Support vector approach using data augmentation with copula function applied to load forecasting

Advanced metering infrastructure (AMI) is spreading to households in some countries, and could be a source for forecasting the residential electric demand. However, load forecasting of a single household is still a fairly challenging topic because of the high volatility and uncertainty of the electric demand of households. Moreover, there is a limitation in the use of historical load data because of a change in house ownership, change in lifestyle, integration of new electric devices, and so on. The paper proposes a novel method to forecast the electricity loads of single residential households. The proposed forecasting method is based on convolution neural networks (CNNs) combined with a data-augmentation technique, which can artificially enlarge the training data. This method can address issues caused by a lack of historical data and improve the accuracy of residential load forecasting. Simulation results illustrate the validation and efficacy of the proposed method.

Download Full-text

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

Sensors ◽

10.3390/s21051568 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1568

Author(s):

Junmo Kim ◽

Geunbo Yang ◽

Juhyeong Kim ◽

Seungmin Lee ◽

Ko Keun Kim ◽

...

Keyword(s):

Incremental Learning ◽

Physical State ◽

Acceptance Rate ◽

Support Vector ◽

Ecg Signal ◽

Biometric Authentication ◽

False Acceptance Rate ◽

Ecg Signals ◽

Using Data ◽

False Acceptance

Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.

Download Full-text

UAV Image Multi-Labeling with Data-Efficient Transformers

Applied Sciences ◽

10.3390/app11093974 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3974

Author(s):

Laila Bashmal ◽

Yakoub Bazi ◽

Mohamad Mahmoud Al Rahhal ◽

Haikel Alhichri ◽

Naif Al Ajlan

Keyword(s):

Data Augmentation ◽

Feature Representation ◽

Aerial Image ◽

Remote Sensing Images ◽

Training Set ◽

Proposed Model ◽

Class Labels ◽

Using Data ◽

Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

Download Full-text

Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation

Communications Chemistry ◽

10.1038/s42004-021-00528-9 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Nadin Ulrich ◽

Kai-Uwe Goss ◽

Andrea Ebert

Keyword(s):

Partition Coefficient ◽

Data Augmentation ◽

Environmental Chemistry ◽

Chemical Properties ◽

Predictive Performance ◽

Chemical Structures ◽

Tautomeric Forms ◽

Learning Techniques ◽

Water Partition Coefficient ◽

Using Data

AbstractToday more and more data are freely available. Based on these big datasets deep neural networks (DNNs) rapidly gain relevance in computational chemistry. Here, we explore the potential of DNNs to predict chemical properties from chemical structures. We have selected the octanol-water partition coefficient (log P) as an example, which plays an essential role in environmental chemistry and toxicology but also in chemical analysis. The predictive performance of the developed DNN is good with an rmse of 0.47 log units in the test dataset and an rmse of 0.33 for an external dataset from the SAMPL6 challenge. To this end, we trained the DNN using data augmentation considering all potential tautomeric forms of the chemicals. We further demonstrate how DNN models can help in the curation of the log P dataset by identifying potential errors, and address limitations of the dataset itself.

Download Full-text

Sensor Data Augmentation Strategy for Load Forecasting in Smart Grid Context

2021 18th International Multi-Conference on Systems, Signals & Devices (SSD) ◽

10.1109/ssd52085.2021.9429417 ◽

2021 ◽

Author(s):

Asma Maalej ◽

Chiheb Rebai

Keyword(s):

Smart Grid ◽

Data Augmentation ◽

Load Forecasting ◽

Sensor Data ◽

Augmentation Strategy

Download Full-text

Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints

Friction ◽

10.1007/s40544-021-0516-2 ◽

2021 ◽

Author(s):

Xiaobin Hu ◽

Jian Song ◽

Zhenhua Liao ◽

Yuhong Liu ◽

Jian Gao ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Support Vector ◽

Wear Particles ◽

Artificial Joints ◽

Performance Deterioration ◽

Overall Performance ◽

Correct Category ◽

Intelligent Recognition

AbstractFinding the correct category of wear particles is important to understand the tribological behavior. However, manual identification is tedious and time-consuming. We here propose an automatic morphological residual convolutional neural network (M-RCNN), exploiting the residual knowledge and morphological priors between various particle types. We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution. Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance. M-RCNN demonstrates a much higher accuracy (0.940) than the deep residual network (0.845) and support vector machine (0.821). This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints.

Download Full-text

Early Detection of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier), Infestation Using Data Mining

Plants ◽

10.3390/plants10010095 ◽

2021 ◽

Vol 10 (1) ◽

pp. 95

Author(s):

Heba Kurdi ◽

Amal Al-Aldawsari ◽

Isra Al-Turaiki ◽

Abdulrahman S. Aldawood

Keyword(s):

Data Mining ◽

Plant Size ◽

Support Vector ◽

Classification Algorithms ◽

Palm Tree ◽

Rhynchophorus Ferrugineus ◽

Red Palm Weevil ◽

Palm Weevil ◽

Using Data ◽

F Measure

In the past 30 years, the red palm weevil (RPW), Rhynchophorus ferrugineus (Olivier), a pest that is highly destructive to all types of palms, has rapidly spread worldwide. However, detecting infestation with the RPW is highly challenging because symptoms are not visible until the death of the palm tree is inevitable. In addition, the use of automated RPW weevil identification tools to predict infestation is complicated by a lack of RPW datasets. In this study, we assessed the capability of 10 state-of-the-art data mining classification algorithms, Naive Bayes (NB), KSTAR, AdaBoost, bagging, PART, J48 Decision tree, multilayer perceptron (MLP), support vector machine (SVM), random forest, and logistic regression, to use plant-size and temperature measurements collected from individual trees to predict RPW infestation in its early stages before significant damage is caused to the tree. The performance of the classification algorithms was evaluated in terms of accuracy, precision, recall, and F-measure using a real RPW dataset. The experimental results showed that infestations with RPW can be predicted with an accuracy up to 93%, precision above 87%, recall equals 100%, and F-measure greater than 93% using data mining. Additionally, we found that temperature and circumference are the most important features for predicting RPW infestation. However, we strongly call for collecting and aggregating more RPW datasets to run more experiments to validate these results and provide more conclusive findings.

Download Full-text

Estimation of Cognitive Distraction from Driver Gazing

International Journal of Software Science and Computational Intelligence ◽

10.4018/ijssci.2017040104 ◽

2017 ◽

Vol 9 (2) ◽

pp. 50-60 ◽

Cited By ~ 6

Author(s):

Akira Yoshizama ◽

Hiroyuki Nishiyama ◽

Hirotoshi Iwasaki ◽

Fumio Mizoguchi

Keyword(s):

Qualitative Data ◽

Mental Arithmetic ◽

Support Vector ◽

Simulation Environment ◽

Time Intervals ◽

Arithmetic Task ◽

Task Load ◽

Cognitive Distraction ◽

Mental Arithmetic Task ◽

Using Data

In their study, the authors sought to generate rules for cognitive distractions of car drivers using data from a driving simulation environment. They collected drivers' eye-movement and driving data from 18 research participants using a simulator. Each driver drove the same 15-minute course two times. The first drive was normal driving (no-load driving), and the second drive was driving with a mental arithmetic task (load driving), which the authors defined as cognitive-distraction driving. To generate rules of distraction driving using a machine-learning tool, they transformed the data at constant time intervals to generate qualitative data for learning. Finally, the authors generated rules using a Support Vector Machine (SVM).

Download Full-text