deep learning, they proposed short-term voltage stability. ey managed the clustering algorithm to obtain short-term voltage stability to increase the reliability.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning-Based Image Processing for Cotton Leaf Disease and Pest Diagnosis

Azath

Zekiwos²,

Bruck

2021

Journal of Electrical and Computer Engineering

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Cotton is one of the economically significant agricultural products in Ethiopia, but it is exposed to different constraints in the leaf area. Mostly, these constraints are identified as diseases and pests that are hard to detect with bare eyes. This study focused to develop a model to boost the detection of cotton leaf disease and pests using the deep learning technique, CNN. To do so, the researchers have used common cotton leaf disease and pests such as bacterial blight, spider mite, and leaf miner. K-fold cross-validation strategy was worn to dataset splitting and boosted generalization of the CNN model. For this research, nearly 2400 specimens (600 images in each class) were accessed for training purposes. This developed model is implemented using python version 3.7.3 and the model is equipped on the deep learning package called Keras, TensorFlow backed, and Jupyter which are used as the developmental environment. This model achieved an accuracy of 96.4% for identifying classes of leaf disease and pests in cotton plants. This revealed the feasibility of its usage in real-time applications and the potential need for IT-based solutions to support traditional or manual disease and pest’s identification.

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“…So far, various neural networks have appeared one after another and have been well applied. Zhang et al [21] used deep learning for short-term voltage stability (STVS) evaluation of power systems, and their study established an evaluation model based on long and short-term memory (LSTM) by learning the time dependence of system dynamics after perturbation; the trained evaluation model is used to judge the stability of the system in real time, and this method can accurately and timely evaluate the stability of the system, which is better than traditional evaluation methods based on shallow learning. Lin et al [22] used deep learning to identify fatigue driving and got a high recognition rate, and many other applications such as [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

An Improved Gesture Segmentation Method for Gesture Recognition Based on CNN and YCbCr

Luo

Cui

2021

Journal of Electrical and Computer Engineering

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With the continuous improvement of people’s requirements for interactive experience, gesture recognition is widely used as a basic human-computer interaction. However, due to the environment, light source, cover, and other factors, the diversity and complexity of gestures have a great impact on gesture recognition. In order to enhance the features of gesture recognition, firstly, the hand skin color is filtered through YCbCr color space to separate the gesture region to be recognized, and the Gaussian filter is used to process the noise of gesture edge; secondly, the morphological gray open operation is used to process the gesture data, the watershed algorithm based on marker is used to segment the gesture contour, and the eight-connected filling algorithm is used to enhance the gesture features; finally, the convolution neural network is used to recognize the gesture data set with fast convergence speed. The experimental results show that the proposed method can recognize all kinds of gestures quickly and accurately with an average recognition success rate of 96.46% and does not significantly increase the recognition time.

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Deep Learning for Short-Term Voltage Stability Assessment of Power Systems

Cited by 65 publications

References 56 publications

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Deep Learning-Based Image Processing for Cotton Leaf Disease and Pest Diagnosis

An Improved Gesture Segmentation Method for Gesture Recognition Based on CNN and YCbCr

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